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The University of New South Wales
Australian School of Business | School of Economics
______________________________________________________
The Impact of Macroeconomic News on
Australian Equity Markets
______________________________________________________
Deepika Patwardhan 3189183
Supervisor: Glenn Otto
Honours Thesis
Bachelor of Economics Economics and Finance
October 26, 2009
Page 2
DECLARATION
I, Deepika Patwardhan, hereby declare that this submission is my own work and any
contributions or materials by other authors used in this thesis have been appropriately
acknowledged. This thesis has not been previously submitted to any other university or
institution as part of the requirements for another degree or award.
DEEPIKA PATWARDHAN
26th OCTOBER 2009
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ACKNOWLEDGEMENTS
I would like to thank my supervisor Glenn Otto for his wise counsel, unwavering and
more than generous support, as well as for his insightful and critical engagement with
the research and writing of this thesis. Glenn has been a wonderful mentor, and has
dealt most graciously with all the dramas of my candidature! An enormous amount of
gratitude is also owed to Valentyn Panchenko for all his help with the econometrics and
for his ideas and suggestions for presenting my thesis results.
One friend is owed a special debt and acknowledgement. Sujatha has, more than anyone
else, endured the quotidian trials of my honours year and on a daily basis reminded me
of the humour, humility and perspective that was necessary to successfully complete
this thesis. For her friendship, loyalty, most of the innumerable provocations, and
unfaltering belief in my ability, I am very grateful. I would also like to thank Piyush and
Devshree for helping me edit my thesis and for being constant and ever enthusiastic
companions throughout the year.
Fellow honours students and staff at the School of Economics made this journey most
enjoyable! I would especially like to thank Rahul, Dave, Shasheen, Hien, Michael,
Andrew, Gordon and Spiro for their help and support. Guys, your constant and
undivided attention was indeed flattering!
Finally, I would like to thank my father for all his help, support and advice that made my
work a lot easier. I would like to thank my mother for her emotional support and my
sister for distracting me when I needed it the most!
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TABLE OF CONTENTS
DECLARATION .................................................................................................................................................... 2
ACKNOWLEDGEMENTS .................................................................................................................................. 3
ABSTRACT ............................................................................................................................................................ 7
1. INTRODUCTION ........................................................................................................................................ 9
1.1 Stock Prices and News ................................................................................................................... 9
1.2 Macroeconomy and Stock Prices ............................................................................................ 10
1.3 Econometric Modelling ............................................................................................................... 12
2. LITERATURE REVIEW .......................................................................................................................... 13
2.1 Theoretical models ....................................................................................................................... 13
2.2 Early Research ................................................................................................................................ 14
2.3 Econometric Challenges ............................................................................................................. 16
2.4 Development of Non‐Linear Models ..................................................................................... 17
2.5 Asymmetric ARCH and GARCH models ................................................................................ 20
3. DATA ............................................................................................................................................................ 23
3.1 Index data ......................................................................................................................................... 23
3.2 Announcement dummy variables .......................................................................................... 24
3.3 Macroeconomic variables: ......................................................................................................... 25
3.4 Expectations dummy variables ............................................................................................... 28
4. PRELIMINARY DATA ANALYSIS ...................................................................................................... 30
4.1 Introduction .............................................................................................................................................. 30
4.2 Stylized facts ............................................................................................................................................. 30
4.3 Model diagnostics ................................................................................................................................... 31
5. METHODOLOGY ...................................................................................................................................... 40
6. EMPIRICAL RESULTS ............................................................................................................................ 48
6.1 Impact of news announcements ............................................................................................. 48
6.2 Impact of news content on the stock market .................................................................... 53
7. CONCLUSION ............................................................................................................................................ 62
8. BIBLIOGRAPHY ....................................................................................................................................... 66
9. APPENDIX .................................................................................................................................................. 72
APPENDIX A .............................................................................................................................................. 72
APPENDIX B .............................................................................................................................................. 72
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APPENDIX C .............................................................................................................................................. 74
APPENDIX D .............................................................................................................................................. 77
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LIST OF TABLES AND FIGURES
Table 1 : Summary characteristics of macroeconomic indicator announcements ........... 25
Table 2: Definition of expectations dummy variable .......................................................... 28
Table 3 Summary statistics of ASX200 ................................................................................. 31
Table 4 Ljung‐Box Q‐statistics for equation 1 ...................................................................... 35
Table 5 Breusch Godfrey Test for Serial Correlation .......................................................... 36
Table 6 BDS Independence Test for equation 1 ................................................................... 37
Table 7 ARCH LM TEST for equation 1 ................................................................................. 38
Table 8 Correlation between ASX200 and SP500 ................................................................ 38
Table 9 Sign bias test results for GARCH 1,1 ..................................................................... 45
Table 10 Mean equation estimates for equation 10 ............................................................ 50
Table 11 Variance equation estimates for equation 10 ...................................................... 51
Table 12 Mean equation estimates of equation 11 .............................................................. 55
Table 13 Estimates of the variance equation of equation 11 ............................................. 58
Figure 1 Returns on ASX200 ....................................................................................................................... 23
Figure 2 Return on SP500 ............................................................................................................................ 24
Figure 3 Distribution of the Returns on ASX200 .............................................................................. 32
Figure 4 Daily Squared Returns on ASX200 ........................................................................................ 33
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ABSTRACT
The research conducted in this thesis entails analysing the impact of certain
macroeconomic variables on the Australian stock market for the period 1999‐2008.
Using data from the Australian Bureau of Statistics’ Release Calendar, news
announcement dates were recorded for four key economic indicators in Australia,
namely, CPI, Unemployment, GDP and Retail Trade. In addition, positive news and
negative news variables were constructed to examine the impact of news content on the
equity markets. The presence of asymmetry was also investigated. These models
primarily comprised of GJR‐GARCH 1, 3 specifications.
Key results indicate that the Australian equity markets respond significantly to
information spillovers from the US stock markets. The impact of US stock markets was
found to be statistically significant at 1% in both the mean as well as the variance
equations. However, it seems that Australian equity markets do not respond to pure
news announcements of CPI, GDP, Retail Trade or Unemployment. None of the
announcements have a statistically significant impact on the mean return or the
conditional variance of the stock market. This suggests that market participants do not
respond to the act of releasing news.
Conversely, some evidence was found for the reaction of Australian market participants
to the content of news released. Results suggest that good news about the CPI causes a
significant and negative reaction in the conditional variance of the Australian stock
market. Good news about the CPI would suggest that inflationary pressures in the
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economy were less than those expected by market participants. This shows that market
participants consider inflation as a leading indicator signalling the health of the
economy. This seems to be intuitive since future interest rate decisions are closely
linked to inflationary pressures within the economy because of the Reserve Bank of
Australia’s inflation targeting regime.
Results also reflect that bad news on retail trade figures cause a significant and positive
reaction in the mean returns of the Australian stock market. This positive reaction was
unexpected, but is effectively explained by the data‐period. It is possible that such a
reaction only occurred during this data‐period because the Australian economy
sustained high levels of economic growth. There is also a strong presence of asymmetry
in the data, with the asymmetry term being positive and significant, suggesting that
negative shocks to the equity market cause greater volatility as opposed to positive
shocks.
The significance of the content of CPI and Retail Trade news suggests that market
participants monitor the level of inflation and economic activity within the economy,
and adjust their positions in the market accordingly. It suggests that macroeconomic
variables have an impact on aggregate stock market returns as well as on the
conditional variance of the market.
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1. INTRODUCTION
The impact of news releases on financial markets is a common subject of enquiry. In
particular, researchers have attempted to analyse the impact of certain specific news on
stock markets, bond markets as well as foreign exchange and options markets. This
research aims to analyse the impact of the news releases of macroeconomic variables on
the Australian stock market.
1.1 Stock Prices and News
Market performances are rarely entirely data‐driven. Trade in today’s markets is
influenced by numerous extraneous factors. These multitudes of extraneous factors
include news events. It is believed that news events dominate the markets on any given
day. Furthermore, as technology reaches new heights the ease of access to details of
worldwide and local news is easier. However, the question remains, exactly what type
of news affects the financial markets of a particular economy?
Stock prices are believed to reflect all available information at a particular point of time
Fama E. F., 1970 . Theoretical models derive the price of a stock at time ‘t’ as:
1Ω
The formula suggests that stock prices reflect the present value of discounted value of
expected future earnings dividends , given all information available at time‘t’ Ω
The discount rate can be divided into two components namely, the risk‐free rate and the
risk premium. “3 primitive factors” that have an important impact on stock prices are
suggested Campbell & Mei, 1993 . These factors are the risk‐free rate of interest,
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growth expectations this is derived from the expected rate of growth of corporate
earnings and dividends , and the equity risk premium. Any news that has an impact on
the stock market must be because of the information conveyed on one of these 3
primitive factors. News brings with it additional information that can be exploited by
market participants to further understand the movement of prices, allowing them to
hedge their portfolios and thus take positions in the market. This attempt at
exploitation by market participants in order to increase their personal profits by
reacting to certain news events makes this a very interesting topic to examine.
1.2 Macroeconomy and Stock Prices
Macroeconomic news released about some of the key indicators of the economy affects
all firms and market‐participants. As market‐wide measures, scheduled macroeconomic
news announcements may cause significant changes in the price generating processes
of different assets, which can then be priced as risk factors see e.g. Flannery &
Protopapadakis, 2002 . News of many different types can affect the market, for
example, the September 11 attacks triggered a stock market collapse, as did the Global
Financial Crisis in mid‐2008.
Macroeconomic news often signals market participants about the level of economic
activity and inflationary pressures. Inflation is considered as an important economic
indicator. An increase in inflation levels would lead to a decline in the value of the
investors’ assets. The level of economic activity signals the prospects of future growth
and the overall level of production within an economy. It is therefore expected that any
news that can be interpreted for containing additional information on either the level of
economic activity or inflation will be priced by market participants.
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Intuitive expectations suggest that an increase in the level of economic activity during
an expansionary phase would lead to an increase in market volatility. Increasing activity
during an expansion could lead market participants to revise future inflationary
expectations as well as future expectations of monetary policy in the economy.
