МЕТОДИКА ОЦІНЮВАННЯ ЕФЕКТИВНОСТІ DEFI-ПЛАТФОРМ У ДИВЕРСИФІКАЦІЇ ІНВЕСТИЦІЙНИХ ПОРТФЕЛІВ

Abstract

The article substantiates the critical inadequacy of traditional static risk assessment methods (specifically, VaR and standard deviation) for analyzing the effectiveness of integrating Decentralized Finance (DeFi) assets into investment portfolios. It is proven that the returns of DeFi assets are characterized by a non-normal distribution with pronounced «fat tails», which creates a significant risk of underestimating catastrophic losses. The purpose of the study is to develop and theoretically substantiate a methodology for evaluating the effectiveness of DeFi platforms in diversifying investment portfolios. The methodological gap between the requirements of the volatile DeFi market and the limitations of classical financial models is investigated, particularly in the areas of controlling Tail Risk and the dynamic nature of correlational dependence, which critically increases during market shocks (the «correlation-to-one» effect). A four-stage methodology is proposed, which includes the theoretical integration of Conditional Value-at-Risk (CVaR) as a basic coherent measure of extreme risk and a developed algorithm for proactive diversification management based on the DCC-GARCH model. This made it possible to calculate the Optimal Dynamic Hedging Weight, necessary for the daily adjustment of the portfolio structure to prevent the loss of the diversification effect. The comprehensive methodology developed provides a complete cycle of proactive risk management and offers a clear algorithm for making decisions about the structure of an investment portfolio. The scientific and practical significance of the research lies in formulating methodological recommendations and evaluation criteria that ensure a transition from static analysis to proactive risk management in investment activities. The developed methodology provides a toolkit for making informed decisions regarding the optimal share of DeFi assets in a portfolio, combining return maximization with extreme risk minimization. The application of this methodology is beneficial for investors, financial analysts, quantitative strategists, and hedge fund managers working with high-risk and innovative asset classes that require advanced risk control tools.