1. Introduction

The task of determining good trading strategies is a fundamental problem in mathematical finance. A typical approach to this problem aims at finding the trading strategy that maximizes, for example, the final expected utility, which is defined as a concave and increasing function of the final wealth. There are, however, many applications where a utility function varies with the underlying securities, or even random. For example, if the market is incomplete, it is often more beneficial to allow certain degree of “shortfall” in order to reduce the “super-hedging cost” (see, e.g., [1, 2] for more details). Mathematically, such a shortfall risk is often quantified by the expected loss

where is a convex increasing “loss” function, is a contingent claim, and is the value process that is subject to the constraint , for a given initial endowment .

The above shortfall minimizing problem can be easily recast as a utility maximization problem with a bounded state-dependent utility of the form

as it was first pointed out by Föllmer and Leukert [3] (see Definition 2.3 for a formal description of the general bounded state-dependent utility). Then, the minimal shortfall risk cost is given by

where the supremum is taken over all wealth processes generated by admissible trading strategies. We should point out here that it is the boundedness and potential nondifferentiability of such utility function that give rise to some technical issues which make the problem interesting.

The existence and essential uniqueness of the solution to the problem (1.3) in its various special forms have been studied by many authors see, for example, Cvitanić [4], Föllmer and Leukert [3], Xu [5], and Karatzas and Žitković [6], to mention a few. However, while the convex duality approach in [3] succeeds in dealing with the non-Markovian nature of the model, it does not seem to shed any light on how to compute, in a feasible manner, the optimal trading strategy, partly due to the generality of the model considered there. In this paper we will consider a specific but popular model driven by a Lévy process. Our goal is to narrow down the domain of dual problem so that the convex duality method holds true. Furthermore, we will try to give an explicit construction of the dual domain that contains the dual optimizer. Although at this point our results are still rather general, and at a theoretical level, we believe that this is a necessary step towards a feasible computational implementation of the convex duality method.

While the utility maximization problem of this kind can be traced back to Merton [7, 8], in this paper we shall follow the convex duality method, suggested by Karatzas et al. [9], and later extended by Kunita [10] to general Lévy market models. However, we note that in [9] the utility function was required to be unbounded, strictly increasing and concave, continuously differentiable, and other technical assumptions including the so-called Inada conditions. On the other hand, since one of the key tools in [10] is an exponential representation for positive local supermartingales (see, e.g., [11, Lemma 4.2]), it is required that the utility function satisfies the same conditions as in [9] (in particular, unboundedness), plus that the dual domain contains all positive “risk-neutral” local supermartingales. The boundedness and potential nondifferentiability of the utility function in our case thus cause some technical subtleties. For example, the dual optimal process can be with positive probability, thus the representation theorem of Kunita [11, Lemma 4.2] does not apply anymore.

A key element that we use to overcome these technical difficulties is an exponential representation theorem for nonnegative supermartingales by Föllmer and Kramkov [12]. This result leads to an explicit construction of the dual domain, based on those nonnegative supermartingales that can be written as stochastic exponentials , with being an increasing process and belonging to a class of semimartingales that is closed under Émery's topology. To validate this approach we prove a closure property for integrals with respect to a fixed compensated Poisson random measures, a result of interest on its own, which extends the analog property for integrals with respect to a fixed semimartingale due to Mémin [13]. Finally, unlike some previous works on the subject (see, e.g., Föllmer and Leukert [3] and Xu [5]), we do not use the so-called bipolar theorem of Kramkov and Schachermayer [14] to argue the attainability of the optimal final wealth. Instead, we shall rely on the fundamental characterization of contingent claims that are super replicable [1, 2], reducing the problem of finding the optimal primal solution to a super-eplication problem.

We believe that the dual problem proposed in this paper offers several advantages. For example, since the dual class enjoys a fairly “explicit” description and “parametrization,” our results could be considered as a first step towards a feasible computational implementation of the covex duality method. Furthermore, the specific results we obtained for the Lévy market can be used to characterize the elements of the dual domain and the admissible trading strategies. In particular, if either (i) the jumps of the price process are driven by the superposition of finitely many shot-noise Poisson processes, or (ii) for a process and a deterministic function , we show that the dual solution is a risk-neutral local martingale.

