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.
- Language
-
Englisch
- Bibliographic citation
-
Series: IES Working Paper ; No. 36/2023
- Classification
-
Wirtschaft
- Subject
-
Stochastic volatility
price jumps
SVJD
neural networks
deep learning
CNN
- Event
-
Geistige Schöpfung
- (who)
-
Fičura, Milan
Witzany, Jiří
- Event
-
Veröffentlichung
- (who)
-
Charles University in Prague, Institute of Economic Studies (IES)
- (where)
-
Prague
- (when)
-
2023
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Fičura, Milan
- Witzany, Jiří
- Charles University in Prague, Institute of Economic Studies (IES)
Time of origin
- 2023
Other Objects (12)
Deep calibration of financial models: turning theory into practice
The potential of deep learning for gas sensor evaluation and calibration
Agent-based model calibration using machine learning surrogates
Polynomial semimartingales and a deep learning approach to local stochastic volatility calibration
Model-free control and deep learning
Deep reading, deep learning
A deep learning integrated Lee-Carter model
How Booking.com serves Deep Learning model predictions
Cardinality Estimation with Local Deep Learning Models
Calibration of probability predictions from machine-learning and statistical models
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
Improving automotive radar spectra object classification using deep learning and multi-class uncertainty calibration
Deep calibration of financial models: turning theory into practice
The potential of deep learning for gas sensor evaluation and calibration
Agent-based model calibration using machine learning surrogates
Polynomial semimartingales and a deep learning approach to local stochastic volatility calibration
Model-free control and deep learning
Deep reading, deep learning
A deep learning integrated Lee-Carter model
How Booking.com serves Deep Learning model predictions
Cardinality Estimation with Local Deep Learning Models
Calibration of probability predictions from machine-learning and statistical models
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
Improving automotive radar spectra object classification using deep learning and multi-class uncertainty calibration
Deep calibration of financial models: turning theory into practice
The potential of deep learning for gas sensor evaluation and calibration
Agent-based model calibration using machine learning surrogates
Polynomial semimartingales and a deep learning approach to local stochastic volatility calibration
Model-free control and deep learning
Deep reading, deep learning
A deep learning integrated Lee-Carter model
How Booking.com serves Deep Learning model predictions
Cardinality Estimation with Local Deep Learning Models
Calibration of probability predictions from machine-learning and statistical models
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning