Particle filtering of volatility dynamics for KOSPI200 and its sequential prediction

This paper examines a method of filtering the volatility dynamics of the KOSPI200 index under a stochastic volatility model. This study applies a particle filter algorithm for sequential estimation of volatility dynamics. In order to improve our estimation, the cross‐asset class approach is adopted by adding option price information to the model. The entire estimation procedure including the derivation of theoretical option price is based on Bayesian Markov chain Monte Carlo methods, so the method presented in this paper can be applied to diversified volatility models. Through the simulation study, we confirm that this method can estimate unknown volatility dynamics correctly, and the use of additional option prices improves both the accuracy and efficiency of volatility filtering. The sequential one‐step‐ahead prediction of the distribution of the KOSPI 200 index and index option prices shows that the additional option price information also enhances the prediction performance.