Similarly, dampening consumer demand in the same scenario would lead to an increase
in the mean returns of the market, as this would possibly be considered to temporarily
relieve future inflationary pressures in the economy. Variables such as GDP, Retail
Trade, and Consumer Price Index CPI have been found to be important in previous
research undertaken.
Since macroeconomic announcements affect price generating processes, it is likely that
they also affect the entire return distributions. Over the recent years, the impact of
macroeconomic news announcements on financial markets has received considerable
attention in the literature. The overall opinion is that asset prices and volatilities in
exchange markets see e.g. Andersen & Bollerslev, 1998 ; bond markets see e.g.
Balduzzi, Elton, & Green, 2001 and stock markets see e.g. Becker, Finnerty, &
Friedman, 1995 ; Jones, Lamont, & Lumsdaine, 1998 ; Veronesi, 1999 are affected
by macroeconomic news announcements. The general conclusion is that asset prices
and volatilities react almost instantaneously to macroeconomic news announcements
with employment and inflationary news announcements having the greatest impact.
Furthermore, the studies show that GARCH Generalized Autoregressive Conditional
Heteroskedasticity or other time‐series volatilities remain high for the following few
hours and gradually decline after the news announcement see e.g. Ederington & Lee,
1996 .
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1.3 Econometric Modelling
The modelling strategy has developed significantly in the past two decades. Early
researchers used simple Ordinary Least Squares OLS methodology to examine the
impact of news on a aggregate stock returns. As econometric knowledge grew, and
problems associated with time‐series data and tests to check data for these issues were
developed, new models were devised to examine this impact. Autoregressive
Conditional Heteroskedasticity ARCH models Engle R. F., 1982 and Generalized
Autoregressive Conditional Heteroskedasticity GARCH models Bollerslev, 1987
were shown to be incredibly effective in modelling financial time series since they were
powerful enough to model the stylized facts of the data and Furthermore, the
development of extensions to ARCH and GARCH models allowed researchers to account
for a large variety of stylized factors such as I have used the most current and up to date
methods and models to account for certain observed features of the data. ARCH/GARCH
models have been used widely in finance and as such are well understood and easy to
estimate. In addition to distinguishing the news announcement from the news content,
this research also considers the differential impact of good and bad news.
Extensive research has been carried out on the impact of news on all markets in the US
and up to a certain extent for markets in Europe also. However, minimal research has
been carried out on the impact of news announcements on the Australian stock
markets. This thesis attempts to bridge this gap in research by conducting an empirical
analysis of the impact of news announcements on the volatility of the Australian stock
market ASX200 for the period 1999‐2008.
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2. LITERATURE REVIEW
The impact of news releases and news content has been a popular subject of enquiry
amongst researchers in both economics and finance. This impact has been subjected to
extensive research, and has led to the formulation of contesting theories and a variety of
different results. Studies have been incredibly dynamic in nature, with researchers
using a variety of different datasets, employing several different econometric
techniques and varying these over the type of markets, the time period and in different
economies.
2.1 Theoretical models
Nofsinger & Prucyk, 2003 provide a discussion of the different theoretical models
used to examine the effect of anticipated news announcements, such as macroeconomic
news announcements. These models are based on different assumptions and therefore
predict different reactions. For example, Kim & Verrecchia, 1994 provide a model in
which it is assumed that traders cannot acquire private information before the
announcement. This further causes volatility to increase after the announcement until a
consensus is reached on the outcome. In another model, Kim & Verrecchia, 1991
assume that traders are able to collect private information and use this information to
trade according to their opinions before the announcement. After the announcement,
price changes are caused proportionally by the unexpected part of the news. This causes
volatility to increase after the announcement until a consensus is reached on the
outcome. In another model, Kim & Verrecchia, 1991 assume that traders are able to
collect private information and use this information to trader according to their
opinions before the announcement. After the announcement, prices changes are caused
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proportionally by the unexpected part of the news. In one additional model, they
assume that the traders collect private information and that the information on the
news is highly anticipated. They suggest that the variance declines as the quality of
announcement increases.
Closely related to Kim and Verrecchia’s research, Ederington & Lee, 1996 derived
their hypothesis based on a model in which it was assumed that investors gathered
private information, but there was still some uncertainty before the announcement.
Their empirical results on the options markets show that the implied volatilities
increase before and decrease after the announcement as the uncertainty is resolved by
the market participants. This finding is consistent with the increase of realized volatility
after the news announcement, as Ederington & Lee, 1996 show.
2.2 Early Research
The earliest research in the arena of the impact of news on financial markets has
concentrated on analysing the impact of macroeconomic variables on the mean returns
of bond markets, stock markets as well as foreign exchange markets.
For example, Pearce & Roley, 1985 used a model such that a change in stock prices
was hypothesized to be dependent on unexpected news announcements and on
anticipated news announcements based on information known as of close on the
previous trading day. They hypothesized the model:
∆ . . .
Where ∆ is the change in stock prices from the closing of trading on day t‐1 to the
close of trading day t in percent; 1 X k vectors of unanticipated components of
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economic data announcements, computed as the difference between announced
values, , and expected values, ; , 1 X k vector of expected announced values of
economic data based on information known as of close of trading day on day t‐1;
1 X k vector of surprises.
Pearce & Roley, 1985 used the model above to assess the impact of news
announcements. Additionally, they also tested the Efficient Markets Hypothesis by
assessing the impact of anticipated news versus news surprises on stock prices. The
Efficient Market Hypothesis as envisioned by Fama E. F., 1970 suggested that if
markets were efficient, any information that could be used by market participants to
predict stock prices would be immediately incorporated into the stock prices. If
investors could predict stock prices, then they would reap endless benefits by
purchasing stocks that were predicted to increase in price and by selling those that
were predicted to fall. Pearce and Roley suggested that if the markets were efficient
then the anticipated component of information presented by economic data would be
incorporated into the price and only news surprises would have an impact on the
aggregate returns. Their empirical results suggested that the anticipated components of
economic announcements did not significantly affect daily stock prices, thereby
providing evidence for the efficient markets hypothesis.
Chen, Roll, & Ross, 1986 used a four factor model to explore a set of economic state
variables as systematic influences on stock market returns and examined their influence
on asset prices using a similar model. They found that industrial production, changes in
the risk premium, and twists in the yield curve to be significant variables in explaining
returns. They also discuss that the changes in expected inflation and unanticipated
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inflation were found to be marginally significant during periods when these variables
were highly volatile.
Other studies found that aggregate stock returns were negatively related to inflation
and money growth Bodie, 1976 , Fama E. F., 1981 , Geske & Roll, 1983 . Evidence
from these studies suggests early researchers found increases in inflation to lead to a
decrease in the average return on the market considered. Subsequently, other
researchers Chan, Chen, & Hsieh, 1985 , Chen, Roll, & Ross, 1986 ; and Ferson &
Harvey, 1993 attempted to identify other macroeconomic variables that were possibly
related to aggregate stock returns. Cutler, Poterba, & Summers, 1989 also found that
industrial production growth was significantly positively correlated with real stock
returns over the period 1926‐1986. This is a major result, and may also indicate the
interplay of expectations and news variables. However, Cutler, Poterba, & Summers,
1989 were not able to find any evidence for inflation, money supply and the long term
interest rate argument.
2.3 Econometric Challenges
A common theme amongst the earliest econometric approaches was the use of linear,
structural models to develop an analysis of the impact of macroeconomic variables on
stock returns. However, as the understanding of the nature of time‐series data grew, the
problems associated with such linear and structural models became more obvious.
Perhaps because they focused on a linear, time‐invariant relationship between the
aggregate returns and macroeconomic variables, early studies found it difficult to show
this impact empirically. Shanken & Weinstein, 1990 showed that Chen, Roll, & Ross,
1986 results were dependent on the econometric methodology they used to test
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portfolios and the statistical importance of the macroeconomic variables would
decrease when their standard error estimates are corrected for errors‐in‐variables.
Mcqueen & Roley, 1993 claimed that the reason why macroeconomic factors seemed
to have an insignificant effect was due to the constant coefficient models used in
general. They argue that the use of models which assume investor response to news is
the same over different stages of the business cycle is limiting. They estimated a model
that allowed investor expectations to vary according to the stage of the business cycle.
In their model, they allowed their macroeconomic variables to depend on overall
economic conditions which they defined according to the monthly growth of industrial
production.
2.4 Development of Non‐Linear Models
Parallel to the development of this literature, a large amount of research was being
carried out into defining and modelling empirical properties observed in time series
data. Tests for non linearity as well as models to incorporate non linearity were
developed subsequently. One of the major assumptions of ordinary least squares OLS
methodology was the assumption of constant variance over time, known as
homoskedasticity. The violation of this assumption made the standard t‐tests and F‐
tests applied during inference procedures to be invalid. Time series analysis however
was subject to changing variances in variables over time.
Engle R. F., 1982 formulated the Auto Regressive Conditional Heteroskedasticity
ARCH model such that the error term in an equation could be given a structure. This
model prescribed that the conditional variance of the error term to be dependent on the
immediately preceding value of the squared error. Since the introduction of the ARCH
Page 18
model, several papers thus began using such autoregressive processes to model daily
information events see for example, Pagan & Schwert, 1990 and Andersen T. G.,
1996
Episodes of volatility are generally characterized as the clustering of large shocks to the
dependent variable. The conditional variance function is formulated to mimic this
phenomenon. In the ARCH regression model, the variance of the current error ,
conditional on the realized values of lagged errors 1, 2,3… is an
increasing function of the magnitude of the lagged errors, irrespective of their signs.
Hence, large errors of either sign tend to be followed by a large error of either sign. And
similarly, small errors of either sign tend to be followed by a small error of either sign.
The order of lag q determines the length of time for which a shock persists in
conditioning the variance of subsequent errors.
In the first empirical observations of ARCH to the relationship between the level and the
volatility of inflation, Engle R. F., 1982 found that a large q was required in the
conditional variance function. This would necessitate estimating a large number of
parameters subject to inequality restrictions. In order to overcome this weakness,
Bollerslev, 1987 proposed an extension of the conditional variance function, known as
the Generalized Autoregressive Conditional Heteroskedasticity GARCH . In their most
general form, univariate GARCH models make the conditional variance at time a
function of exogenous and lagged endogenous variables, past residuals and conditional
variances, time, parameters. GARCH has proven to be extremely useful in empirical
work. The GARCH model allows the conditional variance to be dependent upon previous
own lags. The conditional variance is the one‐period ahead estimate for the variance
calculated based on any past information thought relevant.
