A Comprehensive Look at Financial Volatility Prediction by Economic Variables
University of Aarhus - School of Economics and Management - CREATES
City University London - Sir John Cass Business School
Bank for International Settlements (BIS) - Monetary and Economic Department
January 8, 2011
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.
Number of Pages in PDF File: 45
Keywords: Realized volatility, Forecasting, Data-rich modeling, Bayesian model averaging, Model uncertainty
JEL Classification: G12, G15, G17, C53working papers series
Date posted: January 10, 2011 ; Last revised: March 6, 2012
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