A Neural Network with Shared Dynamics for Multi-Step Prediction of Value-at-Risk and Volatility
37 Pages Posted: 3 Jul 2021
Date Written: June 17, 2021
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
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