We would like to remark that some of our results are related to those in Xu [5], but there are essential differences. For example, the model in [5] exhibits only finite-jump activity and allows only downward price jumps (in fact, this assumption seems to be important for the approach there), while our model allows for general jump dynamics, and our approach is also valid for general additive processes, including the time-inhomogeneous cases considered in [5] (see (ii) of Section 6).

The rest of the paper is organized as follows. In Section 2 we introduce the financial model, along with some basic terminology that will be used throughout the paper. The convex duality method is revised in Section 3, where a potential optimal final wealth is constructed. An explicit description of a dual class and a characterizations of the dual optimum and admissible trading strategies are presented in Section 4. In Section 5 we show that the potential optimal final wealth is attained by an admissible trading strategy, completing the proof of the existence of optimal portfolio. In Section 6 we give some concluding remarks. Some necessary fundamental theoretical results, such as the exponential representation for nonnegative supermartingales of Föllmer and Kramkov [12] and the closure property for integrals with respect to Poisson random measures, are collected in Appendix A. Finally, Appendix B briefly outlines the proofs of the convex duality results used in the paper.

2. Notation and Problem Formulation

Throughout this paper we assume that all the randomness comes from a complete probability space , on which there is defined a Lévy process with Lévy triplet (see Sato [15] for the terminology). By the Lévy-Itô decomposition, there exist a standard Brownian motion and an independent Poisson random measure on with mean measure , such that

where . Let be the natural filtration generated by and , augmented by all the null sets in so that it satisfies the usual conditions (see, e.g., [16]).

2.1. The Market Model

We assume that there are two assets in the market: a risk free bond (or money market account), and a risky asset, say, a stock. The case of multiple stocks, such as the one studied in [10], can be treated in a similar way without substantial difficulties (see Section 6 for more details). As it is customary all the processes are taken to be discounted to the present value so that the value of the risk-free asset can be assumed to be identically equal to . The (discounted) price of the stock follows the stochastic differential equation

where , , , and (see [17] for the terminology). More precisely, , , and are predictable processes such that a.s. (hence, a.s.), and that the processes

are locally integrable with respect to time. Even though we will work with a finite horizon later on, we choose to define our market model on . Finally, we assume that the market is free of arbitrage so that there exists at least one risk-neutral probability measure such that the (discounted) process , , is an -local martingale under . Throughout, will stand for the class of all equivalent risk neutral measures . It is relevant to mention that we do not impose market completeness, and hence, the class is not assumed to be a singleton.

2.2. Admissible Trading Strategies and the Utility Maximization Problem

A trading strategy is determined by a predictable locally bounded process representing the proportion of total wealth invested in the stock. Then, the resulting wealth process is governed by the stochastic differential equation

where stands for the initial endowment. For future reference, we give a precise definition of “admissible strategies.”

Definition 2.1. The process solving (2.4) is called the value process corresponding to the self-financing portfolio with initial endowment and trading strategy . We say that a value process is “admissible” or that the process is “admissible” for if

For a given initial endowment , we denote the set all admissible strategies for by , and the set of all admissible value processes by . In light of the Doléans-Dade stochastic exponential of semimartingales (see, e.g., [17, Section I.4f]), one can easily obtain necessary and sufficient conditions for admissibility.

Proposition 2.2. A predictable locally bounded process is admissible if and only if

To define our utility maximization problem, we begin by introducing the bounded state-dependent utility function.

Definition 2.3. A random function is called a “bounded and state-dependent utility function” if (1) is nonnegative, nondecreasing, and continuous on ; (2)for each fixed , the mapping is -measurable; (3)there is an -measurable, positive random variable such that for all , is a strictly concave differentiable function on , and it holds that

Notice that the -measurability of the random variable is automatic because is -measurable in light of the above conditions and We remark that while assumption (2.7) is merely technical, assumption (2.6) is motivated by the shortfall risk measure (1.2). Our utility optimization problem is thus defined as

for any . We should note that the above problem is relevant only for those initial wealths that are smaller than , the super-hedging cost of . Indeed, if , then there exists an admissible trading strategy for such that almost surely, and consequently, (see [1, 2] for this super-hedging result).

Our main objectives in the rest of the paper are the following: (1) Define the dual problem and identify the relation between the value functions of the primal and the dual problems; (2) By suitably defining the dual domain, prove the attainability of the associated dual problem; (3) Show that the potential optimum final wealth induced by the minimizer of the dual problem can be realized by an admissible portfolio. We shall carry out these tasks in the remaining sections.

