chp.19 term structure of interest rates (ii)

>>

报告人：陈焕华指导老师：郑振龙教授

厦门大学金融系

Chp.19 Term Structure of Interest Rates (II)

>> Continuous time models

• Term structure models are usually more convenient in continuous time.

• Specifying a discount factor process and then find bond prices.

• A wide and popular class models for the discount factor:

>> Implications

• Different term structure models give different specification of the function for

• r starts as a state variable for the drift of discount factor process, but it is also the short rate process since

• Dots(.) means that the terms can be function of state variables.(And so are time-varying)

• Some orthogonal components can be added to the discount factor with on effect on bond price.

,, rru

dtrdE tft /

>> Some famous term structure models

• 1.Vasicek Model:

Vasicek model is similar to AR(1) model.

• 2.CIR Model

• The square root terms captures the fact that higher interest rate seem to be more volatile, and keeps the interest rate from zero.

dzdtrrdrdzrdtdr

)(,

dzrrrdrdzrdtdr

)(,

>> Continuous time models

Having specified a discount factor process, it is simple matter to find bond prices

Two way to solve – 1. Solve the discount factor model forward

and take the expectation– 2. Construct a PDE for prices, and solve

that backward

)()(

t

Ntt

Nt EP

>> Implication

• Both methods naturally adapt to pricing term structure derivatives : call options on bonds, interest rate floors or caps, swaptions and so forth, whose payoff is

• We can take expectation directly or

use PDE with option payoff as boundary conditions.

ts

C

t

st

Nt dssxEP )()(

>> expectation approach

• Example: in a riskless economy • With constant interest rate,

)(

)2/1(ln

)2/1(2/1ln

0 02 )2/1(

0)(

0

0 0

2

0

22

2

Ts

Ts sss dzdsrN

T

s

T

s sssT

eEP

dzdsr

dzdtrddd

,0)(0

T

s sdsrN ePrTeP 0

>> Remark

• In more situations, the expectation approach is analytically not easy.

• But in numerical way, it is a good way. We can just stimulate the interest rate process thousands of times and take the average.

>> Differential Equation Approach

• Similar to the basic pricing equation for a security price S with no dividend

• For a bond with fixed maturity, the return is

• Then we can get the basic pricing equation for the bonds with given maturity:

)()/(

d

SdSErdtSdSE tt

dtNtNP

PPtNdP

),(1),(

)()),(1()(

dPdPEdtr

NtNP

PPdPE tt

>> Differential Equation Solution

• Suppose there is only one state variable, r. Apply Ito’s Lemma

• Then we can get:

dzrPdt

rPu

rPdP rrr

)21( 2

2

2

rrr r

PrPNP

rPu

rP 2

2

2

21

>> Market Price of Risk and Risk-neutral Dynamic Approach• The above mentioned PDE is derived with

discount factors. • Conventionally the PDE is derived without

discount factors.• One approach is write short-rate process

and set market price of risk to

rrr rPrP

NP

rPu

rP

2

2

2

21

>> Implication

• If the discount factor and shocks are imperfectly correlated,

• Different authors use market price of risk in different ways.

• CIR(1985) warned against modeling the right hand side as , it will lead to positive expected return when the shock is zero, thus make the Sharpe ration infinite.

• The covariance method can avoid this.

(.)rP

>> Risk-Neutral Approach

A second approach is risk-neutral approach• Define:• We can then get

• price bonds with risk neutral probability:

rrrr dzdtudrrdtd )(,/

021)( 2

2

2

rP

NP

rPu

rP

rrr

][ 0*)( T

s sdsr

tN

t eEP

>> Remark

• The discount factor model carries two pieces of information. – The drift or conditional mean gives the

short rate– The covariance generates market price

of risk.• It is useful to keep the term structure

model with asset pricing, to remind where the market price of risk comes from.

• This beauty is in the eye of the beholder, as the result is the same.

>>Solving the bond price PDE numerically

• Now we solve the PDE with boundary condition

• numerically. • Express the PDE as

• The first step is

rPrPu

rP

NP

rrr

2

2

2

21)(

>>Solving the bond price PDE

• At the second step,0/,/ 22 rPNrP

22 ),()(),2(

),()(),(),2(

rNPuNrNP

rNrPuNN

rNPrNPNP

rr

rr

>> 5. Three Linear Term Structure Models

• Vasicek Model, CIR Model, and Affine Model gives a linear function for log bond prices and yields:

• Term structure models are easy in principle and numerically. Just specify a discount factor process and find its conditional expectation or solve the differential equation.

rNBNArNP )()(),(ln

>> Overview• Analytical solution is important since the

term structure model can not be reverse-engineered. We can only start from discount factor process to bond price, but don’t know how to start with the bond price to discount factor. Thus, we must try a lot of calculation to evaluate the models.

• The ad-hoc time series models of discount factor should be connected with macroeconomics, for example, consumption, inflation, etc.

>> Vasicek Model

• The discount factor process is:

• The basic bond differential equation is:

• Method: Guess and substitute

dzdtrrdr

dzrdtd

r

)(

rr r

PrPNP

rPrr

rP 2

2

2

21)(

>> PDE solution:(1)

• Guess – Boundary condition: for

any r, so

• The result is

rNBNAerNP )()(),(

0)0()0( rBA

0)0(,0)0( BA

>> PDE solution:(1)

• To substitute back to PDE ,we first calculate the partial derivatives given

rNBNAerNP )()(),(

>> PDE solution:(1)• Substituting these derivatives into PDE

• This equation has to hold for every r, so we get ODEs

>> PDE solution(2)

• Solve the second ODE with

>> PDE solution(3)

• Solve the first ODE with

>> PDE solution(3)

>> PDE solution(4)

• Remark: the log prices and log yields are linear function of interest rates

• means the term structure is always upward sloping.

rNNB

NNArNyrNBNArNp )()(),(,)()(),(

0)(

NNB

ry

>> Vasicek Model by Expectation

• The Vasicek model is simple enough to use expectation approach. For other models the algebra may get steadily worse.

