Volatility forecasting accuracy for Bitcoin

Abstract

We analyse the quality of Bitcoin volatility forecasting of GARCH-type models applying the commonly used volatility proxy based on squared daily returns as well as a jump-robust proxy based on intra-day returns and vary the degrees of asymmetry in robust loss functions. We construct model confidence sets (MCS) which contain superior models with a high probability and find them to be systematically smaller for asymmetric loss functions and the jump robust proxy. Our findings suggest a cautious use of GARCH models in forecasting Bitcoin's volatility.

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Keywords

bitcoin, cryptocurrency, GARCH, volatility, model confidence set, robust loss function

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