3. The Convex Duality Method and the Dual Problems

In this section we introduce the dual problems corresponding to the primal problem (1.3) and revise some standard results of convex duality that are needed in the sequel. Throughout, represents the convex dual function of , defined as

We observe that the function is closely related to the Legendre-Fenchel transformation of the convex function . It can be easily checked that is convex and differentiable everywhere, for each . Furthermore, if we denote the generalized inverse function of by

with the convention that , then it holds that

and the function has the following representation:

Remark 3.1. We point out that the random fields defined in (3.1) and (3.2) are -measurable. For instance, in the case of , we can write and we will only need to check that is jointly measurable for each fixed . This last fact follows because the random field in question is continuous in the spatial variable for each and is -measurable for each In light of (3.3), it transpires that the random field is jointly measurable. Given that the subsequent dual problems and corresponding solutions are given in terms of the fields and (see Definition 3.3 and Theorem 3.5), the measurability of several key random variable below is guaranteed.

Next, we introduce the traditional dual class (cf. [14]).

Definition 3.2. Let be the class of nonnegative supermartingales such that (i), (ii)for each locally bounded admissible trading strategy , is a supermartingale.

To motivate the construction of the dual problems below we note that if and is the value process of a self-financing admissible portfolio with initial endowment , then , and it follows that

for any . The dual problem is defined as follows.

Definition 3.3. Given a subclass , the minimization problem is called the “dual problem induced by .” The class is referred to as a dual domain (or class) and is called its dual value function.

Notice that, by (3.6) and (3.7), we have the following weak duality relation between the primal and dual value functions:

valid for all . The effectiveness of the dual problem depends on the attainability of the lower bound in (3.8) for some (in which case, we say that strong duality holds), and the attainability of its corresponding dual problem (3.7). The following well-known properties will be needed for future reference. Their proofs are standard and are outlined in Appendix B for the sake of completeness.

Proposition 3.4. The dual value function corresponding to a subclass of satisfies the following properties (1) is nonincreasing on and (2)If then is uniformly continuous on , and (3)There exists a process such that (4)If is a convex set, then (i) is convex, and with (ii) there exists a attaining the minimum . Furthermore, the optimum can be “approximated” by elements of in the sense that there exists a sequence for which , a.s.

We now give a result that is crucial for proving the strong duality in (3.8). The result follows from arguments quite similar to those in [9, Theorem 9.3]. For the sake of completeness, we outline the proof in Appendix B.

Theorem 3.5. Suppose that (3.9) is satisfied and is convex, then, for any , there exist and such that (i); (ii), where (iii).

We note that Theorem 3.5 provides essentially an upper bound for the optimal final utility of the form , for certain “reduced” contingent claim . By suitably choosing the dual class , we shall prove in the next two sections that this reduced contingent claim is (super-) replicable with an initial endowment .

4. Characterization of the Optimal Dual

We now give a full description of a dual class for which strong duality, that is, , holds. Denote to be the class of all real-valued càdlàg, nondecreasing, adapted processes null at zero. We will call such a process “increasing.” In what follows we let be the Doléans-Dade stochastic exponential of the semimartingale (see, e.g., [17] for their properties). Let

and consider the associated class of exponential local supermartingales:

In (4.1), we assume that , , and that , for all . The following result shows not only that the class

is convex, but also that the dual optimum, whose existence is guaranteed from Theorem 3.5, remains in . The proof of this result is based on a powerful representation for nonnegative supermartingales due to Föllmer and Kramkov [12] (see Theorem A.1 in Appendix A), and a technical result about the closedness of the class of integrals with respect to Poisson random measures, under Émery’s topology. We shall defer the presentation of these two fundamental results to Appendix A in order to continue with our discussion of the dual problem.

Theorem 4.1. The class is convex, and if (3.9) is satisfied, the dual optimum of Theorem 3.5 belongs to , for any .

