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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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Bibliographic Info

Paper provided by Bank for International Settlements in its series BIS Working Papers with number 374.

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Length: 46 pages
Date of creation: Mar 2012
Date of revision:
Handle: RePEc:bis:biswps:374

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Related research

Keywords: Realised volatility; Forecasting; Data-rich modeling; Bayesian model averaging; Model uncertainty;

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References

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Citations

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Cited by:
  1. Becker, Christoph & Schmidt, Wolfgang M., 2013. "Stressing correlations and volatilities — A consistent modeling approach," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 174-194.
  2. Conrad, Christian & Loch, Karin, 2012. "Anticipating Long-Term Stock Market Volatility," Working Papers 0535, University of Heidelberg, Department of Economics.
  3. Sévi, Benoît, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Economic Modelling, Elsevier, vol. 31(C), pages 189-197.
  4. Charlotte Christiansen & Jonas Nygaard Eriksen & Stig V. Møller, 2013. "Forecasting US Recessions: The Role of Sentiments," CREATES Research Papers 2013-14, School of Economics and Management, University of Aarhus.
  5. Dimpfl, Thomas & Jank, Stephan, 2011. "Can Internet search queries help to predict stock market volatility?," University of Tuebingen Working Papers in Economics and Finance 18, University of Tuebingen, Faculty of Economics and Social Sciences.
  6. Nektarios Aslanidis & Charlotte Christiansen & Christos S. Savva, 2013. "Risk-Return Trade-Off for European Stock Markets," CREATES Research Papers 2013-31, School of Economics and Management, University of Aarhus.

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