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Studies show that ARCH, GARCH or other time‐series volatilities are higher on
important news announcement days see e.g. Bollerslev et al., 2000 ; Ederington &
Lee, 1993, 1995 ; Flannery& Protopapadakis, 2002 ; Jones, Lamont & Lumsdaine,
1998 , Kim, McKenzie and Faff, 2004 . Hamilton & Susmel, 1994 also estimate
GARCH models of monthly US equity returns. They used macroeconomic conditions as
the indicator to switch from high‐volatility to low‐volatility regimes. They conclude that
macroeconomic conditions significantly affect equity returns. Flannery &
Protopapadakis, 2002 looked at seventeen macroeconomic variables and found that six
variables could be priced by markets. Estimating a GARCH model of daily equity returns
and allowed the realized returns and their conditional volatility to depend on these
macroeconomic variables. In their specification tests, they replicate and expand the
analysis of Mcqueen & Roley, 1993 defining alternative economic regimes; and they
also added explanatory variables to their original specification.
Similar models have been adopted by economists to study the Australian market.
Particularly, Kim & In, 2002 investigate the impact of movements in the US, UK and
Japanese stock markets on the Australian stock market. They also investigate the impact
of US macroeconomic news and Australian macroeconomic news on the Australian
stock market. Their results are in line with expectations with some US and a few
Australian macroeconomic news announcements having an impact on the first and
second moments of the Australian equity markets. They also document a significant
impact of the movements of the US, UK and Japanese stock markets on the Australian
stock market.
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2.5 Asymmetric ARCH and GARCH models
Time‐varying models such as ARCH and GARCH were extremely effective in modeling
the time‐varying volatility seen in asset returns. However, neither of these models was
able to model asymmetry. A key feature of scheduled news arrivals however, is that the
market and the people that participate in it formulate expectations about the
upcoming scheduled information release. Traders take positions in the markets based
on their expectations of future events, thus the anticipated estimate for the upcoming
scheduled news announcement is significant in determining the reaction of the market.
Thus, it is possible to expect that the act of releasing information might not be
considered important by market participants, rather the market participants might
actually react to the content of news. News thus can be labeled as “good” or “bad”,
depending on these expectations.
This asymmetry was modeled by Barberis, Shleifer, & Vishny, 1998 in a behavioral
model. They assumed that good news is expected to be followed by good news and vice
versa. This indicates that high low volatility will follow after bad good news.
Behavioral models formulated by Shefrin & Statman, 1985 and Hong & Stein,
1999 suggested that investors trade and react differently after good vs. bad news
announcements. These two models suggest that investors may trade after good news,
but not after bad news and that the reaction is slower in the case of a bad news
announcement.
Many researchers have found evidence for the presence of asymmetry in stock return
data. Black, 1976 , Christie, 1982 , Nelson, 1991 , Pagan & Schwert, 1990 ,
Sentana, 1995 , and Engle & Ng, 1993 all find evidence to suggest that a negative
shock to stock returns would cause much more volatility compared to a positive shock
Page 21
of equal magnitude. There are two contesting theories about the cause of this observed
asymmetry in financial markets. The first major theory is the leverage effect. The
leverage effect explains the asymmetry by suggesting that when the price of an asset
decreases, the financial leverage of the asset increases as does the probability of
bankruptcy. This causes the asset to become riskier, hence leading to an increase in the
volatility of the market. This leverage effect was hypothesized by Black, 1976 and
Christie, 1982 . Although the leverage effect was mainly applied to firms, this effect has
also been applied to stock market indices. The second explanation for asymmetry is
known as the volatility feedback effect see for example, Pindyck, 1984 and French,
Schwert, & Stambaugh, 1987 . This hypothesis basically suggests that if volatility is
priced, an anticipated increase in volatility would increase the required rate of return
thus implying an immediate fall in stock price to allow for higher future returns.
The earliest generation of GARCH models, such as the seminal ARCH p model of Engle
R. F., 1982 the GARCH p,q of Bollerslev, 1987 , and their in‐mean generalization
Engle, Lilien, & Robins, 1987 are able to capture the volatility clustering exhibited by
financial asset returns, however they can only account for the magnitude of the shock,
but not the sign affecting conditional variance. Hence, this first generation of time‐
varying volatility models is unable to capture the differences in impact on volatility
caused by bad news as opposed to good news. In order to overcome this limitation,
more flexible specifications of the conditional variance were introduced. Some of the
most popular models used in empirical studies to take this asymmetry into account are
the Exponential GARCH EGARCH model by Nelson, 1991 , the Asymmetric GARCH
AGARCH by Engle & Ng, 1993 , the threshold GARCH GJR‐GARCH by Glosten,
Jagannathan, & Runkle, 1993 , the threshold GARCH TGARCH by Zakoin, 1994 , and
Page 22
the quadratic GARCH of Sentana, 1995 . These models have been widely used to model
the asymmetry commonly observed in returns.
Impact of information arrivals has been considered extensively in economics and
finance. With theories, models and observations constantly being updated, research in
this field is as dynamic as ever. The contribution of this thesis is the empirical study of
the impact of such information arrivals on the Australian equity market. This aim of this
thesis is to extend the work of the research in this field and to understand the impact of
macroeconomic news releases as well as market participant expectations to examine if
the Australian stock market behaves in line with those in the US, Japan and the UK.
Page 23
3. DATA
In order to effectively measure the impact of news announcements on the Australian
equity markets, several macroeconomic and financial market variables were utilized.
The following section provides a summary of the variables used and created.
3.1 INDEX DATA
Daily closing stock prices on the ASX200 index and the S&P500 Index were used to
calculate the returns, which were calculated using the formula:
Returns LN /
The closing prices were downloaded from DataStream. Figures 1 and 2 below plot the
returns on both indices for the sample period 1999‐2008
Figure 1 Returns on ASX200
‐0.1
‐0.08
‐0.06
‐0.04
‐0.02
0
0.02
0.04
0.06
0.08
1/01/1999
1/06/1999
1/11/1999
1/04/2000
1/09/2000
1/02/2001
1/07/2001
1/12/2001
1/05/2002
1/10/2002
1/03/2003
1/08/2003
1/01/2004
1/06/2004
1/11/2004
1/04/2005
1/09/2005
1/02/2006
1/07/2006
1/12/2006
1/05/2007
1/10/2007
1/03/2008
1/08/2008
RETURN ON ASX 200
RETURN ON ASX 200
Page 24
Figure 2 Return on SP500
3.2 ANNOUNCEMENT DUMMY VARIABLES
The Australian Bureau of Statistics publishes dates and makes official announcements of
all economic indicators for the Australian economy. Four variables were considered in
this analysis, those being CPI, GDP, Retail Trade and Unemployment. CPI and GDP are
announced quarterly by the ABS. Using the ABS Historical Releases information, four
separate dummy variables were created for these four indicators. These dummies took
the value of unity if an announcement regarding the macroeconomic variable was made
at 11:30am on a given day. For example, if CPI figures were released on a 23rd June
2008, then the announcement dummy for CPI equalled unity on 23rd June 2008. Table 1
below presents a succinct summary of the variable, the frequency of announcements
and the measure.
‐0.15
‐0.1
‐0.05
0
0.05
0.1
0.151/01/1999
1/06/1999
1/11/1999
1/04/2000
1/09/2000
1/02/2001
1/07/2001
1/12/2001
1/05/2002
1/10/2002
1/03/2003
1/08/2003
1/01/2004
1/06/2004
1/11/2004
1/04/2005
1/09/2005
1/02/2006
1/07/2006
1/12/2006
1/05/2007
1/10/2007
1/03/2008
1/08/2008
RETURN on S&P500
Page 25
Table 1 : Summary characteristics of macroeconomic indicator announcements
GDP CPIRETAIL
SALES
UNEMPLOYMENT
RATE
FREQUENCY QUARTERLY QUARTERLY MONTHLY MONTHLY
SOURCE ABS ABS ABS ABS
UNIT
OF MEASUREMENT $A BILLION
% CHANGE IN CPI
FROM
PREVIOUS YEAR
% CHANGE IN GROSS RETAIL SALES FROM PREVIOUS MONTH
UNEMPLOYMENT
RATE %
TIME
OF
ANNOUNCEMENT
11:30AM 11:30AM 11:30AM 11:30AM
TOTAL NUMBER 40 41 108 120
3.3 MACROECONOMIC VARIABLES:
1. CPI‐ The consumer price index CPI is one of the main indicators of the
purchasing power of money. One of the main goals of Australian monetary policy
is the maintenance of this purchasing power. The CPI measures the cost of a
representative basket of goods and services relative to the same basket of goods
and services in a fixed year base year . This basket consists of typical goods and
services used by a typical Australian household. CPI figures are announced by
Page 26
the ABS at the end of each quarter three months ending June, March, September
and December .
2. GDP‐ Most market participants consider GDP as a primary indicator of the
prevailing economic conditions and it is keenly monitored. GDP is the gross value
added of all resident producers for a particular period. GDP is usually measured
in three different ways. It can be derived as the sum of factor incomes and net
taxes on production and imports; as the sum of all final expenditures by
residents, changes in inventories and exports less imports of goods and services.
The ABS rebases every year, and introduced chain volume measures in 1998 to
remove the discrepancy between the three approaches. Since this change in
calculating methodology of GDP was introduced in 1998 it does not affect our
data.
3. Unemployment‐ The unemployment rate is a sensitive indicator of conditions in
the labour market. Unemployment is considered a significant macroeconomic
issue. Each month the ABS conducts the Labour Force Survey surveying about
3000 randomly selected households. Every person in the household, over 15
years of age is placed into one of 3 categories:
‐ Employed: A person is employed if he or she worked full‐time or part‐time
during the past week or is on vacation or sick leave from a regular job.
‐ Unemployed: A person is considered unemployed if he or she did not work
during the preceding week but made some effort to find work.
