Maximum likelihood estimation of discretely observed diffusion processes is mostly hampered by the lack of a closed form solution of the transient density. It has recently been argued that a most generic remedy to this problem is the numerical solution of the pertinent Fokker-Planck (FP) or...

This paper shows how exact solutions for the transient density of a large class of continuous-time Markov switching models can be obtained. We illustrate the pertinent approach for both simple diffusion models with a small number of regimes as well as for the more complicated so-called Poisson...

We propose a consistent and computationally efficient 2-step methodology for the estimation of multidimensional non-Gaussian asset models built using Lévy processes. The proposed framework allows for dependence between assets and different tail-behaviors and jump structures for each asset. Our...

In the present paper we consider the Quasi Maximum Likelihood (QML) procedure for the estimation of stationary Stochastic Volatility models. We prove the consistency of the QML estimators and compute explicitly their asymptotic variances. This allows us to obtain also consistent estimators of...

The financial econometrics literature includes several multivariate GARCH models where the model parameter matrices depend on a clustering of financial assets. Those classes might be defined a priori or data-driven. When the latter approach is followed, one method for deriving asset groups is...

We extend the double-well potential process to a three-parameter version in order to model intraday price dynamics, with a focus on the intraday momentum and reversal. The proposed process has a parsimonious form of three parameters controlling momentum, reversal, and volatility respectively. By...

In this article we suggest a new method for solutions of stochastic integrals where the dynamics of the variables in integrand are given by some stochastic differential equation. We also propose numerical simulation of stochastic differential equations which is based on iterated integrals method...