Bitcoin as an alternative asset in emerging markets: portfolio optimization via conditional value-at-risk
Date
2018
Authors
Mukuria, Mary Wanjiru
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
In a bid to diversify their portfolios, investors are venturing into various alternative assets. This study analyses the inclusion of bitcoin, as an alternative asset, in a well-diversified portfolio in South Africa. Recognizing that bitcoin is a relatively new asset, this study has provided detailed information on the features of bitcoin, both as a currency and as an investable asset. The study utilizes a well-diversified portfolio that consists of stocks, bonds, commodities, cash, real estate and international assets. The asset classes in the portfolio are picked from the South-African market as it is a very lucrative emerging market. The study utilizes time series data as the historical data of the asset returns is collected from July 28, 20 I 0 to December 29, 2017. The Mean-CVaR Portfolio Optimization approach is utilized so as to accommodate the highly non-normal return distribution of bitcoin instead of the Mean Variance Optimization approach which assumes that returns are normally distributed. Two different portfolio frameworks are utilized namely; The Minimum CVaR Concentration Portfolio and the Minimum CVaR portfolio under an upper 30% CVaR allocation constraint are used to assess the objectives of the study. Dynamic rebalancing is utilized so as to achieve robust results. The results show that the inclusion of bitcoin increases the risk-return ratios of the different portfolios. The results also show that bitcoin's weight allocation is relatively low, however, even with the low weighting in the portfolios, the risk contribution of bitcoin to the portfolio CVaR is relatively high. The study concludes that bitcoin appears to be an attractive investment that can substantially increase the return of an efficient portfolio as the portfolios with bitcoin outperform their non-bitcoin counterparts. The recommendations of this study are that sophisticated forecasting techniques such as Bayesian methods or Neural Network should be utilized for scenario generation instead of the use of historical data and that the impact of the inclusion of bitcoin to a well-diversified portfolio should be conducted when the asset is at its mature stage.
Description
Submitted in partial fulfillment of the requirements for the Degree of Bachelor of Business Science in Finance at Strathmore University
Keywords
Bitcoin, Emerging markets, Economy