An Unscented Kalman Smoother for Volatility Extraction: Evidence from Stock Prices and Options

23 Pages Posted: 10 Mar 2013

See all articles by Junye Li

Junye Li

Fudan University - School of Management

Date Written: January 5, 2012

Abstract

A smoothing algorithm based on the unscented transformation is proposed for the non-linear 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: Non-linear Gaussian state-space models, Non-linear Kalman filters, Unscented Kalman smoother, Heston stochastic volatility model, Option pricing

JEL Classification: C10

Suggested Citation

Li, Junye, An Unscented Kalman Smoother for Volatility Extraction: Evidence from Stock Prices and Options (January 5, 2012). Available at SSRN: https://ssrn.com/abstract=2229597 or http://dx.doi.org/10.2139/ssrn.2229597

Junye Li (Contact Author)

Fudan University - School of Management ( email )

No. 670, Guoshun Road
No.670 Guoshun Road
Shanghai, 200433
China

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