Volatility has long been a favorite stomping ground for quants. The heady days of options pricing models in the 1970s which earned Nobel prizes for Merton and Scholes in 1997 (Black had died in 1995 and was thus ineligible) may have passed, but research on both realized (historical) and implied volatility continues. Stock Market Volatility, edited by Greg N. Gregoriou (Chapman & Hall/CRC, 2009), is a collection of 31 studies that deal with such themes as modeling stock market volatility, portfolio management and hedge fund volatility, developed country volatility, and emerging market volatility.
Here are a couple of cheap takeaways from this book. First, switching from a static portfolio strategy to a dynamic volatility timing strategy improves performance. This thesis is not new, and financial engineers have been working hard to develop relationships between volatility and predictability. But it might be fertile ground for enterprising traders who know some statistics and ideally can develop multivariate and rolling GARCH estimators.
Two tables from Hsu and Li’s chapter “Cyclicality in Stock Market Volatility and Optimal Portfolio Allocation” may also be of interest, and one needs no quant skills to understand them. Both tables show asset class volatility and asset class return. The first table focuses on an average bull market cycle and an average bear market cycle, using the standard 20% demarcation line. The second looks at average expansion and recession cycles. These tables, it seems, cover the 30 years between January 1977 and December 2006
Of course, one cannot simply take an average and extrapolate to optimal portfolio allocation. But these tables at least provide a first point of reference.