Jump and Volatility Risk Premiums Implied by VIX

Duan, Jin‐Chuan; Yeh, Chia-Hsuan

doi:10.2139/ssrn.966682

Cited by 56 publications

(73 citation statements)

References 21 publications

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“…This relationship can provide a gauge to see whether a stochastic volatility model for VIX can be reliable. The similar idea of finding a proxy for some unobservable factor can also be found in Ait-Sahalia and Kimmel (2007), Duan and Yeh (2010) and Ait-Sahalia et al (2014). In this paper, VIX index is the underlying asset so its dynamics are observed under P measure.…”

Section: Linear Relationship Between Vvix and Volatility Of Vixsupporting

confidence: 60%

Double-Jump Stochastic Volatility Model for VIX: Evidence from VVIX

Zang

Huang

et al. 2015

SSRN Journal

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The paper studies the continuous-time dynamics of VIX with stochastic volatility and jumps in VIX and volatility. Built on the general parametric affine model with stochastic volatility and jump in logarithm of VIX, we derive a linear relation between the stochastic volatility factor and VVIX index. We detect the existence of co-jump of VIX and VVIX and put forward a double-jump stochastic volatility model for VIX through its joint property with VVIX. With VVIX index as a proxy for the stochastic volatility, we use MCMC method to estimate the dynamics of VIX. Comparing nested models on VIX, we show the jump in VIX and the volatility factor is statistically significant. The jump intensity is also statedependent. We analyze the impact of jump factor on the VIX dynamics.

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Section: Linear Relationship Between Vvix and Volatility Of Vixsupporting

confidence: 60%

Double-Jump Stochastic Volatility Model for VIX: Evidence from VVIX

Zang

Huang

et al. 2015

SSRN Journal

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“…Given that the KOSPI200 index options are top-tier options products due to their high trading volume and investor interest, there is good _________________________ 2 If we derive implied volatility from option pricing models, it contains some model bias, of which representative examples are volatility smiles or smirks of the Black-Scholes model. 3 See the recent studies of Giot (2005aGiot ( , 2005b, Banerjee, Doran, and Peterson (2007), Becker, Clements, andMcCelland (2009), andDuan andYeh (2010). 4 Some recent studies, such as Ryu (2008, 2010), Ryu (2011), and Kim and Ryu (2012), have begun to address the market microstructure issues of the KOSPI200 options market.…”

mentioning

confidence: 99%

Stock Returns and Implied Volatility: A New VAR Approach

Lee

Ryu

2013

Economics

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The authors re-examine the return-volatility relationship and its dynamics under a new vector autoregression (VAR) identification framework. By analyzing two model-free impliedvolatility indices -the well-established VIX (in the United States) and the recently published VKOSPI (in Korea) -and their stock market indices, the authors find an asymmetric volatility phenomenon in both the developed and emerging markets. However, the VKOSPI shows impulse response dynamics that are quite different from those of the VIX. This finding can be attributed to the unique characteristics of the KOSPI200 options market, which determine the dynamics of the VKOSPI. JEL G10 G15

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“…This index is computed by Deutsche Börse AG from the prices of call and put options and reflects market expectation under the risk neutral measure of the 30 day ahead square root implied variance for the DAX 30 log-returns, which is then annualized. Duan and Yeh (2010) show that squared volatility index is a good approximation of the expected risk-neutral volatility when the jumps are small. While the volatility index refers to the standard deviation of the log-returns under the risk neutral measure, it can still be used in the regression because the transformation q(log m, τ) = mq(m, τ) maintains the linear-relationship between the dependent and explanatory variables.…”

Section: 3mentioning

confidence: 94%

Functional Principal Component Analysis for Derivatives of Multivariate Curves

et al. 2016

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We present two methods based on functional principal component analysis (FPCA) for the estimation of smooth derivatives of a sample of random functions, which are observed in a more than one-dimensional domain. We apply eigenvalue decomposition to a) the dual covariance matrix of the derivatives, and b) the dual covariance matrix of the observed curves. To handle noisy data from discrete observations, we rely on local polynomial regressions. If curves are contained in a finite-dimensional function space, the second method performs better asymptotically. We apply our methodology in a simulation and empirical study, in which we estimate state price density (SPD) surfaces from call option prices. We identify three main components, which can be interpreted as volatility, skewness and tail factors. We also find evidence for term structure variation.

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Jump and Volatility Risk Premiums Implied by VIX

Cited by 56 publications

References 21 publications

Double-Jump Stochastic Volatility Model for VIX: Evidence from VVIX

Double-Jump Stochastic Volatility Model for VIX: Evidence from VVIX

Stock Returns and Implied Volatility: A New VAR Approach

Functional Principal Component Analysis for Derivatives of Multivariate Curves

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