OPTIMAL CRYPTOCURRENCY PORTFOLIO CONSTRUCTION USING GARCH-BASED MONTE CARLO SIMULATION

Abstract

This study investigates the construction of an optimal cryptocurrency portfolio comprising Ethereum and Solana using a GARCH-based Monte Carlo simulation framework. Asset volatilities were modelled individually through GARCH (1,1) processes, while asset correlations were captured using standardized residuals and Cholesky decomposition. Simulation results over 180- and 360-day horizons showed that the optimized portfolio achieved slightly higher cumulative growth factors and better upside capture compared to an equal-weighted benchmark, particularly during volatile market phases. In out-of-sample testing, the return-to-risk optimized portfolio delivered a total return of 34% over six months, compared to 33% for the equal-weighted strategy, while maintaining a higher return-to-risk ratio (0.06 versus 0.05) and lower volatility (3% versus 4%). Over a one-year period, both portfolios converged closely, with the equal-weighted strategy achieving a slightly higher total return of 45% compared to 43% for the optimized portfolio. These findings suggest that GARCH-based optimization can enhance portfolio resilience and risk-adjusted returns, although its realized return advantage may diminish in synchronized market conditions.