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The Leverage Effect Puzzle: Disentangling Sources of Bias at High Frequency

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  • Yacine Ait-Sahalia
  • Jianqing Fan
  • Yingying Li

Abstract

The leverage effect refers to the generally negative correlation between an asset return and its changes of volatility. A natural estimate consists in using the empirical correlation between the daily returns and the changes of daily volatility estimated from high-frequency data. The puzzle lies in the fact that such an intuitively natural estimate yields nearly zero correlation for most assets tested, despite the many economic reasons for expecting the estimated correlation to be negative. To better understand the sources of the puzzle, we analyze the different asymptotic biases that are involved in high frequency estimation of the leverage effect, including biases due to discretization errors, to smoothing errors in estimating spot volatilities, to estimation error, and to market microstructure noise. This decomposition enables us to propose novel bias correction methods for estimating the leverage effect.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17592.

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Date of creation: Nov 2011
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Handle: RePEc:nbr:nberwo:17592

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Cited by:
  1. Li, Yong & Zeng, Tao & Yu, Jun, 2014. "A new approach to Bayesian hypothesis testing," Journal of Econometrics, Elsevier, vol. 178(P3), pages 602-612.
  2. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2014. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Papers 1401.3911, arXiv.org.

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