In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochasti

"This paper proposes the EGARCH [Exponential Generalized Autoregressive Conditional Heteroskedasticity] model with jumps and heavy-tailed errors, and studies th

We first propose a reduced-form model in discrete time for Samp;P500 volatility showing that the forecasting performance of a volatility model can be significan

While a great deal of attention has been focused on stochastic volatility in stock returns, there is strong evidence suggesting that return distributions have t

This paper is concerned with specification for modelling finanical leverage effect in the context of stochastic volatility models.