Forecasting Value-at-Risk of Cryptocurrencies with RiskMetrics Type Models

Wei Liu, Artur Semeyutin, Chi Keung Marco Lau, Giray Gozgor

Research output: Contribution to journalArticle


Since the financial crisis, risk management has been of growing interest to investors and the approach of Value-at-Risk has gained wide acceptance. Investing in Cryptocurrencies brings not only huge rewards but also huge risks. For this purpose, this paper investigates whether Cryptocurrencies investors’ decisions can rely on the pragmatic and parsimonious approaches for Value-at-Risk forecasting. Specifically, we suggest a parsimonious reflected gamma specification under the GAS framework, consider other GAS special cases and the Exponential Weights driven nonparametric methods, which fall into the same modelling category as the well-known and widely recognised original RiskMetrics

approach. We focus on the returns for BTC, LTC and ETH and find that progress upon RiskMetricks

may provide valuable gains in exposure modelling of Cryptocurrencies under the rough and primary backtesting conditions, though not all of the considered approaches demonstrate consistency at the selected risk confidence levels. In our setting, Laplace GAS specification, which controls for time-variation both in scale (volatility) and skewness (asymmetric responses to positive and negative volatility) parameters, performs the best at the most of the levels. We also find that controlling for time-variation in the degrees of freedom (tails) of the Student's may be a worthwhile consideration, though such approach may still yield more conservative investors’ strategies than its Laplace asymmetric alternative. Reflected gamma and Extreme Value Theory linked Double Pareto specifications also demonstrate a modest performance, but likely suffer from the lack of asymmetry in their parameters, as our Reflected Gamma parametrisation accounts for time-variation in the tails, unlike Pareto specifications and does not outperform asymmetric Laplace specification. Data- driven nonparametric methods seem to struggle the most in approximating downside tail risks due to the sharp corrections in Cryptocurrencies’ value.
Original languageEnglish
Article number101259
Pages (from-to)(In-Press)
JournalResearch in International Business and Finance
Early online date13 Jun 2020
Publication statusE-pub ahead of print - 13 Jun 2020


  • RiskMetrics
  • Exponential smoothing
  • Generalised autoregressive score models
  • Kernel density estimation
  • Time-varying quantiles
  • Value-at-Risk
  • Cryptocurrencies

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