Proof. Let us check that meets with the conditions in Theorem A.1. Indeed, each in is locally bounded from below since, defining where . Condition (i) of Theorem A.1 is straightforward, while condition (ii) follows from Theorem A.3. Finally, condition (iii) holds because the processes in are already local martingales with respect to and hence with . By Proposition A.2 we conclude that is convex and closed under Fatou convergence on dense countable sets. On the other hand, is also convex and closed under Fatou convergence, and thus so is the class . To check the second statement, recall that the existence of the dual minimizer in Theorem 3.5 is guaranteed from Proposition 3.4, where it is seen that is the Fatou limit of a sequence in (see the proof of Proposition 3.4). This suffices to conclude that since is closed under under Fatou convergence.

In the rest of this section, we present some properties of the elements in and of the dual optimum . In particular, conditions on the “parameters” so that is in are established. First, we note that without loss of generality, can be assumed predictable.

Lemma 4.2. Let Then, there exist a predictable process and a process such that .

Proof. Let . Since , there are stopping times such that Compare with [17, Theorem II.1.33]. Now, define and . Then, where we used that . Therefore, is locally integrable, increasing, and thus, its predictable compensator exists. Now, by the representation theorem for local martingales (see [17, Theorem III.4.34]), the local martingale admits the representation Finally, The conclusion of the proposition follows since is necessarily in .

The following result gives necessary conditions for a process to belong to . Recall that a predictable increasing process can be uniquely decomposed as the sum of three predictable increasing processes,

where is the absolutely continuous part, is the singular continuous part, and is the jump part (cf. [18, Theorem 19.61]).

Proposition 4.3. Let , where , and is an increasing predictable process. Let be the “sinking time” of the supermartingale : Also, let . Then, is a supermartingale if and only if the following two conditions are satisfied. (i)There exist stopping times such that (ii)For -a.e. , for almost every , where

Proof. Recall that and satisfy the SDE’s Integration by parts and the predictability of yield that Suppose that is a nonnegative supermartingale. Then, the integral must have locally integrable variation in light of the Doob-Meyer decomposition for supermartingale (see, e.g., [16, Theorem III.13]). Therefore, there exist stopping times such that Then, (i) is satisfied with , where and . Next, we can write (4.17) as By the Doob-Meyer representation for supermartingales and the uniqueness of the canonical decomposition for special semimartingales, the last integral must be increasing. Then, for since and for (see [17, Theorem I.4.61]).
We now turn to the sufficiency of conditions (i) and (ii). Since is locally bounded,
is locally integrable. Then, from (4.17), we can write Condition (ii) implies that is a supermartingale, and by Fatou, will be a supermartingale. This concludes the prove since for , and thus, , for all .

The following result gives sufficient and necessary conditions for to belong to . Its proof is similar to that of Proposition 4.3.

Proposition 4.4. Under the setting and notation of Proposition 4.3, belongs to if and only if condition (i) in Proposition 4.3 holds and, for any locally bounded admissible trading strategies ,

The previous result can actually be made more explicit under additional information on the structure of the jumps exhibited by the stock price process. We consider two cases: when the jumps come from the superposition of shot-noise Poisson processes, and when the random field exhibit a multiplicative structure. Let us first extend Proposition 2.2 in these two cases.

Proposition 4.5. (i) Suppose that is atomic with finitely many atoms then, a predictable locally bounded strategy is admissible if and only if -a.e.

Suppose that for a predictable locally bounded process such that -a.e. and is locally bounded, and a deterministic function such that , then, a predictable locally bounded strategy is admissible if and only if -a.e.
where and .

Proof. From Proposition 2.2, recall that -a.s. for a.e. . Then, for any closed set , , and , Taking expectation, we get Since such processes generate the class of predictable processes, we conclude that -a.e. Let us prove (ii) (the proof of (i) is similar). Notice that where is the support of . Suppose that . Then, by considering closed sets such that as , we can prove the necessity. The other two cases (namely, or ) are proved in a similar way. Sufficiency follows since, -a.s.,

Example 4.6. It is worth pointing out some consequences (a)In the time homogeneous case, where , the extreme points of the support of (or what accounts to the same, the infimum and supremum of all possible jump sizes) determine completely the admissible strategies. For instance, if the Lévy process can exhibit arbitrarily large or arbitrarily close to jump sizes, then a constraint that can be interpreted as absence of shortselling and bank borrowing (this fact was already pointed out by Hurd [19]). (b)In the case that , the admissibility condition takes the form If in addition (such that the stock prices exhibit only downward sudden movements), then and , with arbitrary, is admissible. In particular, from Proposition 4.4, if belongs to , then a.s. This means that