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

MoreLess

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

freely available

Full text |

More access options

doi.org

Check Google Scholar

In German libraries (KVK)

I need help

Extent:	1 Online-Ressource (37 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 17, 2021 erstellt
Other identifiers:	10.2139/ssrn.3871096 [DOI]
Classification:	G17 - Financial Forecasting ; C32 - Time-Series Models
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013220893