Modelling Stochastic Volatility with Leverage and Jumps: A Simulated Maximum Likelihood Particle Filtering Approach
Speaker
Abstract
| In this paper we provide a unified methodology in order to conduct likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility models, characterized by both a leverage effect and jumps in returns. Given the non-linear/non-Gaussian state-space form, approximating the likelihood for the parameters is conducted with output generated by the particle filter. Methods are employed to ensure that the approximating likelihood is continuous as a function of the unknown parameters; thus enabling the use of Newton-Raphson type maximization algorithms. Our approach is robust and efficient relative to alternative Markov Chain Monte Carlo schemes employed in such contexts. The technique is applied to daily returns data of various leading stock price indices. |
| Contact information: |
| Erik Kole |
