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A contingent claim paying one at maturity if a specified price path has remained below an upper barrier, and zero otherwise. This survival payoff differs from a terminal digital put option, which tests only the terminal price. In the dividend-free Black-Scholes model, its value is the discount factor times the risk-neutral probability of avoiding the barrier, obtainable from the linear-boundary Brownian first-passage distribution.

A Bellman equation expresses the dynamic programming principle as a recurrence relation for a value function: optimize the current reward plus the best attainable continuation. Let $s$ be a dynamic programming state, $A_t(s)$ the allowed actions at time $t$, $r_t(s,a)$ the immediate reward, and $F_t(s,a)$ the next dynamic programming state. With terminal time $T$ and terminal reward $g$, the deterministic finite-horizon Bellman equation isHere $V_t(s)$ measures total reward from time $t$ through the terminal reward. Start with $g$ and evaluate backwards; the terminal condition anchors the recurrence relation. For finite nonempty action sets, finite rewards, and finite terminal rewards, a maximum exists at each step. If an optimum need not be attained, use a supremum. A minimization problem instead uses a minimum or infimum. The 0-1 knapsack problem above is the same comparison between an immediate item value plus a continuation and the continuation after skipping. See Bertsekas, MIT lecture 2: the backwards recursion.

In a Markov decision process, the next dynamic programming state $S_{t+1}$ is random. If $r_t(s,a)$ denotes the expected immediate reward and $\beta$ is a discount factor, the finite-horizon Bellman equation becomesThe conditional expectation averages over the next dynamic programming state before choosing the current action; that action cannot use a future outcome that has not yet been observed. For a finite state space with transition probabilities $p_t(s^{\prime }\mid s,a)$, the continuation term is $\beta {\sum }_{s^{\prime }}{p}_t(s^{\prime }\mid s,a)V_{t+1}(s^{\prime })$. Taking $\beta =1$ gives the undiscounted finite-horizon problem. With a discount factor $0\le \beta <1$, rewards $k$ steps ahead carry a factor ${\beta }^k$, including the terminal reward. A maximizing action for each $(t,s)$ specifies a Markov policy, which contains decisions rather than just optimal scores.

For an infinite horizon with a finite state space, finite nonempty action sets, time-independent transition probabilities, bounded rewards $|r(s,a)|\le M$, and a discount factor $0\le \beta <1$, the optimal value function satisfiesIn this setting the maximizing dynamic programming operator is a contraction mapping in the supremum norm, so the Banach fixed-point theorem gives a unique bounded fixed point and convergence of repeated application to it. See Bertsekas, MIT lecture 15: bounded discounted problems. These hypotheses matter: with one state, one action, zero reward, and $\beta =1$, the Bellman equation is merely $V(s)=V(s)$, so every finite constant is a fixed point, although the actual total-reward value function is zero. The average-reward Bellman equation and Hamilton-Jacobi-Bellman equation address other settings with their own conditions.

In a zero-interest one-period market with a continuous terminal stock law under a pricing measure, $C(K)={\mathbb{E}}_Q(S-K{)}^{+}$ implies $-C^{\prime }(K)=Q(S>K)$ and $C^{\prime \prime }(K)=f_Q(K)$. Thus a full differentiable call curve determines its pricing density. A finite collection of strikes generally does not. With a deterministic nonunit discount factor, divide the call curve by that factor before recovering the probability density.

Two assets with linearly independent payoff vectors across two positive-probability states span all terminal payoffs. The state-price equations have a unique solution. A positive solution gives the state-price density after division by physical state probabilities. Arrow state prices sum to the riskless discount factor, while normalized Arrow state prices are risk-neutral probabilities; these three quantities must not be confused.

Assume deterministic discount factors, deterministic risk-neutral default intensity $\lambda$, fixed fractional recovery $\mathcal{R}$, and continuous premium until default or maturity. Survival is $G(t)=\exp (-{\int }_0^t\lambda (u)du)$. Expected discounted protection is $N(1-\mathcal{R})\int B\lambda G$, while premium is $Ns\int BG$. Equating them gives the par spread. Constant intensity gives $s=(1-\mathcal{R})\lambda$. Discrete premiums, default accrual, stochastic recovery and counterparty losses require changing the corresponding cash-flow expectations.

