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Let ${\mathcal{F}}_n$ represent the information at decision time $n$ and $E_n$ conditional expectation given it. $V_n(w)$ is the largest conditional expected utility from the remaining consumption opportunities with current wealth $w$; it may depend on the information state as well as $w$. The controls $c$ and $\theta$ are chosen using current information. In the usual allocation interpretation $0\le c\le w$ and $0\le \theta \le 1$, and next wealth is $(w-c)[r+\theta (X_{n+1}-r)]$. Other borrowing/short-selling rules require explicitly replacing this admissible set while preserving solvency.

The Bellman equation follows by separating current utility from future utility: after any current decision the best continuation has conditional value $V_{n+1}$, and maximizing over the current admissible decisions gives the stated recursion. At the last opportunity all wealth is consumed, because utility is increasing, giving $V_N(w)=U(w)$.

There are two printed qualifications. Strict convexity is not needed for this recursion, but it conflicts with the concave power utility in part (ii) and does not support the interior maximizing first-order conditions in part (iii). Also the displayed recursion starting at $n=0$ includes consumption at times $0,\dots ,N$, whereas the preceding sum starts at one. The same recursion represents that sum when begun at time one; alternatively relabel the consumption dates. Below the policy is given for the recursion's remaining horizon, so this indexing discrepancy does not leave the optimization unspecified.

For a competitive equilibrium with one productive asset, the primitives are the probability law and information filtration of total output $d$, each agent's utility function and discount factor, the initial ownership shares ${\theta }_0^j$ summing to one, and the permitted consumption and trading strategies. Prices are not primitives of the equilibrium problem. The derived objects are the ex-dividend price $S$, each agent's consumption $C^j$ and holdings ${\theta }^j$, and their marginal-utility pricing processes. At a common price, every agent solves the individual problem, and resource and share clearing requireThe individual discrete dividend budget equations, optimality Euler equations and terminal conditions from part (i), together with these clearing equations, define equilibrium even when the market is incomplete. Summing the individual budget constraints is consistent with total fruit consumption.

For the given constant absolute risk aversion utility, $U_j^{\prime }(c)=e^{-{\gamma }_{j}c}$. Hence each interior optimum satisfiesThe same $S$ must satisfy all these equations, but their conditional expectation identities do not by themselves identify the different agents' pricing densities pointwise.

To illustrate explicit exponential-utility risk sharing, first consider a complete or otherwise implementable interior allocation. With positive constant welfare weights $a_j$, a pointwise resource multiplier ${\nu }_t$ satisfiesWriteSolving the marginal equations and summing the consumptions givesThe output risk is shared in proportion to risk tolerance ${\gamma }_j^{-1}$; differences in patience produce the time-dependent transfer term. The common normalized state-price density is ${\zeta }_t={\nu }_t/{\nu }_0$. The weights, up to a common scale, are determined by the initial wealth budgetsand, for a fundamental price,If consumption is required to be nonnegative and the interior formula produces a negative allocation, replace it byFor $d_t>0$ this determines the resource multiplier uniquely. At zero total output all constrained consumptions are zero and the corresponding marginal inequalities apply.

A single traded tree need not span the contingent transfers in the general risk-sharing formula. A genuine equilibrium example within the literal one-tree market is obtained by taking all ${\beta }_j=\beta$ and initial shares ${\theta }_0^j=\Gamma /{\gamma }_j$. ThenEvery agent simply keeps the initial shares, all budget constraints and clearing conditions hold, and their discounted marginal utilities coincide. The fundamental price above satisfies every Euler equation. For example, nonnegative bounded output ensures the required price and utility integrability and the terminal terms vanish; the supporting-line argument from part (i) then verifies individual optimality. For heterogeneous discount factors or arbitrary initial ownership, the displayed coupled Euler-budget-clearing system remains the correct one-tree formulation; using the unconstrained Pareto allocation as a traded equilibrium requires an additional financing check.

