The Effect of Energy Cryptos on Efficient Portfolios of Key Energy Companies in the S&P Composite 1500 Energy Index

Abstract

The purpose of this paper is to investigate if energy block chain based cryptocurrencies can help diversify equity portfolios consisting primarily of leading energy companies in the US S&P Composite 1500 Energy Index. The key contributions are firstly, in terms of assessing the importance of energy cryptos as alternative investments in portfolio management, and secondly, whether different volatility models such as Autoregressive Moving Average – Generalized Autoregressive Heteroskedasticity (ARMA-GARCH) and Machine Learning (ML) can help investors make better informed decisions in investments. The methodology utilizes the traditional Markowitz mean-variance framework to obtain optimized portfolio risk and return combinations. Different volatility measures, derived from the Cornish-Fisher adjusted variance, ARMA family classes and machine learning models are used to compare efficient portfolios which include or exclude the energy cryptos. To capture the negative performance of cryptos, the study also analyses the effect of adding cryptos to equity portfolios with non-positive excess returns. The different models are assessed using the Sharpe performance measure. Daily data is used, spanning from 21st November 2017 to 31st January 2019. Findings suggest that the energy based cryptos do not have a significant impact on energy equity portfolios, despite the use of different risk measures. This was attributable to the relatively poor performance of energy cryptos which did not contribute in improving the excess return per unit of risk of efficient portfolios based on the leading US energy stocks.