Sequential Learning, Asset Allocation, and Bitcoin Returns

61 Pages Posted: 2 Aug 2021 Last revised: 1 Sep 2023

See all articles by James Yae

James Yae

University of Houston - C. T. Bauer College of Business

George Zhe Tian

University of Houston - C. T. Bauer College of Business

Date Written: July 23, 2021

Abstract

Despite high volatility, Bitcoin is known to offer diversification benefits through its relatively low correlation with stock markets. Unlike traditional safe-haven assets, Bitcoin is highly sensitive to time-varying correlations and diversification benefits. We find that a decrease (an increase) in correlation between Bitcoin and S&P500 index strongly predicts higher (lower) Bitcoin returns the next day. Under the classical mean-variance framework, we develop a stylized model of Bitcoin prices utilizing extreme disagreement among heterogeneous Bitcoin investors. When the model is calibrated to Bitcoin's predictability results, it simultaneously explains the lack of predictability in gold and long-term treasuries.

Keywords: Safe-Haven Asset, Bitcoin, Time-Varying Correlation, Return Predictability

JEL Classification: G12, G15, D83

Suggested Citation

Yae, James and Tian, George Zhe, Sequential Learning, Asset Allocation, and Bitcoin Returns (July 23, 2021). Journal of Financial Stability, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3896611 or http://dx.doi.org/10.2139/ssrn.3896611

James Yae (Contact Author)

University of Houston - C. T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

George Zhe Tian

University of Houston - C. T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

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