Download This PaperA Neural Network with Shared Dynamics for Multi-Step Prediction of Value-at-Risk and Volatility
31 Pages Posted: 3 Jul 2021 Last revised: 21 Jun 2022
Date Written: January 31, 2022
Abstract
We develop a LSTM neural network for the joint prediction of volatility, realized volatility and Value-at-Risk. Regularization by means of pooling the dynamic structure for the different outputs of the models is shown to be a powerful method for improving forecasts and smoothing VaR estimates. The method is applied to daily and high-frequency returns of the S&P500 index over a period of 25 years.
Keywords: Neural Network, Value-at-Risk, Volatility Models, Equity Returns, Risk Management
JEL Classification: G17,C32
Suggested Citation: Suggested Citation
Basturk, Nalan and Schotman, Peter C. and Schyns, Hugo, A Neural Network with Shared Dynamics for Multi-Step Prediction of Value-at-Risk and Volatility (January 31, 2022). Available at SSRN: https://ssrn.com/abstract=3871096 or http://dx.doi.org/10.2139/ssrn.3871096
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