We investigate if asset return volatility is predictable by macroeconomic and financial variables and shed light on the economic drivers of financial volatility. Our approach is distinct due to its comprehensiveness: First, we employ a data-rich forecast methodology to handle a large set of potential predictors in a Bayesian Model Averaging approach, and, second, we take a look at multiple asset classes (equities, foreign exchange, bonds, and commodities) over long time spans. We find that proxies for credit risk and funding (il)liquidity consistently show up as common predictors of volatility across asset classes. Variables capturing time-varying risk premia also perform well as predictors of volatility. While forecasts by macro-finance augmented models also achieve forecasting gains out-of-sample relative to autoregressive benchmarks, the performance varies across asset classes and over time.
“This is where gay marriage is absolutely necessary: at its best, it provides a model for a voluntary union of equals. Unless we’re going to go the full Republic route, it seems that more or less autonomous households are here to stay — and so we might as well have them forming without all the baggage of patriarchal presuppositions. This is the good way that gay marriage challenges the traditional family: by pushing it further in the direction of being a realm of love and affinity rather than a regime of property.”—http://itself.wordpress.com/2012/03/06/the-questionability-of-the-traditional-family/