Abhatoo - Centre Nationale de Documentation

We investigate if asset return volatility is predictable by macroeconomic and nancial variables and shed light on the economic drivers of nancial 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 nd 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- nance augmented models also achieve forecasting gains out-ofsample relative to autoregressive benchmarks, the performance varies across asset classes and over time.