Portfolio Management with Cryptocurrencies: The Role of Estimation Risk

Abstract

This paper contributes to the literature on cryptocurrencies, portfolio management and estimation risk by comparing the performance of naïve diversification, Markowitz diversification and the advanced Black-Litterman model with VBCs that controls for estimation errors in a portfolio of cryptocurrencies. We show that the advanced Black-Litterman model with VBCs yields superior out-of-sample risk-adjusted returns as well as lower risks. Our results are robust to the inclusion of transaction costs and short-selling, indicating that sophisticated portfolio techniques that control for estimation errors are preferred when managing cryptocurrency portfolios.

Portfolio optimization in the era of digital financialization using cryptocurrencies

“…In the literature, many studies have investigated the performance of cryptocurrency inclusive portfolios ( Platanakis et al., 2018 ; Platanakis and Urquhart, 2019 ; Borri, 2019 ; Liu, 2019 ; Brauneis and Mestel, 2019 ). Most of these studies have discussed the portfolio diversification using only Bitcoin as a benchmark.…”

“…Symitsi and Chalvatzis provided an extensive analysis of the economic value of Bitcoin and suggested that the decrease in the portfolio risk comes from the low correlations between the assets and Bitcoin. Also, further contributing to the literature, Platanakis and Urquhart highlighted that, given the higher potential estimation error in a portfolio of cryptocurrencies, the portfolio theory might face significant difficulties ( Platanakis and Urquhart, 2019 ). In another research study, Liu, (2019 ) analyzed the results of portfolio diversification on six different portfolios of the ten cryptocurrencies taken into account and found that the Naïve Portfolio performed better than all the other portfolios when calculating the return and risk profile using the Sharpe ratio.…”

“…The observation is more useful for high-targeted returns strategies. ( Platanakis and Urquhart, 2019 ) Bitcoin, Litecoin, Ripple, and Dash on (cryptocurrencies only) February 21, 2014 – May 4, 2018 (weekly data) Sophisticated portfolio techniques that control the estimation errors are preferred ( Brauneis and Mestel, 2019 ) 500 cryptocurrencies January 2015- December 2017 (daily) Substantial potential for risk reduction, when several cryptocurrencies are mixed. In terms of Sharpe ratio and equivalent certainty returns, the 1/N portfolio outperforms single cryptocurrencies and more than 75% of the mean-variance optimal portfolios.…”

THE EFFECT OF ENERGY CRYPTOS ON EFFICIENT PORTFOLIOS OF KEY ENERGY LISTED COMPANIES IN THE S&P COMPOSITE 1500 ENERGY INDEX

“…For instance, Scala et al (2019) adopted the Markowitz framework to propose a simplified strategy in managing portfolios of renewable energy sources. Similarly, Platanakis and Urquhart (2019) analyzed the effect of including cryptocurrencies, and found the Markowitz model to rank second after the Black-Litterman model. Platanakis et al (2018) also found there is very little difference when choosing between an equally weighted portfolio and the optimal mean-variance portfolio.…”

Weekly Dynamic Conditional Correlations Among Cryptocurrencies and Traditional Assets

“…Two related topics have emerged in the literature: (a) Bitcoin's diversification potential (Liu, 2018;Platanakis and Urquhart, 2019;Aslanidis et al, 2019); (b) daily seasonality in Bitcoin markets (Mbanga, 2019;Ma and Tanizaki, 2019;Caporale and Plastun, 2019;Aharon and Qadan, 2019). Considering the growing interest of the financial industry to provide cryptocurrency related products, such as exchange traded funds, mutual or hybrid funds, we consider that it is relevant to address both issues together.…”