Simulated likelihood inference for stochastic volatility models using continuous particle filtering

Michael Pitt, Sheheryar Malik and Arnaud Doucet ()

Annals of the Institute of Statistical Mathematics, 2014, vol. 66, issue 3, 527-552

Abstract: Discrete-time stochastic volatility (SV) models have generated a considerable literature in financial econometrics. However, carrying out inference for these models is a difficult task and often relies on carefully customized Markov chain Monte Carlo techniques. Our contribution here is twofold. First, we propose a new SV model, namely SV–GARCH, which bridges the gap between SV and GARCH models: it has the attractive feature of inheriting unconditional properties similar to the standard GARCH model but being conditionally heavier tailed. Second, we propose a likelihood-based inference technique for a large class of SV models relying on the recently introduced continuous particle filter. The approach is robust and simple to implement. The technique is applied to daily returns data for S&P 500 and Dow Jones stock price indices for various spans. Copyright The Institute of Statistical Mathematics, Tokyo 2014

Keywords: Stochastic volatility; Particle filter; Simulated likelihood; State space; Leverage effect; Jumps (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (17)

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DOI: 10.1007/s10463-014-0456-y

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