Malliavin Calculus Methods Research Articles

Influence of risk tolerance on long-term investments: a Malliavin calculus approach

This study investigates the influence of risk tolerance on the expected utility in the long run. We estimate the extent to which the expected utility of optimal portfolios is affected by small changes in the risk tolerance. For this purpose, we adopt the Malliavin calculus method and the Hansen–Scheinkman decomposition, through which the expected utility is expressed in terms of the eigenvalues and eigenfunctions of an operator. We conclude that the influence of risk aversion on the expected utility is determined by these eigenvalues and eigenfunctions in the long run.

Optimal Hedging Strategies for Options in Electricity Futures Markets

In this paper, we derive optimal hedging strategies for options in electricity futures markets. Optimality is measured in terms of minimal variance and the associated minimal variance hedging portfolios are obtained by a stochastic maximum principle. Our explicit results are particularly useful for electricity retailers, who have sold an option to a client, and now want to hedge the payoff of this option by investing into an electricity futures and into the issued option itself. Another innovative aspect of the paper lies in the derivation of the time dynamics of the stochastic option price process by Malliavin calculus methods and the Clark-Ocone formula. We also apply our theoretical results to several practical examples. Our investigations are based upon the popular arithmetic multi-factor electricity spot price model proposed by Benth, Kallsen & Meyer-Brandis (Appl. Math. Finance 14(2):153-169, 2007).

A Malliavin Calculus Approach to Minimal Variance Hedging

In this paper, we investigate the following problem: How can a financial institution, which has sold an option to a client, optimally hedge the payoff of this option by investing into a stock and into the option itself? Optimality is measured in terms of minimal variance and the associated optimal hedging portfolio is derived by a stochastic maximum principle. Moreover, we deduce the time dynamics of the stochastic option price process by Malliavin calculus methods, particularly by an application of the Clark-Ocone formula. We finally apply our theoretical results to several examples.

An Asymptotic Expansion for Forward–Backward SDEs: A Malliavin Calculus Approach

This paper proposes a new analytical approximation scheme for the representation of the forward–backward stochastic differential equations (FBSDEs) of Ma and Zhang (Ann Appl Probab, 2002). In particular, we obtain an error estimate for the scheme applying Malliavin calculus method for the forward SDEs combined with the Picard iteration scheme for the BSDEs. We also show numerical examples for pricing option with counterparty risk under local and stochastic volatility models, where the credit value adjustment is taken into account.

SIMULATION OF MULTI-ASSET OPTION GREEKS UNDER A SPECIAL LÉVY MODEL BY MALLIAVIN CALCULUS

We discuss simulation of sensitivities or Greeks of multi-asset European style options under a special Lévy process model: that is, the subordinated Brownian motion model. The Malliavin calculus method combined with Monte Carlo and quasi-Monte Carlo methods is used in the simulations. Greeks are expressed in terms of the expectations of the option payoff functions multiplied by the weights involving Malliavin derivatives for multi-asset options. Numerical results show that the Malliavin calculus method is usually more efficient than the finite difference method for options with nonsmooth payoffs. The superiority of the former method over the latter is even more significant when both are combined with quasi-Monte Carlo methods.

Efficient simulation of Greeks of multiasset European and Asian style options by Malliavin calculus and quasi-Monte Carlo methods

This paper discusses simulation of sensitivities or Greeks of multiasset European and Asian style options by Malliavin calculus combined with Monte Carlo and quasi-Monte Carlo methods. By using the Malliavin calculus, we are able to express Greeks of both European and Asian style multiasset options explicitly in terms of the expectations of the option payoff functions multiplied by the Malliavin weights. For European multiasset options, these expectations are further converted to integrals over hypercubes in order to make use the better uniformity of low discrepancy sequences. Numerical results show the advantages of Malliavin calculus method to the finite difference method for options with nonsmooth payoffs. The superiority of the first method over the second one is even more significant when these methods are combined with quasi-Monte Carlo methods. For example, when simulating Δ1 of a basket binary option or a basket “double” binary option, the efficiencies reach up to tens of thousands with good lattice points sequences; and the efficiencies are up to tens of millions or higher with the same type of sequences when simulating Γ11 or Γ12 for the same type of options.

