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