It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in...

In a nonlinear framework, temporal dependence of time series is sensitive to transformations. The aim of this paper is to examine in detail the relationships between various forms of persistence and nonlinear transformations of stationary and nonstationary processes. We introduce the concept of...

We develop a unified approach with closed-form solutions for pricing bonds, stocks,currencies and their derivatives. The specification assumes a fundamental risk factorrepresented by a stochastic positive definite matrix following a Wishart autoregressive(WAR) process. By assuming a...

Risks are usually represented and measured by volatility-covolatility matrices.Wishart processes are models for a dynamic analysis of multivariaterisk, that describe the evolution of stochastic volatility-covolatility matrices,constrained to be symmetric positive definite. The autoregressive...

This paper provides a uni¯ed statistical framework for the analysis of distortion riskmeasures (DRM) and of their sensitivities with respect to parameters representing riskaversion and/or pessimism. We derive the general formula for calculating the functionalasymptotic distribution of the...