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A Moment Matching Market Implied Calibration

Authors: Florence Guillaume; Wim Schoutens;

A Moment Matching Market Implied Calibration

Abstract

Abstract This paper provides a new market implied calibration based on a moment matching methodology where the moments of the risk-neutral density function are inferred from at-the-money and out-the-money European vanilla option quotes. In particular, we derive a model-independent risk-neutral formula for the moments of the asset log-return distribution function by expanding power returns as a weighted sum of vanilla option payoffs (based on results of Breeden and Litzenberger and Carr and Madan). For the numerical study, we develop different popular exponential Levy models, namely the VG, NIG and Meixner models. The new calibration methodology rests on closed-form formulae only: it is shown that the moment matching system can be transformed into a system of algebraic equations that computes directly the optimal value of the model parameters in terms of the second to the th market standardized moments under the different Levy models under investigation. Hence, the proposed calibration can be performed alm...

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Top 10%
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