Of Smiles and Smirks: A Term Structure Perspective

Das, Sanjiv Ranjan; Sundaram, Rangarajan K.

doi:10.2307/2676279

Cited by 316 publications

(89 citation statements)

References 45 publications

(68 reference statements)

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“…This paper provides some additional insights into the SNJD model and contributes to the existing literature in several directions: Firstly, from a theoretical point of view, we provide an analytical expression for the process distribution and several statistical properties, generalizing the results obtained by Das and Sundaram (1999). Secondly, we introduce a brief treatment of the Shot-Noise process in the economic literature.…”

Section: Summary Of Main Results and Contributionsmentioning

confidence: 99%

“…We also provide expressions for the first moments, and compare our results with those obtained for the JD processes in Das and Sundaram (1999) and Aït-Sahalia (2004).…”

Section: The Statistical Distribution Of the Processmentioning

confidence: 91%

“…See the Appendix This result includes the one for JD processes without Shot-Noise effects as obtained by Das and Sundaram (1999). Indeed, if we set h ≡ 1, (7) leads to Example 1 In the case of a JD process, expression (7) reduces to…”

Section: Propositionmentioning

confidence: 97%

See 2 more Smart Citations

Statistical properties and economic implications of jump-diffusion processes with shot-noise effects

Moreno¹,

Serrano

Stute

2011

European Journal of Operational Research

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This paper analyzes the Shot-Noise Jump-Diffusion model of Altmann, Schmidt and Stute (2008), which introduces a new situation where the effects of the arrival of rare, shocking information to the financial markets may fade away in the long run. We analyze several economic implications of the model, providing an analytical expression for the process distribution. We also prove that certain specifications of this model can provide negative serial persistence. Additionally, we find that the degree of serial autocorrelation is related to the arrival and magnitude of abnormal information. Finally, a GMM framework is proposed to estimate the model parameters.

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Section: Summary Of Main Results and Contributionsmentioning

confidence: 99%

“…We also provide expressions for the first moments, and compare our results with those obtained for the JD processes in Das and Sundaram (1999) and Aït-Sahalia (2004).…”

Section: The Statistical Distribution Of the Processmentioning

confidence: 91%

See 1 more Smart Citation

Statistical properties and economic implications of jump-diffusion processes with shot-noise effects

Moreno¹,

Serrano

Stute

2011

European Journal of Operational Research

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“…As pointed out by Das and Sundaram (1999), the parametric stochastic volatility model can only produce the level of skewness and kurtosis observed in the data when it takes on implausible parameter values. By design a nonparametric joint distribution allows the stochastic volatility model to capture these types of characteristics in the data without sacrificing the time dependent nature of the stochastic volatility model.…”

Section: Introductionmentioning

confidence: 99%

Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture

Jensen

Maheu

2012

SSRN Journal

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In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional, return-volatility distribution with an infinite mixture of bivariate Normal distributions with mean zero vectors, but having unknown mixture weights and covariance matrices. This semiparametric ASV model nests stochastic volatility models whose innovations are distributed as either Normal or Student-t distributions, plus the response in volatility to unexpected return shocks is more general than the fixed asymmetric response with the ASV model. The unknown mixture parameters are modeled with a Dirichlet process prior. This prior ensures a parsimonious, finite, posterior mixture that best represents the distribution of the innovations and a straightforward sampler of the conditional posteriors. We develop a Bayesian Markov chain Monte Carlo sampler to fully characterize the parametric and distributional uncertainty. Nested model comparisons and out-ofsample predictions with the cumulative marginal-likelihoods, and one-day-ahead, predictive logBayes factors between the semiparametric and parametric versions of the ASV model shows the semiparametric model projecting more accurate empirical market returns. A major reason is how volatility responds to an unexpected market movement. When the market is tranquil, expected volatility reacts to a negative (positive) price shock by rising (initially declining, but then rising when the positive shock is large). However, when the market is volatile, the degree of asymmetry and the size of the response in expected volatility is muted. In other words, when times are good, no news is good news, but when times are bad, neither good nor bad news matters with regards to volatility. JEL classification: C11, C14, C53, C58

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“…There have been various attempts to deal with this apparent failure of the BS valuation model. In principle, as explained by Das and Sundaram (1998) and others, the existence of the smile may be attributed to the well known presence of excess kurtosis in the conditional return distributions of the underlying assets. It is clear that excess kurtosis makes extreme observations more likely than in the BS case.…”

Section: Introductionmentioning

confidence: 99%

A semiparametric estimation of liquidity effects on option pricing

Ferreira

Gago

Rubio

2003

Spanish Economic Review

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This paper proposes a semiparametric option pricing model with liquidity, as proxied by the relative bid-ask spread. The nonparametric volatility function with liquidity as an explanatory variable is estimated using the Symmetrized Nearest Neighbors (SNN) estimator rather than the traditional kernel estimator. Moreover, special care is taken in obtaining the smoothing parameter. The in-sample performance of the model turns out to be statiscally favorable relative to a competing model without liquidity. However, the out-of-sample performance of both models is quite disappointing despite the fact that we are not able to reject the stability of risk-neutral densities estimated over different quarters during our sample period.

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Of Smiles and Smirks: A Term Structure Perspective

Cited by 316 publications

References 45 publications

Statistical properties and economic implications of jump-diffusion processes with shot-noise effects

Statistical properties and economic implications of jump-diffusion processes with shot-noise effects

Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture

A semiparametric estimation of liquidity effects on option pricing

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