A Comprehensive Look at Financial Volatility Prediction by Economic Variables
Maik Schmeling and
No 374, BIS Working Papers from Bank for International Settlements
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.
Keywords: Realised volatility; Forecasting; Data-rich modeling; Bayesian model averaging; Model uncertainty (search for similar items in EconPapers)
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Journal Article: A comprehensive look at financial volatility prediction by economic variables (2012)
Working Paper: A Comprehensive Look at Financial Volatility Prediction by Economic Variables (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:bis:biswps:374
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