Time-varying Variance Scaling: Application of the Fractionally Integrated ARMA Model

Abstract Time-varying variance scaling is the technique by which a mean-variance investor can volatility manage a portfolio by adjusting allocation according to the attractiveness of the mean-variance trade-off, μ t / σ t 2 . This study shows that the choice of volatility forecasting models significantly affects performance. We find that the fractionally integrated ARMA model provides significantly better scaling input than the simple historical average volatility for most test portfolios examined by this study. For standard momentum portfolios, however, using the simple historical average as input for volatility scaling resulted in the best performance in many cases.