Simple Robust Hedging with Nearby Contracts
Liuren Wu and
Jingyi Zhu
Journal of Financial Econometrics, 2017, vol. 15, issue 1, 1-35
Abstract:
This paper proposes a new hedging strategy based on approximate matching of contract characteristics instead of risk sensitivities. The strategy hedges an option with three options at different maturities and strikes by matching the option function expansion along maturity and strike rather than risk factors. Its hedging effectiveness varies with the maturity and strike distance between the target and the hedge options, but is robust to variations in the underlying risk dynamics. Simulation analysis under different risk environments and historical analysis on S&P 500 index options both show that a wide spectrum of strike-maturity combinations can outperform dynamic delta hedging.
Keywords: characteristics matching; hedging; jumps; Monte Carlo; payoff matching; risk sensitivity matching; strike-maturity triangle; stochastic volatility; S&P 500 index options; Taylor expansion (search for similar items in EconPapers)
JEL-codes: G11 G13 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1093/jjfinec/nbw007 (application/pdf)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:oup:jfinec:v:15:y:2017:i:1:p:1-35.
Ordering information: This journal article can be ordered from
https://academic.oup.com/journals
Access Statistics for this article
Journal of Financial Econometrics is currently edited by Allan Timmermann and Fabio Trojani
More articles in Journal of Financial Econometrics from Oxford University Press Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().