Let $X$ be a special semimartingale of the form $X=X_0+M+\int d\langle M\rangle\,\widehat\lambda$ and denote by $\widehat K=\int \widehat\lambda^{\rm tr}\,d\langle M\rangle\,\widehat\lambda$ the mean-variance tradeoff process of $X$. Let $\Theta$ be the space of predictable processes $\theta$...

We consider the mean-variance hedging problem when asset prices follow ItÆ processes in an incomplete market framework. The hedging numÊraire and the variance-optimal martingale measure appear to be a key tool for characterizing the optimal hedging strategy (see GouriÊroux et al. 1996;...

In the context of complete financial markets, we study dynamic measures of the form \[ \rho(x;C):=\sup_{\nu\in\D} \inf_{\pi(\cdot)\in\A(x)}{\bf E}_\nu\left(\frac{C-X^{x, \pi}(T)}{S_0(T)}\right)^+, \] for the risk associated with hedging a given liability C at time t = T. Here x is the initial...

This paper solves the following problem of mathematical finance: to find a solution to the problem of maximizing utility from terminal wealth of an agent with a random endowment process, in the general, semimartingale model for incomplete markets, and to characterize it via the associated dual...

We define (d,n)-coherent risk measures as set-valued maps from <InlineEquation ID="Equ1"> <EquationSource Format="TEX">$L^\infty_d$</EquationSource> </InlineEquation> into <InlineEquation ID="Equ2"> <EquationSource Format="TEX">$\mathbb{R}^n$</EquationSource> </InlineEquation> satisfying some axioms. We show that this definition is a convenient extension of the real-valued risk measures introduced by Artzner et al. [2]. We then discuss the aggregation issue, i.e., the...</equationsource></inlineequation></equationsource></inlineequation>

This paper presents an original probabilistic method for the numerical computations of Greeks (i.e. price sensitivities) in finance. Our approach is based on the {\it integration-by-parts} formula, which lies at the core of the theory of variational stochastic calculus, as developed in the...

Given a multi-dimensional Markov diffusion X, the Malliavin integration by parts formula provides a family of representations of the conditional expectation E[g(X <Subscript>2</Subscript>)|X<Subscript>1</Subscript>]. The different representations are determined by some localizing functions. We discuss the problem of variance reduction...</subscript></subscript>