Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks

Ma, Feng; Liao, Yin; Zhang, Yaojie; Cao, Yang

doi:10.1016/j.jempfin.2019.01.004

Cited by 167 publications

(67 citation statements)

References 40 publications

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“…Following Ma et al. [ 38 ];

within the function

is set to be 1. Thus, it is the parameter

that only determines the weighted values of RV lags.…”

Section: Methodsmentioning

confidence: 99%

“…Differently, following Breitung and Roling (2015), Ma et al. [ 38 ]; we set

equal to 40 and60 8 in this part, and further carry out out-of-sample prediction. The results of alternative

values are presented in Table 8 .…”

Section: Empirical Designmentioning

confidence: 99%

“…First, MIDAS-RV model is capable of better reflecting heterogeneity than the HAR-RV model because HAR-RV model is a special evolution from the MIDAS-RV model [ 20 , 21 ]; 2009). In addition, MIDAS-RV model is popularly applied and practical applications further confirm that MIDAS models obtain more powerful forecasting performances than other models [ 3 , 38 , 49 ]. For instance, Santos and Ziegelmann [ 49 ] find MIDAS prediction is superior to several multi-period volatility forecasting models such as HAR, MIDAS and their combination methods.…”

Section: Introductionmentioning

confidence: 99%

“…Ma et al. [ 38 ] find the MIDAS-RV may better reflect heterogeneity than the HAR-RV model. As far as we know, only two literature predict intraday oil futures volatility relying on MIDAS framework [ 16 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

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Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

Wang

et al. 2020

Energy

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This study evaluates whether CBOE crude oil volatility index (OVX) owns forecasting ability for China’s oil futures volatility using Markov-regime mixed data sampling (MS-MIDAS) models. In-sample empirical result shows that, OVX can significantly lead to high future short-term, middle-term and long-term volatilities with regard to Chinese oil futures market. Moreover, our proposed model, the Markov-regime MIDAS with including the OVX (MS-MIDAS-RV-OVX), significantly outperforms the MIDAS and other competing models. Unsurprising results further confirm that OVX indeed contain predictive information for oil realized volatility (especially significant and robust in middle-term and long-term horizons) and regime switching is useful to deal with the structural break within the energy market. We carry out economic value analysis and discuss OVX’s asymmetric effects concerning different trading hours and good (bad) OVX, and find OVX performs better in day-time trading hours and the good OVX is more predictive for the oil futures RV than the bad OVX. The further discussion also confirms our previous conclusions are robust during the highly volatile period of the COVID-19 pandemic.

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“…Following Ma et al. [ 38 ];

within the function

is set to be 1. Thus, it is the parameter

that only determines the weighted values of RV lags.…”

Section: Methodsmentioning

confidence: 99%

“…Differently, following Breitung and Roling (2015), Ma et al. [ 38 ]; we set

equal to 40 and60 8 in this part, and further carry out out-of-sample prediction. The results of alternative

values are presented in Table 8 .…”

Section: Empirical Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

Wang

et al. 2020

Energy

Self Cite

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“…The modeling and forecasting of stock market volatility has always been a hotspot and difficulty in academic research (see, e.g., Wei et al., 2010 ; Wen et al., 2016 ; Hong and Lee, 2017 ; Ma et al., 2019 ; Liang et al., 2020a , 2020b ; Zhang et al., 2020b ). At the same time, it is very important for risk management and option pricing in practical applications.…”

Section: Introductionmentioning

confidence: 99%

The role of the IDEMV in predicting European stock market volatility during the COVID-19 pandemic

Liang

et al. 2020

Finance Research Letters

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Highlights We investigate the predictive power of the IDEMV and VIX for the European stock indices volatilities during the COVID-19 pandemic. The IDEMV has stronger predictive power for the France and UK stock markets volatilities during the COVID-19 pandemic. The VIX has superior predictive ability for the three European stock markets during the COVID-19 pandemic.

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Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects

Wang

Liu

2020

Journal of Forecasting

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This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH‐MIDAS‐ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short‐ and long‐term effects caused by extreme events, we extend the standard GARCH‐MIDAS model to characterize the different responses of the stock market for short‐ and long‐term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in‐sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH‐MIDAS‐SLES model by including the short‐ and long‐term impacts of extreme shocks for all forecasting horizons. The out‐of‐sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short‐ and long‐term effects to improve the accuracy of the DJIA volatility forecasts.

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Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks

Cited by 167 publications

References 40 publications

Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models

The role of the IDEMV in predicting European stock market volatility during the COVID-19 pandemic

Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects

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