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Modelling and Forecasting of Stochastic Volatility and Jumps

Autor práce: Fičura, Milan
Typ práce: Disertační práce
Vedoucí práce: Witzany, Jiří
Osoba oponující práci: Kodera, Jan; Vácha, Lukáš

Informace o vysokoškolské kvalifikační práci

Název práce: Modelling and Forecasting of Stochastic Volatility and Jumps
Typ práce: Doctoral thesis
Jazyk práce: English
Abstrakt: The thesis reviews the most commonly used volatility forecasting models from the ARCH/GARCH, realized volatility and stochastic volatility forecasting frameworks, with the main focus being placed on Stochastic-Volatility Jump-Diffusion (SVJD) models, on the ways of how high-frequency power-variation estimators can be used in SVJD model setting, and on the use of Bayesian methods for the estimation of SVJD model parameters and latent states. 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 developed by the author. 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.
Klíčová slova: Price jumps; SVJD models; Realized volatility; Z-Estimator; Stochastic volatility; Bayesian methods; Particle filters

Informace o studiu

Studijní program a Studijní obor: Finance a účetnictví/Finance
Typ studijního programu: Doktorský studijní program
Jméno přidělované hodnosti: Ph.D.
Instituce přidělující hodnost: University of Economics, Prague
Název fakulty: Faculty of Finance and Accounting
Název katedry: Department of Banking and Insurance
Instituce archivující a zpřístupňující VŠKP: University of Economics, Prague

Informace o odevzdání a obhajobě

Datum zadání práce: 30. 8. 2018
Datum podání práce: 30. 8. 2018
Datum obhajoby: 26.09.2018
Výsledek obhajoby: Závěrečná práce byla úspěšně obhájena

Soubory ke stažení

Hlavní práce66507_xficm03.pdf [6,30 MB]
Oponentura58708_kodera.pdf [309,29 kB]
Oponentura58709_Vácha.pdf [66,59 kB]
Hodnocení vedoucího66507_witzanyj.pdf [351,01 kB]

Údaje ze systému InSIS

Identifikátor https://insis.vse.cz/zp/66507/podrobnosti