Use ${\theta }_t$ for the dollar amount invested in the stock; the number of shares is ${\theta }_t/S_t$. The bank account contains $w_t-{\theta }_t$ dollars. With no intermediate consumption, the self-financing portfolio equation isIf instead $\theta$ denotes share holdings, replace each dollar ${\theta }_t$ here by $S_t{\theta }_t$. The equation says that trading only reallocates existing portfolio wealth; gains come from cash interest and stock returns.

For the standard unconstrained complete-diffusion formulation, assume the completed natural Brownian filtration, initial wealth $w_0>0$, and nonnegative admissible wealth. Define the market price of risk ${\kappa }_t=({\mu }_t-r_t)/{\sigma }_t$. The boundedness assumptions make $\kappa$ bounded. The state-price density normalized at zero isIts money-market-adjusted process $Z_t=e^{{\int }_0^t{r}_{s}ds}{\zeta }_t$ is a true martingale by the Novikov condition. Under $d\mathbb{Q}=Z_{T}d\mathbb{P}$, the Girsanov theorem gives $W_t^{\mathbb{Q}}=W_t+{\int }_0^t{\kappa }_{s}ds$ as a Brownian motion, and the discounted stock has zero drift.

The Itô product rule applied to the wealth equation givesFor nonnegative admissible wealth this is a nonnegative local martingale, hence a supermartingale. Therefore every affordable terminal payoff $X=w_T$ satisfies the state-price budget constraint $\mathbb{E}[{\zeta }_{T}X]\le w_0$. Nonzero volatility and the Brownian martingale representation theorem allow replication of nonnegative claims with finite state-price cost.

For a differentiable strictly concave utility function with the usual conditions ensuring an interior integrable optimizer, put $I=(U^{\prime }{)}^{-1}$, the inverse marginal utility. Pointwise maximization of $U(x)-y{\zeta }_{T}x$ givesThe second equation determines the positive constant $y$. To prove optimality, use the concave supporting-tangent inequality:Taking expectations and using the budget bound proves that the candidate dominates every admissible affordable payoff, whenever the utility expectations are well defined. This is the complete-market terminal utility optimizer.

The actual wealth and dollar portfolio are as explicit as general adapted coefficients permit. LetThe Brownian martingale representation theorem, with localization if only $L^1$ integrability is initially known, gives $h$. The dollar portfolio from a deflated wealth martingale isThe bank holds $w_t^{\ast }-{\theta }_t^{\ast }$ dollars. Equivalently, with $B_t=e^{{\int }_0^t{r}_{s}ds}$, $w_t^{\ast }=B_t{\mathbb{E}}^{\mathbb{Q}}[w_T^{\ast }/B_T\mid {\mathcal{F}}_t]$. These formulas need no unjustified differentiability of a value function with random coefficients.

The printed assumption that $U$ is only increasing and concave does not guarantee that the interior formula exists, or even that the optimal value is finite. In general replace the inverse formula by an affordable selectionwhere $\mathcal{D}$ is the wealth domain; boundary choices or supporting slopes handle nondifferentiability. Existence, finite cost and replication still have to hold. A concrete obstruction is the unbounded linear terminal-wealth utility: take $U(x)=x$, $r=0$, $\mu =\sigma =1$, and the Brownian filtration. Then ${\zeta }_T=e^{-W_T-T/2}$. For $A_n=\{{\zeta }_T<1/n\}$, the bounded nonnegative claimis replicable, costs $w_0$, and has $\mathbb{E}{X}_n>n{w}_0$. Thus there is no finite optimal expected utility under the literal utility hypotheses alone. Also, bounded coefficients do not ensure market completeness in an arbitrarily enlarged filtration containing untraded randomness; the full replication formula uses the Brownian market information assumption stated above.

Interpret the exponential distribution change time as an exogenous time independent of the stock's Brownian motion, or, equivalently for this calculation, as having constant conditional intensity $\lambda$ while the rate remains high. Its marginal distribution alone would not imply that conditional intensity if the change time carried information about stock returns. Write $r_1=r+\epsilon$, $p=1-R$, $B_1=B(r_1)$, and let $V_1$ be the prechange value function.

