Arbeitspapier
Historical calibration of SVJD models with deep learning
We propose how deep neural networks can be used to calibrate the parameters of Stochastic-Volatility Jump-Diffusion (SVJD) models to historical asset return time series. 1-Dimensional Convolutional Neural Networks (1D-CNN) are used for that purpose. The accuracy of the deep learning approach is compared with machine learning methods based on shallow neural networks and hand-crafted features, and with commonly used statistical approaches such as MCMC and approximate MLE. The deep learning approach is found to be accurate and robust, outperforming the other approaches in simulation tests. The main advantage of the deep learning approach is that it is fully generic and can be applied to any SVJD model from which simulations can be drawn. An additional advantage is the speed of the deep learning approach in situations when the parameter estimation needs to be repeated on new data. The trained neural network can be in these situations used to estimate the SVJD model parameters almost instantaneously.
- Sprache
-
Englisch
- Erschienen in
-
Series: IES Working Paper ; No. 36/2023
- Klassifikation
-
Wirtschaft
- Thema
-
Stochastic volatility
price jumps
SVJD
neural networks
deep learning
CNN
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Fičura, Milan
Witzany, Jiří
- Ereignis
-
Veröffentlichung
- (wer)
-
Charles University in Prague, Institute of Economic Studies (IES)
- (wo)
-
Prague
- (wann)
-
2023
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Fičura, Milan
- Witzany, Jiří
- Charles University in Prague, Institute of Economic Studies (IES)
Entstanden
- 2023
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