Historical Calibration of SVJD Models with Deep Learning

Milan Ficura and Jiří Witzany

No 2023/36, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

Abstract: 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.

Keywords: Stochastic volatility; price jumps; SVJD; neural networks; deep learning; CNN (search for similar items in EconPapers)
Pages: 26 pages
Date: 2023-12, Revised 2023-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:fau:wpaper:wp2023_36

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