Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

Chang, Chia‐Lin; McAleer, Michael; Tansuchat, Roengchai

doi:10.1142/s2010495212500108

Cited by 29 publications

(29 citation statements)

References 34 publications

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“…Extended from the family of GARCH models, Baillie et al [76] proposed the FIGARCH model, which provides additional features for volatility clustering with good in-sample estimates [65,66]. Chang et al [77] suggest that the FIGARCH(1,d,1) model outperforms its GARCH(1,1) counterpart (see also Ho et al [14]). Since the introduction of the model, many significant empirical studies on long memory have emerged in the existing literature [14,[78][79][80][81].…”

Section: Methodology and Model Specificationmentioning

confidence: 99%

Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market

2018

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This paper examines the impact of firms' sustainability engagement on their stock returns and volatility by employing the EGARCH and FIGARCH models using data from the major financial firms listed in the Chinese stock market. We find evidence of a positive association between sustainability engagement and stock returns, suggesting firms' sustainability news release in favour of the market. Although volatility persistence can largely be explained by news flows, the results show that sustainability news release has the significant and largest drop in volatility persistence, followed by popularity in Google search engine and the general news. Sustainability news release is found to affect positively stock return volatility. We also find evidence that market expectation can be driven by the dominant social paradigm when sustainability is included. These findings have important implications for market efficiency and effective portfolio management decisions.

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Section: Methodology and Model Specificationmentioning

confidence: 99%

Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market

2018

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“…The study suggests the existence of a seasonal pattern in convenience yields and volatility, in line with the storage theory. Chang et al (2012) examined a long memory volatility model for 16 agricultural commodity futures. The empirical results are obtained using unit root tests, GARCH, EGARCH, APARCH, FIGARCH, FIEGARCH, and FIAPARCH model.…”

Section: Literature Reviewmentioning

confidence: 99%

The volatility of returns from commodity futures: evidence from India

Mukherjee

Goswami

2017

Financ Innov

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“…Traditionally, the fractionally integrated generalized autoregressive conditional heteroscedasticity (GARCH) class models have been utilized to forecast the volatility of agricultural commodity futures in the literature. As long memory is found in most volatility series for agricultural commodity futures, the introduction of fractional integration will improve the model's forecast performance (see Crato and Ray, 2000;Jin and Frechette, 2004;Baillie and Kapetanios, 2007;Coakley et al, 2008;Hyun-Joung, 2008;Sephton, 2009;Chang et al, 2012). However, due to the unobservable nature of the volatility, these studies treat the volatility of agricultural commodity futures as a latent process in the GARCH class models.…”

Section: Introductionmentioning

confidence: 99%

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

Tian

Yang

Chen

2016

Journal of Forecasting

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We investigate the dynamic properties of the realized volatility of five agricultural commodity futures by employing the high‐frequency data from Chinese markets and find that the realized volatility exhibits both long memory and regime switching. To capture these properties simultaneously, we utilize a Markov switching autoregressive fractionally integrated moving average (MS‐ARFIMA) model to forecast the realized volatility by combining the long memory process with regime switching component, and compare its forecast performances with the competing models at various horizons. The full‐sample estimation results show that the dynamics of the realized volatility of agricultural commodity futures are characterized by two levels of long memory: one associated with the low‐volatility regime and the other with the high‐volatility regime, and the probability to stay in the low‐volatility regime is higher than that in the high‐volatility regime. The out‐of‐sample volatility forecast results show that the combination of long memory with switching regimes improves the performance of realized volatility forecast, and the proposed model represents a superior out‐of‐sample realized volatility forecast to the competing models. Copyright © 2016 John Wiley & Sons, Ltd.

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Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

Cited by 29 publications

References 34 publications

Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market

Does Sustainability Engagement Affect Stock Return Volatility? Evidence from the Chinese Financial Market

The volatility of returns from commodity futures: evidence from India

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

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