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‐ Out of Labour Force: A person is considered to be out of the labour force if he
or she did not work in the past week and was not actively seeking work.
Unemployment rate announcements are made every month at 11:30am. There
have been no changes to the structure of the Labour Force Survey or to the
definitions used in the Labour Force Survey between 1999 and 2008.
4. Retail Trade‐ Retail trade is a measure of the total sales of goods and services by
retail stores in Australia. Retail sales are a very important measure of consumer
spending and inflationary pressures in the Australian economy. The ABS releases
a “Retail Trade Series” presenting estimates of the value of turnover of “retail
trade” businesses classified by industry, and by state and territory. These
estimates of turnover are compiled from the monthly Retail Business Survey and
are in current price terms. These figures are also released by the ABS at
11:30am.
As seen in the variable definitions above, all the news releases are made at 11:30am by
the ABS. Information about all four of the variables were collected through surveys at
some point making them subject to measurement error. It is important to note that ABS
utilizes methods to correct these biases however, all data is subject to biases resulting
from misreporting of data items, deficiencies in coverage, non‐response and coverage
errors.
Page 28
3.4 EXPECTATIONS DUMMY VARIABLES
Market expectations data was derived from the Economic Outlook Reports released
monthly by the St. George Bank. These report both the actual value of the indicator as
well as the consensus expectation of the variables. I have used these consensus
expectations, and by comparing them with the actual value released I have either
labelled news as ‘positive’ or ‘negative’. Subsequently, dummy variables for each
macroeconomic indicator GDP, CPI, Retail Trade and Unemployment were created.
Table 2 below lists the individual variable definition and the parameters according to
which they were defined as positive and negative.
Table 2: Definition of expectations dummy variable
VARIABLE POSITIVE NEWS IF NEGATIVE NEWS IF
CPI % Change in CPI from
previous quarter expected
% change in CPI from
previous quarter
% change in CPI from previous quarter
expected % change in CPI from previous
quarter
GDP % Change in GDP since last
quarter expected %
change in GDP since last
quarter
% change in GDP since last quarter
expected % change in GDP since last
quarter
UNEMPLOYMENT Unemployment rate last
month expected
unemployment rate
unemployment rate last month
expected unemployment rate
Page 29
RETAIL TRADE % Change in retail sales
from previous month
expected % change in retail
trade from previous month
% change in retail sales from previous
month expected % change in retail trade
from previous month
As seen in Table 2 above, the positive dummy variable equalled unity if the news
released was ‘good’ and zero otherwise. The negative dummy variable equalled unity if
the news released was ‘bad’ and zero otherwise. Actual numbers were not used due to
difference in the units of measurement for each of the variables. Using actual numbers
would have involved using some sort of standardization procedure. The process is made
simpler by just classifying the ‘positive’ and ‘negative’ news. This method also takes into
account those days on which no news was released for any of the macroeconomic
indicators.
Page 30
4. PRELIMINARY DATA ANALYSIS
4.1 Introduction
The major goal of this research is to assess the impact of scheduled macroeconomic
news releases on Australian equity markets.
In order to determine the type of model that needs to be used, it is important to
examine the data set. When dealing with financial time series data, certain stylized facts
are known to exist. Running diagnostic tests and carrying out certain checks will enable
us to determine the nature of modelling required to assess the impact of news
announcements on Australian equities.
4.2 Stylized Facts
It is likely that many relationships in finance are intrinsically non‐linear. As Campbell,
Lo and McKinley 1997 state, the payoffs to options are non‐linear in some of the input
variables, and investors’ willingness to trade off returns and risks are also non‐linear.
These observations provide clear motivations for consideration of non‐linear models in
a variety of circumstances in order to capture better the relevant features of the data.
There are several common features of financial data that cannot be explained by linear
structural or univariate time series models. Time series models are usually a‐
theoretical, implying that their construction and use is not based upon any underlying
theory, rather they are an attempt to capture empirically relevant features of the
observed data. Examining the stylized features of the data will allow us to determine
Page 31
what type of model should be used to investigate the impact of news announcements on
the ASX200.
4.3 Model Diagnostics
Table 3 presents the summary statistics of the daily returns data. The mean represents
the average percentage daily return on the ASX200. The distribution of the returns
appears to be negatively skewed as evidenced by a negative coefficient of ‐0.645.
Table 3 Summary statistics of ASX200
ASX200Observations 2609
Mean 0.00012058
Standard Error 0.000193747
Standard Deviation 0.009896279
Sample Variance 9.79363E‐05
Kurtosis 8.893888324
Skewness ‐0.645080962
Range 0.14332508
Jacque‐Bera Test Statistic 8742.358
LEPTOKURTOSIS: An important stylized fact concerning financial data is that there are
frequent extreme observations in both tails of the empirical distribution of many
financial series, which are not consistent with the assumption of normality. The
distributions exhibit “fatness in tails” which corresponds to points in time where large
movements in returns have been excessive relative to the normal distribution. The
0
50
100
150
200
250
300
350
400
450
500 RETURNS ON ASX200
RETURNS
sharp peaks correspond to periods when there is very little movement in the return
series. Figure 3 below represents the returns distribution of the ASX200. The returns
distribution of the ASX200 is fat‐tailed as evidenced by the high coefficient of kurtosis
8.893 . This feature is also supported by the Jacque Bera test which yields a statistic of
8742.358 thus rejecting the null hypothesis of normality.
VOLATILITY CLUSTERING: This is the tendency for volatility in stock markets to
appear in bunches. Thus large returns of either sign are expected to follow large
returns, and small returns of either sign are expected to follow small returns. A
plausible explanation for this phenomenon, which seems to be an almost universal
feature of asset return series in finance, is that the information arrivals which drive
price changes themselves occur in bunches rather than being evenly spaced over
time. The important point to note from Figure 4 is that volatility occurs in bursts.
Figure 3 Distribution of the Returns on ASX200
There appears to have been a prolonged period in the market in the early 2000s,
evidenced by only small positive and negative returns. On the other hand, post 2005;
the ASX200 seems to have many large positive and large negative returns, and
thereby far greater volatility.
The figures present above also suggest the presence of heteroskedasticity. The returns
data is negatively skewed coefficient of ‐0.645 and also points towards the likely
presence of heteroskedasticity. One of the most important assumptions of the Classical
Figure 4‐ Daily Squared Returns for January 1999‐December 2008
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
1/01/1999
1/06/1999
1/11/1999
1/04/2000
1/09/2000
1/02/2001
1/07/2001
1/12/2001
1/05/2002
1/10/2002
1/03/2003
1/08/2003
1/01/2004
1/06/2004
1/11/2004
1/04/2005
1/09/2005
1/02/2006
1/07/2006
1/12/2006
1/05/2007
1/10/2007
1/03/2008
1/08/2008
SQUARED RETURN ON ASX200
SQUARED RETURN ON ASX200
Figure 4 Daily Squared Returns on ASX200
Page 34
Linear Regression Model is the assumption of constant variance homoskedasticity .
Although heteroskedasticity does not destroy the unbiasedness and consistency
properties of Ordinary Least Squares OLS , in the presence of heteroskedasticity, OLS
estimators are no longer minimum variance or efficient i.e. they are no longer BLUE
Best Linear Unbiased Estimators . If we use OLS estimators in this scenario, the t and F
tests based on them can be misleading and may result in erroneous conclusions.
In order to test for heteroskedasticity a Breusch‐Pagan test for heteroskedasticity was
conducted on a preliminary model. The estimated model was:
1
In equation 1 above reprsented the returns on the ASX200, X represents the
matrix of the dummy variables created for announcement effects and is the error.
The BP test emphatically rejected the null hypothesis of constant variance, thereby
confirming the presence of heteroskedasticity1. We can therefore conclude that the
initial analysis of the returns data through graphs and the heteroskedasticity tests
suggest that the variances of the returns series are not constant over time.
Kendall & Hill, 1953 found that stock market prices could not be predicted. He found
that weekly changes in prices could not be predicted either from past changes in the
series or from past changes in other series. This suggests that prices follow a ‘random
walk’. The random walk suggests that changes in prices of financial assets are
independent. Autocorrelation tests are a way of detecting departure from a random
walk. Financial applications often need to be tested for the presence of serial
correlation.
1 REFER TO APPENDIX A
Page 35
The Ljung‐Box Q statistic, presented in Table 4 was computed to check for
autocorrelations within the data. The results suggest that there exists higher order of
autocorrelation in the data, with Q‐statistic being significant for a large number of lags
for equation one.
Table 4 Ljung‐Box Q‐statistics for equation 1
LAG AC PAC Q‐STATISTIC
1 ‐0.042** ‐0.042** 4.6565
2 ‐0.023** ‐0.024** 5.9884
3 ‐0.046* ‐0.049* 11.633
4 0.016** 0.011** 12.265
5 0.033* 0.032* 15.184
* Means coefficient is significant at 1%
** Means coefficient is significant at 5%
Table 4 above lists the Autocorrelations AC and Partial Autocorrelations PAC of the
residuals of equation 1. The Q‐statistic represents the Ljung‐Box Q‐statistic for the
Ljung‐Box Q test. This test is implemented to check for the presence of autocorrelations
between the error terms of equation 1. The test rejects the null hypothesis of no
autocorrelations emphatically, with at least five lags of AC and PAC being significant.
Since the Ljung‐Box Q test suggested that there was a presence of higher order serial
correlation in the data and hence the Breusch‐Godfrey test was used. The Breusch‐
Godfrey test for serial correlation detects if error terms in a regression are correlated
over time. Table 5 below presents the results of the Breusch‐Godfrey test. The
coefficient is significant at 1%, and it can be clearly seen that higher‐order serial
Page 36
correlation exists in the data, with the null hypothesis of no correlation between errors
being emphatically rejected.
Table 5 Breusch Godfrey Test for Serial Correlation
F‐statistic 3.0209 Probability 0.010069
Obs*R‐squared 15.05198 Probability 0.010143
Testing for nonlinearity in financial asset returns has been an important area of
econometric research in the last couple of decades as a consequence of the rapid
development in the econometric methodologies necessary to compute the tests, and as a
consequence of the increasingly widely held view that the world is probably not linear.
