The diversification benefits of cryptocurrency asset categories and estimation risk: Pre and post covid-19

X Huang; W Han; David Newton; Emmanouil Platanakis; Dimitrios Stafylas; Charles Sutcliffe

The diversification benefits of cryptocurrency asset categories and estimation risk: Pre and post covid-19

X Huang, W Han, David Newton, Emmanouil Platanakis, Dimitrios Stafylas, Charles Sutcliffe

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Abstract

We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-Covid-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models.

Original language	English
Journal	European Journal of Finance
Publication status	Accepted/In press - 2022

Cite this

@article{aac2cd73658045e3885372e93db119be,

title = "The diversification benefits of cryptocurrency asset categories and estimation risk: Pre and post covid-19",

abstract = "We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-Covid-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models. ",

author = "X Huang and W Han and David Newton and Emmanouil Platanakis and Dimitrios Stafylas and Charles Sutcliffe",

year = "2022",

language = "English",

journal = "European Journal of Finance",

issn = "1351-847X",

publisher = "Routledge",

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T1 - The diversification benefits of cryptocurrency asset categories and estimation risk: Pre and post covid-19

AU - Huang, X

AU - Han, W

AU - Newton, David

AU - Platanakis, Emmanouil

AU - Stafylas, Dimitrios

AU - Sutcliffe, Charles

PY - 2022

Y1 - 2022

N2 - We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-Covid-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models.

AB - We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-Covid-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models.

M3 - Article

SN - 1351-847X

JO - European Journal of Finance

JF - European Journal of Finance

ER -