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Why Do Absolute Returns Predict Volatility So Well?

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Author Info
Lars Forsberg
Eric Ghysels
Abstract

Our objective is volatility forecasting, which is core to many risk management problems. We provide theoretical explanations for (i) the empirical stylized fact recognized at least since Taylor (1986) and Ding, Granger, and Engle (1993) that absolute returns show more persistence than squared returns and (ii) the empirical finding reported in recent work by Ghysels, Santa-Clara, and Valkanov (2006) showing that realized absolute values outperform square return-based volatility measures in predicting future increments in quadratic variation. We start from a continuous time stochastic volatility model for asset returns suggested by Barndorff-Nielsen and Shephard (2001) and study the persistence and linear regression properties of various volatility-related processes either observed directly or with sampling error. We also allow for jumps in the asset return processes and investigate their impact on persistence and linear regression. Extensive empirical results complement the theoretical analysis. Copyright 2007, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/jjfinec/nbl010
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Publisher Info
Article provided by Oxford University Press in its journal Journal of Financial Econometrics.

Volume (Year): 5 (2007)
Issue (Month): 1 ()
Pages: 31-67
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Handle: RePEc:oup:jfinec:v:5:y:2007:i:1:p:31-67

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  1. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics. [Downloadable!]
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  2. Dimitris Politis & Dimitrios Thomakos, 2007. "NoVaS Transformations: Flexible Inference for Volatility Forecasting," Working Papers 0005, University of Peloponnese, Department of Economics. [Downloadable!]
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  3. Visser, Marcel P., 2008. "Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure," MPRA Paper 11100, University Library of Munich, Germany. [Downloadable!]
  4. Christian T. Brownlees & Giampiero Gallo, 2007. "Volatility Forecasting Using Explanatory Variables and Focused Selection Criteria," Econometrics Working Papers Archive wp2007_04, Universita' degli Studi di Firenze, Dipartimento di Statistica "G. Parenti". [Downloadable!]
  5. Fulvio Corsi & Davide Pirino & Roberto RenĂ², 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena. [Downloadable!]
  6. Marwan Izzeldin & Ana-Maria Fuertes & Elena Kalotychou, 2008. "On forecasting daily stock volatility: the role of intraday information and market conditions," Working Papers 005439, Lancaster University Management School, Economics Department. [Downloadable!]
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  7. Fulvio Corsi & Davide Pirino & Roberto Reno, 2009. "Volatility Forecasting: The Jumps Do Matter," Global COE Hi-Stat Discussion Paper Series gd08-036, Institute of Economic Research, Hitotsubashi University. [Downloadable!]
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