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A1295
Title: Quantitative risk management for cryptocurrencies Authors:  Francesco Ravazzolo - Free University of Bozen-Bolzano (Italy) [presenting]
Leopoldo Catania - Aarhus BBS (Denmark)
Stefano Grassi - University of Rome 'Tor Vergata' (Italy)
Abstract: Cryptocurrencies have recently gained a lot of interest from investors, central banks and governments worldwide. The lack of any form of political regulation and their market far from being efficient, requires new forms of regulation in the near future. From an econometric viewpoint, the process underlying the evolution of the cryptocurrencies' volatility has been found to exhibit at the same time differences and similarities with other financial time-series, e.g. foreign exchanges returns. We analyse how quantitative risk management techniques need to be implemented when dealing with cryptocurrencies time-series. We focus on the estimation and backtesting of the Value-at-Risk (VaR) and Expected Shortfall (ES) risk measures and report advices for quantitative risk managers and investors. Our results indicate that naive approaches generally used by practitioners, like variance estimation via exponential smoothing, can be extremely dangerous when dealing with cryptocurrencies.