Forecasting the good and bad uncertainties of crude oil prices using a HAR framework

Gong, Xu; Lin, Boqiang

doi:10.1016/j.eneco.2017.08.035

Cited by 160 publications

(41 citation statements)

References 43 publications

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“…2 The earlier studies of Haugom et al (2014) , Sévi (2014) , Prokopczuk et al (2015) have led to the conclusion that all models fail to beat the forecast accuracy of the simple HAR-RV model which utilizes only the information embedded in past realized volatility, while incorporating structural breaks to the model is found to help improve the predictive performance ( Wen et al, 2016 ). Moreover, Gong and Lin (2017) indicate signed jumps contain forecasting information for good and bad RVs, derived from positive and negative oil returns respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To some extent, under the same theme, the role of various metrics capturing financial and oil market uncertainty and sentiment, has been stressed by numerous other studies including Gong and Lin (2018a) , Wen et al (2019) , Yang et al (2019) , and Bonato et al (2020) . Gong and Lin (2017) and Wen et al (2016) . At the same time, Liu et al (2018) and Chen et al (2019) argue that the benchmark HAR-RV model can be outperformed when considering the time-variation and asymmetric volatility jumps and co-jumps with the equity (S&P 500) market.…”

Section: Literature Reviewmentioning

confidence: 99%

“… 2 See for example, Haugom et al (2014) , Sévi (2014) , Prokopczuk et al (2015) , Wen et al (2016 , 2019) , Degiannakis and Filis (2017) , Gong and Lin (2017 , 2018a) , Liu et al (2018) , Chen et al (2019) , Yang et al (2019) , Bonato et al (2020) and Gkillas et al (2020) . …”

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confidence: 99%

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The predictive power of oil price shocks on realized volatility of oil: A note

et al. 2020

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This paper examines the predictive power of oil supply, demand and risk shocks over the realized volatility of intraday oil returns. Utilizing the heterogeneous autoregressive realized volatility (HAR-RV) framework, we show that all shock terms on their own, and particularly financial market driven risk shocks, significantly improve the forecasting performance of the benchmark HAR-RV model, both in- and out-of-sample. Incorporating all three shocks simultaneously in the HAR-RV model yields the largest forecasting gains compared to all other variants of the HAR-RV model, consistently at short-, medium-, and long forecasting horizons. The findings highlight the predictive information captured by disentangled oil price shocks in accurately forecasting oil market volatility, offering a valuable opening for investors and corporations to monitor oil market volatility using information on traded assets at high frequency.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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The predictive power of oil price shocks on realized volatility of oil: A note

et al. 2020

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“…And, more remarkably, the coefficients of GDP and its spatially lagged term in this model are negative. This may be attributable to the negative effect of GDP, which in fact it is quite common when using econometric models for empirical analysis (Wen et al, 2018[ 47 ]; Dai and Wen, 2018[ 48 ]; Gong and Lin, 2017[ 49 ])…”

Section: Empirical Analysismentioning

confidence: 99%

Effect of urban tourist satisfaction on urban macroeconomics in China: A spatial panel econometric analysis with a spatial Durbin model

Zhou¹,

Liu²,

Tang³

2018

PLoS ONE

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Tourist satisfaction has always been a crucial research issue in the tourism economy. This paper utilizes the Spatial Durbin Model (SDM) to analyze the impact of urban tourism satisfaction on urban macroeconomics from a macro perspective, using quarterly data on tourist satisfaction in 35 large and medium-sized cities along with major urban macroeconomic variables. This study is quite distinct from previous research that focused on constructing a tourist satisfaction index and analyzing the influence factors of tourism satisfaction from the perspective of the micro-level internal composition of tourism. The empirical results show: Firstly, in respect of the impact of urban tourists' satisfaction on the GDP income of cities, the SDM and the SDM with a lagged first-order dependent variable (SDM_dlag) show that the short-term and long-term indirect effects of log-tourist satisfaction are significantly positive, indicating that city satisfaction has a significant positive spatial spillover effect on GDP growth in other cities; Secondly, in respect of the influence of urban tourist satisfaction on the cost of urban life in the SDM, the long-term direct and indirect effects of logarithmic satisfaction are significantly positive, implying, in the long run, that tourist satisfaction has a positive intraregional spillover effect and spatial spillover effect on urban living costs; Finally, the SDM_dlag for the regression of urban tourist satisfaction on the cost of urban daily life shows that the short-run direct and indirect effects of city tourist satisfaction are significantly negative, indicating that tourist satisfaction has intra-regional and spatial spillover effects, and its rise will reduce the cost of living expenses in local and other cities in the short term. Overall, we have further elucidated the role of different levels of urban tourist satisfaction in city macroeconomics from the spatial dimension, thereby enriching the existing research on tourist satisfaction to some certain extent.

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“…Naser found that using the dynamic model averaging (DMA) with empirical evidence is better than linear models such as autoregressive (AR) model and its variants [5]. Gong and Lin proposed several new 2 Complexity heterogeneous autoregressive (HAR) models to forecast the good and bad uncertainties in crude oil prices [6]. Wen et al also used HAR models with structural breaks to forecast the volatility of crude oil futures [7].…”

Section: Introductionmentioning

confidence: 99%

A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices

Zhou

Shi

et al. 2019

Complexity

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Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices. However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices. To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices. Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue. Secondly, XGBOOST is used to predict each IMF and the residue individually. Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices. The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.

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Forecasting the good and bad uncertainties of crude oil prices using a HAR framework

Cited by 160 publications

References 43 publications

The predictive power of oil price shocks on realized volatility of oil: A note

The predictive power of oil price shocks on realized volatility of oil: A note

Effect of urban tourist satisfaction on urban macroeconomics in China: A spatial panel econometric analysis with a spatial Durbin model

A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices

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