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Intertemporal CAPM

In mathematical finance, the intertemporal capital asset pricing model, or ICAPM, created by Robert C. Merton, is an alternative to the capital asset pricing model (CAPM). 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.

01Continuous time version

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

dX=\mu dt+sdZ

The investor maximizes his Von Neumann-Morgenstern utility:

E_{o}\left\{\int _{o}^{T}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 w_{i} be the weight invested in the asset i. Then:

W(t+dt)=[W(t)-C(t)dt]\sum _{i=0}^{n}w_{i}[1+r_{i}(t+dt)]

where r_{i} is the return on asset i. The change in wealth is:

dW=-C(t)dt+[W(t)-C(t)dt]\sum w_{i}(t)r_{i}(t+dt)

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

\max E_{0}\left\{\sum _{t=0}^{T-dt}\int _{t}^{t+dt}U[C(s),s]ds+B[W(T),T]\right\}

Then, a Taylor expansion gives:

\int _{t}^{t+dt}U[C(s),s]ds=U[C(t),t]dt+{\frac {1}{2}}U_{t}[C(t^{*}),t^{*}]dt^{2}\approx U[C(t),t]dt

where t^{*} is a value between t and t+dt.

Assuming that returns follow a Brownian motion:

r_{i}(t+dt)=\alpha _{i}dt+\sigma _{i}dz_{i}

with:

E(r_{i})=\alpha _{i}dt\quad ;\quad E(r_{i}^{2})=var(r_{i})=\sigma _{i}^{2}dt\quad ;\quad cov(r_{i},r_{j})=\sigma _{ij}dt

Then canceling out terms of second and higher order:

dW\approx [W(t)\sum w_{i}\alpha _{i}-C(t)]dt+W(t)\sum w_{i}\sigma _{i}dz_{i}

Using Bellman equation, we can restate the problem:

J(W,X,t)=max\;E_{t}\left\{\int _{t}^{t+dt}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]=J_{t}dt+J_{W}dW+J_{X}dX+{\frac {1}{2}}J_{XX}dX^{2}+{\frac {1}{2}}J_{WW}dW^{2}+J_{WX}dXdW

and the expected value:

E_{t}J[W(t+dt),X(t+dt),t+dt]=J[W(t),X(t),t]+J_{t}dt+J_{W}E[dW]+J_{X}E(dX)+{\frac {1}{2}}J_{XX}var(dX)+{\frac {1}{2}}J_{WW}var[dW]+J_{WX}cov(dX,dW)

After some algebra , we have the following objective function:

max\left\{U(C,t)+J_{t}+J_{W}W[\sum _{i=1}^{n}w_{i}(\alpha _{i}-r_{f})+r_{f}]-J_{W}C+{\frac {W^{2}}{2}}J_{WW}\sum _{i=1}^{n}\sum _{j=1}^{n}w_{i}w_{j}\sigma _{ij}+J_{X}\mu +{\frac {1}{2}}J_{XX}s^{2}+J_{WX}W\sum _{i=1}^{n}w_{i}\sigma _{iX}\right\}

where r_{f} is the risk-free return. First order conditions are:

J_{W}(\alpha _{i}-r_{f})+J_{WW}W\sum _{j=1}^{n}w_{j}^{*}\sigma _{ij}+J_{WX}\sigma _{iX}=0\quad i=1,2,\ldots ,n

In matrix form, we have:

(\alpha -r_{f}{\mathbf {1} })={\frac {-J_{WW}}{J_{W}}}\Omega w^{*}W+{\frac {-J_{WX}}{J_{W}}}cov_{rX}

where \alpha is the vector of expected returns, \Omega the covariance matrix of returns, {\mathbf {1} } a unity vector cov_{rX} the covariance between returns and the state variable. The optimal weights are:

{\mathbf {w} ^{*}}={\frac {-J_{W}}{J_{WW}W}}\Omega ^{-1}(\alpha -r_{f}{\mathbf {1} })-{\frac {J_{WX}}{J_{WW}W}}\Omega ^{-1}cov_{rX}

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

\alpha _{i}=r_{f}+\beta _{im}(\alpha _{m}-r_{f})+\beta _{ih}(\alpha _{h}-r_{f})

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

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Sources and credits

This article is adapted from the Wikipedia article Intertemporal CAPM, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.

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