Realized Volatility Forecasting of Agricultural Commodity Futures Using Long Memory and Regime Switching

Tian, Fei; Yang, Ke; Chen, Langnan

doi:10.1002/for.2443

Cited by 18 publications

(23 citation statements)

References 42 publications

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“…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%

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: 99%

Forecasting realized volatility of agricultural commodities

Degiannakis

Filis

Klein

et al. 2022

International Journal of Forecasting

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show abstract

“…There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013). There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013).…”

Section: Preliminary Datamentioning

confidence: 99%

“…In this article, we choose four common agricultural futures-corn, wheat, cotton, and soybean-and the CSI300 index to represent the Chinese agricultural futures market and the stock market. There are two essential reasons for this choice: (a) Corn, wheat, cotton, and soybean futures are very active in China, and the same choice is also made by Li, Zhang, and Zhou (2017), Jiang et al (2016), Tian et al (2017aTian et al ( , 2017b, Yang, Yang, and Zhou (2012), and Zhang and Qu (2015); and (b) the CSI300 index is a well-applied prospect, because it is commonly used as a representative index to measure the overall performance of the Chinese stock market (Chen, Han, Li, & Wu, 2013). The raw transaction prices are obtained from the Wind Financial Terminal in Chinese markets during the period from April 8, 2005, to June 31, 2014.…”

Section: Preliminary Datamentioning

confidence: 99%

“…The first contribution of this paper is that it considers cojumps within the agricultural futures market, based on the corn, wheat, cotton, and soybean commodities, and cojumps between the agricultural futures market and the stock market to explore cojumps' predictive ability. There are two main motivations for this research: (a) In recent years, commodity markets have received more attention from scholars and practitioners-for example, Kellard, Newbold, Rayner, and Ennew (1999), Tomek and Peterson (2001), Sørensen (2002), Tang and Xiong (2012), Anderson, Rausser, and Swinnen (2013), Nazlioglu, Erdem, and Soytas (2013), Jiang, Su, Todorova, and Roca (2016), Le Pen and Sévi (2017), Tan and Ma (2017), Tian, Yang, and Chen (Tian, Yang, & Chen, 2017a;Tian, Yang, & Chen, 2017b), Bakas and Triantafyllou (2018), Du (2018), Gong and Lin (2018), and Wu, Dorfman, and Karali (2018); and (b) as documented by Le Pen and Sévi (2017), commodity markets are now more closely related to the financial market. Moreover, the existing literature (see, e.g., Berger & Uddin, 2016;Büyükşahin & Robe, 2014;Hammoudeh, Nguyen, Reboredo, & Wen, 2014) indicates that the dependence between equities and commodities becomes stronger in market turmoil, especially after the Lehman-filed bankruptcy.…”

Section: Introductionmentioning

confidence: 99%

“…As we know, agricultural commodity futures have received more attention in academia-for example, Kellard et al (1999), Tomek and Peterson (2001), Sørensen (2002), Anderson et al (2013), Nazlioglu et al (2013), Jiang et al (2016, Tan and Ma (2017), Tian et al (2017aTian et al ( , 2017b, Du (2018), and Gong and Lin (2018). However, none of these studies has investigated cojumps between the agricultural futures market and the stock market, nor explored cojumps' abilities to predict stock market volatility.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

Zhang

Wahab

et al. 2019

Journal of Forecasting

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In this study, we explore the effect of cojumps within the agricultural futures market, and cojumps between the agricultural futures market and the stock market, on stock volatility forecasting. Also, we take into account large and small components of cojumps. We have several noteworthy findings. First, large jumps may lead to more substantial fluctuations and are more powerful than small jumps. The effect of cojumps and their decompositions on future volatility are mixed. Second, a model including large and small cojumps between the agricultural futures market and the stock market can achieve a higher forecasting accuracy, implying that large and small cojumps contain more useful predictive information than cojumps themselves. Third, our conclusions are robust based on various robustness tests such as the realized kernel, expanding forecasts, different forecasting windows, different jump tests, and different threshold values.

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Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach

Wen

Zhang

et al. 2021

Journal of Forecasting

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In this study, we propose a new family of the heterogeneous autoregressive realized volatility (HAR-RV) models by considering truncated methods for predicting the RV in China's stock market. By adopting three types of critical values to recognize extremely large values of RV, we show that the modified models are simple but efficient to consistently deliver stronger in-sample and out-of-sample forecasting performances than those of existing methods. Models that take truncated approaches into account can generate substantial economic gains in applications. We further provide evidence that the superiority of our proposed models is derived from the reduced variance of the measurement errors during days including truncated RVs. Additionally, the improved performances of the modified models still hold after considering the effects of jump components and leverage, as well as a wide range of extensions and robustness analyses. K E Y W O R D SChina's stock market, HAR-RV model, truncated method, volatility forecasting | INTRODUCTIONVolatility and accurate forecasts of volatility in the stock market are crucial for asset pricing and risk management. Given the helpful role of high-frequency data in forecasting volatility, realized volatility (RV) based on intraday data, as pioneered by Andersen and Bollerslev (1998), receives much attention with regard to modeling and predicting financial market volatility (see, e.g.,

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Realized Volatility Forecasting of Agricultural Commodity Futures Using Long Memory and Regime Switching

Cited by 18 publications

References 42 publications

Forecasting realized volatility of agricultural commodities

Forecasting realized volatility of agricultural commodities

The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach

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