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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

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

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!

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

APPENDIX C .............................................................................................................................................. 74

APPENDIX D .............................................................................................................................................. 77

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

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

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.

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,

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.

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 .

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.

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

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

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

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

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

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.

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.

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

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

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.

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

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

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

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.

‐ 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.

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

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.

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

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

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

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

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

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

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

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.

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

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.

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

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

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.

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 .

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

Australian Stock Market. Subsequent sections will discuss the nature of results obtained

and the implications of these results.

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

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:

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

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


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



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

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:

∑ ∑ 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

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


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



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

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.

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.

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%


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

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

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

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.

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.

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

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

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

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


‐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

APPENDIX C

Alternative Models Estimated to check for the robustness of equation 10

ARCH 5


‐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

GJR‐GARCH 1,1


‐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


GJR‐GARCH 1,2


‐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


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


ARCH LM Test for Equation 10


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