A common finding is that there is substantial evidence in favour of some nonlinear
structure see for example, Scheinkman & LeBaron, 1989 , Hseih, 1991 for
applications to stock returns .
There are numerous tests for non‐linear patterns in time series that are available. These
tests can be broadly split into two types: general test and specific tests. General tests, ,
are usually designed to detect many departures from randomness in data. The
implication is that such tests will detect a variety of non‐linear structures in data.
However, these tests do not indicate the type of non‐linearity that is present. The BDS
test tests the null hypothesis that the data is pure noise. It has been argued that the BDS
test has the power to detect a wide variety of departures from randomness. It follows a
standard normal distribution under the null hypothesis. Most applications of the above
tests conclude that there is non‐linear dependence in financial asset returns series, but
Page 37
that the dependence is best characterized by a GARCH‐type process. For the purposes of
this research, the BDS test for independence was applied to equation 1. The results
from this test are listed below in Table 6. It can be clearly seen that there still exists
considerable non‐linearity in the data. This confirmation of the non‐linearity hypothesis
can lead to our modelling strategy.
Table 6 BDS Independence Test for equation 1
Dimension BDS Statistic Standard Error z‐Statistic
2 0.027406* 0.00179 15.30822
3 0.052946* 0.002843 18.62099
4 0.070989* 0.003384 20.9766
5 0.082427* 0.003526 23.37911
6 0.087449* 0.003399 25.7309
* Means coefficient is significant at 1%
This confirms that the data is nonlinear in nature and hence an appropriate model
would be required to effectively assess the impact of scheduled macroeconomic news
announcements on the Australian equity market. Prior to proceeding further, an ARCH
effects test was also conducted on the preliminary OLS regression equation 1 . The
results of this test are shown in Table 7 below. The ARCH‐LM test was devised by Engle
R. F., 1982 . The ARCH‐LM test has the null hypothesis of homoskedasticity, i.e. constant
variance of the error term and an alternative hypothesis of heteroskedasticity. The test
procedure is to run the regression:
2
Page 38
The test statistic is and is asymptotically distributed as a chi‐squared distribution.
If any of the coefficients in that series are significant then there may be potential ARCH
effects in the regression. The model showed strong signs of potential ARCH effects and
thereby made the case for using a nonlinear model stronger.
Table 7 ARCH LM TEST for equation 1
Variable Coefficient Std. Error t‐Statistic
δ 3.07E‐05* 6.16E‐06 4.978323
u 0.079748* 0.019572 4.074575
u 0.181182* 0.019376 9.351014
u 0.193687* 0.019329 10.02064
u 0.16167* 0.019376 8.343826
u 0.070524* 0.019571 3.603414
*means coefficient is significant at 1%
It is well known that US stock markets influence stock markets in most economies of the
world. In order to confirm this observation, correlations between the closing prices on
the ASX200 in Australia and the US SP500 were checked. As can be seen in Table 7
below, the correlation between the closing prices of both the indices is very high. This
suggests that the US stock market is very likely to have a significant impact on the
Australian stock market.
Table 8 Correlation between ASX200 and SP500
ASX200 SP500ASX200 1 0.534111SP500 0.534111 1
Page 39
In this chapter, several important features of the data‐set have been seen. It has been
shown that the data exhibits leptokurtosis, volatility clustering, heteroskedasticity and
also the presence of ARCH effects.
The next chapter will consider the most effective methodology required to model the
data.
Page 40
5. METHODOLOGY
In this section I will consider the econometric methodology adopted to analyse the
impact of news announcements and news content on the mean returns and the
volatility of the Australian stock market.
Over many decades and in countless papers, certain observations about the nature of
financial time series data have been made. Known as ‘stylized facts’ these observations
motivate the adoption of certain types of econometric models. A variety of diagnostic
tests were conducted in chapter 4 of this thesis to explore the returns data on the
Australian equity market and to confirm the presence of such observations. As seen in
chapter 3, the data for the ASX200 for the period between 1999 and 2008 exhibits
volatility clustering, skewness and peakedness in the distribution of returns as well as
leptokurtosis. In addition, tests such as the Breusch‐Godfrey test for serial correlation,
Breusch‐Pagan test for heteroskedasticity and the ARCH‐LM test confirmed the
presence of serial correlation, heteroskedasticity as well as ARCH effects in the data. In
this section, I will attempt to formulate a modelling strategy such that the model utilized
to derive results will incorporate and adjust for as many features of the data as possible.
In chapter 4, the Q‐statistic with a statistically significant lag‐1 autocorrelation suggests
that the lagged return on ASX200 might be useful in predicting the return at time‘t’.
Therefore, I have used a simple mean equation for models postulated, the equation took
the form:
3
Where is the return on the ASX200, is the constant,
is the last periods return on the ASX200 and is the error term
Page 41
Additionally, in chapter 3, it was also identified that the closing prices on the ASX and
the previous day’s closing price on the SP500 were highly correlated the correlation
coefficient took the value of 0.534 . This coupled with extensive literature on
information spillovers between economies suggested that the return on the US SP500
could be a significant variable in the modelling process. Hence, the mean equation for all
models was altered to take the form: Madelbrot, 1963
4
An important parallel development in literature was the introduction of nonlinear time
series models. Such non linear models can take into account the changing variances that
seem to be a stylized fact for this particular dataset. Uncertainty is central to modern
finance theory. According to most asset pricing theories the risk premium is determined
by the covariance between the future return on the asset and one of more benchmark
portfolios. It has long been recognized that the uncertainty of speculative prices, as
measured by the variances and covariance, are changing through time see for example,
Madelbrot, 1963 Fama E. , 1965 . Subsequently, applied research started explicitly
modelling time variation in second or higher‐order moments. Foremost amongst these
was the Auto Regressive Conditional Heteroskedasticity ARCH model developed by
Engle R. F., 1982 . The advantage of the ARCH model is that it allows us to
parameterize volatility clustering. The model allows the conditional variance of the
error term, to depend on the immediately previous value of the squared error,
therefore accounting for volatility clustering. The mean equation of an ARCH q model
can take any structure. I only impose the ARCH q structure on the variance equation.
Page 42
For example, a basic ARCH 1 model to assess the impact of macroeconomic news
would be:
5
In the equation above, represents the returns in Australia at time‘t’, X represents
the vector of covariates that includes all explanatory variables and n represents the
error term. The variance equation has a standard ARCH structure such that the
conditional variance of the error term, to depend on the immediately previous
value of the squared error .
The data was fitted with a basic ARCH structure with up to 5 lags2. All these 5 lags were
very highly significant at the 1% level. However, choosing an appropriate lag length in
an ARCH model is challenging. It is possible that the value of q, the number of lags on
the squared error that are required to capture all of the dependence in the conditional
variance, might be very large. The ARCH model is therefore not parsimonious. Another
issue is that the ARCH model imposes non‐negativity constraints on the model. Other
things being equal, the more the parameters in the variance equation the more likely it
is that these non‐negativity constraints are violated.
Bollerslev, 1987 extended Engle’s original work by developing a technique that allows
the conditional variance to be an ARMA process. The generalized ARCH p, q model‐
called GARCH p, q allows for both autoregressive and moving average components in
the heteroskedastic variance. Clearly, the GARCH model is more parsimonious and
entails fewer coefficient constraints. The GARCH p, q model allows the conditional
2 REFER TO APPENDIX B
Page 43
variance to be dependent upon its previous own lags. A basic GARCH 1, 1 model to
assess the impact of macroeconomic news would be:
6
In the equation above, represents the returns in Australia at time‘t’, X represents
the vector of covariates that includes all explanatory variables and represents the
error term. The variance equation has a standard GARCH structure such that the
conditional variance is dependent on its own lag .
Several basic models were run to estimate the best model for the data. For every model,
a test of model adequacy was performed. The main test used in this context was the
ARCH‐LM test to check for the appropriateness of the ARCH or GARCH model run3. The
ARCH‐LM test checks data for any remaining ARCH effects that might require modelling.
It provides a means of checking the number of lags of ARCH and GARCH used in the
postulated model.
It is plausible that an increase in stock market volatility raises required stock returns,
and thus lowers stock prices. An asymmetric model would be able to capture the
differing impacts of good and bad news in the Australian stock market. GARCH 1,1
models appear to fit financial data really well, however GARCH models do not allow for
asymmetries such as the leverage effect, and it has been this shortcoming which has led
to nonlinear GARCH specifications which allow positive and negative news to have
different effects on volatility.
3 REFER TO APPENDIX C
Page 44
The models for the impact of news announcements have known to display an
asymmetric impact. Hence the above models were tested for the presence of
asymmetry. Engle & Ng, 1993 proposed a set of tests for asymmetry in volatility
known as sign and size bias tests. These Engle and Ng tests were used to determine
whether an asymmetric model was required to assess the impact of macroeconomic
news announcements on the ASX200, or whether the symmetric GARCH model was
adequate. The Engle‐Ng tests are usually applied to the residuals of a GARCH fit to the
returns data.
Defining as an indicator dummy that takes the value of 1 is 0 and zero
otherwise. The test for sign bias is based on the significance or otherwise of in
7
Where is an iid error term. If positive and negative shocks to impact differently
upon the conditional variance, then will be statistically significant. It could also be
the case that the magnitude or size of the shock will affect whether the response of
volatility to shocks is symmetric or not. In this case, a negative size bias test would be
conducted, based on a regression where is now used as a slope dummy variable.
Negative size bias is argued to be present if is statistically significant in the
regression:
8
Significance of indicates the presence of sign bias, where positive and negative
shocks have differing impacts upon future volatility, compared with the symmetric
response required by the standard GARCH formulation.
Page 45
The GARCH 1, 1 models for evaluating the impact of announcements as well as for
evaluating the differential created by bad and good news was tested for sign bias. If sign
bias existed, then GARCH 1, 1 models would not be valid as they will not be able to
capture the asymmetric impact that exists. Table 9, below presents the coefficient
estimates of the sign‐bias test conducted on both the models.
