Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests

45 Pages Posted: 4 Apr 2022 Last revised: 24 Jun 2022

See all articles by Konstantin Görgen

Konstantin Görgen

Karlsruhe Institute of Technology

Jonas Meirer

affiliation not provided to SSRN

Melanie Schienle

Karlsruhe Institute of Technology (KIT)

Date Written: June 24, 2022

Abstract

We study the prediction of Value at Risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that Generalized Random Forests (GRF) (Athey et al., 2019) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly-volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study also indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns and clearly superior in the cryptocurrency setup.

Keywords: Generalized Random Forests, Value at Risk, Quantile Prediction, Backtesting, Cryptocurrencies, Conditional Predictive Ability

JEL Classification: C58,G17,C22

Suggested Citation

Görgen, Konstantin and Meirer, Jonas and Schienle, Melanie, Predicting Value at Risk for Cryptocurrencies With Generalized Random Forests (June 24, 2022). Available at SSRN: https://ssrn.com/abstract=4053537 or http://dx.doi.org/10.2139/ssrn.4053537

Konstantin Görgen (Contact Author)

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

Jonas Meirer

affiliation not provided to SSRN

Melanie Schienle

Karlsruhe Institute of Technology (KIT) ( email )

Institute of Economics (ECON)
Karlsruhe
Germany

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