The value of an account that continuously reinvests at the short rate $r$, with $B_0=1$. Its reciprocal is the discount factor $D_t=B_t^{-1}$.

In the one-factor Heath-Jarrow-Morton model, put $b_t^T={\int }_t^T\sigma (t,u)du$. The Itô formula gives $dP/P=r_{t}dt-b_t^{T}d{W}_t$. Thus multiplying by the discount factor yields the displayed stochastic exponential. Bounded forward volatility on a finite maturity horizon implies the Novikov condition, so the discounted bond is a true martingale.

An integral of future flows weighted by a specified discount factor, such as ${\int }_0^{\infty }f(t+s)e^{-rs}ds$. For an exponentially growing flow, convergence requires the discount rate to exceed its growth rate.

A grim trigger is a history-dependent pure strategy in a repeated game that switches permanently to punishment after a trigger event. In the iterated prisoner's dilemma above, the trigger is any player's observed defection, and punishment is always defect. Unlike Tit for tat, grim trigger never restores cooperation after the opponent cooperates again. Whether mutual grim trigger is a subgame perfect equilibrium depends on the stage payoffs, discount factor, and monitoring assumptions; it is not an unconditional property of the rule. See Ozdaglar, lecture 15, slides 12–15.

The iterated prisoner's dilemma is a repeated game with a prisoner's dilemma at each round. Earlier choices can change later best responses: a present gain from defection may be outweighed by lost future cooperation. A pure strategy now specifies an action after every possible observed history, including histories that its own prescribed play would never reach. The horizon, observations, initial actions, and payoff convention must be specified before assessing a Nash equilibrium or subgame perfect equilibrium.

For the following worked example, repeat the prisoner's dilemma above infinitely at rounds $t=0,1,\dots$. Assume public perfect monitoring: after each round both players observe both actions exactly, without error. They share a discount factor $0\le \delta <1$ and evaluate a path of stage payoffs $u_i(t)$ using the normalized discounted payoffThe round-zero weight is one before normalization, including when $\delta =0$. A larger discount factor gives future payoffs more weight; the geometric series makes a constant stage payoff have the same normalized value. These assumptions and normalization follow ^[ref]. The numerical payoffs here are our $3,5,0,1$ example; the lecture uses a different table.

Suppose both players use grim trigger: play $C$ initially and while every past action of both players was $C$; after any observed $D$, play $D$ forever. With no unilateral deviation, each gets normalized payoff $3$. A first unilateral deviation to $D$ earns $5$ now, then at most $1$ each round because the opponent switches permanently to always defect. The deviator attains that continuation payoff by choosing $D$ thereafter. At any history of mutual cooperation, the comparison is thereforeCooperation is a best response precisely whenEquivalently, without normalization the geometric series givesMultiplying both unnormalized payoffs by the same positive factor $1-\delta$ leaves the best response comparison unchanged.

For example, with discount factor $\delta =1/4$, the first unilateral deviation gives normalized payoff $4>3$, so mutual grim trigger is not a Nash equilibrium. At $\delta =3/4$, it gives $2<3$, so cooperation is preferable. At $\delta =1/2$ the two payoffs tie: the incentive to cooperate is weak, which is sufficient for a subgame perfect equilibrium.

The punishment is credible after every history containing a defection: the opponent's pure strategy then prescribes $D$ forever, and $D$ remains the stage best response, earning $1$ rather than $0$. Together with the cooperative-history comparison, this establishes mutual grim trigger as a subgame perfect equilibrium for $1/2\le \delta <1$. Delaying the first unilateral deviation merely delays the same gain or loss; further cooperation during punishment only lowers the deviator's payoff. Mutual always defect also remains a subgame perfect equilibrium, so patience permits cooperation without making it inevitable.

