Constrained Merton Portfolio Optimization with a Terminal Wealth Floor
Summary
The document asks how to enforce a minimum terminal wealth in a continuous-time consumption and investment problem with CRRA utility. It presents a proposed construction: scale down the unconstrained Merton strategy and use the remaining initial capital to buy a put option that protects the scaled portfolio at maturity. The scale is selected so the strategy and put together use the investor’s initial wealth.
The question connects this construction to the static optimization and martingale-replication steps used to derive the unconstrained solution, but provides no derivation or evidence establishing that the proposed strategy solves the constrained problem. It also asks whether a minimum consumption requirement would have a similar form, without answering. The setup assumes a Black–Scholes market and deterministic floor; applicability depends on the exact constraints and market assumptions.
Key ideas
- A terminal wealth floor changes the attainable terminal payoffs in the Merton consumption and investment problem.
- The proposed construction scales the unconstrained strategy and funds a put option on its terminal wealth.
- The option premium and scaled strategy are required to fit within initial wealth.
- The document raises, but does not resolve, how static optimization and replication lead to this construction.
- A minimum consumption constraint is posed as a separate question without a proposed solution.
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Full text
# Merton's portfolio problem with constraints
# Merton's portfolio problem with constraints
Suppose the investor can invest in a Black-Scholes market with one risky asset $S$ with drift $\alpha$ and volatility $\sigma$ and a riskless asset $B$ with a riskless rate of return $r$, and the investor seeks to solve the problem $$\max_{c,\lambda} E\bigg[\int_0^Tu(c_t)dt+u(X_T)\bigg],$$ where $c_t>0$ is a consumption process and $\lambda_t$ is the dollar amount invested in the risky asset. u is a CRRA utility function given by $u(x)=\frac{x^{\gamma}}{\gamma}$ for $\gamma$ $\in$ $(-\infty,1)$\{0}. $X_t$ is the wealth process that solves $$dX_t=[rX_t+\lambda_t(\alpha-r)-c_t]dt+\sigma \lambda_tdW_t, X(0)=x.$$ Using the martingale method (same as Karatzas & Shreve 1998, Methods of Mathematical Finance) , I have arrived at a feedback form solution $$c^*_t=\frac{X_t}{f_t}, \lambda^*_t=\frac{\alpha-r}{\sigma^2(1-\gamma)}X_t$$ for a deterministic function $f$. Now I want to impose the constraint $X_T\geq G$ for a positive constant $G$. The litterature seems to suggest a solution where one divides the initial wealth $x$ into 2 parts, $kx$ and $(1-k)x$, use the amount $kx$ to pursue the strategy ($kc^*_t$, $k\lambda^*_t$) and the remaining ($1-k)x$ to buy a European put option on $kX$ with maturity $T$ and strike $G$. k is chosen such that $$x=kx+P_{kX}(0,T,G)$$ where $P_{kX}(0,T,G)$ is the price of the put option at time 0.
My question is how we arrive at this kind of solution? The original problem involved static optimization to find the $c$ and $X_T$ that maximize the total utility and a martingale representation result to find a $\lambda$ that replicated that $X_t$ How do we go from static optimization to a strategy of this form?
Also, if one wanted to impose the constraint $c_t\geq C$ for all $t$ some some positive constant $C$, would the solution have the same form?Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.