Artikel

Use of adapted particle filters in SVJD models

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.

Sprache
Englisch

Erschienen in
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

Klassifikation
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
Thema
Particle Filters
Price Jumps
Stochastic Volatility

Ereignis
Geistige Schöpfung
(wer)
Fičura, Milan
Witzany, Jiří
Ereignis
Veröffentlichung
(wer)
University of Economics, Faculty of Finance and Accounting
(wo)
Prague
(wann)
2018

DOI
doi:10.18267/j.efaj.211
Handle
Letzte Aktualisierung
10.03.2025, 11:46 MEZ

Datenpartner

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Fičura, Milan
  • Witzany, Jiří
  • University of Economics, Faculty of Finance and Accounting

Entstanden

  • 2018

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