We develop infinitesimally robust statistical procedures for general diffusion processes. We first prove existence and uniqueness of the times series influence function of conditionally unbiased M-estimators for ergodic and stationary diffusions, under weak conditions on the (martingale)...

Asset prices exhibit characteristics that significantly deviate from log-normality and display time-varying stochastics. There is ample evidence of jumps in one asset price or market leading to jumps in other assets' prices or markets. We propose a multivariate jump diffusion model with...

The risky assets prices of the bi-variate model are reviewed under the hegemonize concentration filtered physical probability space. In the stochastic variance of the Cox-Ingersoll-Ross process. The Mean-variance hedging expanse on the Föllmer-Schweizer decomposition is stringent to the...

In an efficient stock market, the returns and their time-dependent volatility are often jointly modeled by stochastic volatility models (SVMs). Over the last few decades several SVMs have been proposed to adequately capture the defining features of the relationship between the return and its...

We propose a mixed frequency stochastic volatility (MFSV) model for the dynamics of intraday asset return volatility. In order to account for long-memory we separate stochastic daily and intraday volatility patterns by introducing a long-run component that changes at daily frequency and a...

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility clustering and asymmetry characterize the logarithmic realized volatility of both indices. ARFIMAX model with time-varying conditional heteroscedasticity is the best performing...

Affine jump diffusion models in general and affine stochastic volatility models in particular are important modeling tools in finance. Their popularity resides in their exibility coupled with their analytical tractability, especially with respect to characteristic functions and polynomial...

We build a class of copula models that captures time-varying dependence across large panels of financial assets. Our models nest Gaussian, Student's t, grouped Student's t, and generalized hyperbolic copulas with time-varying correlations matrices, as special cases. We introduce time-variation...

We propose a new Stochastic Dominance (SD) criterion based on standard risk aversion, which assumes decreasing absolute risk aversion and decreasing absolute prudence. To implement the proposed criterion, we develop linear systems of optimality conditions for a given prospect relative to a...

We consider a multivariate continuous time process, generated by a system of linear stochastic differential equations, driven by white noise and involving coefficients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points...