Use of adapted particle filters in SVJD models

Artikel

Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump-Diffusion (SVJD) models are being explained. Three versions of the SIR particle filter with adapted proposal distributions to the jump occurrences, jump sizes, and both are derived and their performance is compared in a simulation study to the un-adapted particle filter. The filter adapted to both the jump occurrences and jump sizes achieves the best performance, followed in their respective order by the filter adapted only to the jump occurrences and the filter adapted only to the jump sizes. All adapted particle filters outperformed the un-adapted particle filter.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: European Financial and Accounting Journal ; ISSN: 1805-4846 ; Volume: 13 ; Year: 2018 ; Issue: 3 ; Pages: 5-20 ; Prague: University of Economics, Faculty of Finance and Accounting

Classification and Topics

• Classification: Management ; Bayesian Analysis: General ; Semiparametric and Nonparametric Methods: General ; Statistical Simulation Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Subject: Particle Filters ; Price Jumps ; Stochastic Volatility

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Fičura, Milan ; Witzany, Jiří
• Event: Veröffentlichung
• (who): University of Economics, Faculty of Finance and Accounting
• (where): Prague
• (when): 2018

Further information

• DOI: doi:10.18267/j.efaj.211
• Handle: http://hdl.handle.net/10419/242257
• Last update: 10.03.2025, 11:46 AM CET