SEM217: Dangxing Chen, UC Berkeley: Predicting Portfolio Return Volatility at Median Horizons

Tuesday, October 2nd @ 11:00-12:30 PM

Predicting Portfolio Return Volatility at Median Horizons

Dangxing Chen, UC Berkeley

Commercially available factor models provide good predictions of short-horizon (e.g. one day or one week) portfolio volatility, based on estimated portfolio factor loadings and responsive estimates of factor volatility. These predictions are of significant value to certain short-term investors, such as hedge funds. However, they provide limited guidance to long-term investors, such as Defined Benefit pension plans, individual owners of Defined Contribution pension plans, and insurance companies. Because return volatility is variable and mean-reverting, the square root rule for extrapolating short-term volatility predictions to medium-horizon (one year to five years) risk predictions systematically overstates (understates) medium-horizon risk when short-term volatility is high (low). In this paper, we propose a computationally feasible method for extrapolating to medium-horizon risk predictions in one-factor models that substantially outperforms the square root rule.