Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Diep Duong and Norman Swanson ()

Journal of Econometrics, 2015, vol. 187, issue 2, 606-621

Abstract: Many recent modeling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility predictability, some from the perspective of the usefulness of jumps in forecasting volatility. Key papers in this area include Andersen et al. (2003), Corsi (2004), Andersen et al. (2007), Corsi et al. (2008), Barndorff et al. (2010), Patton and Shephard (2011), and the references cited therein. In this paper, we review the extant literature and then present new empirical evidence on the predictive content of realized measures of jump power variations (including upside and downside risk, jump asymmetry, and truncated jump variables), constructed using instantaneous returns, i.e., |rt|q,0≤q≤6 in the spirit of Ding et al. (1993) and Ding and Granger (1996). We also present new empirical evidence on the predictive content of realized measures of truncated large jump variations, constructed using truncated squared instantaneous return, i.e., rt2×I|rt|>γ, where γ is the threshold jump size. Our prediction experiments use high frequency price returns constructed using S&P500 futures data as well as stocks in the Dow 30, and our empirical implementation involves estimating linear and nonlinear heterogeneous autoregressive realized volatility (HAR-RV) type models. We find that past “large” jump power variations help less in the prediction of future realized volatility, than past “small” jump power variations. Additionally, we find evidence that past realized signed jump power variations, which have not previously been examined in this literature, are strongly correlated with future volatility, and that past downside jump variations matter in prediction. Finally, incorporation of downside and upside jump power variations does improve predictability, albeit to a limited extent.

Keywords: Realized volatility; Jump power variations; Downside risk; Semivariances; Market microstructure; Volatility forecasts; Jump test (search for similar items in EconPapers)
JEL-codes: C22 C53 C58 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (59)

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Related works:
Working Paper: Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:187:y:2015:i:2:p:606-621

DOI: 10.1016/j.jeconom.2015.02.042

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