Over a short interval $dt$, a rate change occurs with probability $\lambda dt+o(dt)$ and changes continuation value from $V_1(w)$ to $V_0(w)$. The dynamic programming equation is consequentlyThis jump term distinguishes the exponential interest-rate switch from a permanent higher bank rate.

By the homogeneity of CRRA utility, put $V_1(w)=K_1{w}^p/p$, with $K_1>0$. Differentiating the strictly concave expressions in $c$ and $\pi$ givesThe optimized consumption terms contribute $R{K}_1^{1-1/R}{w}^p/p$, and the optimized investment terms contribute $K_1{B}_1{w}^p$. With $K_0={\gamma }_0^{-R}$, the Hamilton-Jacobi-Bellman equation reduces toEquivalently,The left side is continuous and strictly increasing for ${\gamma }_1>0$, with limits zero and infinity. Thus $D_1>0$ gives one and only one positive root. This scalar equation is an explicit characterization for arbitrary real $R$; elementary closed forms are not generally available. For example, when $R=2$,Use $({\pi }_1^{\ast },{\gamma }_1)$ until the rate changes, and $({\pi }_0^{\ast },{\gamma }_0)$ afterwards. If the two rates coincide, the root is ${\gamma }_1={\gamma }_0$; as $\lambda$ tends to infinity, it also tends to ${\gamma }_0$.

For a direct attainability check, use the proposed prechange constant fractions. The geometric Brownian motion moment is $E[w_t^p]=w^p\exp [p(B_1-{\gamma }_1)t]$ on the prechange evolution. Conditioning on the independent exponential distribution time therefore givesThe denominator is positive because the scalar equation gives $D_1+p{\gamma }_1={\gamma }_1+\lambda K_0/K_1>0$. Hence the candidate policy actually achieves the stated value.

One can also verify the upper bound for both signs of $p$ without relying on a sign-sensitive terminal-value argument. Let $j_t$ be the current regime, $w^{\ast }$ the candidate portfolio wealth, andIf $N_t=1_{\{\tau \le t\}}$ and ${\tilde{N}}_t=N_t-\lambda (t\wedge \tau )$, the Itô formula and the scalar equation givewhere the jump term stops after the switch and continuous coefficients are understood predictably. Thus $M$ is a positive martingale deflator: for any admissible portfolio wealth $w$, the process $M_t{w}_t+{\int }_0^t{M}_s{c}_{s}ds$ is a nonnegative local martingale. It follows thatFor the candidate policy, $E[M_t{w}_t^{\ast }]\to 0$. Indeed its surviving prechange term decays at rate $D_1+p{\gamma }_1>0$, and its postchange terms decay at rate ${\gamma }_0>0$; integrating over the switch time preserves convergence to zero. The finite-time moment computations also make its budget equality exact. Consequently $E{\int }_0^{\infty }{M}_t{c}_t^{\ast }dt=M_0{w}_0$. Apply the concave supporting-tangent inequalityand integrate in time and expectation. The budget inequality proves that no admissible policy exceeds the candidate. This verifies the value function, including when $R>1$.

Both ${\gamma }_0>0$ and $D_1>0$ are needed for a finite prechange answer. If the postchange value is already infinite, that problem persists after the almost surely finite switch. If ${\gamma }_0>0$ but $D_1\le 0$, then for $p>0$ a small constant prechange consumption fraction makes the expected utility function before the switch diverge, or makes its supremum diverge when $D_1=0$. For $p<0$, the optimal no-consumption terminal moment before the switch is $w^p{e}^{p{B}_{1}t}$; withdrawals only increase this negative-power moment. The expected magnitude of the negative postchange continuation utility function therefore contains the divergent integral $\lambda K_0{w}^p{\int }_0^{\infty }{e}^{-D_{1}t}dt/|p|$. Thus these cases yield respectively $+\infty$ or $-\infty$, rather than a positive-root solution.

Define the positive constantSince $S=\delta /k$, the stock price has capital-appreciation dynamics $dS/S=\alpha dt+\sigma dW$. But each share also pays the flow $\delta =kS$. Its dividend-inclusive portfolio return therefore has driftThis is the drift to use in the Merton consumption-investment problem; using only capital appreciation would omit an actual source of portfolio wealth.

