Department of Economics and Business Economics

Risk Everywhere: Modeling and Managing Volatility

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Risk Everywhere: Modeling and Managing Volatility. / Bollerslev, Tim; Hood, Benjamin; Huss, John; Pedersen, Lasse Heje.

In: The Review of Financial Studies, Vol. 31, No. 7, 2018, p. 2729-2773.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Bollerslev, T, Hood, B, Huss, J & Pedersen, LH 2018, 'Risk Everywhere: Modeling and Managing Volatility', The Review of Financial Studies, vol. 31, no. 7, pp. 2729-2773. https://doi.org/10.1093/rfs/hhy041

APA

Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H. (2018). Risk Everywhere: Modeling and Managing Volatility. The Review of Financial Studies, 31(7), 2729-2773. https://doi.org/10.1093/rfs/hhy041

CBE

Bollerslev T, Hood B, Huss J, Pedersen LH. 2018. Risk Everywhere: Modeling and Managing Volatility. The Review of Financial Studies. 31(7):2729-2773. https://doi.org/10.1093/rfs/hhy041

MLA

Bollerslev, Tim et al. "Risk Everywhere: Modeling and Managing Volatility". The Review of Financial Studies. 2018, 31(7). 2729-2773. https://doi.org/10.1093/rfs/hhy041

Vancouver

Bollerslev T, Hood B, Huss J, Pedersen LH. Risk Everywhere: Modeling and Managing Volatility. The Review of Financial Studies. 2018;31(7):2729-2773. https://doi.org/10.1093/rfs/hhy041

Author

Bollerslev, Tim ; Hood, Benjamin ; Huss, John ; Pedersen, Lasse Heje. / Risk Everywhere: Modeling and Managing Volatility. In: The Review of Financial Studies. 2018 ; Vol. 31, No. 7. pp. 2729-2773.

Bibtex

@article{4b88fa2aba5645ca8a897e2732b03630,
title = "Risk Everywhere: Modeling and Managing Volatility",
abstract = "Based on high-frequency data for more than fifty commodities, currencies, equity indices, and fixed-income instruments spanning more than two decades, we document strong similarities in realized volatility patterns within and across asset classes. Exploiting these similarities through panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and conventional procedures that do not incorporate the similarities in volatilities. We develop a utility-based framework for evaluating risk models that shows significant economic gains from our new risk model. Lastly, we evaluate the effects of transaction costs and trading speed in implementing different risk models.Received March 7, 2016; editorial decision February 3, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.",
author = "Tim Bollerslev and Benjamin Hood and John Huss and Pedersen, {Lasse Heje}",
year = "2018",
doi = "10.1093/rfs/hhy041",
language = "English",
volume = "31",
pages = "2729--2773",
journal = "Review of Financial Studies",
issn = "0893-9454",
publisher = "Oxford University Press",
number = "7",

}

RIS

TY - JOUR

T1 - Risk Everywhere: Modeling and Managing Volatility

AU - Bollerslev, Tim

AU - Hood, Benjamin

AU - Huss, John

AU - Pedersen, Lasse Heje

PY - 2018

Y1 - 2018

N2 - Based on high-frequency data for more than fifty commodities, currencies, equity indices, and fixed-income instruments spanning more than two decades, we document strong similarities in realized volatility patterns within and across asset classes. Exploiting these similarities through panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and conventional procedures that do not incorporate the similarities in volatilities. We develop a utility-based framework for evaluating risk models that shows significant economic gains from our new risk model. Lastly, we evaluate the effects of transaction costs and trading speed in implementing different risk models.Received March 7, 2016; editorial decision February 3, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

AB - Based on high-frequency data for more than fifty commodities, currencies, equity indices, and fixed-income instruments spanning more than two decades, we document strong similarities in realized volatility patterns within and across asset classes. Exploiting these similarities through panel-based estimation of new realized volatility models results in superior out-of-sample risk forecasts, compared to forecasts from existing models and conventional procedures that do not incorporate the similarities in volatilities. We develop a utility-based framework for evaluating risk models that shows significant economic gains from our new risk model. Lastly, we evaluate the effects of transaction costs and trading speed in implementing different risk models.Received March 7, 2016; editorial decision February 3, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

U2 - 10.1093/rfs/hhy041

DO - 10.1093/rfs/hhy041

M3 - Journal article

VL - 31

SP - 2729

EP - 2773

JO - Review of Financial Studies

JF - Review of Financial Studies

SN - 0893-9454

IS - 7

ER -