Enhancing portfolio covariance estimation: a hybrid prediction approach

Accurate estimation of financial portfolio covariance matrix is crucial for effective risk management and portfolio optimization. Traditional methods, such as Rolling Window, Exponentially Weighted Moving Average (EWMA), and Multivariate GARCH (MGARCH), often struggle in its prediction due to the harsh predictability of the dynamic relationships among financial assets data. This paper introduces a hybrid predictor that linearly combines traditional covariance prediction methods with an Expanding Window (EW) model, exploiting a time-varying weighting mechanism to enhance prediction accuracy. The proposed method's efficacy is validated on actual time-series data from 2010 to 2024 of 9 major S&P500 stocks. Results show that the proposed strategy significantly improves covariance estimation, particularly in the end of financial quarters. The higher covariance prediction quality directly reflects into more accurate risk-return trade-offs, as a consequence of portfolio optimization when using predicted covariance as portfolio risk proxy. These results can ultimately lead to sharper investment decisions.