Equilibrium variance risk premium in a cost-free production economy

Ruan, Xinfeng; Zhang, Jin E.

doi:10.1016/j.jedc.2018.08.011

Cited by 16 publications

(11 citation statements)

References 100 publications

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“…This strongly supports the negative variance risk premium in the literature, for example, Bardgett et al (2019) and Egloff et al (2010). Consistent with the equilibrium model in Ruan and Zhang (2018), most of the price jump risk premium

η^{x} = μ_{x} - μ_{x}^{Q}

and the volatility jump risk premium

η^{y} = μ_{y}^{Q} - μ_{y}

are positive and the jump intensity risk premium

η^{λ} = λ_{t}^{Q} ∕ λ_{t} > 1

. In other words, jumps contribute a very large part of the negative variance risk premium, for example, Bollerslev and Todorov (2011), Li and Zinna (2018), and Todorov (2010).…”

Section: Resultssupporting

confidence: 57%

“…The negative sign is consistent with Du and Luo (2019). According to the equilibrium model in Ruan and Zhang (2018), η S should be positive. Actually η S estimates are very noisy with large standard deviations (which are almost the same as the absolution values of the estimates).…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, we consider three typical models. The first model is the stochastic volatility model with contemporaneous jumps in returns and volatility (SVCJ), which is the most popular affine model in the literature, for example, Bakshi, Cao, and Chen (1997); Broadie, Chernov, and Johannes (2007); Da Fonseca and Ignatieva (2019); Duan and Yeh (2010); Eraker (2004); Eraker, Johannes, and Polson (2003); Kaeck, Rodrigues, and Seeger (2017); Lin and Chang (2010); Neuberger (2012); Neumann, Prokopczuk, and Simen (2016); Ruan and Zhang (2018); Zhu and Lian (2011, 2012); and others. Bakshi et al (1997), Broadie et al (2007), Eraker (2004), and Neumann et al (2016) document that the SVCJ model is good enough to fit options and returns data simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Inferring information from the S&P 500, CBOE VIX, and CBOE SKEW indices

Cao

Ruan

Zhang

2020

Journal of Futures Markets

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This paper compares the information extracted from the S&P 500, CBOE VIX, and CBOE SKEW indices for the S&P 500 index option pricing. Based on our empirical analysis, VIX is a very informative index for option prices. Whether adding the SKEW or the VIX term structure can improve the option pricing performance depends on the model we choose. Roughly speaking, the VIX term structure is informative for some models, while the SKEW is very noisy and does not contain much important information for option prices. This paper also extends Zhang et al.(2017, J Futures Markets, 37, 211-237) into three typical affine models.

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η^{x} = μ_{x} - μ_{x}^{Q}

and the volatility jump risk premium

η^{y} = μ_{y}^{Q} - μ_{y}

are positive and the jump intensity risk premium

η^{λ} = λ_{t}^{Q} ∕ λ_{t} > 1

. In other words, jumps contribute a very large part of the negative variance risk premium, for example, Bollerslev and Todorov (2011), Li and Zinna (2018), and Todorov (2010).…”

Section: Resultssupporting

confidence: 57%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inferring information from the S&P 500, CBOE VIX, and CBOE SKEW indices

Cao

Ruan

Zhang

2020

Journal of Futures Markets

Self Cite

View full text Add to dashboard Cite

show abstract

“…Examining the relationship quantitatively requires us to estimate model parameters and latent stochastic variance and jump intensity variables see e.g. Ruan and Zhang (2018). The task seems to be nontrivial and hence is left for further research.…”

Section: Variable Measurementsmentioning

confidence: 99%

“…One is able to extend our model by includingEpstein and Zin's (1992) recursive utility, see e.g Ruan and Zhang (2018). andBansal and Yaron's (2004) long-run risk.…”

mentioning

confidence: 99%

Market Excess Returns, Variance and the Third Cumulant

2018

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In this paper, we develop an equilibrium asset pricing model for market excess returns, variance and the third cumulant by using a jump‐diffusion process with stochastic variance and jump intensity in Cox et al. (1985) production economy. Empirical evidence with the S&P 500 index and options from January, 1996 to December, 2005 strongly supports our model prediction that the lower the third cumulant, the higher the market excess returns. Consistent with existing literature, the theoretical mean–variance relation is supported only by regressions on risk‐neutral variance. We further demonstrate empirically that the third cumulant explains significantly the variance risk premium.

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Bakshi, Kapadia, and Madan (2003) risk‐neutral moment estimators: An affine jump‐diffusion approach

Aschakulporn

Zhang

2021

Journal of Futures Markets

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This is the first study of the errors in the Bakshi, Kapadia, and Madan riskneutral moment estimators under the Duffie, Pan, and Singleton affine jumpdiffusion model benchmarked against their true values. This is accomplished by extending the exact solutions from Zhen and Zhang. To mitigate errors in skewness, interpolating the implied volatility curve linearly and applying constant extrapolation to have a step size of $1 and strikes ranging from a quarter to quadruple the forward price should yield skewness values with errors less than 10 −3 , based on simulated data using parameters calibrated with the S&P 500 index during 2020.

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Equilibrium variance risk premium in a cost-free production economy

Cited by 16 publications

References 100 publications

Inferring information from the S&P 500, CBOE VIX, and CBOE SKEW indices

Inferring information from the S&P 500, CBOE VIX, and CBOE SKEW indices

Market Excess Returns, Variance and the Third Cumulant

Bakshi, Kapadia, and Madan (2003) risk‐neutral moment estimators: An affine jump‐diffusion approach

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