Option Pricing and Distribution Characteristics

Mauler, David J.; McDonald, James B.

doi:10.1007/s10614-014-9441-z

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Recent academic literature shows a rising interest in using non-normal innovation for option pricing (Bellini and Mercuri 2014;Christoffersen et al 2006;Liu et al 2015;Mauler and McDonald 2014). It would be interesting to investigate the performance of our accelerated FHS option pricing method relative to these methods, which will be our future research.…”

Section: Discussionmentioning

confidence: 99%

Accelerating FHS Option Pricing Under Linear GARCH

Xie

Fan

2020

Comput Econ

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We propose an analytical approximation technique to accelerate the filtered historical simulation (FHS) option pricing method. The analytical approximation technique has at least two advantages over the FHS method: first, it does not suffer from random sampling error as it needs no simulation; second, it is fast in calculating option price as it is analytical. Simulation results indicate our technique approximates the FHS method quite well, and empirical results show that our technique has very good option pricing performance.

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Section: Discussionmentioning

confidence: 99%

Accelerating FHS Option Pricing Under Linear GARCH

Xie

Fan

2020

Comput Econ

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“…In other fields, for instance for healthcare costs, the choice of the appropriate distribution has been a subject with wide coverage (Jones et al (2014); Jones et al (2015); Mauler and McDonald (2015)). McDonald et al (2013) utilized the observed Skewness & Kurtosis on GB1 and GB2 distributions.…”

Section: Literature Reviewmentioning

confidence: 99%

The Skewness‐Kurtosis plane for cryptocurrencies' universe

2023

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Cryptocurrency returns diverge excessively from normality, with the interrelationship of Skewness and Kurtosis being accordant with a parabolic form, yet this connection is scantly documented. We begin by demonstrating diagrammatically the attributes of the S‐K plane for cryptocurrencies. Moreover, by taking advantage of the panel structure of the data, we estimate a quadratic model for the S‐K plane. Then we investigate whether the type and the infrastructure of the cryptocurrency, as well as the period under examination, alter the architecture of the plane. We find that the squared Skewness of tokens substantially lowers the slope of Kurtosis, while the same applies to the earlier era of the market.

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“…Mauler and McDonald [17] apply the generalized beta of the second kind, the inverse hyperbolic sine, the g-and-h, and others to generalize the Black-Scholes option pricing model to explore potential improvements relative to the original log-normal specification of the options model. All alternative flexible pdf's considered generated improvements in the accuracy of options price estimates relative to the log-normal pdf.…”

Section: Literature Reviewmentioning

confidence: 99%

Partially Adaptive and Robust Estimation of Asset Models: Accommodating Skewness and Kurtosis in Returns

McDonald¹,

Michelfelder²

2017

JMF

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Robust regression estimation deals with selecting estimators that have desirable statistical properties when applied to data drawn from a wide range of distributional characteristics. Ideally, robust estimators are insensitive to small departures from the assumed distributions and hopefully would be unbiased and have variances close to estimators based on the true distribution. The approach explored in this paper is to select an estimator based on a flexible distribution which includes, for example, the normal as a limiting case. Thus, the corresponding estimator can accommodate normally distributed data as well as data having significant skewness and kurtosis. In the case when an assumed distribution over-parameterizes the true distribution, the variance of the estimator is larger than necessary, but often the increases are modest and much smaller than assuming a model which does not include the true distribution. The selection of a flexible probability distribution can impact the efficiency and biasedness of the corresponding robust estimator. Knowing the relations among potential distributions can lead to a better estimator that improves efficiency, avoids bias, and reduces the impact of misspecification.

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Option Pricing and Distribution Characteristics

Cited by 5 publications

References 12 publications

Accelerating FHS Option Pricing Under Linear GARCH

Accelerating FHS Option Pricing Under Linear GARCH

The Skewness‐Kurtosis plane for cryptocurrencies' universe

Partially Adaptive and Robust Estimation of Asset Models: Accommodating Skewness and Kurtosis in Returns

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