Stochastic Volatility

Stochastic Volatility (SV) models are a popular class of models to analyze the dependency structure between stocks and their volatility. In this paper, we develop a new class of SV models by incorporating carefully selected copula structures to reconstruct stylised empirical behaviours that cannot be captured by symmetric Gaussian innovations.

My doctoral research evolves around the topic of Sequential Bayesian Learning for State Space Models. More generally, I am working on estimating model parameters in a batch as well as in a times series setting, with a focus on latent variable models. This thesis includes projects with Hidden Semi-Markov Models, Copulae, and Epidemic Models that have latent factors attached.