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A1712
Title: Forecasting stochastic volatility with realized volatility estimators and particle filters Authors:  Milan Ficura - University of Economics in Prague (Czech Republic) [presenting]
Jiri Witzany - University of Economics in Prague (Czech Republic)
Abstract: SVJD-RV-Z class of models is developed, utilizing the realized variance for better estimation of the stochastic variances, and the non-parametric $Z$-estimator for more accurate estimation of price jumps. Several adapted particle filters, specifically designed for latent-state filtering in SVJD models, are derived, and a Sequential Gibbs Particle Filter (SGPF) algorithm is developed for the sequential learning of their parameters. In the empirical study, four SVJD models (with intraday data, self-exciting jumps in prices and volatility, as well as multiple volatility components) are applied for the task of realized volatility forecasting on the time series of 7 foreign exchange rates and 10 ETF/ETN securities in the daily, weekly and monthly forecast horizon. The performance of the SVJD models is compared with 3 GARCH models (GARCH, EGARCH and GJRGARCH), 15 HAR model specifications (HAR, AHAR, SHAR, HARJ and HARQ), and 15 Echo State Neural Network (ESN) based volatility models previously developed. The SVJD-RV-Z models with jumps in volatility and prices are shown to exhibit the highest out-sample predictive power, comparable to the best HAR and ESN model specifications.