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Particle filtering of volatility dynamics for KOSPI200 and its sequential prediction
Author(s) -
Kwon Tae Yeon
Publication year - 2018
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2546
Subject(s) - stochastic volatility , particle filter , volatility (finance) , econometrics , markov chain monte carlo , computer science , sabr volatility model , implied volatility , bayesian probability , markov chain , economics , kalman filter , artificial intelligence , machine learning
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.