Revealing the implied risk-neutral MGF from options: The wavelet method

Emmanuel Haven; Xiaoquan Liu; Chenghu Ma; Liya Shen

doi:10.1016/j.jedc.2008.09.001

Revealing the implied risk-neutral MGF from options: The wavelet method

Emmanuel Haven, Xiaoquan Liu, Chenghu Ma, Liya Shen

Research output: Journal Publication › Article › peer-review

13 Citations (Scopus)

Abstract

Options are believed to contain unique information on the risk-neutral moment generating function (MGF) or the risk-neutral probability density function (PDF) of the underlying asset. This paper applies the wavelet method to approximate the implied risk-neutral MGF from option prices. Monte Carlo simulations are carried out to show how the risk-neutral MGF can be obtained using the wavelet method. With the Black-Scholes model as the benchmark, we offer a novel method to reveal the implied MGF, and to price in-sample options and forecast out-of-sample option prices with the estimated MGF.

Original language	English
Pages (from-to)	692-709
Number of pages	18
Journal	Journal of Economic Dynamics and Control
Volume	33
Issue number	3
DOIs	https://doi.org/10.1016/j.jedc.2008.09.001
Publication status	Published - Mar 2009
Externally published	Yes

Keywords

Laplace transform
Option pricing
Wavelet analysis

ASJC Scopus subject areas

Economics and Econometrics
Control and Optimization
Applied Mathematics

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10.1016/j.jedc.2008.09.001

Cite this

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title = "Revealing the implied risk-neutral MGF from options: The wavelet method",

abstract = "Options are believed to contain unique information on the risk-neutral moment generating function (MGF) or the risk-neutral probability density function (PDF) of the underlying asset. This paper applies the wavelet method to approximate the implied risk-neutral MGF from option prices. Monte Carlo simulations are carried out to show how the risk-neutral MGF can be obtained using the wavelet method. With the Black-Scholes model as the benchmark, we offer a novel method to reveal the implied MGF, and to price in-sample options and forecast out-of-sample option prices with the estimated MGF.",

keywords = "Laplace transform, Option pricing, Wavelet analysis",

author = "Emmanuel Haven and Xiaoquan Liu and Chenghu Ma and Liya Shen",

note = "Funding Information: We thank the three referees and the editor for their detailed and helpful advice that greatly improved the paper. Thanks are also due to Jerry Coakley for his comments on the previous version of the paper. Chenghu Ma acknowledges funding from the Economic and Social Research Council (ESRC), UK. Liya Shen acknowledges funding from the Overseas Research Students Awards Scheme (ORSAS), UK. This paper is part of her PhD thesis. ",

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doi = "10.1016/j.jedc.2008.09.001",

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AU - Liu, Xiaoquan

AU - Ma, Chenghu

AU - Shen, Liya

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AB - Options are believed to contain unique information on the risk-neutral moment generating function (MGF) or the risk-neutral probability density function (PDF) of the underlying asset. This paper applies the wavelet method to approximate the implied risk-neutral MGF from option prices. Monte Carlo simulations are carried out to show how the risk-neutral MGF can be obtained using the wavelet method. With the Black-Scholes model as the benchmark, we offer a novel method to reveal the implied MGF, and to price in-sample options and forecast out-of-sample option prices with the estimated MGF.

KW - Laplace transform

KW - Option pricing

KW - Wavelet analysis

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Revealing the implied risk-neutral MGF from options: The wavelet method

Abstract

Keywords

ASJC Scopus subject areas

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