Estimating value at risk: LSTM vs. GARCH

Weronika Ormaniec, Marcin Pitera, Sajad Safarveisi and Thorsten Schmidt

Papers from arXiv.org

Abstract: Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes and correctly identify the underlying risk dynamics in a non-parametric fashion. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data, whereas on real market data it is more sensitive towards increasing or decreasing volatility and outperforms all existing estimators of value-at-risk in terms of exception rate and mean quantile score.

Date: 2022-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets and nep-rmg
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2207.10539

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