VIX forecasting and variance risk premium: A new GARCH approach

Liu, Qiang; Guo, Shuxin; Qiao, Gaoxiu

doi:10.1016/j.najef.2015.10.001

Cited by 26 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, VIX, the Cboe 30-day implied volatility index (in calendar days), can be forecasted as the average of the projected variances for the 30 days in the future under GARCH (Barone-Adesi et al, 2008). Given Equation (2) and the law of iterated expectation, one can obtain the following variance-VIX equation (Liu et al, 2015):…”

Section: Vix-variance Equationmentioning

confidence: 99%

“…Therefore, our speedup is on par with the anticipated quantum hardware, which is much more expensive to construct and requires much more energy to run. Liu et al (2015) recover the risk-neutral parameters from the empirical variance and the market VIX. Guo and Liu (2020) argue that with the current market VIX, they can extract the risk-neutral measure and price VIX futures out-ofsample and in real time.…”

Section: Speedupmentioning

confidence: 99%

“…Liu et al (2015) recover the risk‐neutral parameters from the empirical variance and the market VIX. Guo and Liu (2020) argue that with the current market VIX, they can extract the risk‐neutral measure and price VIX futures out‐of‐sample and in real time.…”

Section: Empirical Studiesmentioning

confidence: 99%

“…Further, VIX, the Cboe 30‐day implied volatility index (in calendar days), can be forecasted as the average of the projected variances for the 30 days in the future under GARCH (Barone‐Adesi et al, 2008). Given Equation (2) and the law of iterated expectation, one can obtain the following variance‐VIX equation (Liu et al, 2015):

{X}_{t}^{2}={100}^{2}\times 365(c{v}_{t+1}+d)=C{v}_{t+1}+D,

c=\frac{1-{\xi }^{30}}{30(1-\xi )},d={V}_{L}(1-c),

C={100}^{2}\times 365c,D={100}^{2}\times 365d,

\xi =\alpha +\beta +0.5\gamma ,{V}_{L}=\omega /(1-\xi ),

where

{X}_{t}

denotes VIX on day t . Guo and Liu (2020) argue and we agree that it is easy and better to work with calendar days in Equation (3).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

GARCH pricing and hedging of VIX options

Liu

Jiao

Guo

2022

Journal of Futures Markets

Self Cite

View full text Add to dashboard Cite

We are the first to study the pricing and hedging of VIX options via Monte Carlo (MC) under GARCH(1,1) and Glosten–Jagannathan–Runkle GARCH(1,1) models. Our pricing is ab initio and out‐of‐sample and can be implemented in real time. Importantly, we propose the so‐called single‐option hedge error, a better measure than that of Bakshi et al., and suggest several techniques to expedite MC by over 1000 times, which rivals the potential speedup of quantum computers. Empirically, our proposed approach outperforms two types of benchmarks; further, the asymmetric Glosten–Jagannathan–Runkle outperforms the symmetric GARCH. Overall, our paper has both theoretical and practical implications.

show abstract

Section: Vix-variance Equationmentioning

confidence: 99%

Section: Speedupmentioning

confidence: 99%

Section: Empirical Studiesmentioning

confidence: 99%

{X}_{t}^{2}={100}^{2}\times 365(c{v}_{t+1}+d)=C{v}_{t+1}+D,

c=\frac{1-{\xi }^{30}}{30(1-\xi )},d={V}_{L}(1-c),

C={100}^{2}\times 365c,D={100}^{2}\times 365d,

\xi =\alpha +\beta +0.5\gamma ,{V}_{L}=\omega /(1-\xi ),

where

{X}_{t}

denotes VIX on day t . Guo and Liu (2020) argue and we agree that it is easy and better to work with calendar days in Equation (3).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

GARCH pricing and hedging of VIX options

Liu

Jiao

Guo

2022

Journal of Futures Markets

Self Cite

View full text Add to dashboard Cite

show abstract

“…The VIX also forms the foundation of many theoretical studies. For example, Liu, Guo, and Qiao used VIX forecasting to relate variance risk premiums.…”

Section: Literature Reviewmentioning

confidence: 99%

Complex relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect

Hsiao

Chang²,

Liu³

2016

Complexity

View full text Add to dashboard Cite

In finance, the asymmetric volatility phenomenon (AVP) and volatility skew are two well‐known topics related to firm risk. In early research, the AVP was documented by Black (Proc 1976 Meetings Business Econ Stat Sect 1976, 177–181), who proposed two possible explanations. The veracity of one such explanation, the “leverage effect,” has long remained controversial. The volatility skew is considered to represent collective phenomena caused by heterogeneous beliefs regarding firm risk among investors. Although the relationship between leverage and firm size has been investigated, the investigations have yielded inconsistent conclusions. All related empirical evidence indicates that the relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect are complex. Regarding accounting principles, Ryan (Account Horizons 1997, 11, 85–95) proposed two concepts for describing firm risk. One of those concepts, “sources of operating risk versus leverage,” revealed the key to linking firm risk to leverage. Among the multidisciplinary methodologies, cybernetics plays a crucial role in complex systems research. The causal nets and feedback loop analysis of cybernetics offer accurate descriptions of realities in terms of causality, nonlinearity, and temporality. This study uses cybernetics to connect the accounting risk concept in microlevel analysis to volatility changes in macrolevel phenomena. This not only validates interdisciplinary analysis with different evidence but also enables the development of a managerial interpretation of different states of firm risk. This study contributes a novel approach that involves the application of a microlevel concept to macrolevel analysis and evidences the “less is more” art of modeling complex phenomena (Schuster, Complexity 2005, 11, 11–13). © 2016 Wiley Periodicals, Inc. Complexity 21: 329–341, 2016

show abstract

Hybrid forecasting of crude oil volatility index: The cross‐market effects of stock market jumps

Jiang,

Qiao,

Wang

et al. 2024

Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

From the cross‐market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data‐driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH‐type models with jumps, and the forecasting effects of five non‐parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross‐market research.

show abstract

VIX forecasting and variance risk premium: A new GARCH approach

Cited by 26 publications

References 21 publications

GARCH pricing and hedging of VIX options

GARCH pricing and hedging of VIX options

Complex relationships among firm risk, asymmetric volatility, volatility skew, and the leverage effect

Hybrid forecasting of crude oil volatility index: The cross‐market effects of stock market jumps

Contact Info

Product

Resources

About