Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches

Yang, Ke; Tian, Fei; Chen, Langnan; Li, Steven

doi:10.1016/j.iref.2017.01.030

Cited by 33 publications

(15 citation statements)

References 58 publications

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“…Despite the recent evidence provided by policy institutions for the need of successful agricultural price volatility forecasts, the fact that modelling approaches for agricultural price volatility have developed for over 15 years now, as well as, the fact that the first effort to produce real out-of-sample forecasts was by Giot & Laurent (2003), we observe the paradox that there are only four other recent studies in this strand of the literature, those by Tian et al (2017a,b), Yang et al (2017), and Luo et al (2019).…”

Section: Introduction and Brief Review Of The Literaturementioning

confidence: 66%

“…Starting with the former study, Giot & Laurent (2003) focus on Cocoa, Coffee, and Sugar futures price volatility and use GARCH-type models to generate the forecasts. By contrast, Tian et al (2017a), Tian et al (2017b) and Yang et al (2017) utilize the increased availability of ultra-high frequency data and extend Corsi (2009) Heterogeneous AutoRegressive (HAR) model to produce short-run volatility forecasts (up to 20-days ahead).…”

Section: Introduction and Brief Review Of The Literaturementioning

confidence: 99%

“…They find evidence that regime switching dynamics offer predictive gains compared to both a simple AR(1) and a Markov-Switching AR(1) model. Yang et al (2017) also use intra-day data from the Chinese commodity futures markets (Zhenzhou Commodity Exchange and Dalian Commodity Exchange) of Soybean, Cotton, Gluten Wheat and Corn futures prices and employ a similar strategy with Tian et al (2017b), where the HAR model is extended with potential predictors (such as day-of-the-week dummies, past cumulative returns and the jump component) and forecasts are generated based on bagging and combination methods. Their conclusions suggest the forecasts based on the HAR models with bagging and principal component combination methods are able to outperform the AR model.…”

Section: Introduction and Brief Review Of The Literaturementioning

confidence: 99%

“…2017a),Tian et al (2017b),Yang et al (2017), andLuo et al (2019) who maintain that the jump component as well as the introduction of time-varying HAR coefficients help improving forecast accuracy of agricultural commodities price volatility. These results are not contradicting, however.…”

mentioning

confidence: 99%

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Forecasting realized volatility of agricultural commodities

Degiannakis

Filis

Klein

et al. 2022

International Journal of Forecasting

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We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous AutoRegressive (HAR) model. We obtain tick-by-tick data for five widely traded agricultural commodities (Corn, Rough Rice, Soybeans, Sugar, and Wheat) from the CME/ICE. Real out-of-sample forecasts are produced for 1-up to 66-days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we convincingly demonstrate that such HAR extensions do not offer any superior predictive ability in the out-of-sample results, since none of these extensions produce significantly better forecasts compared to the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit by adding more complexity, related to volatility decomposition or relative transformations of volatility, in the forecasting models.

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Section: Introduction and Brief Review Of The Literaturementioning

confidence: 66%

Section: Introduction and Brief Review Of The Literaturementioning

confidence: 99%

Section: Introduction and Brief Review Of The Literaturementioning

confidence: 99%

mentioning

confidence: 99%

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Forecasting realized volatility of agricultural commodities

Degiannakis

Filis

Klein

et al. 2022

International Journal of Forecasting

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show abstract

“…Among the copula functions, one can distinguish their particularly important class, namely, the copula of extreme values [27][28][29][30][31][32][33]. The use of two-dimensional extreme value copulas in empirical studies has been greatly simplified by using the representation introduced into the subject literature by J. Pickands in 1981 [34].…”

Section: Theoretical Models Of Extreme Value Copulas the Inference Fmentioning

confidence: 99%

Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function

Gródek-Szostak

Malik²,

Kajrunajtys

et al. 2019

Sustainability

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The purpose of the article is to identify and estimate the dependency model for the extreme prices of agricultural products listed on the Chicago Mercantile Exchange. The article presents the results of the first stage of research covering the time interval 1975–2010. The selected products are: Corn, soybean and wheat. The analysis of the dependency between extreme price values on the selected futures was based on the estimation of five models of two-dimensional extreme value copulas, namely, the Galambos copula, the Gumbel copula, the Husler–Reiss copula, the Tawn asymmetric copula and the t-EV copula. The next stage of the analysis was to test whether the structure of the dependency described with the estimated copulas is a sufficient approximation of reality, and whether it is suitable for modeling empirical data. The quality of matching the estimated copulas to empirical data of return rates of agricultural products was assessed. For this purpose, the Kendall coefficient was calculated, and the methodology of the empirical combining function was used. The conducted research allowed for the determination of the conduct for this kind of phenomena as it is crucial in the process of investing in derivatives markets. The analyzed phenomena are highly dependent on e.g., financial crises, war, or market speculation but also on drought, fires, rainfall, or even crop oversupply. The conducted analysis is of key importance in terms of balancing agricultural production on a global scale. It should be emphasized that conducting market analysis of agricultural products at the Chicago Mercantile Exchange in the context of competition with the agricultural market of the European Union is of significant importance.

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Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

Fang

Guan

et al. 2020

Journal of Forecasting

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Improving the prediction accuracy of agricultural product futures prices is important for the investors, agricultural producers and policy makers. This is to evade the risks and enable the government departments to formulate appropriate agricultural regulations and policies. This study employs Ensemble Empirical Mode Decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model were then compared with the benchmark individual models, SVM, NN, and ARIMA. Our main interest in this study is on the short-term forecasting, and thus we only consider 1-day and 3-days forecast horizons. The results indicated that the prediction performance of EEMD combined model is better than that of individual models, especially for the 3-days forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods to forecast high-frequency volatile components. However, there is no obvious difference between individual models in predicting the low-frequency components.

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Realized volatility forecast of agricultural futures using the HAR models with bagging and combination approaches

Cited by 33 publications

References 58 publications

Forecasting realized volatility of agricultural commodities

Forecasting realized volatility of agricultural commodities

Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function

Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

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