Quadratic Backward Stochastic Differential Equations Research Articles

CREDIT RISK PREMIA AND QUADRATIC BSDEs WITH A SINGLE JUMP

This paper is concerned with the determination of credit risk premia of defaultable contingent claims by means of indifference valuation principles. Assuming exponential utility preferences we derive representations of indifference premia of credit risk in terms of solutions of Backward Stochastic Differential Equations (BSDE). The class of BSDEs needed for that representation allows for quadratic growth generators and jumps at random times. Since the existence and uniqueness theory for this class of BSDEs has not yet been developed to the required generality, the first part of the paper is devoted to fill that gap. By using a simple constructive algorithm, and known results on continuous quadratic BSDEs, we provide sufficient conditions for the existence and uniqueness of quadratic BSDEs with discontinuities at random times.

A Note on FBSDE characterization of mean exit times

In this Note, we present a new explicit characterization for a mean exit time problem recently treated by the author, in form of a quadratic Forward–Backward Stochastic Differential Equation (FBSDE) with a random terminal time. An a priori estimate and a uniqueness result for such a type of FBSDE are also proved, under certain conditions. To cite this article: C. Makasu, C. R. Acad. Sci. Paris, Ser. I 347 (2009).

Quadratic BSDEs driven by a continuous martingale and applications to the utility maximization problem

In this paper, we study a class of quadratic backward stochastic differential equations (BSDEs), which arises naturally in the utility maximization problem with portfolio constraints. We first establish the existence and uniqueness of solutions for such BSDEs and then give applications to the utility maximization problem. Three cases of utility functions, the exponential, power, and logarithmic ones, are discussed.

Quadratic BSDEs with Random Terminal Time and Elliptic PDEs in Infinite Dimension

In this paper we study one dimensional backward stochastic differential equations (BSDEs) with random terminal time not necessarily bounded or finite when the generator $F(t,Y,Z)$ has a quadratic growth in $Z$. We provide existence and uniqueness of a bounded solution of such BSDEs and, in the case of infinite horizon, regular dependence on parameters. The obtained results are then applied to prove existence and uniqueness of a mild solution to elliptic partial differential equations in Hilbert spaces. Finally we show an application to a control problem.

Classical and Variational Differentiability of BSDEs with Quadratic Growth

We consider Backward Stochastic Differential Equations (BSDEs) with generators that grow quadratically in the control variable. In a more abstract setting, we first allow both the terminal condition and the generator to depend on a vector parameter $x$. We give sufficient conditions for the solution pair of the BSDE to be differentiable in $x$. These results can be applied to systems of forward-backward SDE. If the terminal condition of the BSDE is given by a sufficiently smooth function of the terminal value of a forward SDE, then its solution pair is differentiable with respect to the initial vector of the forward equation. Finally we prove sufficient conditions for solutions of quadratic BSDEs to be differentiable in the variational sense (Malliavin differentiable).

On Exponential Hedging and Related Quadratic Backward Stochastic Differential Equations

The dual optimization problem for the exponential hedging problem is addressed with a cone constraint. Without boundedness conditions on the terminal payoff and the drift of the Ito-type controlled process, the backward stochastic differential equation, which has a quadratic growth term in the drift, is derived as a necessary and sufficient condition for optimality via a variational method and dynamic programming. Further, solvable situations are given, in which the value and the optimizer are expressed in closed forms with the help of the Clark-Haussmann-Ocone formula.

Dynamic Portfolio Choice and Risk Aversion

This chapter presents a theory of optimal lifetime consumption-portfolio choice in a continuous information setting, with emphasis on the modeling of risk aversion through generalized recursive utility. A novel contribution is a decision theoretic development of the notions of source-dependent firstor second-order risk aversion. Backward stochastic differential equations (BSDEs) are explained heuristically as continuous-information versions of backward recursions on an information tree, and are used to formulate utility functions as well as optimality conditions. The role of scale invariance and quadratic BSDEs in obtaining tractable solutions is explained. A final section outlines extensions, including optimality conditions under trading constraints, and tractable formulations with nontradeable income.

Efficient consumption set under recursive utility and unknown beliefs

In a context of complete financial markets where asset prices follow Itô’s processes, we characterize the set of consumption processes which are optimal for a given stochastic differential utility (e.g. [Duffie and Epstein, Econometrica 60 (1992) 353]), when beliefs are unknown. Necessary and sufficient conditions for the efficiency of a consumption process, consists of the existence of a solution to a quadratic backward stochastic differential equation and a martingale condition. We study the efficiency condition in the case of a class of homothetic stochastic differential utilities and derive some results for those particular cases. In a Markovian context, this efficiency condition becomes a partial differential equation.