Forecasting volatility in the petroleum futures markets: A re-examination and extension

Hasanov, Akram Shavkatovich; Shaiban, Mohammed Sharaf; Alfreedi, Ajab A.

doi:10.1016/j.eneco.2019.104626

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Forecasting volatility is an extremely arduous task which has garnered growing attention amongst the researchers ( Chatziantoniou et al, 2020 ; Hasanov, 2020 ; Cheng et al, 2021 ). Model based estimation of volatility through conventional and advanced econometric modelling, auto regressive conditional heteroscedasticity (ARCH), Generalized ARCH (GARCH), etc.…”

Section: Previous Literaturementioning

confidence: 99%

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Ghosh¹,

Sanyal²

2021

International Journal of Information Management Data Insights

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Financial markets across the globe have seen rapid volatility and uncertainty owing to scary and disruptive impacts of COVID-19 pandemic. Mayhem wrecked by frequent lockdowns, curfews, emergencies, etc. has stoked the high quantum of chaotic movement in equity markets and resulted in perplexed investor behaviour. It, therefore, is of paramount practical relevance to measure predictability of market fear at such a crucial juncture of time. Market fear can effectively be measured in terms of implied and historic volatility of equity markets. The present study chooses India VIX and 20-day rolling standard deviation of NIFTY returns to account for implied and historic volatility respectively during the ongoing COVID-19 timeline. Pertinent macroeconomic constructs, technical indicators and Google search volume index on meaningful keywords have been chosen as raw explanatory features for inspecting predictability. Boruta feature selection methodology has been used in a supervised manner to select significant features. State-Of-The-Art machine and deep learning algorithms namely Gradient Boosting (GB), Extra Tree Regression (ERT), Deep Neural Network (DNN), Long Short Term Memory Network (LSTM) are then used on processed feature set to scrupulously evaluate the quantum of predictability of said assets. The integrated predictive frameworks have been subjected to a battery of numerical and statistical checks to draw inferences. Additionally, Explainable AI frameworks have been used to analyse the nature of influence of respective features. Findings indeed suggest that despite exhibiting high degree of volatile traits, both India VIX and historic volatility can be predicted utilizing the proposed architectures effectively and serve practical actionable insights.

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Section: Previous Literaturementioning

confidence: 99%

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Ghosh¹,

Sanyal²

2021

International Journal of Information Management Data Insights

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“…According to Hansen et al (2011), MCS entails performing a series of statistical tests to generate a ‘superior set of models (SSM)’. Notably, MCS is mainly used to evaluate the performance of volatility forecasting models (for more details, see Charles & Darné, 2017; Hasanov et al, 2020; Qu et al, 2018; Zhang et al, 2019). One appealing characteristic of the MCS procedure is that the statistic test is estimated for an arbitrary loss function, enabling the user to build a loss function suited to the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

Tail risk of coal futures in China's market

Shen

Wang

Wan

2022

Accounting & Finance

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China has been performing a significant role in the world's energy market, and the coal sector is a dominant part of China's energy market. According to the China Statistical Yearbook 2020 compiled by the National Bureau of Statistics, the total domestic primary energy production and consumption (10,000 tce) by 2019 are 397,000 and 487,000, while raw coal accounts for 68.6% and 57.7%, respectively. Therefore, appraising the Chinese coal market and providing implications to the market participants and policymakers are of great importance.The main feature of China's coal market is price fluctuation, which drives a speculative and risky market. Various factors contribute to the volatility of coal prices. One of the essential factors is China's supply-side reforms in the coal sector that reduce overcapacity, which requires the phase-out of enormous amounts of backward production capacity starting from 2016, resulting in a sharp decline in coal supply and a surge in coal futures. Another vital issue is importing. For instance, Indonesia, which supplies over 60% of China's coal, declared a ban on coal exports in January 2022, triggering a price spike in China's thermal coal futures.

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“…Yan et al (2018) and Ames et al (2020) discuss other impact factors on crude oil futures prices. By contrast, more scholars focus on the study of the volatility of crude oil futures market (Agnolucci, 2009;Ergen and Rizvanoghlu, 2016;Hasanov et al, 2020;Joët et al, 2017;Kang and Yoon, 2013;Liu et al, 2018;Zhang et al, 2019). Undoubtedly, exploring the sources of the crude oil futures market volatility is crucial to energy researchers, financial practitioners and policy makers.…”

Section: Introductionmentioning

confidence: 99%

The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model

Dai,

Xiong,

Zhou

2020

Preprint

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This paper aims to examine whether the global economic policy uncertainty (GEPU) and uncertainty changes have different impacts on crude oil futures volatility. We establish single-factor and two-factor models under the GARCH-MIDAS framework to investigate the predictive power of GEPU and GEPU changes excluding and including realized volatility. The findings show that the models with rolling-window specification perform better than those with fixedspan specification. For single-factor models, the GEPU index and its changes, as well as realized volatility, are consistent effective factors in predicting the volatility of crude oil futures. Specially, GEPU changes have stronger predictive power than the GEPU index. For two-factor models, GEPU is not an effective forecast factor for the volatility of WTI crude oil futures or Brent crude oil futures. The two-factor model with GEPU changes contains more information and exhibits stronger forecasting ability for crude oil futures market volatility than the single-factor models. The GEPU changes are indeed the main source of long-term volatility of the crude oil futures.

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Forecasting volatility in the petroleum futures markets: A re-examination and extension

Cited by 9 publications

References 35 publications

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI

Tail risk of coal futures in China's market

The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model

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