Sequential Bayesian Analysis of Time-Changed Infinite Activity Derivatives Pricing Models

Junye Li

Journal of Business & Economic Statistics, 2011, vol. 29, issue 4, 468-480

Abstract: This article investigates time-changed infinite activity derivatives pricing models from the sequential Bayesian perspective. It proposes a sequential Monte Carlo method with the proposal density generated by the unscented Kalman filter. This approach overcomes to a large extent the particle impoverishment problem inherent to the conventional particle filter. Simulation study and real applications indicate that (1) using the underlying alone cannot capture the dynamics of states, and by including options, the precision of state filtering is dramatically improved; (2) the proposed method performs better and is more robust than the conventional one; and (3) joint identification of the diffusion, stochastic volatility, and jumps can be achieved using both the underlying data and the options data.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:29:y:2011:i:4:p:468-480

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DOI: 10.1198/jbes.2010.08310

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