Forecasting oil price volatility: Forecast combination versus shrinkage method

Zhang, Yaojie; Wei, Yu; Zhang, Yi; Jin, Daxiang

doi:10.1016/j.eneco.2019.01.010

Cited by 155 publications

(68 citation statements)

References 43 publications

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“…It is obvious that a model with a larger MCS p ‐value is likely to have better forecasting power. Following Hansen et al (2011) and Zhang, Wei, et al, 2019), among others, we consider a significance level of 10%. That is, models with an MCS p ‐value larger than 0.1 are included in the MCS.…”

Section: Resultsmentioning

confidence: 99%

Is implied volatility more informative for forecasting realized volatility: An international perspective

Liang

Wei

Zhang

2020

Journal of Forecasting

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Inspired by the commonly held view that international stock market volatility is equivalent to cross-market information flow, we propose various ways of constructing two types of information flow, based on realized volatility (RV) and implied volatility (IV), in multiple international markets. We focus on the RVs derived from the intraday prices of eight international stock markets and use a heterogeneous autoregressive framework to forecast the future volatility of each market for 1 day to 22 days ahead. Our Diebold-Mariano tests provide strong evidence that information flow with IV enhances the accuracy of forecasting international RVs over all of the prediction horizons. The results of a model confidence set test show that a market's own IV and the first principal component of the international IVs exhibit the strongest predictive ability. In addition, the use of information flows with IV can further increase economic returns. Our results are supported by the findings of a wide range of robustness checks.

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Section: Resultsmentioning

confidence: 99%

Is implied volatility more informative for forecasting realized volatility: An international perspective

Liang

Wei

Zhang

2020

Journal of Forecasting

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“…Degiannakis and Filis [65] opine that Direction-of-Change (DoC) is the core of market timing and portfolio trading strategies. us, following Degiannakis and Filis [65] and Zhang et al [59], we adopt the Direction-of-Change (DoC) test as another model evaluation approach. In detail, DoC is a ratio that accounts for the accurate predictions to the total predictions in the direction of a forecasted variable by a model.…”

Section: Robustness Checks Of Model Forecasting Resultsmentioning

confidence: 99%

“…However, the two loss functions discussed above can hardly offer us the significance levels of forecasting difference among various models. erefore, this paper utilizes the model confidence set (MCS) test which is proposed by Hansen et al [58] and widely used in recent research studies [24,25,59], to achieve this goal and to determine the superior models. e MCS test is developed from several traditional and standard model evaluation methods [60][61][62][63] but with more obvious advantages over these traditional ones.…”

Section: Model Evaluationmentioning

confidence: 99%

Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China

Zhang

Chen

2020

Discrete Dynamics in Nature and Society

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Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.

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“…Further, we implement least absolute shrinkage and selection operator (LASSO) shrinkage method to handle all the considered endogenous and exogenous explanatory factors and select the most powerful influential factors automatically. Although previous studies [6,[29][30][31][32][33] simulate that LASSObased approaches show better out-of-sample forecasts and surpass both AR class models and time-varying parameter models, it is unclear whether LASSO operator is also outperforming other commonly used benchmark models in oil price forecasting. Examining the LASSO operator effectiveness may help oil market decision-makers identify significant influential indicators efficiently and seize investment opportunities.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models

Liu

Wei

Liu

et al. 2020

Discrete Dynamics in Nature and Society

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The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying parameter (TVP) predictive regression should be employed in each out-of-sample forecasting step. This newly developed method has the advantages of both model shrinkage and automatic switch between CC and TVP forecasting models; thus, this may produce more accurate predictions of crude oil prices. The empirical results show that this hierarchical shrinkage model can outperform many commonly used forecasting benchmark methods, such as AR, unobserved components stochastic volatility (UCSV), and multivariate regression models in forecasting crude oil price on various forecasting horizons.

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Forecasting oil price volatility: Forecast combination versus shrinkage method

Cited by 155 publications

References 43 publications

Is implied volatility more informative for forecasting realized volatility: An international perspective

Is implied volatility more informative for forecasting realized volatility: An international perspective

Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China

Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models

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