Forecasting using alternative measures of model‐free option‐implied volatility

Yao, Xingzhi; Izzeldin, Marwan

doi:10.1002/fut.21881

Cited by 4 publications

(3 citation statements)

References 39 publications

(104 reference statements)

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“…Thereby, the sample comprises 168 observations. Following Andersen et al (2015) and Yao and Izzeldin (2018), we consider a replicated

V I X

index,

R X

, using the exact CBOE procedure every day. The

R X

provides an equivalent of the

V I X

using SPX option prices from the Optionmetrics data set.…”

Section: Datamentioning

confidence: 99%

Return predictability of variance differences: A fractionally cointegrated approach

Izzeldin

Yao

2020

Journal of Futures Markets

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This paper examines the fractional cointegration between downside (upside) components of realized and implied variances. A positive association is found between the strength of their cofractional relation and the return predictability of their differences. That association is established via the common longmemory component of the variances that are fractionally cointegrated, which represents the volatility-of-volatility factor that determines the variance premium. Our results indicate that market fears play a critical role not only in driving the long-run equilibrium relationship between implied-realized variances but also in understanding the return predictability. A simulation study further verifies these claims. K E Y W O R D S fractional cointegration, return predictability, variance risk premium J E L C L A S S I F I C A T I O N C14; C32; C51 | INTRODUCTIONNumerous empirical studies suggest that, unlike the variance, the variance risk premium (VRP) carries nontrivial predictive power for aggregate stock market returns over quarterly to annual horizons, where the degree of return predictability is greater than that afforded by more conventional predictors, see, Bollerslev, Tauchen, and Zhou (2009), Drechsler and Yaron (2010), Bollerslev, Marrone, Xu, and Zhou (2014), and Bali and Zhou (2016), among others. Those studies also provide strong evidence that much of the predictability implicit in the VRP may be attributed to its close relation with macroeconomic uncertainty and aggregate risk aversion.The VRP is formally defined as the wedge between option-implied and realized variances. It is measured as the difference between (the square of) the CBOE VIX index and the statistical expectation of the future return variation. In Bollerslev et al. (2009), Drechsler andYaron (2010) and Bollerslev, Sizova, and Tauchen (2012), the return predictability of the VRP is investigated using a self-contained general equilibrium model. This is in the spirit of the long-run risks (LRR) model pioneered by Bansal and Yaron (2004). Specifically, Bollerslev et al. (2009) andBollerslev et al. (2012) extend the LRR model by allowing the time-varying volatility-of-volatility (vol-of-vol) within the economy to be generated by its own stochastic process. They further show that the difference between the risk-neutral and the objective expectations of return variation isolates the vol-of-vol factor, which then serves as the sole source of the true VRP. The VRP, therefore, displays good predictive power for future returns in cases where the vol-of-vol plays a more dominant role in determining variation in the equity premium. A direct link between the VRP and the vol-of-vol is also demonstrated in a purely probabilistic model introduced by Barndorff-Nielsen and Veraart (2013). U D . The SJ and IVA are, respectively, measured as the difference between RV U and RV D and the difference between IV U and IV D . Using a semiparametric approach, we demonstrate the existence of a fractionally cointegrating relation in each pair of the semivariances...

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“…Thereby, the sample comprises 168 observations. Following Andersen et al (2015) and Yao and Izzeldin (2018), we consider a replicated

V I X

index,

R X

, using the exact CBOE procedure every day. The

R X

provides an equivalent of the

V I X

using SPX option prices from the Optionmetrics data set.…”

Section: Datamentioning

confidence: 99%

Return predictability of variance differences: A fractionally cointegrated approach

Izzeldin

Yao

2020

Journal of Futures Markets

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then lots of researchers use realized volatility to measure volatility. RV becomes one of the most popular proxies for volatility (see, for example, Buncic & Gisler, 2017;Luo & Qi, 2017;Shin & Zhong, 2017;Wang, Wu, & Xu, 2015;and Yao & Izzeldin, 2018). Andersen, Bollerslev, Diebold, and Labys (2003); Andersen, Bollerslev, and Meddahi (2004) strongly suggested that simple models with RV are better than popular GARCH-class models for volatility prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes

Gong

Lin

2020

Int. J Fin Econ

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This paper investigates whether leverage effects and structural changes have positive effects on the volatility prediction of crude oil futures. On the basis of existing HAR models, this paper proposes three classes of new HAR models by considering leverage effects, structural changes, or both. The in-sample and out-of-sample results show that leverage effects and structural changes contain significant information for predicting oil volatility. In most cases, structural changes have more in-sample and out-of-sample incremental information than leverage effect, whereas leverage effects have more out-of-sample information for predicting 1-day volatility. In addition, HAR models with leverage effects and structural changes have better in-sample and out-of-sample performances than the corresponding other three classes of HAR models. The above results mean that leverage effects and structural changes should be considered while modelling and forecasting oil volatility.

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“…long horizons. Yao and Izzeldin (2018) further extended the work of Zhang, Taylor, and Wang (2013) and evaluated the performance of various measures of model-free implied volatility in predicting returns and realized volatility. In this paper, we prefer the model-free integrated implied volatility, but we use a discrete form of the integral and consider the process risk-neutral.…”

mentioning

confidence: 99%

A novel term structure stochastic model with adaptive correlation for trend analysis

Lai

et al. 2020

Int. J Fin Econ

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The prediction of underlying price continues to draw extensive attention in academic research. Based on a review of the advantages and disadvantages of different volatility models, we find that the Heston stochastic volatility model with an adaptive correlation coefficient is most suitable for analysing the Hong Kong options market. We subsequently propose a model-free implied volatility term structure formulated using options with different strikes and different maturities. The implied volatility is calculated by integrating the option price and strike price from the current time to the expiry date. Discrete points of term structure data are used to fit a term structure curve. Finally, we use the model-free implied volatility term structure as the long-run mean level of the Heston model to fully exploit the information content contained in the implied volatility term structure. We simulate the distribution of the underlying asset price based on the Heston model and constant elasticity of variance (CEV) model. The adaptive correlation Heston model provides superior results in terms of one-day-ahead prediction performance and the 79-day distribution of the underlying asset price compared with the CEV model. K E Y W O R D S implied volatility, stochastic volatility model, term structure | INTRODUCTIONVolatility term structure is represented by the curve of the variation of the implied volatility with different times to maturity. A key aspect of analysing implied volatility term structure is how to calculate the implied volatility from market option data. In the literature, the two main types of implied volatility valuation methods are model-based implied volatility and modelfree implied volatility.The most widely used implied volatility valuation model is the Black-Scholes (BS) model. In this calculation method, the BS model is reversed to obtain a deterministic volatility function. However, this approach is restricted by a number of constraints.Among studies of model-free volatility, Carr and Madan (1998), Demeterfi, Derman, Kamal, and Zou (1999), Britten-Jones andNeuberger (2000) and Carr and Liuren (2009) presented a volatility expectation based on variance swap contracts. Dotsis and Vlastakis (2016) found that out-the-money (OTM) put options do not contain important information with respect to equity volatility risk. However, Andersen, Fusari, and Todorov (2015) showed that the left tail risk, which drives a substantial part of the OTM put option dynamics, exhibits strong predictive power for future excess market returns over

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Forecasting using alternative measures of model‐free option‐implied volatility

Cited by 4 publications

References 39 publications

Return predictability of variance differences: A fractionally cointegrated approach

Return predictability of variance differences: A fractionally cointegrated approach

Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes

A novel term structure stochastic model with adaptive correlation for trend analysis

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