Table 9 Sign bias test results for GARCH 1,1
Announcement impact
GARCH 1,1
News Content Impact
GARCH 1,1
Sign‐bias test
8.56E‐05*
‐6.39E‐06
8.56E‐05*
‐6.34E‐06
Negative sign‐bias test
‐0.015034*
‐0.000698
‐0.014934*
‐0.000693
* means coefficient is significant at 1%
As seen in Table 9 above, for the model hypothesized in this research, there was found
to be a significant negative asymmetric impact. It seems from the preliminary results
that a linear GARCH specification will not suffice. Hence, the model was further altered
to include terms that would allow for asymmetry.
There are several competing models which have been devised by researchers over the
past few years. One of the most commonly used asymmetric GARCH model is the GJR‐
GARCH model devised by Glosten, Jagannathan, & Runkle, 1993 .
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They specify the variance equation as:
9
1 0
0
If the future variance of the series is not a function solely of the squared innovation of
the current return, then a simple GARCH p, q model would be misspecified and the
empirical results derived from such an estimation procedure will not be entirely
reliable. Looking at the coefficients of the BDS test presented in Table 6 earlier in this
section, we can clearly see that some non‐linearity still exists in the data even though a
GARCH model has been specifically used to correct for this non linearity. This combined
with the sign bias test suggests that the series of Australian stock returns is not solely a
function of the squared innovation of the current return.
Several competing models exist for modelling asymmetries. However, EGARCH devised
by Nelson, 1991 and GJR‐GARCH devised by Glosten, Jagannathan, & Runkle, 1993
are the only models that EViews runs. Both models were estimated for this dataset,
however the GJR‐GARCH was chosen over the EGARCH, since it achieved convergence
through the BHHH optimization algorithm. EGARCH did not converge. The reason for
this non‐convergence can be traced to the presence of news dummy variables in the
variance equation, which causes issues for optimization algorithms Doornik & Ooms,
2003 . This suggested that the estimates obtained through GJR‐GARCH would be much
more reliable.
It can thus be concluded that the GJR‐GARCH specification is optimal to assess the
impact of macroeconomic news announcements and the impact of expectations on the
Page 47
Australian Stock Market. Subsequent sections will discuss the nature of results obtained
and the implications of these results.
Page 48
6. EMPIRICAL RESULTS
Following from the methodology presented in chapter 4, I will now use the variables
discussed in chapter 3 to estimate the impact of macroeconomic news announcements
on the conditional mean and volatility of the returns on the Australian stock market. All
estimation presented has been done using EViews5.
In chapter 3, we identified several stylized features of the data which strongly suggested
that the data was non‐linear in structure. Hence, all regressions presented in this
section have been estimated using the BHHH optimization algorithm Berndt, Hal, Hall,
& Hausman, 1974 .
Chapter 4 showed the basic econometric techniques that were used to estimate the
results. The GJR‐GARCH model was employed in analysing the dataset owing to the
more parsimonious nature of the model as well as its ability to explain the asymmetry in
the response of stock returns to news. In reaching this conclusion, several different
models were postulated and estimated4. All postulated models were tested for any
remaining ARCH effects post‐estimation. This was done to ensure that the actual model
used was adequate, and robust.
6.1 IMPACT OF NEWS ANNOUNCEMENTS
The following results correspond to the announcement impact of news on the
Australian equity market. It is hypothesized that news arrivals cause market volatility to
increase. Macroeconomic news is released by the ABS at 11:30am. The ABS has an
embargo on such announcements, and as such our model assumes that market
4 REFER TO APPENDIX D
Page 49
participants have no private information about the content of this news, they are only
aware that figures for a particular macroeconomic indicator, will be released at
11:30am on a certain date.
Using data extracted from the ABS historical release calendar and the announcement
dummy variables hence created, the following model was estimated to assess the
impact of news announcements on the Australian stock market:
∑ 10
Where represents the returns on the Australian stock market, , represents
the one period lagged return on the US stock market, are the dummy variables, such
that j 1 GDP, j 2 CPI, j 3 Retail Sales and j 4 Unemployment. These dummy
variables take the value of one on those days in which a scheduled news announcement
occurs for each of the four economic variables described in Section 3 and 0 otherwise
and is the error term which is assumed~ 0, . is the indicator dummy variable
which takes the value of one if 0 and zero otherwise. The coefficient accounts
for the asymmetric impact caused by news.
The model above was run to estimate the maximum likelihood estimates of the impact
of news announcements on the four economic variables. The results were obtained by
jointly estimating the mean and variance equations shown above. These results are
presented in Table 10 below:
Page 50
Table 10 Mean equation estimates for equation 10
Variable Coefficient Standard Error z‐Statistic
RAUS 0.072033 0.01771 4.067
RUS 0.342452 0.012004 28.529
CPI 0.000529 0.000915 0.578
Unemployment 0.000339 0.000541 0.627
Retail Trade 0.000249 0.000607 0.409
GDP 0.000313 0.00134 0.233
means coefficient is significant at 1%
The coefficients on the lagged US and Australian stock market variable are highly
significant, proving the importance of their inclusion in this model. The significance of
these lags suggests that past returns on the Australian and US stock markets are
influential in determining current returns on the Australian stock market. This provides
evidence that foreign stock markets have an impact on the returns of the Australian
stock market.
The result obtained is similar to those that have investigated information spillovers
between markets. Particularly, Kim & In, 2002 found that major stock markets UK,
US and Japan have a significant impact on the returns of the Australian stock and
futures market. This result is not entirely unexpected since these countries represent
some of Australia’s major trading partners and the high degree of financial market
integration has increased the significance of these markets.
The announcement impact is estimated by the coefficients of CPI, GDP, Unemployment
and Retail Trade. None of these variables are statistically significant for the mean
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equation. This suggests that there is no impact of a news announcement on the CPI,
GDP, Unemployment rate or Retail Trade being made on the mean returns of the
ASX200. Studies undertaken by Kim, McKenzie, & Faff, 2004 and Kim & In,
2002 found similar results, with the announcement effect of macroeconomic variables
being insignificant.
Examining the variance equation will allow us to assess the impact of news
announcements on volatility of the market. Table 11 below presents the results of the
variance equation of the model.
Table 11 Variance equation estimates for equation 10
Variable Coefficient Standard Error z‐Statistic
C 1.38E‐06* 4.34E‐07 3.181
β 0.028065** 0.017294 1.622
θ 0.125343* 0.036769 3.409
γ 0.423878* 0.175275 2.418
γ 0.460049* 0.16058 2.865
CPI ‐9.18E‐07 4.95E‐06 ‐0.185
Unemployment ‐2.85E‐06 4.52E‐06 ‐0.630
Retail Trade ‐1.02E‐06 4.41E‐06 ‐0.231
GDP 1.02E‐05 9.26E‐06 1.105
RUS ‐0.000304* 9.77E‐05 ‐3.106
means coefficient is significant at 1%
means coefficient is significant at 5%
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All of the ARCH and GARCH terms are significant, as shown in Table 11. Model
estimates can also provide insight into the degree of persistence of the volatility shocks
in the market. For shocks to be highly persistent, the sum of the coefficients on the
ARCH and GARCH terms should be close to unity. For this equation, the
coefficients , , is approximately 0.92, suggesting that volatility shocks are
persistent in this particular case. This result is consistent with that obtained by Kim &
In, 2002
Table 11, also provides evidence that the returns on US stock markets cause volatility in
the Australian markets. The coefficient on the variable is is highly statistically
significant at 1% and has a negative sign. This proves that a decrease in the mean return
on the US stock market leads to an increase in the volatility of the Australian stock
market. This impact could be seen as Australia’s reaction to a loss of confidence in the
US stock markets. This suggests an increase in the risk in the global economy, and
thereby is reflected through increased volatility episodes in the domestic market. For
example, in the period between 1999 and 2008, two major stock market crashes took
place in the US 2001 and 2008 triggering large falls in the asset prices in Australia.
The returns on the US stock market also seem to be significantly increasing volatility in
the Australian equity markets. The coefficient on the US stock returns is negative,
suggesting that a fall in the mean return on the US SP500 leads to an increase in the
volatility of the Australian ASX200. This result may be explained by the increase in
uncertainty caused due to the fall of the US SP500. A fall in the US SP500 may result in
significant falls in other share markets worldwide due to the increased global
integration amongst countries. This ‘domino effect’ causes the risk of investing in
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smaller markets to increase exponentially, and market participants will move to hedge
their positions in the market, thereby increasing the volatility of the market.
None of the other variables are statistically significant, and hence represent no notable
change in the conditional variance of the returns on the ASX 200. The terms on the
GARCH and ARCH are also highly statistically significant, hence representing evidence
for model acceptance.
Additionally, a sign‐bias test conducted on the GARCH 1, 1 model for the data in
chapter 3 suggested a strong presence of negative sign bias. This suggested that there
exists considerable asymmetric impact in the data. The asymmetry term is positive and
highly statistically significant. This implies that a negative announcement has a bigger
impact on the market as opposed to a positive announcement. More importantly, it
suggests that the nature of announcement could be very important in understanding the
impact of news announcements on the mean returns and the volatility of the Australian
stock market.
6.2 IMPACT OF NEWS CONTENT ON THE STOCK MARKET
The model presented above looked at the impact of announcements on the ASX 200; it
did not take into account the nature of news. It is intuitive that the nature of news could
also have an impact on the mean and variance of the stock returns. The impact of news
content on the stock market was modelled using the equation below:
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∑ ∑ 11
Where represents the returns on the Australian stock market, , represents
the one period lagged return on the US stock market, are the dummy variables such
that j 5 positive CPI, k 1 negative CPI, j 6 positive unemployment, k 2 negative
unemployment, k 7 positive retail trade, j 3 negative retail trade, j 8 positive GDP
and k 4 negative GDP. These dummy variables take the value of one on those days in
which a positive scheduled news announcement occurs for each of the four economic
variables described in Chapter 3 and 0 otherwise, are the dummy variables which
take the value of one on those days in which a negative scheduled news announcement
occurs for each of the four economic variables described in Chapter 3 and is the error
term which is assumed~ 0, . is the indicator dummy variable which takes the
value of one if 0 and zero otherwise. The coefficient accounts for the
asymmetric impact caused by news.
