Optimal asset allocation using a combination of implied and historical information
Optimal asset allocation using a combination of implied and historical information
This paper investigates the contribution of option-implied information for strategic asset allocation for individuals with minimum-variance preferences and portfolios with a variety of assets. We propose a covariance matrix that exploits a mixture of historical and option-implied information. Implied variance measures are proposed for those assets for which option-implied information is available. Historical variance and correlation measures are applied to the remaining assets. The performance of this novel approach for constructing optimal investment portfolios is assessed out-of-sample using statistical and economic measures. An empirical application to a sophisticated portfolio comprised by a combination of equities, fixed income, alternative securities and cash deposits shows that implied variance measures with risk premium correction outperform variance measures constructed from historical data and implied variance without correction. This result is robust across investment portfolios, volatility and portfolio performance metrics, and rebalancing schemes.
Sharpe ratio, asset allocation, historical volatility, implied volatility, realized variance
1-14
Cheang, Chi Wan
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Olmo, Jose
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Ma, Tiejun
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Sung, Ming-Chien
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McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
January 2020
Cheang, Chi Wan
f9aaaf3c-6edc-4cb1-a780-daabfd5822a0
Olmo, Jose
706f68c8-f991-4959-8245-6657a591056e
Ma, Tiejun
1f591849-f17c-4209-9f42-e6587b499bae
Sung, Ming-Chien
2114f823-bc7f-4306-a775-67aee413aa03
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
Cheang, Chi Wan, Olmo, Jose, Ma, Tiejun, Sung, Ming-Chien and McGroarty, Frank
(2020)
Optimal asset allocation using a combination of implied and historical information.
International Review of Financial Analysis, 67, , [101419].
(doi:10.1016/j.irfa.2019.101419).
Abstract
This paper investigates the contribution of option-implied information for strategic asset allocation for individuals with minimum-variance preferences and portfolios with a variety of assets. We propose a covariance matrix that exploits a mixture of historical and option-implied information. Implied variance measures are proposed for those assets for which option-implied information is available. Historical variance and correlation measures are applied to the remaining assets. The performance of this novel approach for constructing optimal investment portfolios is assessed out-of-sample using statistical and economic measures. An empirical application to a sophisticated portfolio comprised by a combination of equities, fixed income, alternative securities and cash deposits shows that implied variance measures with risk premium correction outperform variance measures constructed from historical data and implied variance without correction. This result is robust across investment portfolios, volatility and portfolio performance metrics, and rebalancing schemes.
Text
IRFA_OptAA
- Accepted Manuscript
More information
Accepted/In Press date: 8 November 2019
e-pub ahead of print date: 20 November 2019
Published date: January 2020
Additional Information:
Funding Information:
We thank editor, the anonymous reviewers, Johnnie Johnson, Richard McGee and Jason Wang for their helpful comments and feedback. We also acknowledge the support from Seven Investment Management and in particular the personal contributions of Christopher Cowell, Alessandro Laurent, Ian Jensen-Humphreys and Christopher Darbyshire. Parts of the research reported in this paper has been supported by Innovate UK, EPSRC and ESRC as part of a KTP project (Ref: KTP010262). Appendix A
Publisher Copyright:
© 2019 Elsevier Inc.
Keywords:
Sharpe ratio, asset allocation, historical volatility, implied volatility, realized variance
Identifiers
Local EPrints ID: 435804
URI: http://eprints.soton.ac.uk/id/eprint/435804
ISSN: 1057-5219
PURE UUID: 3d4703c9-c688-4df0-8dd3-c28a754af8e8
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Date deposited: 21 Nov 2019 17:30
Last modified: 17 Mar 2024 05:03
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Author:
Chi Wan Cheang
Author:
Frank McGroarty
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