• Bond price


• First we solve r from

• The main idea is to find a function of r, and by applying Ito’s Lemma we get a SDE whose drift is only a function of t. Thus we can just take intergral directly.

• Define


• Take intergral


• So

• We have


• Next we solve the discount factor process

• Plugging r


• The first integral includes a deterministic function, so gives rise to a normally distributed r.v. for

• Thus is normally distributed with mean


• And variance


• So

• Plugging the mean and variance


• Rearrange into

• Which is the same as in the PDE approach


• In the risk-neutral measure

>> CIR Model

>> CIR Model

• Guess• Take derivatives and substitue

• So

>> CIR Model

• Solve these ODEs

• Where

>> CIR Model

>> Multifactor Affine Models

• Vasicek Model and CIR model are special cases of affine models (Duffie and Kan 1996, Dai and Singleton 1999).

• Affine Models maintain the convenient form that the log bond prices are linear functions of state variables(The short rate and conditional variance be linear functions of state variables).

• More state variables, such as long interest rates, term spread, (volatility),can be added as state variable.

>> Multifactor Affine Model

>>Multifactor Affine Model

Where, ,

, , 1,: ' 0i i

K K

y y b KCon y

0 . .0 . .

i

i

CIR MV M

>> PDE solution• Guess

• Basically, recall that

• Use Ito’s Lemma21 1 1' '

2 'dP P Pdy dy dyP P y P y y

>> PDE solution


Rearrange we get the ODEs for Affine Model

>> Bibliography and Comments

• The choice between discrete and continuous time is just for convenience. Campbell, Lo and MacKinlay(1997) give a discrete time treatment, showing that the bond prices are also linear in discrete time two parameters square root model.

• In addition to affine, there are many other kinds of term structure models, such as Jump, regime shift model, nonlinear stochastic volatility model, etc. For the details, refer to Lin(2002).

>> Bibliography and Comments

Constantinides(1992)• Nonlinear Model based on CIR Model,• Analytical solution.• Allows for both signs of term premium.

>> Risk-neutral method

The risk-neutral probability method rarely make reference to the separation between drifts and market price of risk. This was not a serious problem for the option pricing, since volatility is more important.

However, it is not suitable for the portfolio analysis and other uses. Many models imply high and time-varying market price of risk and conditional Sharpe ratio.

Duffee(1999) and Duarte(2000) started to fit the model to the empirical facts about the expected returns in term structure models.

>> Term Structure and Macroeconomics

• In finance, term structure models are often based on AR process.

• In macroeconomics, the interest rates are regressed on a wide variety of variables, including lagged interest rate, lagged inflation, output, unemployment, etc.

• This equation is interpreted as the decision-making rule for the short rate.

• Taylor rule(Taylor,1999), monetary VAR literature (Eichenbaum and Evans(1999).

>> The criticism of finance model

• The criticism of term structure model in finance is hard when we only use one factor model.

• Multifactor models are more subtle.• But if any variable forecasts future

interest rate, it becomes a state variable, and should be revealed by bond yields.

• Bond yields should completely drive out other macroeconomic state variables as interest rate forecasters.

• But in fact, it is not.

>> High-frequency research

• Balduzzi,Bertola and Foresi (1996), Piazzesi(2000) are based on diffusions with rather slow-moving state variable. The one-day ahead densities are almost exactly normal.

• Johannes(2000) points out the one day ahead densities have much fatter tails than normal distribution. This can be modeled by fast-moving state variables. Or, it is more natural to think of a jump process.

>> Other Development

• All the above mentioned models describe the bond yields as a function of state variables.

• Knez, Litterman and Scheinkman(1994) make a main factor analysis on the term structure and find that most of the variance of yields can be explained by three main factors, level, slope, hump. It is done by a simple eigenvalue decomposition method.

>> Remark

• Remark: This method is mainly used in portfolio management, for example, to realize the asset immunation of insurance fund.

• It is a good approximation, but just an approximation. The remaining eigenvalues are not zero. Then the maximum likelihood method is not suitable, maybe GMM is better.

• The importance of approximation depends on how you use the model, if you want to find some arbitrage opportunity, it has risk. The deviation from the model is at best a good Sharpe ratio but K factor model can not tell you how good.

>> Possible Solution

• Different parameters at each point in time (Ho and Lee 1986). It is useful, but not satisfactory.

• The whole yield curve as a state variable, Kennedy(1994), SantaClara and Sornette(1999) may be the potential way.

>> Market Price of Risk

• The market price of interest rate risk reflects bond the market price of real interest rate change and the market price of inflation.

• The relative contribution is very important for the nature of risk.

• If the real interest rate is constant and nominal rates change with inflation, the short term bonds are safest long term investment.

>> Market Price of Risk

• If the inflation is constant and nominal rates change with the real rate, the long term bonds are safest long term investment.

• Little work is done on the separation of interest rate premia between real and inflation premium components. Buraschi and Jiltsov(1999) is one recent effort.

>>

• Thanks!

chp.19 term structure of interest rates (ii)

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