Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/242257 
Erscheinungsjahr: 
2018
Quellenangabe: 
[Journal:] European Financial and Accounting Journal [ISSN:] 1805-4846 [Volume:] 13 [Issue:] 3 [Publisher:] University of Economics, Faculty of Finance and Accounting [Place:] Prague [Year:] 2018 [Pages:] 5-20
Verlag: 
University of Economics, Faculty of Finance and Accounting, Prague
Zusammenfassung: 
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.
Schlagwörter: 
Particle Filters
Price Jumps
Stochastic Volatility
JEL: 
C11
C14
C15
C22
G1
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
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