Artikel
Maximum entropy evaluation of asymptotic hedging error under a generalised jump-diffusion model
In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the asymptotic hedging error for options under a generalised jump-diffusion model with kernel bias, which nests a number of very important processes in finance. We then obtain an estimation for the distribution of hedging error by maximising Shannon's entropy subject to a set of moment constraints, which in turn yields the value-at-risk and expected shortfall of the hedging error. The significance of this approach lies in the fact that the maximum entropy estimator allows us to obtain a consistent estimate of the asymptotic distribution of hedging error, despite the non-normality of the underlying distribution of returns.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 3 ; Pages: 1-19 ; Basel: MDPI
- Classification
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Wirtschaft
Estimation: General
Model Construction and Estimation
Contingent Pricing; Futures Pricing; option pricing
- Subject
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expected shortfall
value-at-risk
asymptotic hedging error
esscher transform
generalised jump
kernel biased
maximum entropy density
- Event
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Geistige Schöpfung
- (who)
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Fard, Farzad Alavi
Doko Tchatoka, Firmin
Sriananthakumar, Sivagowry
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2021
- DOI
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doi:10.3390/jrfm14030097
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
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Object type
- Artikel
Associated
- Fard, Farzad Alavi
- Doko Tchatoka, Firmin
- Sriananthakumar, Sivagowry
- MDPI
Time of origin
- 2021
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