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Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach · Study

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ArticleValue-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach

Kshitij Abhay Kakade2022

Article2022

Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach

Kshitij Abhay Kakade · Aswini Kumar Mishra

61 citations·0/0 files stored·Resources Policy

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Financial Risk and Volatility Modeling Forecasting Techniques and Applications Stock Market Forecasting Methods

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1970Efficient Capital Markets: A Review of Theory and Empirical Work 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 2001Empirical properties of asset returns: stylized facts and statistical issues 1998Evaluating Interval Forecasts 1995Techniques for Verifying the Accuracy of Risk Measurement Models 1994Threshold heteroskedastic models

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2024Volatility forecasting and assessing risk of financial markets using multi-transformer neural network based architecture 2024Intelligent crude oil price probability forecasting: Deep learning models and industry applications

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Year2022

Typearticle

Published2022-08-11

VenueResources Policy

ISSN-L0301-4207

Languageen

Citations61

Files0 public / 0

ShelfFinancial Risk and Volatility Modeling

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doi10.1016/j.resourpol.2022.102903

openalex_workW4290990734

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