Option pricing with a dynamic fat-tailed model

Aboura, Sofiane; Valeyre, Sebastien; Wagner, Niklas

doi:10.1057/jdhf.2014.16

Cited by 4 publications

(4 citation statements)

References 51 publications

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“…Because leverage can be a measurement of firm risk, it can also influence the degree of volatility skew. Results provided by Vargas et al , Aboura et al , Kim et al , Chang and Lin , Borland and Bouchaud , and Jones are consistent with the leverage hypothesis. The relationship between the leverage effect and the volatility smile seems to be closer than that between the leverage effect and the AVP.…”

Section: Literature Reviewmentioning

confidence: 51%

“…The curve of volatility against strike prices in equity options exhibits a negative slope. The close relationship between a firm's leverage and volatility skewness has been confirmed in studies by Vargas, Dao, and Bouchaud , Aboura, Valeyre, and Wagner , Dennis and Mayhew , Kim, Lee, Hwang, Kim, and Ko , Chang and Lin , Borland and Bouchaud , and Jones .…”

Section: Introductionmentioning

confidence: 57%

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Complex relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect

Hsiao

Chang²,

Liu³

2016

Complexity

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In finance, the asymmetric volatility phenomenon (AVP) and volatility skew are two well‐known topics related to firm risk. In early research, the AVP was documented by Black (Proc 1976 Meetings Business Econ Stat Sect 1976, 177–181), who proposed two possible explanations. The veracity of one such explanation, the “leverage effect,” has long remained controversial. The volatility skew is considered to represent collective phenomena caused by heterogeneous beliefs regarding firm risk among investors. Although the relationship between leverage and firm size has been investigated, the investigations have yielded inconsistent conclusions. All related empirical evidence indicates that the relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect are complex. Regarding accounting principles, Ryan (Account Horizons 1997, 11, 85–95) proposed two concepts for describing firm risk. One of those concepts, “sources of operating risk versus leverage,” revealed the key to linking firm risk to leverage. Among the multidisciplinary methodologies, cybernetics plays a crucial role in complex systems research. The causal nets and feedback loop analysis of cybernetics offer accurate descriptions of realities in terms of causality, nonlinearity, and temporality. This study uses cybernetics to connect the accounting risk concept in microlevel analysis to volatility changes in macrolevel phenomena. This not only validates interdisciplinary analysis with different evidence but also enables the development of a managerial interpretation of different states of firm risk. This study contributes a novel approach that involves the application of a microlevel concept to macrolevel analysis and evidences the “less is more” art of modeling complex phenomena (Schuster, Complexity 2005, 11, 11–13). © 2016 Wiley Periodicals, Inc. Complexity 21: 329–341, 2016

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Section: Literature Reviewmentioning

confidence: 51%

Section: Introductionmentioning

confidence: 57%

Complex relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect

Hsiao

Chang²,

Liu³

2016

Complexity

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“…One limitation is that the CCM assumes Gaussian distributed probability distributions with constant volatility, while risky-asset returns have empirical distributions with fat tails and volatility clusters and actual default probability distributions are fat tailed. In the literature various methods are proposed to remedy the assumption of a Gaussian distribution for the underlying asset returns, such as alternative volatility models or adjustments of the default probability (see Aboura et al, 2014 for an overview). We apply the latter method by mapping the risk-neutral default probabilities onto actual sovereign default data.…”

Section: Application Of the Ccmmentioning

confidence: 99%

How quantitative easing changes the nature of sovereign risk

Broeders

End

Haan

2023

Journal of International Money and Finance

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“…(2) asymmetric distributions, such as skewed-t [19], variance gamma [20], Weibull [21], and distorted lognormal distribution [22]; and (3) mixture distribution such as the mixture of Gaussian and heavy-tailed model [23].…”

Section: Introductionmentioning

confidence: 99%

Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

2023

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Connecting derivative pricing with tail risk management has become urgent for financial practice and academia. This paper proposes a novel option pricing model based on the exponential generalized beta of the second kind (EGB2) distribution. The newly proposed model is of generality, simplicity, robustness, and financial interpretability. Most importantly, one can detect tail risk signals by calibrating the proposed model. The model includes the seminal Black–Scholes (B−S) formula as a limit case and can perfectly “replicate” the option prices from Merton’s jump-diffusion model. Based on the proposed pricing model, three tail risk warning measures are introduced for tail risk signals detection: the EGB2 implied tail index, the EGB2 implied Value-at-Risk (EGB2-VaR), and the EGB2 implied risk-neutral density (EGB2 R.N.D.). Empirical results show that the new pricing model can yield higher pricing accuracy than existing models in normal and crisis periods, and three model-based tail risk measures can perfectly detect tail risk signals before financial crises.

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Option pricing with a dynamic fat-tailed model

Cited by 4 publications

References 51 publications

Complex relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect

Complex relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect

How quantitative easing changes the nature of sovereign risk

Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

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