Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets

67 Pages Posted: 30 May 2020 Last revised: 5 Jun 2020

See all articles by Daniele Bianchi

Daniele Bianchi

School of Economics and Finance, Queen Mary University of London

Massimo Guidolin

Bocconi University - Department of Finance

Manuela Pedio

Bocconi University - CAREFIN - Centre for Applied Research in Finance

Date Written: May 1, 2020

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 coefficients, 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

JEL Classification: E40, E52

Suggested Citation

Bianchi, Daniele and Guidolin, Massimo and Pedio, Manuela, Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets (May 1, 2020). BAFFI CAREFIN Centre Research Paper No. 2020-143, Available at SSRN: https://ssrn.com/abstract=3609949 or http://dx.doi.org/10.2139/ssrn.3609949

Daniele Bianchi

School of Economics and Finance, Queen Mary University of London ( email )

Mile End Rd
Mile End Road
London, London E1 4NS
United Kingdom

HOME PAGE: http://whitesphd.com

Massimo Guidolin (Contact Author)

Bocconi University - Department of Finance ( email )

Via Roentgen 1
Milano, MI 20136
Italy

Manuela Pedio

Bocconi University - CAREFIN - Centre for Applied Research in Finance ( email )

Via Sarfatti, 25
Milan, 20136
Italy

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
72
Abstract Views
284
rank
352,558
PlumX Metrics