Seminar 217, Risk Management: Nonparametric prediction of portfolio return volatility and its extension to the longer horizon

Seminar | February 18 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

 Dangxing Chen, UC Berkeley

 Consortium for Data Analytics in Risk

ABSTRACT: Medium-horizon portfolio volatility predictions are of significant value to long-term investors, such as Defined Benefit pension plans, insurance companies, sovereign wealth funds, endowments, and individual owners of Defined Contribution pension plans. In this paper, we propose a simple, accurate, and efficient nonparametric method predict the conditional variance of log return (CVLR) at an arbitrary horizon. At a relatively short horizon, we introduce a regression framework for the stochastic volatility model. Beyond the short horizon, we study the properties of the dispersion function, which is defined as the ratio of the CVLR function and its converged stationary value, in the weak interaction case (e.g. for portfolios of large stocks). We provide tight upper and lower bounds of the CVLR function at longer horizons based on the results from the regression and analytical properties of the dispersion function. Empirical results suggest that our method is very promising. Finally, we provide a mean-field single-factor framework, validated by varied empirical evidence, for the optimization of portfolios defined by weights on multiple country indices.