Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets

Daniele Bianchi, Massimo Guidolin and Manuela Pedio (manuela.pedio@bristol.ac.uk)

No 20143, BAFFI CAREFIN Working Papers from BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy

Abstract: In this paper we take an empirical asset pricing perspective and investigate the dominant view (possibly, an instinctive reflection of the media hype surrounding the surge of Bitcoin valuations) that cryptocurrencies represent a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. Methodologically, we rely on a flexible dynamic econometric model that allows not only time-varying coeficients, but also allow that the entire forecasting model be changing over time. We estimate such model by looking at the time variation in the exposures of major cryptocurrencies to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). The main empirical results suggest that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with the exception of some occasional spikes of the coefficients during our sample. On the contrary, crypto assets are characterized by a time-varying but significant exposure to a sentiment index and to crypto-momentum. Despite the lack of predictability compared to traditional asset classes, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can lead to a moderate improvement in the realized Sharpe ratios and certainty equivalent returns within the context of a typical portfolio problem.

Keywords: Cryptocurrencies; predictability; portfolio diversification; dynamic model averaging; time-varying; parameter regressions. (search for similar items in EconPapers)
JEL-codes: E40 E52 (search for similar items in EconPapers)
Pages: 67
Date: 2020
New Economics Papers: this item is included in nep-fmk, nep-mac, nep-ore, nep-pay and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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