Generally it is expected that the conditional variance of the stock market would increase
when negative news about certain macroeconomic variables is released. This increase
in conditional variance occurs because market participants frame news as good or bad
depending on their a‐priori expectations. For example, suppose market participants
expect the upcoming retail trade news to be an increase in x‐percent. An increase in the
retail trade figures represents an increase in consumption expenditure, and hence
suggests an improvement in economic activity. If the news released was not as
expected, it is likely, market participants will react unfavourably to it, and hence cause
the conditional variance of returns to increase. In order to capture this impact of
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expectations, the model above was estimated, such that market participant’s reaction
was also taken into consideration when observing the effect of news on the market.
Table 12 Mean equation estimates of equation 11
Variable Coefficient Standard Error z‐Statistic
RAUS ‐0.073415* 0.017715 4.144
RUS 0.341414* 0.012042 28.351
Positive CPI ‐0.000197 0.001236 1.238
Negative CPI 0.000636 0.00127 0.501
Positive Unemployment ‐5.92E‐05 0.000858 ‐0.069
Negative Unemployment ‐0.000433 0.000805 ‐0.538
Positive Retail Trade 0.000934 0.000813 1.149
Negative Retail Trade 0.000681*** 0.00043 1.584
Positive GDP ‐0.001912 0.003354 ‐0.570
Negative GDP 0.000467 0.001124 0.416
means coefficient is significant at 1%
means coefficient is significant at 15%
Table 12 above presents the maximum likelihood estimates of the mean equation of the
GJR‐ GARCH 1, 3 regression of the above model. The GJR‐GARCH 1, 3 model was
used to capture the asymmetric impacts that were found to be present in the data after
conducting a sign‐bias test, as detailed in chapter 4. Similar to Equation 10, the
coefficients on the lagged return on the ASX200 and the US SP500 are highly significant.
The coefficient on the lagged return of the US SP500 is positive, suggesting that an
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increase in the mean return on the US SP500 index leads to an increase in the mean
return on the ASX200. This result is similar to the result obtained for Equation 10.
The coefficient on the dummy variable for negative retail trade is also significant. This
result suggests that market participants react to the news content of the retail sales
figures rather than just the release of the announcement. The coefficient on variable is
positive, suggesting that the release of retail sales news leads to an increase of the mean
return on the ASX 200. This could be explained by the notion that retail sales figures
depict the level of consumer spending in the economy. Negative retail trade news can be
viewed by market participants as a signal for dampening consumer demand eventually
leading to a fall in the level of economic activity in the country.
For an economy such as Australia, where one of the most important goals of the Reserve
Bank of Australia RBA is inflation‐targeting, the levels of economic activity can
influence the RBA to take precautionary action against rising inflation by increasing
interest rates. This is seen by market participants as very significant, as increasing
interest rates leads to increased costs of borrowing and thereby leads to a slowdown of
economic activity. Since retail trade signals consumer demand we can view the news
content leading to an increase in uncertainty in the economy. It increases the risk of an
economic downturn and hence market participants react to this news leading to a
significant reaction in the mean returns on the ASX200. However, the sign on the
negative retail trade news is surprising. It was expected that bad news about the level of
consumer spending in the economy would lead to a short‐term decrease in the market
return. Since the sign on the variable is positive, it suggests that release of bad news
regarding retail trade results in a positive jump in the mean returns on the market.
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If the jump in economic activity is considered to push Australian inflation out of the
inflation target then it is possible that the Reserve Bank of Australia could increase the
overnight cash rate. In this scenario it is possible that bad news could be in fact seen by
the market as good news. The news would effectively then have the same impact as
good news.
None of the other variables are significant in the mean returns equation. It is rather
interesting to note that the variable CPI has had no impact so far. This does not;
however imply that markets were indifferent to the CPI news. Kim S.‐J. , 1996 suggests
that prior to April 1988, higher future inflation expectations was the dominant response
to news about the CPI, however, now the anticipation of a tightening monetary policy
response by the RBA is more relevant. The data used in this study only ranged from
1999‐2008, and thus the impact of the significance of the CPI announcement was
neutralized by the market participant’s reaction to the news on the overnight cash rate.
Similarly, news regarding the unemployment rate in the economy also wasn’t deemed
significant by market participants.
The same variables have been added to the variance equation to examine the impact of
news releases on the conditional variance of the ASX 200. Table 13 below presents the
results for the variance equation.
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Table 13 Estimates of the variance equation of equation 11
Variable Coefficient Std. Error z‐Statistic
C 1.13E‐06** 4.85E‐07 2.338
0.040591** 0.020911 1.941
0.15131* 0.043576 3.472
0.235603*** 0.142482 1.654
0.357966** 0.168585 2.123
0.262451*** 0.155507 1.687
‐0.000323 9.69E‐05 0.287
Positive CPI 3.17E‐05* 2.56E‐05 1.238
Negative CPI 4.60E‐07 6.31E‐06 0.626
Positive Unemployment 3.58E‐06 5.61E‐06 2.861
Negative Unemployment 3.81E‐06 5.66E‐06 0.638
Positive Retail Trade ‐1.84E‐06 5.47E‐06 0.673
Negative Retail Trade 7.89E‐07 2.74E‐06 1.237
Positive GDP ‐1.37E‐05 4.77E‐06 0.073
Negative GDP 3.80E‐06 6.07E‐06 3.337
means coefficient is significant at 1% means coefficient is significant at 5%
means coefficient is significant at 10%
All of the ARCH and GARCH terms are significant, as shown in Table 13. Model
estimates can also provide insight into the degree of persistence of the volatility shocks
in the market. For shocks to be highly persistent, the sum of the coefficients on the
ARCH and GARCH terms should be close to unity. For this equation, the
coefficients , , , is approximately 0.90, suggesting that volatility shocks are
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persistent in this particular case. This result is consistent with that obtained by Kim &
In, 2002
Additionally, similar to equation 10, the coefficient on the lagged US return variable is
highly statistically significant, again implying that a fall in the return on US stock
markets leads to increased volatility in the Australian stock market. This result is
expected, as follows through from the high correlation between these two markets.
Unlike equation 10, we can clearly see that the coefficient on positive CPI news is
negative and highly statistically significant. This implies that a less than expected
increase in prices in the economy leads to a decrease in the volatility of the Australian
stock markets. The coefficient is statistically significant at 1% level. This result is
intuitive and very important. A variety of studies conducted on the US stock markets
have also found a similar result see for example, Kim, McKenzie, & Faff, 2004
Positive news about the inflation leads to the adjustment of inflationary expectations. As
discussed before, the RBA monitors the rate of inflation within the economy and
attempts to keep it between 2‐3% on average. Positive news about the CPI would mean
that inflation in the economy was lower than expected and hence lead to a downward
revision of inflationary expectations. This in turn would cause interest rates to remain
constant. Market participant’s price risk and this good news would lead to downward
revisions of associated risk and lead the volatility within the economy to decrease. An
increase in inflationary pressures could possibly cause inflation to rise in the medium
term which would cause the value of investments and assets to decline. It is also
possible that the market participant reaction may be due to an expectation of a change
in interest rates that might be taken by the Reserve Bank of Australia RBA . If the RBA
feels that the growth will be overheating the economy, it will take steps to dampen
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consumer spending in the economy by increasing interest rates. This increase in
interest rates will cause the costs of borrowing to increase, thereby affecting businesses.
Hence, positive news about the CPI is seen to be so statistically significant. More
importantly, in combination with the marginal significance of the positive GDP news,
these results suggest that market participants react to that information which conveys
certain facts about the inflationary pressure in the economy. A high level of economic
activity would lead to increased inflationary pressures, causing an increase in the risk as
asset values decline. This implies that market participants react to news that could
possibly lead to increased inflationary pressures. Similarly, market participants also
react significantly to the opposite type of news. Positive news about the CPI reduces
market volatility, suggesting that a reduction in inflationary pressures increases
confidence in the economy.
Additionally, the coefficient estimate on the asymmetry term is positive and highly
significant. This implies that negative shocks imply a higher conditional variance in the
next period than positive shocks of the same sign. This impact can be explained by two
contesting theories, leverage effects as well as volatility feedback effects. In their study
of the impact of foreign stock markets and news on the Australian financial markets,
Kim & In, 2002 found a similar result.
Similar to Equation 10, none of the other variables included in model 2 are statistically
significant. It is especially surprising that unemployment news causes no increase in the
conditional variance in the Australian stock market. A plausible explanation for this
could be that since inflation and unemployment are closely related, our model fails to
capture any stock market reaction to unemployment news. Unemployment akin to CPI
also essentially conveys information about the level of economic activity. It is possible
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that market participants consider the CPI as a more accurate estimate of the state of the
economy, and disregard unemployment.
Similarly, retail trade news was also not found to affect the conditional variance of the
Australian stock market. This again supports the hypothesis that since investors derive
information about inflationary expectations and levels of economic activity from CPI
and GDP news respectively, they disregard estimates of consumer spending and
unemployment.
Overall, the models presented in this chapter have been able to capture all the stylized
features of the data presented in chapter 3. The results show that the Australian market
responds mostly to information that affects the inflationary expectations within the
economy. Additionally, negative shocks in the market have a greater impact than
positive shocks, with such shocks being highly persistent in nature. However, this model
also provides evidence that the US stock market presents an important source of
volatility in the Australian stock market, and as such US news announcements are also
likely to impact Australian equity returns.
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7. CONCLUSION
The research undertaken in this thesis has shown evidence that news announcements
have an impact on the mean returns as well as the volatility of the equity market in
Australia. In particular, the release of CPI has a marked effect on the conditional
variance of the ASX200. Our modelling strategy predicts that the release of CPI figures
lead to an increase in the volatility of the Australian Stock exchange. This is a clear
indication that market participants are concerned with the level of inflation in Australia.
Secondly, our results also show that negative news on retail trade i.e. a surprise in retail
trade figures, labelled as bad news if the outcome was below expectations led to an
increase in the mean returns of the ASX200. This result is not as expected, but perhaps
provides an interesting avenue for the exploration of this trend. Negative retail trade
news is a signal for dampening consumer demand, suggesting a possible fall in the level
of economic activity. This fall in economic activity could be seen as putting downward
pressure on inflationary expectations. Given the time‐period investigated and the high
levels of sustained growth achieved by the Australian economy, this result could
possibly be feasible.
