Volatility dynamics for the S&amp;P 500: Further evidence from non-affine, multi-factor jump diffusions

Kaeck, Andreas; Alexander, Carol

doi:10.1016/j.jbankfin.2012.07.012

Cited by 77 publications

(48 citation statements)

References 44 publications

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“…Only a few recent papers show that VIX can improve option valuation performance. Kaeck and Alexander () use several continuous‐time models with Markov chain Monte Carlo, which takes the VIX term structure into account, and apply these models to option valuation. Yang and Kanniainen () investigate how infinite‐activity variance γ and normal inverse Gaussian increases with affine and nonaffine volatility processes improve returns data fitting and option pricing performance.…”

Section: Introductionmentioning

confidence: 99%

Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

Pan

Wang

Liu

et al. 2019

Journal of Futures Markets

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We develop a new generalized autoregressive conditional heteroskedasticity (GARCH) model that accounts for the information spillover between two markets. This model is used to detect the usefulness of the CBOE volatility index (VIX) for improving the performance of volatility forecasting and option pricing. We find the significant ability of VIX to predict stock volatility both in‐sample and out‐of‐sample. VIX information also helps to greatly reduce the option pricing error. The proposed volatility spillover GARCH model performs better than the related approaches proposed by Kanniainen et al. (2014, J Bank Finance, 43, pp. 200‐211) and P. Christoffersen et al. (2014, J Financ Quant Anal, 49, pp. 663–697).

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Section: Introductionmentioning

confidence: 99%

Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

Pan

Wang

Liu

et al. 2019

Journal of Futures Markets

View full text Add to dashboard Cite

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“…This 4 Because closed-form solutions are typically unavailable, empirical studies of non-affine models are most often based either on pure time-series evidence or are restricted to diagnosing simple volatility contracts such as variance swaps or the VIX index. In recent literature, Kaeck and Alexander (2012) estimate a variety of affine and non-affine models on S&P 500 index returns and the VIX term structure. Christoffersen, Jacobs and Mimouni (2010) advance the estimation of single-factor non-affine models using a particle filter, as discussed below.…”

Section: Introductionmentioning

confidence: 99%

What is beneath the surface? Option pricing with multifrequency latent states

Calvet

Fearnley

Fisher

et al. 2015

Journal of Econometrics

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We introduce a tractable class of non-ane price processes with multifrequency stochastic volatility and jumps. The specications require few xed parameters and deliver fast option pricing. One key ingredient is a tight link between jumps and volatility regimes, as asset pricing theory suggests. Empirically, the model matches implied volatility surfaces and their dynamics with-out requiring parameter recalibration. A variety of metrics show improvements over traditional benchmarks in-and out-of-sample. AbstractWe introduce a tractable class of non-affine price processes with multifrequency stochastic volatility and jumps. The specifications require few fixed parameters and deliver fast option pricing. One key ingredient is a tight link between jumps and volatility regimes, as asset pricing theory suggests. Empirically, the model matches implied volatility surfaces and their dynamics without requiring parameter recalibration. A variety of metrics show improvements over traditional benchmarks in-and out-of-sample.

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“…A large literature on affine stochastic volatility models has emerged focusing on improving the path-breaking model of Heston (1993): models that allow for jumps in the dynamics of the price of the financial asset, in order to account for large moves such in the case of crashes (Bates, 1996;Bakshi, Cao and Chen, 1997), models allowing that the long-run variance is itself a stochastic process, modeled as a diffusion process or as a discrete state Markov process (Bardgett, Gourier and Leippold, 2013;Kaeck and Alexander, 2012), models that allow for jumps in the dynamics of the variance (Eraker, 2004;Broadie, Chernov and Johannes, 2007), two-factor models that generates stochastic correlation between returns and volatility (Christoffersen, Heston and Jacobs, 2009), or three-factor models with jumps both in the dynamics of the underlying and of the volatility (Andersen, Fusari and Todorov, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Further support for non-affine stochastic volatility models is provided by Kaeck and Alexander (2012) which present comprehensive empirical results regarding the option pricing performance of affine and non-affine continuous time stochastic volatility models. The authors consider an exogenous non-affine constant elasticity of volatility (CEV)-type stochastic volatility model augmented by jumps in both the price and variance process and by a stochastic long-run variance level.…”

Section: Introductionmentioning

confidence: 99%

Herding and Stochastic Volatility

2015

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In this paper we develop a one-factor non-affine stochastic volatility option pricing model where the dynamics of the underlying is endogenously determined from micro-foundations. The interaction and herding of the agents trading the underlying asset induce an amplification of the volatility of the asset over the volatility of the fundamentals. Although the model is non-affine, a closed form option pricing formula can still be derived by using a Gauss-Hermite series expansion methodology. The model is calibrated using S&P 500 index options for the period 1996-2013. When its results are compared to some benchmark models we find that the new non-affine one-factor model outperforms the affine one-factor Heston model and it is competitive, especially out-of-sample, with the affine two-factor double Heston model.

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Volatility dynamics for the S&P 500: Further evidence from non-affine, multi-factor jump diffusions

Cited by 77 publications

References 44 publications

Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

What is beneath the surface? Option pricing with multifrequency latent states

Herding and Stochastic Volatility

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