This calculation assumes an infinite horizon and $\delta <1$; it does not define a discounted payoff at $\delta =1$. With a finite, commonly known final round and positive weight on each round, backward induction instead gives mutual defection in every round of this stage game. Observational errors change the model too: one mistaken defection permanently activates grim trigger. Historical dependence, initial memories, and the payoff time convention also affect whether a cooperative rule resists or invades other pure strategies; the existing reactive strategy and Tit for tat discussions concern those additional questions.

Under the risk-neutral measure, writeComplete the square in the normal density after multiplying by the power of $S_T/s$. The truncated lognormal moment gives, for $K>0$,All real powers have finite moments in this lognormal distribution. If $K=0$, the indicator is identically one and $V_p=M_p$. The function $V_p$ is an undiscounted conditional moment; the option prices below additionally include the discount factor.

For a starting price above the trigger, maximizing $V_L(S)$ amounts to maximizing $g(L)=(X-L)L^{-{\beta }_{-}}$ on $0<L<X$. Its derivative isBecause ${\beta }_{-}<0$, this is positive below ${\beta }_{-}X/({\beta }_{-}-1)$ and negative above it. Both endpoint limits of $g$ are zero. Thus the unique maximizing trigger isEquivalently smooth fit requires ${\beta }_{-}(X-S^{\ast })/S^{\ast }=-1$, matching the derivative of $X-S$ below the boundary. The optimized perpetual put option value is $X-S$ for $S\le S^{\ast }$ and $(X-S^{\ast })(S/S^{\ast }{)}^{{\beta }_{-}}$ above it.

We can verify optimality among all stopping rules, not only constant triggers. The continuation branch is convex, with value and slope matching $X-S$ at $S^{\ast }$, so it dominates $X-S$ above the boundary; it is also positive, and hence dominates $(X-S{)}^{+}$ everywhere. Its discounted generator vanishes in continuation. In the exercise region, the generator of $X-S$ is $\delta S-rX$. The root equation givesso for $\delta \ge 0$ this generator is nonpositive throughout $S\le S^{\ast }$. Smooth fit eliminates a local-time contribution at the boundary. Itô formula and localization therefore make $e^{-rt}V(S_t)$ a nonnegative supermartingale, providing an upper bound for every discounted exercise payoff. The threshold strategy attains that bound by the martingale calculation in part (a). This proves the boundary is globally optimal.

The positive-interest assumption is substantive. If $r=0$ and $\delta \ge 0$, the stock tends to zero almost surely, so the supremum of perpetual put payoffs has value $X$, approached by triggers $L\downarrow 0$. No positive finite trigger attains that supremum for positive spot. The displayed finite-boundary formula belongs to the $r>0$ case. If $r<0$ and $\delta \ge 0$, the stock still tends to zero, while the discount factor grows without bound. Even exercise at deterministic times tending to infinity makes the expected discounted put payoff unbounded, so there is no finite perpetual price in that extension.

A credit derivative separates an exposure to a borrower's credit quality from outright ownership of its loan or bond. In the early-2002 setting, these were a growing, largely dealer-intermediated over-the-counter market. For scale, the contemporaneous BIS comparison reports ISDA member positions of about US$919billionatend-2001,versusUS$632 billion at mid-2001. These are notional amounts, not market values or expected losses, and the survey did not eliminate double-counting; estimates from other surveys cannot simply be combined because their coverage differed. This historical description uses the BIS Quarterly Review, June 2002, derivatives-market comparison, rather than projecting later market totals back into the paper's date.

The basic unfunded contract is a credit default swap. The protection buyer pays a periodic spread until maturity or a defined credit event; the seller pays the contracted default loss, often notional times one minus recovery. Physical settlement instead delivers eligible defaulted debt in exchange for par. A total return swap exchanges the reference asset's coupons and price changes against a funding leg: its receiver earns gains, bears price losses and pays funding, obtaining broader market and credit exposure than default protection alone. A credit spread option pays on widening or narrowing of a quoted credit spread, transferring mark-to-market credit risk even without a default. A credit-linked note is funded: the investor supplies principal and accepts a reduction of coupons or principal after a reference credit event, in return for enhanced yield. The investor is economically selling protection and can also face the note issuer's own credit risk.

