Assessing the Accuracy of Exponentially Weighted Moving Average Models for Value-at-Risk and Expected Shortfall of Crypto Portfolios

50 Pages Posted: 20 Apr 2022

See all articles by Carol Alexander

Carol Alexander

University of Sussex Business School; Peking University HSBC Business School

Michael Dakos

University of Sussex Business School

Date Written: April 7, 2022

Abstract

A plethora of academic papers on generalized autoregressive conditional heteroscedasticity (GARCH) models for bitcoin and other cryptocurrencies have been published in academic journals. Yet few, if indeed any, of these are employed by practitioners. Previous academic studies produce results that are fragmented, confusing and conflicting, so there is no commercial incentive to drive an expensive implementation of complex multivariate GARCH models, which anyway would commonly require more data for calibration than are available in the history of most cryptocurrencies, at least at the daily frequency. We provide extensive backtests of hourly and daily Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts that are regarded as best practice in the industry and commonly used for regulatory approval. Our results demonstrate that much simpler models in the exponentially weighted moving average (EWMA) class are just as accurate as GARCH models for VaR and ES forecasting, provided they capture an asymmetric volatility response and a heavy-tailed returns distribution. Moreover, on ranking each model's variance and covariance forecasts using average scores generated from proper univariate and multivariate scoring rules, there is no evidence of superior performance of variance and covariance forecasts generated by GARCH models, using either daily or hourly data.

Keywords: Volatility clustering, Conditional VaR, Continuous ranked probability score, Energy score, Traffic light tests

JEL Classification: C22, C5, F31, G1, G2

Suggested Citation

Alexander, Carol and Dakos, Michael, Assessing the Accuracy of Exponentially Weighted Moving Average Models for Value-at-Risk and Expected Shortfall of Crypto Portfolios (April 7, 2022). Available at SSRN: https://ssrn.com/abstract=4078032 or http://dx.doi.org/10.2139/ssrn.4078032

Carol Alexander (Contact Author)

University of Sussex Business School ( email )

Falmer, Brighton BN1 9SL
United Kingdom

HOME PAGE: http://www.coalexander.com

Peking University HSBC Business School ( email )

Michael Dakos

University of Sussex Business School ( email )

Falmer, Brighton BN1 9SL
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
138
Abstract Views
467
rank
292,489
PlumX Metrics