A Neural Network with Shared Dynamics for Multi-Step Prediction of Value-at-Risk and Volatility

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

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Year of publication:	[2021]
Authors:	Basturk, Nalan ; Schotman, Peter C. ; Schyns, Hugo
Publisher:	[S.l.] : SSRN
Subject:	Volatilität \| Volatility \| Neuronale Netze \| Neural networks \| Theorie \| Theory \| Prognoseverfahren \| Forecasting model \| Risikomaß \| Risk measure

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