At the proposed bank rate,Thus the optimal stock fraction from part (i) isMoreover,Substituting this into the optimal consumption coefficient yieldsHence the finite-value condition in part (i) is automatically satisfied.

The agent's endowment of the single productive share has portfolio wealth $w=S$. Investing fraction one in the stock means holding $w/S=1$ share, leaving $w-S=0$ in the bank account. The optimal consumption rate isThis policy also satisfies the self-financing portfolio equation: with one share and no bank balance,Thus $w_t=S_t$ remains valid at all times. The agent holds the entire one-share supply, has zero net borrowing or lending, and consumes exactly the supplied goods. The stock, bank-account and consumption-good markets all clear.

Choose the utility function primitive $U(c)=c^{1-R}/(1-R)$. Any other primitive with the same derivative differs by a constant $C$; this adds $C/\rho$ to the value function and does not alter the policy. Put $p=1-R$ and let ${\pi }_t$ be the stock fraction. The self-financing portfolio equation, including consumption withdrawals, isFor a time-independent infinite-horizon value function $V$, the Hamilton-Jacobi-Bellman equation isScaling initial portfolio wealth and all consumption rates by the same positive number preserves admissibility and scales utility function by its $p$th power. This homogeneity gives $V(w)=K{w}^p/p$. In particular,The consumption first-order condition $c^{-R}=V^{\prime }(w)$ and stock first-order condition $w(\mu -r)V^{\prime }(w)+{\sigma }^2\pi w^2{V}^{\prime \prime }(w)=0$ giveThe second derivatives of the optimized expressions are negative, so these are their global maximizers. Writing ${\gamma }_M=K^{-1/R}$ and substituting into the Hamilton-Jacobi-Bellman equation givesThereforeThe given inequality is precisely ${\gamma }_M>0$, so the Merton consumption-investment problem value is $V(w)={\gamma }_M^{-R}{w}^{1-R}/(1-R)$. This derives the requested policy; no additional verification argument is needed here.

Use the primitive $U(c)=c^{1-R}/(1-R)$, as in Question 3, and the usual positive spot volatility convention $\sigma >0$. An additive utility function constant shifts both agents' value functions equally and does not affect their policies or the comparison. SetThe Itô formula applied to the stock price and the binary Brownian drift filter givesLet ${\theta }_t$ denote the dollar stock holding. The separated state equations areBoth states use the same observed Brownian motion. In particular, their quadratic covariation is $d[w,X{]}_t=\sigma {\theta }_{t}dt$.

Let $J(w,x)$ be the Bayesian value function. Its Hamilton-Jacobi-Bellman equation isThe cross derivative is necessary: learning and portfolio wealth are correlated through the same observation shock. At a smooth, finite, strictly concave value, the optimal consumption and stock holding areThe number of shares is ${\theta }^{\ast }/S$, and the wealth fraction is ${\theta }^{\ast }/w$.

More explicitly, homogeneity gives $J(w,x)=K(x)w^p/p$, where $p=1-R$. ThenThe additional term is the learning hedge in a binary-drift investment model, a form of intertemporal hedging demand. Merely putting the Bayesian posterior mean drift into a constant-coefficient investment formula would omit it. Eliminating the controls gives the coefficient equationThe economic solution is selected by admissibility and the infinite-horizon value condition, rather than by an arbitrary solution of this ordinary differential equation.

For the comparison, now suppose $0<R<1$ and $\mu >r$. Write $m=\mu -r>0$, $b_{+}=m+\sigma a$, andWhen ${\gamma }_{+}>0$, the informed agent's Merton consumption-investment problem has valueThese are the policies available to an agent who knows the true drift is $+a$.