SPDEs with polynomial growth coefficients and the Malliavin calculus method

In this paper we study the existence and uniqueness of the Lρ2p(Rd;R1)×Lρ2(Rd;Rd) valued solutions of backward doubly stochastic differential equations (BDSDEs) with polynomial growth coefficients using weak convergence, equivalence of norm principle and Wiener–Sobolev compactness arguments. Then we establish a new probabilistic representation of the weak solutions of SPDEs with polynomial growth coefficients through the solutions of the corresponding BDSDEs. This probabilistic representation is then used to prove the existence of stationary solutions of SPDEs on Rd via infinite horizon BDSDEs. The convergence of the solution of a finite horizon BDSDE, when its terminal time tends to infinity, to the solution of the infinite horizon BDSDE is shown to be equivalent to the convergence of the pull-back of the solution of corresponding SPDE to its stationary solution. This way we obtain the stability of the stationary solution naturally.

An Asymptotic Expansion for Forward-Backward SDEs: A Malliavin Calculus Approach

This paper proposes a new analytical approximation scheme for the representation of the forward- backward stochastic differential equations (FBSDEs) of Ma and Zhang (2002). In particular, we obtain an error estimate for the scheme applying Malliavin calculus method for the forward SDEs combined with the Picard iteration scheme for the BSDEs. We also show numerical examples for pricing option with counterparty risk under local and stochastic volatility models, where the credit value adjustment (CVA) is taken into account.

A Malliavin Calculus method to study densities of additive functionals of SDE’s with irregular drifts

On introduit une méthode générale qui permet l’utilisation du Calcul de Malliavin pour des fonctionnelles additives générées par des équations stochastiques avec une dérive irrégulière. Cette méthode utilise le théorème de Girsanov avec l’expansion d’Itô–Taylor pour obtenir la régularité de la densité. On applique cette méthodologie pour au cas de l’intégrale en temps d’une diffusion avec derive mesurable bornée.

Hypoelliptic heat kernel inequalities on Lie groups

This paper discusses the existence of gradient estimates for the heat kernel of a second order hypoelliptic operator on a manifold. For elliptic operators, it is now standard that such estimates (satisfying certain conditions on coefficients) are equivalent to a lower bound on the Ricci tensor of the Riemannian metric. For hypoelliptic operators, the associated “Ricci curvature” takes on the value − ∞ at points of degeneracy of the semi-Riemannian metric. For this reason, the standard proofs for the elliptic theory fail in the hypoelliptic setting. This paper presents recent results for hypoelliptic operators. Malliavin calculus methods transfer the problem to one of determining certain infinite dimensional estimates. Here, the underlying manifold is a Lie group, and the hypoelliptic operators are given by the sum of squares of left invariant vector fields. In particular, “ L p -type” gradient estimates hold for p ∈ ( 1 , ∞ ) , and the p = 2 gradient estimate implies a Poincaré estimate in this context.

A new technique for calibrating stochastic volatility models: the Malliavin gradient method

We discuss the application of gradient methods to calibrate mean reverting stochastic volatility models. For this we use formulas based on Girsanov transformations as well as a modification of the Bismut–Elworthy formula to compute the derivatives of certain option prices with respect to the parameters of the model by applying Monte Carlo methods. The article presents an extension of the ideas to apply Malliavin calculus methods in the computation of Greek's.

Hypoelliptic heat kernel inequalities on the Heisenberg group

We study the existence of “ L p -type” gradient estimates for the heat kernel of the natural hypoelliptic “Laplacian” on the real three-dimensional Heisenberg Lie group. Using Malliavin calculus methods, we verify that these estimates hold in the case p > 1 . The gradient estimate for p = 2 implies a corresponding Poincaré inequality for the heat kernel. The gradient estimate for p = 1 is still open; if proved, this estimate would imply a logarithmic Sobolev inequality for the heat kernel.