Basket products extend these ideas. A first-to-default swap pays when the first member of a reference basket defaults. A synthetic CDO uses credit default swaps to create portfolio credit exposure and allocates aggregate losses among tranches. For monetary portfolio loss $L$ and attachment/exhaustion amounts $A<D$, the credit tranche loss function is $\min ((L-A{)}^{+},D-A)$. These contracts require a model of joint defaults and recoveries, not just a list of individual default probabilities. The separation of credit exposure from financing and the role of synthetic structures were already central to the Federal Reserve's December 2001 discussion of credit derivatives.

For risk management, a bank can buy protection on a concentrated borrower exposure while retaining the loan and customer relationship. It can transfer sectors or names it already holds heavily and diversify the remaining portfolio. Recognition of a hedge for capital purposes depends on the actual transfer and applicable rules, not merely on the instrument's name. Investors can instead sell protection or buy credit-linked notes to obtain chosen credit exposure without buying the cash bond, seek a risk premium, or take relative-value positions between cash bonds and swaps. Such positions still depend on funding and on the credit-spread basis: contractual events, maturity, currency and recovery terms may fail to match the original exposure. Protection also replaces part of the reference risk with counterparty credit risk, particularly if the seller may fail when the protected borrower is distressed.

Pricing equates expected discounted contractual cash flows under a risk-neutral measure, calibrated to traded bonds and derivatives rather than substituting historical default frequencies. To make this concrete, suppose the risk-neutral default intensity $\lambda (u)$ and discount factor $B(0,u)$ are deterministic, fractional recovery $\mathcal{R}$ is fixed, and premiums are continuous. Survival is $G(u)=\exp (-{\int }_0^u\lambda (v)dv)$. A notional-$N$ protection leg pays $N(1-\mathcal{R})$ at default, so its value is $N(1-\mathcal{R}){\int }_0^{T}B(0,u)\lambda (u)G(u)du$. Premium at annual rate $s$ has value $Ns{\int }_0^{T}B(0,u)G(u)du$. A zero-upfront par swap therefore has the continuous-premium credit-default swap spreadFor constant intensity this reduces exactly to $s=(1-\mathcal{R})\lambda$ in this simplified model. Actual discrete premium dates and premium accrued at default change the premium leg; stochastic recovery, discounting and counterparty losses change the expectations. A quoted spread thus cannot universally be read as a physical default probability. Default-risk premiums and other market effects enter the calibrated pricing measure.

A structural alternative is the Merton model. If firm assets at debt maturity are $V_T$ and promised debt is $F$, equity pays $(V_T-F{)}^{+}$ and debt pays $\min (V_T,F)=F-(F-V_T{)}^{+}$. Under the model's diffusion assumptions, debt value is consequently the discounted face value less a put on firm assets. This derives credit prices from asset value, volatility and capital structure, while the simple model allows default only at the debt maturity. Intensity models instead calibrate a default-arrival law directly to market credit prices. Neither approach removes model risk in recovery, dependence or calibration, and incomplete credit markets need not select a unique pricing measure. A useful credit hedge and a fair derivative price both depend on the actual cash-flow and default specifications, not the product label alone.

Specify the trading conventions first. Let the bank account have value $R_r>0$, with $R_0=1$ and its next gross return known at time $r$. Write $B_r=1/R_r$ for the discount factor. Let $S_r$ be the ex-dividend asset-price vector, and $d_r$ the vector of cash dividends paid per unit at time $r$; set $d_r=0$ for non-dividend-paying assets. Under an equivalent martingale measure $Q$, each discounted cumulative asset gainis a martingale. Equivalently, ${\mathbb{E}}_Q[B_{r+1}(S_{r+1}+d_{r+1})\mid {\mathcal{F}}_r]=B_r{S}_r$. This is the gain-process form of risk-neutral pricing, rather than a martingale assumption on raw ex-dividend prices.

Let $X_r$ be the ${\mathcal{F}}_r$-measurable holdings after trading and distributions at time $r$. The previous holdings $X_{r-1}$ earn the next price change and dividends. Define the strategy's net cash payment and its residual portfolio wealth byA negative payment is an external infusion; a self-financing portfolio with no consumption has $D_r^X=0$. The discounted budget equation isAssume these gains and payments are integrable under $Q$, for example by bounded predictable holdings and integrable discounted asset gains. Thenis a true martingale, by conditioning the displayed one-step gains. Merely assuming a local martingale without appropriate integrability would not justify the conditional equality.

