Intertemporal CAPM explained

Within mathematical finance, the intertemporal capital asset pricing model, or ICAPM, is an alternative to the CAPM provided by Robert Merton. It is a linear factor model with wealth as state variable that forecasts changes in the distribution of future returns or income.

In the ICAPM investors are solving lifetime consumption decisions when faced with more than one uncertainty. The main difference between ICAPM and standard CAPM is the additional state variables that acknowledge the fact that investors hedge against shortfalls in consumption or against changes in the future investment opportunity set.

Continuous time version

Merton[1] considers a continuous time market in equilibrium.The state variable (X) follows a Brownian motion:

dX=\mudt+sdZ

The investor maximizes his Von Neumann–Morgenstern utility:

Eo

T
\left\{\int
o

U[C(t),t]dt+B[W(T),T]\right\}

where T is the time horizon and B[W(T),T] the utility from wealth (W).

The investor has the following constraint on wealth (W). Let

wi

be the weight invested in the asset i. Then:

W(t+dt)=[W(t)-C(t)

n
dt]\sum
i=0

wi[1+ri(t+dt)]

where

ri

is the return on asset i.The change in wealth is:

dW=-C(t)dt+[W(t)-C(t)dt]\sumwi(t)ri(t+dt)

We can use dynamic programming to solve the problem. For instance, if we consider a series of discrete time problems:

maxE0

T-dt
\left\{\sum
t=0
t+dt
\int
t

U[C(s),s]ds+B[W(T),T]\right\}

Then, a Taylor expansion gives:
t+dt
\int
t

U[C(s),s]ds=U[C(t),t]dt+

1
2

Ut[C(t*),t*]dt2U[C(t),t]dt

where

t*

is a value between t and t+dt.

Assuming that returns follow a Brownian motion:

ri(t+dt)=\alphaidt+\sigmaidzi

with:

E(ri)=\alphaidt;

2)=var(r
E(r
i)=\sigma
2dt
i

;cov(ri,rj)=\sigmaijdt

Then canceling out terms of second and higher order:

dW[W(t)\sumwi\alphai-C(t)]dt+W(t)\sumwi\sigmaidzi

Using Bellman equation, we can restate the problem:

J(W,X,t)=maxEt\left\{\int

t+dt
t

U[C(s),s]ds+J[W(t+dt),X(t+dt),t+dt]\right\}

subject to the wealth constraint previously stated.

Using Ito's lemma we can rewrite:

dJ=J[W(t+dt),X(t+dt),t+dt]-J[W(t),X(t),t+dt]=Jtdt+JWdW+JXdX+

1
2

JXXdX2+

1
2

JWWdW2+JWXdXdW

and the expected value:

EtJ[W(t+dt),X(t+dt),t+dt]=J[W(t),X(t),t]+Jtdt+JWE[dW]+JXE(dX)+

1
2

JXXvar(dX)+

1
2

JWWvar[dW]+JWXcov(dX,dW)

After some algebra[2], we have the following objective function:

max\left\{U(C,t)+Jt+JWW

n
[\sum
i=1

wi(\alphai-rf)+rf]-JWC+

W2
2

JWW

n
\sum
j=1

wiwj\sigmaij+JX\mu+

1
2

JXXs2+JWXW

n
\sum
i=1

wi\sigmaiX\right\}

where

rf

is the risk-free return.First order conditions are:

JW(\alphai-rf)+JWWW

n
\sum
j=1
*
w
j

\sigmaij+JWX\sigmaiX=0i=1,2,\ldots,n

In matrix form, we have:

(\alpha-rf{1})=

-JWW
JW

\Omegaw*W+

-JWX
JW

covrX

where

\alpha

is the vector of expected returns,

\Omega

the covariance matrix of returns,

{1}

a unity vector

covrX

the covariance between returns and the state variable. The optimal weights are:

{w*}=

-JW
JWWW

\Omega-1(\alpha-rf{1})-

JWX
JWWW

\Omega-1covrX

Notice that the intertemporal model provides the same weights of the CAPM. Expected returns can be expressed as follows:

\alphai=rf+\betaim(\alpham-rf)+\betaih(\alphah-rf)

where m is the market portfolio and h a portfolio to hedge the state variable.

See also

References

Notes and References

  1. Robert . Merton. An Intertemporal Capital Asset Pricing Model . Econometrica. 1973. 867–887. 1913811. 41. 5 . 10.2307/1913811.
  2. E(dW)=-C(t)dt+W(t)\sumwi(t)\alphaidt

    var(dW)=[W(t)-C(t)dt]2var[\sumwi(t)ri(t+dt)]=W(t)2\sumi=1\sumi=1wiwj\sigmaijdt

    n
    \sum
    i=o

    wi(t)\alphai=

    n
    \sum
    i=1

    wi(t)[\alphai-rf]+rf