Forecasting volatility in the biofuel feedstock markets in the presence of structural breaks: A comparison of alternative distribution functions

Hasanov, Akram Shavkatovich; Poon, Wai Ching; Alfreedi, Ajab A.; Heng, Zin Yau

doi:10.1016/j.eneco.2018.01.011

Cited by 13 publications

(5 citation statements)

References 59 publications

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“…Following Charles and Darné (2017), Hasanov, Poon, Al‐Freedi, and Heng (2018), Wei et al (2017) and Zhang, Wei, Zhang, and Jin (2019), we utilized the model confidence set (MCS) test proposed by Hansen, Lunde, and Nason (2011) as one of the evaluation methods to assess the out‐of‐sample forecasting performance of all employed predictive methods. The MCS test is developed from several traditional and standard model evaluation methods (Diebold & Mariano, 1995; Hansen & Lunde, 2005; West, 1996; White, 2000) but with more obvious advantages.…”

Section: Methodsmentioning

confidence: 99%

Which uncertainty is powerful to forecast crude oil market volatility? New evidence

Wei

Chen

et al. 2020

Int. J Fin Econ

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This paper focuses on distinguishing the predictive power of five newly developed uncertainty indices, that is, global and US economic policy uncertainty (GEPU and US EPU), global geopolitical risk (GPR), US monetary policy uncertainty (MPU) and equity market volatility (EMV), on crude oil market volatility. We construct the extended GARCH‐MIDAS models to assess the forecasting ability of these indices and then compare them with four commonly used oil volatility drivers: oil supply, oil demand, oil speculation and interest rate. Firstly, the in‐sample analysis suggests that all uncertainty indices and traditional determinants have significant impacts on oil volatility. Secondly, the out‐of‐sample evaluations suggest that US MPU, EMV and EPU indices are more powerful than other predictors to improve the forecasting accuracy of crude oil volatility, and US EMV exhibits the best prediction ability. Then, we find that EPU and MPU indices are more effective in forecasting high volatility in crude oil market, while traditional determinants together with US EMV index are more helpful in predicting low oil volatility. Finally, the forecasts of bad oil volatility can be improved by all predictors except for GEPU index, while the forecasts of good oil volatility can only be enhanced by US MPU, EMV and EPU.

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Section: Methodsmentioning

confidence: 99%

Which uncertainty is powerful to forecast crude oil market volatility? New evidence

Wei

Chen

et al. 2020

Int. J Fin Econ

View full text Add to dashboard Cite

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“…This will be the case when we consider the inventories in level or in difference as covariates. Furthermore, Hasanov et al (2018) found no clear-cut dominance for the innovation distribution, although Gaussian and skewed-Gaussian distributions were among the best for most biofuel-related commodities. In this study, we retained an asymmetrical GARCH-based model with a Gaussian distribution and fundamental covariates both in the conditional variance and mean without structural changes so as to simplify multivariate estimations and interpretation of empirical results.…”

Section: Econometric Modelmentioning

confidence: 89%

“…However, neither RSO nor PMO were included. Finally, Hasanov et al (2018), who concluded that an asymmetric generalised autoregressive conditional heteroskedasticity (GARCH) model with structural breaks should be considered for agricultural markets, only studied univariate time series and considered soybeans instead of SBO.…”

Section: Introductionmentioning

confidence: 99%

On the dependence structure of European vegetable oil markets

Menier,

Bagnarosa,

Gohin

2023

Applied Economics

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Motivated by the current context of high and volatile energy and food prices, we examine the conditional variance-covariance matrix dynamics and price discovery process characterising European vegetable oils. In addition to outcomes differences using daily or monthly datasets, we find that both the marginal distributions and volatility dependence structures relate to distinct economic fundamentals. The energy price mainly influences the vegetable oil trend component, and stock-to-use ratios are crucial for variance-covariance dynamics once their particular cold filter plugging point is considered. This latter point also reveals interesting interplays among vegetable oils according to the economic concepts of complementarity and substitutability.

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“…One group of empirical studies has been directed toward modelling and forecasting commodity volatility as an important issue in asset allocation, asset pricing, and financial risk management (see, among others, [1][2][3][4][5][6][7][8]). A considerable number of these studies have examined the predictive power of distinctive volatility measures, using conditional volatility, such as GARCH family models [2,4] or realized volatility (RV) [1,3].…”

Section: Literature Reviewmentioning

confidence: 99%

Commodity and Equity Markets: Volatility and Return Spillovers

Pinho

Maldonado

2022

Commodities

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The present paper provides an empirical analysis of the relationship between shocks to commodity markets and stock markets. By employing a total volatility connectedness measure, we study the relationship between shocks to oil, gold, copper, and agricultural commodity markets and emerging and developed stock markets. We conduct a connectivity analysis in the time and frequency domain to quantify market linkages using volatility spillovers over the period from 2004 to 2021. In addition, we analyze the spillovers of returns in these markets over the same period. The results suggest that both on volatility and returns spillovers, slightly more than 35% of the total variance of forecast errors is explained by shocks to markets during the period January 2004 to June 2021. We also show that, in terms of both volatility and returns, the contribution of equity market shocks to other markets is substantially more important than that of commodities; however, our analysis reveals that the total link between market returns is larger in the short run than in the long run, while in the case of volatility, the long-run frequencies concentrate the market link. Additionally, we use dynamic analysis to assess both the time evolution of total connectivity and all directional partial connectivity between markets. Our results show that both volatility and return linkages change significantly over time and that a set of events has a significant impact on them.

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Forecasting volatility in the biofuel feedstock markets in the presence of structural breaks: A comparison of alternative distribution functions

Cited by 13 publications

References 59 publications

Which uncertainty is powerful to forecast crude oil market volatility? New evidence

Which uncertainty is powerful to forecast crude oil market volatility? New evidence

On the dependence structure of European vegetable oil markets

Commodity and Equity Markets: Volatility and Return Spillovers

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