Conditioning $M_n$ on ${\mathcal{F}}_r$ proves the general multi-period dividend pricing identityFor a strategy liquidated at time $n$, $X_n=0$ and all liquidation proceeds are included in $D_n^X$. The terminal value disappears, giving exactly the requested dividend-only relation. That liquidation convention is necessary: holding one unit of the bank account with no payments would otherwise have a positive left side and zero right side.

An attainable claim $H$ paid at $n$ can be replicated by a strategy with no earlier payments and final liquidation payment $D_n^X=H$. Its time-$r$ value is thereforeTwo admissible replicating strategies have the same value by this identity, and the value is the same under every equivalent martingale measure for which the replication gains are true martingales. For unattainable claims this expectation is a possible model price, but the replication argument does not establish a unique price. With density process $Z_r={\mathbb{E}}_P[dQ/dP\mid {\mathcal{F}}_r]$, the equivalent state-price density form is $V_r=(B_r{Z}_r{)}^{-1}{\mathbb{E}}_P[B_n{Z}_{n}H\mid {\mathcal{F}}_r]$.

Let $P_{s,t}>0$ denote the time-$s$ price of a zero-coupon bond paying one at maturity $t$, with $P_{t,t}=1$. Information available at $s$ is the sigma-algebra ${\mathcal{F}}_s$ in the market filtration. For a curve differentiable in maturity, the instantaneous forward rate and its diagonal short rate areThe last identity follows by integrating the maturity derivative and using $P_{s,s}=1$. The continuous-time bank account reinvesting at the short rate is $B_s=\exp ({\int }_0^s{R}_{u}du)$, and its reciprocal is the discount factor. Modelling forward rates means specifying a random maturity-indexed curve with an evolution consistent with the same pricing measure for every bond. The expectation below is under that money-market risk-neutral measure, not an unspecified physical measure.

For fixed maturity $t$, if $P_{s,t}/B_s$ is a true martingale on $0\le s\le t$, its terminal value is $1/B_t$. ConsequentlyConversely, this representation gives $P_{s,t}/B_s={\mathbb{E}}_Q[B_t^{-1}\mid {\mathcal{F}}_s]$, a conditional-expectation martingale by the tower property. For example, for $r<s$, conditioning the right side on ${\mathcal{F}}_r$ returns ${\mathbb{E}}_Q[B_t^{-1}\mid {\mathcal{F}}_r]$. These claims assume ${\mathbb{E}}_Q{B}_t^{-1}<\infty$. A positive discounted local martingale alone need not equal the conditional expectation of its terminal value, so the word “martingale” cannot be weakened without an extra integrability argument.

In the Vasicek model, the Brownian motion $W$ and coefficients in $d{R}_s=\alpha (\beta -R_s)ds+\sigma d{W}_s$ must be understood under this pricing measure. Multiplying by $e^{\alpha s}$ and integrating gives, conditionally on ${\mathcal{F}}_s$,Assume first $\alpha \ne 0$, and put $\tau =t-s$ and $B(\tau )=(1-e^{-\alpha \tau })/\alpha$. Integrating the explicit solution over $s\le u\le t$ yieldsThe interchange of the stochastic integrals is justified by their deterministic square-integrable kernels on the finite triangular domain; equivalently approximate the kernels by step functions, interchange finite sums, and pass to the limit using the Itô isometry. The last integral conditionally has a centered normal distribution independent of ${\mathcal{F}}_s$, with varianceA deterministic Brownian integral is Gaussian because its step approximations are sums of independent normal increments; their variances converge by the Itô isometry. Completing the square in the normal density proves $\mathbb{E}{e}^{-Y}=e^{-m+v/2}$ for a normal variable of mean $m$ and variance $v$. Hence the zero-coupon bond price is exponential-affine:An equivalent familiar form isAt $t=s$, both coefficients are zero and the bond value is one. These formulas are valid for negative as well as positive nonzero $\alpha$ on finite horizons, though a genuinely mean-reverting Vasicek model has $\alpha >0$. For $\alpha =0$, direct integration gives $B(\tau )=\tau$, $J(\tau )={\tau }^3/3$ andAll the exponential moments used above are finite, so the prices defined by the conditional expectation do give true discounted martingales.

