GARCH‐Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

Zhang, Huannan; Lan, Qiujun

doi:10.1155/2014/386721

Cited by 5 publications

(12 citation statements)

References 19 publications

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“…2 2 Few studies (Koopman, Jungbacker, & Hol, 2005; Zhang & Lan, 2014) examine the presence of jumps in GARCH‐RV‐type models. For example, Zhang and Lan (2014) show that GARCH‐RV type models accounting for jumps have more power to predict the future volatility of stock prices than other GARCH‐RV type approaches. …”

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confidence: 99%

Modelling the volatility of crude oil returns: Jumps and volatility forecasts

2020

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We contribute to the scarce literature on the oil market volatility index (OVX) by examining the presence of time‐varying jumps in OVX and by assessing the ability of OVX to predict the conditional variance of crude oil returns. Using a GARCH‐jump model, we find evidence that OVX is characterized by jump behaviour that tends to vary over time. Further analysis indicates that accounting for the jump behaviour of OVX helps improve the conditional variance forecasts of crude oil returns. Since the studied features of OVX play a crucial role in asset pricing and risk analyses, our findings have policy implications related to refining volatility prediction models and risk measures.

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confidence: 99%

Modelling the volatility of crude oil returns: Jumps and volatility forecasts

2020

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“…In addition, comparing the results of this study with other studies such as the study of Koutmos (2012) also shows similar result that models which use earnings ratio will mostly yield a positive relationship. The study of Zhang and Lan (2013) also finds that the model with the effect of realized volatility would provide better accuracy. In this study, the results of model 2 are significantly more effective than the results of model 1.…”

Section: Discussionmentioning

confidence: 98%

“… Source: The authors. Notes: *Continuous sample path variation and discontinuous jump variation are computed by using realized volatility concept (Zhang & Lan, 2013). (Example of the computation is shown in Appendix B.…”

Section: Methodsmentioning

confidence: 99%

“…The study finds it in jump process, and it could be compounded in the GARCH-type model by using the realized volatility. The volatility model that composes with realized volatility is based on jump process and continuous sample path, which shows that the model may yield better predictive power than models with no effect of realized volatility (Zhang & Lan, 2013).…”

Section: Jump and Realized Volatilitymentioning

confidence: 99%

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Appropriate Expected Return and the Relationship with Risk

Maneemaroj

Lonkani

Chingchayanurak

2019

Global Business Review

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Theoretically, risk-averse investors who expose to higher level of risks should expect to be compensated with more returns. However, prior studies empirically found inconclusive and often counterintuitive results. Motivated by advanced econometric techniques and choices of risk and return recently introduced in literature, we justify and choose the appropriate models and proxies to study the risk and return trade-off pattern in The Stock Exchange of Thailand. Instead of using historical returns mentioned in prior studies, this study chooses the earning ratio to proxy the expected return. For the risk and return trade-off testing model, we employ the ICAPM model with Glosten, Jagannathan and Runkle-GARCH (GJR-GARCH) and GJR with Continuous and Jump Variations (GJR-CJ) as volatility function. Consistent with the theory, results show that the baseline models can explain the positive relationship between risks and returns. By adding the effect of a rare event which can be estimated from the jump process of the data, the forecast accuracy is more improved. In addition, the results find that it is possible that this proxy can be applied in another model which is based on the mean and variance models. Finally, the results from the forecasting model and other models indicate that using an Earning to Price (E/P) ratio as a proxy for expected returns is more appropriate than using the historical return.

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“…For the study of volatility, scholars have focused on the GARCH models. Under different hypothesis distributions, Zhang and Lan [5] gave the GARCH-type model with continuous and jump variation for stock volatility and its empirical study in China. Zhou et al [6] studied the measurements accuracy of VaR based on GARCH models using spot transactions in Shanghai and London gold markets as objects, and they concluded that the Shanghai gold market was more risky than the London gold market.…”

Section: Introductionmentioning

confidence: 99%

Research on Risk of Stock Index Futures Market Based on EGARCH Model

Wang¹,

Zhu²

2019

dtem

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Volatility is an important indicator to measure the risk of financial market. The EGARCH model is established by using the logarithmic yield of the China Securities Index (CSI) 300 stock index futures from January 2014 to August 2017, then the volatility and yield of the data in the last three months are back tested to confirm the reliability of the model. Furthermore, based on the established EGARCH model, the future volatility and yield are forecasted, and the value at risk of stock index futures contract is calculated. At last, the error of our EGARCH model is determined by compare the predicted yield with actual value.

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GARCH‐Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

Cited by 5 publications

References 19 publications

Modelling the volatility of crude oil returns: Jumps and volatility forecasts

Modelling the volatility of crude oil returns: Jumps and volatility forecasts

Appropriate Expected Return and the Relationship with Risk

Research on Risk of Stock Index Futures Market Based on EGARCH Model

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