An unscented Kalman smoother for volatility extraction: Evidence from stock prices and options

Junye Li

Computational Statistics & Data Analysis, 2013, vol. 58, issue C, 15-26

Abstract: A smoothing algorithm based on the unscented transformation is proposed for the nonlinear Gaussian system. The algorithm first implements a forward unscented Kalman filter and then evokes a separate backward smoothing pass by only making Gaussian approximations in the state but not in the observation space. The method is applied to volatility extraction in a diffusion option pricing model. Both simulation study and empirical applications with the Heston stochastic volatility model indicate that in order to accurately capture the volatility dynamics, both stock prices and options are necessary.

Keywords: Nonlinear Gaussian state-space models; Nonlinear Kalman filters; Unscented Kalman smoother; Heston stochastic volatility model; Option pricing (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:58:y:2013:i:c:p:15-26

DOI: 10.1016/j.csda.2011.06.001

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