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A comprehensive look at financial volatility prediction by economic variables

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  • Charlotte Christiansen
  • Maik Schmeling
  • Andreas Schrimpf

Abstract

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.
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Suggested Citation

  • Charlotte Christiansen & Maik Schmeling & Andreas Schrimpf, 2012. "A comprehensive look at financial volatility prediction by economic variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 956-977, September.
  • Handle: RePEc:wly:japmet:v:27:y:2012:i:6:p:956-977
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    More about this item

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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