JSM 2005 - Toronto

Abstract #303924

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Activity Number: 181
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303924
Title: Sequential Parameter Estimation in Stochastic Volatility Models with Jumps
Author(s): Jonathan Stroud*+ and Michael Johannes and Nicholas Polson
Companies: University of Pennsylvania and Columbia University and The University of Chicago
Address: 400 Jon M Huntsman Hall, Philadelphia, PA, 19104-6340, United States
Keywords: Particle filtering ; Practical filtering ; Sequential parameter estimation ; Stochastic volatility ; Stochastic volatility with jumps ; Stock returns
Abstract:

This paper analyzes the sequential learning problem for both parameters and states in stochastic volatility models with jumps. We describe the existing methods, the particle, and practical filter. Then, we extend these algorithms to incorporate jumps. We analyze the performance of both approaches using both simulated and S&P 500 index return data. On both types of data, we find both algorithms are effective in sequential learning of the jump parameters, although sensitivity analysis indicates the practical filter performs marginally better. These conclusions are similar to those in Stroud, Polson, and Mueller (2004) regarding stochastic volatility models.

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