Evaluation of portfolio optimization methods on decentralized assets and hybridized portfolios

Abstract: The market for decentralised financial instruments, more commonly known as cryptocurrencies, has gained momentum over the past recent years and the application areas are many. Modern portfolio theory has for years demonstrated its applicability to traditional assets, such as equities and other instruments, but to some extent omitted the application of mathematical portfolio theory with respect for cryptocurrencies. This master's thesis aims to evaluate both traditional and DeFi assets from a modern optimization perspective. The focus area includes whichallocation structures that minimize the risk-adjusted return. The optimizations strategies are based on the risk measures, standard deviation, Conditional Value at Risk and First linear partial moment. The method has its structure in different scenarios where the outcome is optimized for traditional assets, DeFi assets and a hybrid set of these. The input data for the optimization methodology is based on weekly and adjusted price data for the assets. The output variables are weight-distribution, risk levels, return, maximum drawdown and graphic visualizations. Our results show that there is a value in incorporating parts of assets from the decentralized financial world in a portfolio provided that the risk-adjusted ratio increases through but through both higher returns and higher potential risk. These results are based on incorporation of certain parts of the new landscape where more established assets such as Bitcoin, Ethereum etc. have proven to perform well while other assets that are less traded shows a significantly worse result relative to risk.