This paper constructs a model for the evolution of a risky security that is consistent with a set of observed call option prices. It explicitly treats the fact that only a discrete data set can be observed in practice. The framework is general and allows for state dependent volatility and jumps....

This article shows how a modeling framework for the evolution of credit spreads can be built up starting from a simple representation with only two states - default and no default. The model is generalized by introducing credit classes, with transitions from one class to another driven by a...

We consider N independent stochastic processes (Xj(t),t∈[0,T]), j=1,…,N, defined by a one-dimensional stochastic differential equation with coefficients depending on a random variable ϕj and study the nonparametric estimation of the density of the random effect ϕj in two kinds of mixed...

Consider a one-dimensional diffusion with unknown positive drift and small variance [var epsilon]. We prove the asymptotic sufficiency of the complete or of some partial observations of the first hitting times process of the diffusion, as [var epsilon] goes to 0.

In this paper, we consider a stochastic volatility model ("Y"_"t", "V"_"t"), where the volatility (V_"t") is a positive stationary Markov process. We assume that ("ln""V"_"t") admits a stationary density "f" that we want to estimate. Only the price process "Y"_"t" is observed at "n" discrete times...

In this paper, we study nonparametric estimation of the Lévy density for pure jump Lévy processes. We consider n discrete time observations with step [Delta]. The asymptotic framework is: n tends to infinity, [Delta]=[Delta]n tends to zero while n[Delta]n tends to infinity. First, we use a...

Let (Vt) be a stationary and [beta]-mixing diffusion with unknown drift and diffusion coefficient. The integrated process is observed at discrete times with regular sampling interval . For both the drift function and the diffusion coefficient of the unobserved diffusion (Vt), we build...