To fit an initial bond curve, let its observed prices $p_0(t)$ be positive, satisfy $p_0(0)=1$, and have a sufficiently smooth instantaneous forward rate $f_0(t)=-d\log p_0(t)/dt$. Set $R_0=f_0(0)$; this compatibility is forced by the infinitesimal-maturity bond price. Replace $\beta$ by a deterministic function $\theta (t)$, with $\alpha \ne 0$, so thatIts mean $m(t)={\mathbb{E}}_Q{R}_t$ satisfies $m^{\prime }=\alpha (\theta -m)$, $m(0)=R_0$. The variance of the integrated rate remains ${\sigma }^{2}J(t)$, since changing deterministic drift does not change the centered Gaussian process. ThereforeThe required mean is consequently $m(t)=f_0(t)+{\sigma }^{2}B(t{)}^2/2$. Solve its mean equation for $\theta$:Combining the last two terms gives the explicit Hull-White model calibration in the question's parameter convention:With $R_0=f_0(0)$, the specified mean solves the same linear differential equation and initial condition as the model mean, so it is the actual mean. The resulting model forward curve equals $f_0$; integrating and using $P_{0,0}=p_0(0)=1$ proves $P_{0,t}=p_0(t)$ for every maturity. If the drift is instead written $\tilde{\theta }(t)-\alpha R_t$, its calibration parameter is $\tilde{\theta }=\alpha \theta$, not $\theta$ itself.

The printed allowance of arbitrary constants needs a qualification for this final claim. If $\alpha =0$, replacing $\beta$ by $\theta (t)$ changes no coefficient at all: $R_t=R_0+\sigma W_t$ regardless of $\theta$. Its initial curve is fixed at $P_{0,t}=\exp (-R_{0}t+{\sigma }^2{t}^3/6)$. With $\sigma \ne 0$, the compatible smooth observed curve $p_0(t)=e^{-R_{0}t}$ is already a counterexample to the unconditional fitting claim. Thus arbitrary compatible smooth positive initial curves can be fitted by the stated replacement when $\alpha \ne 0$; it cannot fit arbitrary curves when $\alpha =0$. In that degenerate case one can instead introduce an independent deterministic drift $k(t)$ in $d{R}_t=k(t)dt+\sigma d{W}_t$, choosing $k(t)=f_0^{\prime }(t)+{\sigma }^{2}t$ to recover the desired initial curve. Likewise an externally fixed $R_0\ne f_0(0)$ is incompatible with exact fitting, and an arbitrary nonsmooth set of prices need not define the differentiable forward curve assumed in the model.

The unit zero-coupon bond pays one at maturity. Its risk-neutral valuation therefore isNo particular rate coefficients were specified, so this expectation is the general answer; evaluating it further requires a chosen one-factor short-rate model.

For the alternative derivation let $D_t=\exp (-{\int }_0^t{r}_{s}ds)$ be the bank discount factor. For an integrable discounted payoff, pricing giveswhich is a martingale by the tower property of conditional expectation. For a payoff determined by the Markov rate state, this conditional expectation is represented by the indicated pricing function. A path-dependent claim requires an appropriately enlarged state, as in part (c). Now $d{D}_t=-r_t{D}_{t}dt$ has finite variation. Applying Itô formula and the product rule under the risk-neutral measure, whose rate drift is $b-\sigma \theta$, yieldsUnder the smoothness and integrability conditions used above, the martingale has zero finite-variation drift. The bracket therefore vanishes, giving againFor the bond the terminal condition is $P(T,T)=1$. This argument also shows why stochastic discounting remains inside the conditional expectation.

