Forecasting Value-at-Risk and Expected Shortfall of Cryptocurrencies using Combinations based on Jump-Robust and Regime-Switching Models

22 Pages Posted: 2 Feb 2021

See all articles by Carlos Trucíos

Carlos Trucíos

Universidade Federal do Rio de Janeiro (UFRJ) - Faculdade de Administracao e Ciencias Contabeis - FACC

James W. Taylor

University of Oxford - Said Business School

Date Written: December 18, 2020

Abstract

Several procedures to estimate daily risk measures in cryptocurrency markets have been recently proposed in the literature. Among them, procedures taking into account the presence of extreme observations, as well as procedures that include more than a single regime, have performed substantially better than standard methods in terms of volatility and Value-at-Risk forecasting. Three of those procedures are revisited in this paper, and their Value-at-Risk forecasting performance is evaluated using recent cryptocurrency data that includes periods of turbulence. Those procedures are also extended to estimate the Expected Shortfall, and a comprehensive backtesting exercise based on both calibration tests and scoring functions is performed. In order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are robust to outliers performed slightly better than regime-switching models. We found some evidence that combining strategies can improve the forecasting of Value-at-Risk and Expected Shortfall, particularly for the 1% risk levels, making them an interesting alternative to be used by practitioners.

Keywords: digital assets, model misspecification, outliers, risk measures, structural breaks, volatility

JEL Classification: C10, C22, C53, G17, G32

Suggested Citation

Trucíos Maza, Carlos César and Taylor, James W., Forecasting Value-at-Risk and Expected Shortfall of Cryptocurrencies using Combinations based on Jump-Robust and Regime-Switching Models (December 18, 2020). Available at SSRN: https://ssrn.com/abstract=3751435 or http://dx.doi.org/10.2139/ssrn.3751435

Carlos César Trucíos Maza (Contact Author)

Universidade Federal do Rio de Janeiro (UFRJ) - Faculdade de Administracao e Ciencias Contabeis - FACC ( email )

Av. Pasteur 250
Rio de Janeiro, 22290-240
Brazil

HOME PAGE: http://https://ctruciosm.github.io

James W. Taylor

University of Oxford - Said Business School ( email )

Park End Street
Oxford, OX1 1HP
Great Britain

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