Artikel
Parameter learning and change detection using a particle filter with accelerated adaptation
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Risks ; ISSN: 2227-9091 ; Volume: 9 ; Year: 2021 ; Issue: 12 ; Pages: 1-18 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
particle filter
model estimation
stochastic volatility
regime switching
genetic algorithm
parameter estimation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Gellert, Karol
Schlögl, Erik
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2021
- DOI
-
doi:10.3390/risks9120228
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Gellert, Karol
- Schlögl, Erik
- MDPI
Entstanden
- 2021
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