A comprehensive look at financial volatility prediction by economic variables
利用贝叶斯模型平均方法处理大量预测变量，研究宏观经济和金融变量对股票、外汇、债券和大宗商品等资产波动率的预测能力，发现信用风险和融资流动性是共同预测因子。
SUMMARY We investigate whether return volatility is predictable by macroeconomic and financial variables to shed light on the economic drivers of financial volatility. Our approach is distinct owing 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 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. Copyright © 2012 John Wiley & Sons, Ltd.