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GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks.

Mateusz Buczynski1,2, Marcin Chlebus2

  • 1Faculty of Economic Sciences, University of Warsaw, Dluga 44/50, Warsaw, Poland.

Computational Economics
|June 26, 2023
PubMed
Summary

This study introduces GARCHNet, a novel nonlinear model combining Long-Term Short-Term Memory (LSTM) neural networks with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) for improved financial volatility modeling. GARCHNet effectively captures complex nonlinear relationships, enhancing risk assessment accuracy.

Keywords:
GARCHLSTMNeural networksValue-at-risk

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Area of Science:

  • Econometrics
  • Computational Finance
  • Machine Learning

Background:

  • Classical Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are widely used in financial modeling, particularly for Value-at-Risk (VaR) calculations, due to their effectiveness in capturing volatility.
  • However, traditional GARCH models often lack the necessary nonlinear structure to accurately represent conditional variance.
  • The advancement of deep learning methods offers powerful tools for modeling intricate nonlinear relationships in financial data.

Purpose of the Study:

  • To propose a novel nonlinear approach, GARCHNet, for modeling conditional variance by integrating Long-Term Short-Term Memory (LSTM) neural networks with GARCH.
  • To address the limitations of classical GARCH models in capturing nonlinear dynamics in financial markets.
  • To evaluate the performance of GARCHNet across various financial indices and volatility regimes.

Main Methods:

  • Development of GARCHNet, a hybrid model combining LSTM architecture with maximum likelihood estimation within the GARCH framework.
  • Application of GARCHNet to model conditional variance using normal, t, and skewed t distributions.
  • Empirical analysis using logarithmic returns from WIG 20, S&P 500, and FTSE 100 indices over multiple time periods (2005-2021).

Main Results:

  • The GARCHNet model demonstrates the ability to effectively capture nonlinear structures in conditional variance, outperforming traditional models in certain aspects.
  • Empirical results on major stock indices confirm the validity and potential of the proposed GARCHNet approach.
  • The study highlights the adaptability of the model to different variance distributions and its robustness across varying market volatility.

Conclusions:

  • GARCHNet offers a promising nonlinear extension to GARCH modeling, enhancing the representation of conditional variance in financial time series.
  • The integration of LSTM networks provides a powerful mechanism for capturing complex dependencies missed by linear GARCH specifications.
  • Further research can explore extensions to other distributions and advanced LSTM architectures for even greater accuracy in financial volatility forecasting.