Testing the Stability of Implied Probability Density Functions

Bliss, Robert R.; Panigirtzoglou, Nikolaos

doi:10.2139/ssrn.230786

Cited by 121 publications

(175 citation statements)

References 12 publications

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“…Going one step further, Bliss and Panigirtzoglou (2002) investigate the impact of a uniformly distributed error in the option prices within the bid-ask spread on the risk-neutral distribution.…”

Section: Perturbations Of Options Datamentioning

confidence: 99%

The pricing kernel puzzle: survey and outlook

Cuesdeanu

Jackwerth

2017

Ann Finance

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Section: Perturbations Of Options Datamentioning

confidence: 99%

The pricing kernel puzzle: survey and outlook

Cuesdeanu

Jackwerth

2017

Ann Finance

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“…Therefore, most efforts have been focused on interpolating option prices within the range of strike prices and extrapolating outside the range to estimate the entire distribution. See for instance, Shimko (1993), Malz (1997), Bates (1991), and Bliss and Panigirtzoglou (2002).…”

Section: Revealing the Risk-neutral Pdfmentioning

confidence: 99%

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

Haven

Liu

et al. 2009

Journal of Economic Dynamics and Control

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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.JEL Classification: C02, C63, G13

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“…Hence we need to use economic models and/or econometric methods to transform risk-neutral densities into real-world 3 densities. Economic theory motivates pricing kernel transformations using power and/or exponential utility functions (Bakshi, Kapadia, & Madan, 2003;Bliss & Panigirtzoglou, 2004;Liu, Shackleton, Taylor, & Xu, 2007) and the hyperbolic absolute risk aversion (HARA) function (Kang & Kim, 2006). Liu et al (2007) use both utility and statistical calibration transformations, and they find that a statistical, parametric calibration gives a higher log-likelihood for observed outcomes than a utility transformation.…”

Section: Introductionmentioning

confidence: 99%

Density forecast comparisons for stock prices, obtained from high‐frequency returns and daily option prices

Fan¹,

Taylor

Sandri

2017

Journal of Futures Markets

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This paper presents the first comparison of the accuracy of density forecasts for stock prices. Six sets of forecasts are evaluated for DJIA stocks, across four forecast horizons. Two forecasts are risk-neutral densities implied by the Black-Scholes and Heston models. The third set are historical lognormal densities with dispersion determined by forecasts of realized variances obtained from 5-min returns. Three further sets are defined by transforming risk-neutral and historical densities into realworld densities. The most accurate method applies the risk transformation to the Black-Scholes densities. This method outperforms all others for 87% of the comparisons made using the likelihood criterion.

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Testing the Stability of Implied Probability Density Functions

Cited by 121 publications

References 12 publications

The pricing kernel puzzle: survey and outlook

The pricing kernel puzzle: survey and outlook

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

Density forecast comparisons for stock prices, obtained from high‐frequency returns and daily option prices

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