For any admissible Bayesian policy, not only its optimum, defineThe integration variable is $s$ throughout the accumulated utility function. Applying the Itô formula in the Bayesian observation filtration giveswhereThe informed-agent Hamilton-Jacobi-Bellman equation says ${\sup }_{c,\theta }{H}_{b_{+}}=0$. Maximizing the stock term at any excess drift $b$ givesSince $V_{+}^{\prime \prime }<0$, this maximum increases with $b^2$. The binary Brownian drift filter satisfies $|h(x)|\le a$, and thereforeIt follows that $H_{b(x)}(w,c,\theta )\le {\sup }_{c,\theta }{H}_{b(x)}\le {\sup }_{c,\theta }{H}_{b_{+}}=0$. Thus $Y$ is a local supermartingale. Because $0<R<1$, both accumulated utility function and $V_{+}$ are nonnegative. A nonnegative local supermartingale is a genuine supermartingale, by localization and the conditional Fatou lemma. This proves the requested assertion under the Bayesian probability law, without conditioning that law on the favorable drift.

ConsequentlyThe monotone convergence theorem lets $t$ tend to infinity. Taking the supremum over Bayesian policies proves the maximal squared Sharpe ratio value boundIn particular this holds at the equal-prior state $x=0$. No vanishing-terminal-value assumption is needed for this upper bound, because the terminal term is nonnegative and can simply be dropped.

The finite-value assumption ${\gamma }_{+}>0$ is missing from the printed comparison. Without it, $V_{+}=+\infty$ and the value inequality is trivial, but $Y$ is not a finite supermartingale. For a concrete allowed example, take $r=0$, $\mu =1$, $\sigma =a=1$, $R=1/2$ and $\rho =1$. Then $b_{+}=2$ and ${\gamma }_{+}=-2$. For the informed agent, the constant stock fraction $\pi =4$ and consumption $c=w/2$ giveThe time integral diverges, so his value is infinite. This is why the nontrivial finite-supermartingale proof above needs the stated qualification.

The holdings ${\varphi }_t$ and ${\pi }_t^i$ are numbers of shares, so the marked-to-market portfolio value isThe stock prices are ex-dividend prices. Over the trading interval, existing holdings gain ${\varphi }_{t}d{B}_t$ from the riskless account and ${\pi }_t\cdot d{S}_t$ from capital-price changes. In addition, ${\pi }_t^i$ shares of asset $i$ receive ${\pi }_t^i{D}_t^{i}dt$ in cash dividends. If this cash remains in the portfolio and changes of holdings are financed internally, the self-financing portfolio equation isRebalancing does not add a separate source of wealth: the cost of purchasing one holding is paid by selling another or by using the cash account. This is why one must specify the gains equation, rather than infer self-financing solely by differentiating the marked-to-market identity. The dividends are reinvested rather than withdrawn as consumption.

The marked-to-market value of the current holdings is $X_t=H_t\cdot P_t$. In a self-financing portfolio, purchases are paid for by sales within the portfolio, so rebalancing itself changes no net wealth. Asset price changes contribute $H_t\cdot d{P}_t$, and nonnegative consumption removes $c_{t}dt$ from wealth. HenceThe holdings must be predictable and stochastically integrable. The second identity is the self-financing definition with withdrawals; it is not obtained by incorrectly setting the changes in the holdings themselves to zero.

Apply the Itô product rule and the quadratic covariation rule for a stochastic integral. The finite variation consumption term has zero quadratic covariation with $Z$, soUsing $X=H\cdot P$ and the wealth equation,Thus the deflated wealth equation with consumption isNo finite variation assumption on $H$ is needed: the self-financing equation and the stochastic-integral quadratic covariation rule already incorporate the portfolio's financing constraint.

Use the usual normalization $Z_0=1$, finite deterministic initial wealth $x=X_0\ge 0$, positive state-price density, and nonnegative consumption. The deflated asset prices $ZP$ are local martingales, so predictable stochastic integrability makes $M=\int H\cdot d(ZP)$ a zero-starting local martingale. Integrating part (b) givesA nonnegative local martingale is a supermartingale: localize to martingales, apply Conditional Fatou lemma, and obtain the conditional expectation inequality and integrability. Therefore $N$, and hence $M=N-x$, are supermartingales. In particular,This is supermartingale control of deflated consumption gains. Nonnegative consumption is essential to the lower bound; it is the ordinary meaning of a consumption rate here. Wealth nonnegativity alone would not supply that bound if arbitrary signed cash flows were allowed.

