Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets

Qiao, Gaoxiu; Teng, Yuxin; Li, Weiping; Liu, Wenwen

doi:10.1016/j.najef.2019.04.003

Cited by 28 publications

(8 citation statements)

References 40 publications

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“…In this study, VIX and VIX futures prices are downloaded from the CBOE website. We apply VIX 5-min highfrequency data to our article, which is extracted from VIX 1-min data obtained from Thomson Reuters, because 5-min data can avoid market noise according to previous studies (Andersen et al, 2011;Ma et al, 2021;Qiao et al, 2019;Seo & Kim, 2015). To avoid any liquidity-related bias, several filters are applied to VIX futures prices with reference to the existing studies (Huang et al, 2018;Wang et al, 2017;Zhu & Lian, 2012) , 100 252 * * .…”

Section: Data Descriptionmentioning

confidence: 99%

Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX

Jiang

Qiao

et al. 2022

Journal of Futures Markets

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This paper proposes to study volatility index (VIX) futures pricing by directly modeling the logarithmic VIX while incorporating observable dynamic jumps of the VIX, which are derived based on VIX high-frequency data. The impacts of several different interday and intraday jump tests for VIX futures prices are investigated. We obtain the analytical expression by deducing the forward iteration relations of the lagged logarithm VIX, as well as the conditional variance and jump intensity, and use the maximum likelihood method to estimate the parameters under the risk-neutral measure. The empirical results prove the superiority of our newly proposed model (especially the model based on the LM jump test), which indicates that considering the heteroscedasticity effect of conditional variance, introducing VIX high-frequency data information and separating realized jump variation from the realized variance are very important to obtain much more accurate VIX futures pricing.

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Section: Data Descriptionmentioning

confidence: 99%

Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX

Jiang

Qiao

et al. 2022

Journal of Futures Markets

Self Cite

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“…S&P 500 daily close prices are obtained from the Oxford‐Man Institute, and 5‐min high‐frequency VIX data are from Thomson Reuters. Previous literature shows that 5‐min high‐frequency data can effectively balance market noise (Andersen et al, 2011; Qiao et al, 2019; Seo & Kim, 2015). In accordance with previous literature (Huang et al, 2018; Wang et al, 2017; Zhu & Lian, 2012), some filters are applied to the VIX futures data.…”

Section: Empirical Analysismentioning

confidence: 99%

“…In addition, another fascinating model is the HAR‐type framework commonly used for realized volatility forecasting, which is easy to estimate and accurately predicts volatility (Andersen et al, 2011; Corsi, 2009; Ma et al, 2019; Patton & Sheppard, 2015; Qiao et al, 2019; Qiao, Teng, et al, 2020). Some studies apply and extend the HAR‐type model to financial derivative pricing, showing the effectiveness of the HAR framework.…”

Section: Introductionmentioning

confidence: 99%

VIX futures pricing based on high‐frequency VIX: A hybrid approach combining SVR with parametric models

Qiao

Jiang

2023

Journal of Futures Markets

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We propose a novel hybrid approach for volatility index (VIX) futures pricing by combining support vector regression (SVR) with parametric models. Realized semivariances calculated based on high‐frequency VIX are used to characterize the asymmetric shocks of VIX, and the direct pricing framework of the heterogeneous autoregressive model is extended by incorporating realized semivariances. VIX futures prices are first obtained via parametric models, then the predicted prices and realized semivariances are input into SVR to obtain the final predicted values. Empirical results indicate that the combination of SVR with parametric models significantly improves the pricing ability. This indicates the important information of high‐frequency VIX and the necessity of combining machine learning methods with parametric models to obtain more accurate predictions.

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“…Many researchers used it as their benchmark model. In addition, compared with other models, this model can predict the performance more accurately [19]. The standard HAR-RV model can be specified as follows: (8) Where 𝜔𝜔 �� i s an unexpected error term, RV � �� is lagged daily RV.…”

Section: Standard Har Model and Its Extensionsmentioning

confidence: 99%

Volatility Forecasting of China Silver Futures: the Contributions of Chinese Investor Sentiment and CBOE Gold and Silver ETF Volatility Indices

Wen-bin

2021

E3S Web Conf.

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This paper is to detect the role of CBOE gold ETF volatility index (GVZ), CBOE silver ETF volatility index (VXSLV), and constructed Chinese investor sentiment (CnSENT) on the volatility forecasting of China silver futures over daily, weekly and monthly horizons. Different types of HAR models and ridge regression models are utilized to do the analysis, and the out-of-sample R-square statistics and different rolling window sizes are used to ensure the robustness of the conclusion. The empirical results suggest that GVZ and VXSLV have the explanatory power on the China silver futures. Particularly, VXSLV has a better performance than GVZ. However, the predictive power of CnSENT is doubtful as some results indicate that it cannot improve the prediction accuracy. Additionally, the ridge regression method does not achieve a better result than all types of HAR models.

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Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets

Cited by 28 publications

References 40 publications

Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX

Directly pricing VIX futures with observable dynamic jumps based on high‐frequency VIX

VIX futures pricing based on high‐frequency VIX: A hybrid approach combining SVR with parametric models

Volatility Forecasting of China Silver Futures: the Contributions of Chinese Investor Sentiment and CBOE Gold and Silver ETF Volatility Indices

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