Let $q(T)$ denote the original zero-coupon bond price $P(0,T)$ computed above. A deterministic shift $h_t$ of the short rate, with $h_0=0$, gives ${\hat{r}}_t=r_t+h_t$. Subtracting the two recursions shows that the required added drift isSince the shift is nonrandom, the new discount factor differs by $\exp (-{\sum }_{s=1}^T{h}_s)$, and hence $\hat{P}(0,T)=q(T)\exp (-{\sum }_{s=1}^T{h}_s)$.

For deterministic calibration of an autoregressive short rate, defineAll these quantities are finite. The telescoping sum ${\sum }_{s=1}^T{h}_s=d_T$ gives $\hat{P}(0,T)=p(T)$. Explicitly,These constants fit every maturity simultaneously. Each successive maturity fixes $h_T$ uniquely, so the deterministic calibration is also unique with the prescribed $r_0$.

Let $z_u,z_d$ be the state-price-density values at the higher and lower risky payoff. Pricing the two assets givesSolving yieldsBoth values are positive, and the two independent equations make the solution unique. The payoff matrix has nonzero determinant, so this is a complete two-state market. The actual Arrow state prices, including physical probabilities, are $q_u=5/9$ and $q_d=5/18$. Their sum is $5/6$, the discount factor. Normalizing gives risk-neutral probabilities $2/3,1/3$. Thus $Z$ itself is not a probability density of mean one: its mean is $5/6$ because the riskless asset earns interest.

Write the discount factor as $D_t=B_t^{-1}=\exp (-{\int }_0^t{r}_{s}ds)$. Splitting the time integral at $t$ givesThe random variable $D_T$ lies in $(0,1]$ because the short rate is nonnegative and continuous on the finite maturity interval. A process of conditional expectations of an integrable terminal variable is a martingale, by the tower property of conditional expectation. Therefore

Concavity gives the supporting-tangent inequalityMultiply by the discount factor $e^{-bt}$ and use the first-order condition $e^{-bt}{U}^{\prime }(c_t)=Y_t$. This gives the pointwise marginal-utility verification of optimal consumption inequalityApply the budget inequality from part (d) to the competing admissible strategy, and use equality for the proposed one:The two weighted consumption integrals are finite, so their difference is integrable. Under the usual positive discount-rate assumption $b>0$, the utility integrals are also integrable: $U(0)\le U(x)\le L$ for a finite upper bound $L$, and ${\int }_0^{\infty }{e}^{-bt}dt=1/b$. Integrating the tangent inequality and taking expectations therefore givesEconomically, both consumers face the same state-price budget, and the candidate spends it exactly where its discounted marginal utility equals the state price. This is utility duality with martingale deflators in its consumption form.

A positive $b$, or another hypothesis making the infinite-horizon objectives well defined and permitting this integration, is needed. The printed question does not specify the sign of $b$. Bounded utility alone does not ensure that an undiscounted infinite time integral exists: a bounded integrand can have both infinite positive and negative parts. Thus the conclusion is established under the standard discount convention $b>0$, and also whenever the displayed objectives satisfy the stated integrability conditions; without either convention the literal infinite-horizon comparison need not be a defined mathematical expression.

This can occur within an admissible financial model, not just for an abstract bounded integrand. Take $U(x)=1-2e^{-x}$ and $b=0$. Choose a deterministic smooth nonnegative $c$ with successive unit-length plateaus alternating between zero and the integer $n$, and connect them over intervals whose lengths have finite sum. Then ${\int }_0^{\infty }2c_t{e}^{-c_t}dt<\infty$: the high-plateau contributions sum to ${\sum }_{n\ge 1}2n{e}^{-n}$ and the transition integrand is bounded by $2/e$. SetThe bank account is a positive deterministic Itô process and $Y_t{B}_t=Y_0$, so $Y$ is a state-price density. Holding $X_t/B_t$ bank units finances consumption with nonnegative wealth and exact budget equality. Also $U^{\prime }(c_t)=Y_t$. Nevertheless every zero plateau contributes $-1$ to the utility integral, while each sufficiently high plateau contributes a fixed positive amount. Both its negative and positive parts are infinite, so the undiscounted objective is undefined. This establishes why the missing discount or objective-integrability hypothesis is substantive.