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    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Empirical Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Empirical Finance, 34, 2015 DOI: 10.1016/j.jempfin.2015.03.019

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The economic value of volatility timing with realized jumps

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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The economic value of volatility timing with realized jumps. / Nolte, Ingmar; Xu, Qi.
In: Journal of Empirical Finance, Vol. 34, 12.2015, p. 45-59.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Nolte, I & Xu, Q 2015, 'The economic value of volatility timing with realized jumps', Journal of Empirical Finance, vol. 34, pp. 45-59. https://doi.org/10.1016/j.jempfin.2015.03.019

APA

Nolte, I., & Xu, Q. (2015). The economic value of volatility timing with realized jumps. Journal of Empirical Finance, 34, 45-59. https://doi.org/10.1016/j.jempfin.2015.03.019

Vancouver

Nolte I, Xu Q. The economic value of volatility timing with realized jumps. Journal of Empirical Finance. 2015 Dec;34:45-59. Epub 2015 Sept 1. doi: 10.1016/j.jempfin.2015.03.019

Author

Nolte, Ingmar ; Xu, Qi. / The economic value of volatility timing with realized jumps. In: Journal of Empirical Finance. 2015 ; Vol. 34. pp. 45-59.

Bibtex

@article{0d5746dbd7524cb4b0742dd1ce665c8c,
title = "The economic value of volatility timing with realized jumps",
abstract = "This paper comprehensively investigates the role of realized jumps detected from high frequency data in predicting future volatility from both statistical and economic perspectives. Using seven major jump tests, we show that separating jumps from diffusion improves volatility forecasting both in-sample and out-of-sample. Moreover, we show that these statistical improvements can be translated into economic value. We find a risk-averse investor can significantly improve her portfolio performance by incorporating realized jumps into a volatility timing based portfolio strategy. Our results hold true across the majority of jump tests, and are robust to controlling for microstructure effects and transaction costs.",
keywords = "high frequency datajumps, nonparametric tests, asset allocation, volatility forecasting, realized volatility",
author = "Ingmar Nolte and Qi Xu",
note = "18 month embargo This is the author{\textquoteright}s version of a work that was accepted for publication in Journal of Empirical Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Empirical Finance, 34, 2015 DOI: 10.1016/j.jempfin.2015.03.019",
year = "2015",
month = dec,
doi = "10.1016/j.jempfin.2015.03.019",
language = "English",
volume = "34",
pages = "45--59",
journal = "Journal of Empirical Finance",
issn = "0927-5398",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - The economic value of volatility timing with realized jumps

AU - Nolte, Ingmar

AU - Xu, Qi

N1 - 18 month embargo This is the author’s version of a work that was accepted for publication in Journal of Empirical Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Empirical Finance, 34, 2015 DOI: 10.1016/j.jempfin.2015.03.019

PY - 2015/12

Y1 - 2015/12

N2 - This paper comprehensively investigates the role of realized jumps detected from high frequency data in predicting future volatility from both statistical and economic perspectives. Using seven major jump tests, we show that separating jumps from diffusion improves volatility forecasting both in-sample and out-of-sample. Moreover, we show that these statistical improvements can be translated into economic value. We find a risk-averse investor can significantly improve her portfolio performance by incorporating realized jumps into a volatility timing based portfolio strategy. Our results hold true across the majority of jump tests, and are robust to controlling for microstructure effects and transaction costs.

AB - This paper comprehensively investigates the role of realized jumps detected from high frequency data in predicting future volatility from both statistical and economic perspectives. Using seven major jump tests, we show that separating jumps from diffusion improves volatility forecasting both in-sample and out-of-sample. Moreover, we show that these statistical improvements can be translated into economic value. We find a risk-averse investor can significantly improve her portfolio performance by incorporating realized jumps into a volatility timing based portfolio strategy. Our results hold true across the majority of jump tests, and are robust to controlling for microstructure effects and transaction costs.

KW - high frequency datajumps

KW - nonparametric tests

KW - asset allocation

KW - volatility forecasting

KW - realized volatility

U2 - 10.1016/j.jempfin.2015.03.019

DO - 10.1016/j.jempfin.2015.03.019

M3 - Journal article

VL - 34

SP - 45

EP - 59

JO - Journal of Empirical Finance

JF - Journal of Empirical Finance

SN - 0927-5398

ER -