The supermartingale conclusion givesDiscard the nonnegative terminal-wealth term and let $t\to \infty$. The cumulative deflated consumption increases, so monotone convergence theorem yields the state-price budget constraintThe normalization is relevant: with $Z_0\ne 1$ the right side is $Z_0{X}_0$; with random integrable initial deflated wealth its expectation is the unconditional budget bound.

Wealth and admissibility. The dollar holding $\theta$ earns the risky return, while $w-\theta$ earns the continuous-time bank account return. Removing consumption therefore givesHere $\theta$ is a dollar amount, not a number of shares; the number of shares is ${\theta }_t/S_t$. Both controls must use available information: take $\theta$ predictable and $c\ge 0$ progressively measurable, withalmost surely on every finite interval. Require a well-defined objective and an admissible trading strategy satisfying $w_t\ge 0$. At zero portfolio wealth this excludes continued risky gambling or positive consumption. The state-price budget constraint rules out doubling strategies. Throughout the diffusion calculations take $\sigma \ne 0$, positive discount $\rho$, positive decay $\lambda$, and finite value; degeneracies are discussed where they affect the conclusions.

Satisfaction and dynamic programming. Write the consumption satisfaction stock asThe product rule gives, almost everywhere in time,This state is a finite-variation process, so it has no quadratic covariation with portfolio wealth. Applying the Itô formula to the discounted value function over a short interval, then using dynamic programming, gives the interior HJB equationAssuming $V_{ww}<0$, completing the square givesThe last supremum is zero if $V_{\xi }-V_w\le 0$ and infinite otherwise. Thus the gradient constraint for unbounded consumption is $V_{\xi }\le V_w$, and $c=0$ wherever the inequality is strict. Since there is no direct penalty for a very large consumption rate, an active boundary can involve singular consumption control. In that relaxed interpretation the HJB equation isWith ordinary rate controls, this describes the supremum and its limiting transfer policy; it does not promise that an instantaneous transfer is attained by a finite rate.

Power reduction. An additive constant in the utility function only adds a control-independent constant divided by $\rho$ to the value, so normalize $U(\xi )={\xi }^{1-R}/(1-R)$. Put $p=1-R$ and $x=w/\xi$ for $\xi >0$. Scaling portfolio wealth, consumption satisfaction and the controls by the same positive number gives the wealth-to-satisfaction reductionConsequently the reduced HJB equation isIn the strict waiting region the first expression vanishes and consumption is zero. On a transfer region the second vanishes; integrating it gives $v(x)=K(1+x{)}^p$. This reflects preservation of $w+\xi$ during an instantaneous wealth-to-satisfaction transfer.

Why a waiting threshold is expected, and its qualification. The gradient constraint for unbounded consumption compares the benefit of increasing consumption satisfaction with the opportunity cost of spending financial portfolio wealth. When consumption satisfaction is already large relative to cash, waiting lets consumption satisfaction decay while financial portfolio wealth earns returns; consuming immediately can be wasteful. Homogeneity makes the comparison depend only on $x$. For fixed $s=w+\xi$, joint concavity of the value function makes $q_s(a)=V(s-a,a)$ concave in financial portfolio wealth $a$. It is nondecreasing because an immediate transfer can reproduce any smaller financial allocation. Consequently its derivative $V_w-V_{\xi }$ is nonnegative and nonincreasing in $a$: a strict waiting region, if present, starts at zero financial portfolio wealth and ends at a single transfer boundary. Homogeneity makes the corresponding boundary a ratio $x_{\ast }$. In the usual finite-boundary regime this gives $c=0$ for $x<x_{\ast }$, and transfers push a larger ratio down towards $x_{\ast }$. The condition is a comparison of marginal values, not the ordinary formula $c=(V_w{)}^{-1/R}$: current utility function here depends on consumption satisfaction, not on current consumption.