Maximum entropy evaluation of asymptotic hedging error under a generalised jump-diffusion model

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 3 ; Pages: 1-19 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Estimation: General ; Model Construction and Estimation ; Contingent Pricing; Futures Pricing; option pricing
• Thema: expected shortfall ; value-at-risk ; asymptotic hedging error ; esscher transform ; generalised jump ; kernel biased ; maximum entropy density

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Fard, Farzad Alavi ; Doko Tchatoka, Firmin ; Sriananthakumar, Sivagowry
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2021

Weitere Informationen

• DOI: doi:10.3390/jrfm14030097
• Handle: http://hdl.handle.net/10419/239513
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