The results also provided no evidence for the importance of the unemployment variable
on the mean returns or the variance of the ASX200. Unemployment rates in the
economy could be viewed as indirect measures for the level of economic activity.
Demand for labour is derived demand, and hence directly influenced by the level of
economic activity. However, structural and frictional unemployment are always existent
in every economy. An increase in unemployment may not be a direct result of falls in
production levels; it could possibly be an impact of new micro structural policy reforms.
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The results obtained thus when viewed in the context of the period of data used are
entirely justified.
Tests of asymmetry were also conducted on the news announcement data. There was a
strong presence of asymmetry in the data. The coefficient of asymmetry was positive,
and thus suggested that negative shocks lead to greater changes in stock market
conditional variance as opposed to positive shocks. In common layman terms, this
would mean that bad news of a certain variable would cause greater uncertainty and
thereby increase volatility of the market, as opposed to good news. Such an impact was
attributed to leverage and volatility feedback effects. Although these theories are
competing in their explanation of the asymmetry, no one theory is enough to explain the
complete impact.
Another important result proved in this thesis was the presence of information
spillovers from the USA to Australia. This was clearly shown through the significant of
US stock markets on the Australian equity markets; US stock markets affected both the
conditional variance of the market, as well as the mean returns of the market very
significantly. It can be thus implied that US news affects the Australian stock markets.
This assumption can be made on the basis of the rationale` that US stock markets would
react to news announcements in the US and thus this would present an indirect impact
of US news on Australian stock markets. Studies such as the one by Kim & In,
2002 have proven this hypothesis using a model similar to the one used in this paper.
The research conducted in this thesis was however limited by time and data availability
constraints. It is important to understand that the research still has considerable
omitted variable bias. One of the primary causes of this omitted variable bias is the
absence of control for firm‐specific news. Collecting firm‐specific news would be a sheer
Page 64
mammoth task, and as such it would be impossible for anyone to account for news from
all firms. Additionally, the data frequency used is daily, and thus does not allow us to
examine the immediate impact of news on the market, it is possible that news shocks
get assimilated into prices by the end of the day. In this case then, it would not be
possible for us to capture reactions to such news. The research controls for US stock
markets, however it is possible that news from other trading partners for example UK
and Japan also have a significant impact on the ASX200.
Another weakness is that the research uses only certain macroeconomic news variables
that are seen as important to market participants. It is possible that announcements of
other variables could have a significant impact on the market, and have not been used in
this study. Lastly, the expectations dummy variables were derived from the monthly
reports by the St. George Bank, these expectations would be less accurate as compared
to weekly or fortnightly expectations forecasted by analysts, as expectations could be
revised by market participants during the course of the month by observing other news
items.
This thesis has made an original contribution to the branch of Australian market
literature focusing on impacts of news announcements on the ASX200. By considering
both announcement impacts as well as expectations to derive news content I have been
able to understand what information is priced by Australian market participants. It has
also enabled me to understand the behaviour of Australian market returns for the
period of 1999‐2008. Research into this field can be extended further by analysing
higher frequency data and by using more accurate expectations data. The
methodologies adopted in this thesis are robust, and as such have been widely used by
researchers worldwide. Although it is feasible for us to assume that macroeconomic
Page 65
news announcements released by the ABS affect both, the conditional variance as well
as the conditional mean of Australian equity markets, stock markets in the US seem to
be considered more important.
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9. APPENDIX
APPENDIX A
BREUSCH PAGAN Test for HETEROSKEDASTICITY
APPENDIX B
Alternative Models Estimated to check for the robustness of equation
ARCH 5 for equation 10
ARC
Coefficient ‐0.006257
Probability 0.00
Mean Equation Coefficient Std. Error
‐0.074341 0.01861 0.358606 0.012342
CPI 0.001498 0.000986GDP 0.002029 0.001543Unemployment 0.000634 0.000592Retail Trade 0.001065 0.000622Variance Equation C 1.69E‐05 1.69E‐06ARCH 1 0.206642 0.049501ARCH 2 0.097285 0.029857ARCH 3 0.179539 0.034293ARCH 4 0.118247 0.026897ARCH 5 0.106198 0.026342
‐0.00017 0.000154CPI 4.00E‐06 6.83E‐06GDP 6.23E‐05 5.71E‐05Unemployment 3.52E‐06 6.12E‐06Retail Trade 8.53E‐06 7.52E‐06
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ARCH LM Test for ARCH 5
F‐statistic 1.704014 Probability 0.13023 Obs*R‐squared 8.511829 Probability 0.130193
GJR‐GARCH 1,1
Mean Equation Coefficient Std. Error
‐0.073859 0.017806 0.341637 0.011859
DCPI 0.000349 0.000923DUNEMP 0.000464 0.000533DGDP 0.000342 0.001298DRET 0.000335 0.000598Variance Equation C 1.25E‐06 2.69E‐07ARCH 1 0.017993 0.012027
0.095641 0.023692GARCH 1 0.915481 0.011473CPI ‐1.73E‐06 3.62E‐06Unemployment ‐5.03E‐06 3.29E‐06GDP 5.68E‐06 5.36E‐06Retail Trade ‐1.53E‐06 3.19E‐06
‐0.000228 6.67E‐05
ARCH LM Test for GJR‐GARCH 1,1
F‐statistic 1.974941 Probability 0.079251
Obs*R‐squared 9.860027 Probability 0.0793
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APPENDIX C
Alternative Models Estimated to check for the robustness of equation 10
ARCH 5
Mean Equation Coefficient Std. Error
‐0.075504 0.018562 0.358571 0.012271
Positive CPI ‐0.000408 0.001599 Negative CPI 0.001443 0.00132 Positive GDP 0.001649 0.003025 Negative GDP 0.000588 0.001285 Positive Unemployment 0.000286 0.000959 Negative Unemployment ‐0.00035 0.000779 Positive Retail Trade 0.001517 0.000865 Negative Retail Trade 0.000944 0.000435 Variance Equation C 1.64E‐05 1.66E‐06 ARCH 1 0.207861 0.048999 ARCH 2 0.098216 0.030174 ARCH 3 0.178225 0.034159 ARCH 4 0.119872 0.026825 ARCH 5 0.112218 0.026376
‐0.000163 0.000151 Positive CPI ‐3.94E‐06 1.22E‐05 Negative CPI 4.20E‐06 7.78E‐06 Positive GDP 0.000161 0.000159 Negative GDP 2.97E‐06 9.70E‐06 Positive Unemployment ‐4.98E‐07 7.65E‐06 Negative Unemployment ‐1.12E‐06 6.79E‐06 Positive Retail Trade 5.21E‐06 1.14E‐05 Negative Retail Trade 5.77E‐06 4.03E‐06
ARCH‐LM Test for ARCH 5
F‐statistic 2.188 Probability 0.052922Obs*R‐squared 10.91928 Probability 0.053004
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GJR‐GARCH 1,1
Mean Equation Coefficient Std. Error
‐0.077049 0.01787 0.34233 0.011849
Positive GDP ‐0.001262 0.003147 Negative GDP 0.00032 0.001147 Positive CPI ‐0.001049 0.001406 Negative CPI 0.000326 0.00128Positive Unemployment 6.31E‐05 0.000844 Negative Unemployment ‐0.000287 0.000781 Positive Retail Trade 0.001163 0.000784 Negative Retail Trade 0.000668 0.000434 Variance Equation C 8.38E‐07 2.77E‐07 ARCH 1 0.019839 0.012196
0.092639 0.024145 GARCH 1 0.916375 0.011043
‐0.000222 5.87E‐05 Positive GDP 1.47E‐05 1.15E‐05 Negative GDP 4.00E‐07 4.40E‐06 Positive CPI ‐6.53E‐06 6.23E‐06 Negative CPI 1.30E‐06 4.26E‐06 Positive Unemployment 1.25E‐06 3.90E‐06 Negative Unemployment 8.77E‐07 3.45E‐06 Positive Retail Trade ‐2.52E‐06 2.96E‐06 Negative Retail Trade 4.69E‐07 1.90E‐06
ARCH‐LM Test for GJR‐GARCH 1, 1
F‐statistic 2.253239 Probability 0.046673Obs*R‐squared 11.24345 Probability 0.046761
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GJR‐GARCH 1,2
Mean Equation Coefficient Std. Error
‐0.075787 0.017647 0.343941 0.011984
Positive GDP ‐0.001838 0.003338 Negative GDP 0.000438 0.001143 Positive CPI ‐0.000751 0.001362 Negative CPI 0.000578 0.001268 Positive Unemployment ‐3.49E‐05 0.000861 Negative Unemployment ‐0.000548 0.000782 Positive Retail Trade 0.000929 0.000812 Negative Retail Trade 0.000703 0.000432 Variance Equation C 9.33E‐07 4.01E‐07 ARCH 1 0.032995 0.018
0.128961 0.034756 GARCH 1 0.280981 0.103635 GARCH 2 0.595723 0.095085
‐0.000305 7.94E‐05 Positive GDP 2.71E‐05 2.04E‐05 Negative GDP 1.85E‐07 5.95E‐06 Positive CPI ‐7.69E‐06 7.43E‐06 Negative CPI 2.85E‐06 5.63E‐06 Positive Unemployment 3.92E‐06 5.43E‐06 Negative Unemployment 5.23E‐06 5.63E‐06 Positive Retail Trade ‐3.44E‐06 4.15E‐06 Negative Retail Trade 1.23E‐06 2.57E‐06
ARCH LM Test GJR‐GARCH 1,2
F‐statistic 1.070849 Probability 0.374468Obs*R‐squared 5.355575 Probability 0.374043
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APPENDIX D
The GJR‐GARCH models for news as well as news content, both seemed robust
adequate. Below are the results from the ARCH‐LM tests for equation 10 and 11:
ARCH LM Test for Equation 11
F‐statistic 0.787433 Probability 0.558601 Obs*R‐squared 3.940288 Probability 0.558045
ARCH LM Test for Equation 10
F‐statistic 1.165935 Probability 0.323469 Obs*R‐squared 5.830057 Probability 0.323108
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