Mean-Variance Optimization

Mean-Variance Optimization is a quantitative method used to determine the optimal asset allocation by balancing the expected return of an investment against its variance, which serves as a proxy for risk. Developed originally for traditional equities, it is adapted for crypto-derivatives by accounting for the high volatility and non-normal distribution of digital asset returns.

The process involves calculating the expected returns for each asset and the covariance between all pairs of assets in the portfolio. By finding the set of weights that minimizes variance for a target return, investors can create a more robust defensive posture.

However, it relies heavily on accurate historical data, which can be misleading during rapid market shifts in the cryptocurrency space. It remains a foundational tool for building diversified crypto portfolios that seek to minimize idiosyncratic risk.