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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.
Original language | English |
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Publisher | SSRN |
DOIs | |
Publication status | Published - 21 Jun 2021 |
JEL classifications
- g17 - Financial Forecasting and Simulation
- c32 - "Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
Keywords
- neural network
- value-at-risk
- volatility models
- equity returns
- risk management
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Dive into the research topics of 'A Neural Network with Shared Dynamics for Multi-Step Prediction of Value-at-Risk and Volatility'. Together they form a unique fingerprint.Activities
- 1 Talk or presentation - at conference
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A Neural Network with Shared Dynamics for Multi-Step Prediction of Value-at-Risk and Volatility
Schyns, H. (Speaker)
23 Jun 2021Activity: Talk or presentation / Performance / Speeches › Talk or presentation - at conference › Academic