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

Abstract

This paper investigates if energy block chain based cryptocurrencies can help diversify equity portfolios consisting primarily of leading energy companies of the US S&P Composite 1500 Energy Index. Key contributions are in terms of assessing the importance of energy cryptos as alternative investments in portfolio management, and whether different volatility models such as Autoregressive Moving Average – Generalized Autoregressive Heteroskedasticity (ARMA-GARCH) and Machine Learning (ML) can help investors make better investment decisions. The methodology utilizes the traditional Markowitz mean-variance framework to obtain optimized portfolio combinations. Volatility measures, derived from the Cornish-Fisher adjusted variance, ARMA family classes and machine learning models are used to compare efficient portfolios. The study also analyses the effect of adding cryptos to equity portfolios with non-positive excess returns. 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 energy based cryptos do not have a significant impact on energy equity portfolios, despite the use of different risk measures. This is 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.

Keywords: Equity Portfolios, Energy Cryptos, Performance Evaluation, Machine Learning, Volatility Measure

JEL Classification: Q40, G11, G12