SPI-based drought simulation and prediction using ARMA-GARCH model

Liu, Qi; Zhang, Guanlan; Ali, Shahzad; Wang, Xiaopeng; Wang, Guodong; Pan, Zhenkuan; Zhang, Jiahua

doi:10.1016/j.amc.2019.02.058

Cited by 37 publications

(22 citation statements)

References 27 publications

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“…Our models are extensions of ARMA-GARCH models, which have the advantages of no need for exogenous variables to predict volatilities. ARMA-GARCH models are used extensively in forecasting volatilities in different areas, including finance, real estate and weather ( Liu et al, 2019 ; Apergis et al, 2020 ; Zou et al, 2020 ). Our models are fitted to futures returns of oil, soybean, copper and gold, which are the most actively traded representative futures in energy, agricultural, industrial and precious metal commodity classes, respectively, with a data period from Jan 1, 2019 to June 30, 2021.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 impact on commodity futures volatilities

Zhang

RuiZhi

2022

Finance Research Letters

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COVID-19 pandemic has affected almost all aspects of the global economy, especially commodity futures markets, due to the disruption risk of global supply chains from the pandemic lockdown. This paper extends ARMA-GARCH models to investigate the pandemic impact on both long-run and short-term volatilities of four major commodity futures. Model-fitting results reveal that the pandemic event has enhanced long-run volatilities for all futures returns, while the daily COVID-19 infection speed has mixed effects on short-term (instantaneous) volatilities. Our extended models and research findings are useful in global supply chain risk management, commodity options trading and regulators’ supervision of inflation risk.

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Section: Introductionmentioning

confidence: 99%

COVID-19 impact on commodity futures volatilities

Zhang

RuiZhi

2022

Finance Research Letters

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“…However, these take an 'impact' approach to observe what is happening in response to drying conditions, rather than forecasting the meteorological conditions that either cause or break a drought. A promising method that may provide this missing link is using the state of ocean-atmosphere climate drivers and local climate variables to develop statistical models and relationships with rainfall anomalies and drought indices (van Dijk et al 2013;Sahin 2015a, 2015b;Mera et al 2018;Liu et al 2019). This relies on an adequate understanding of the regional drought characteristics and the host of potential climate influences specific to that region.…”

Section: Introductionmentioning

confidence: 99%

Characterising the seasonal nature of meteorological drought onset and termination across Australia

Gibson

Verdon‐Kidd

Hancock

2022

J. South. Hemisph. Earth Syst. Sci.

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Drought, and its associated impacts, represents one of the costliest natural hazards worldwide, highlighting the need for prediction and preparedness. While advancements have been made in monitoring current droughts, prediction of onset and termination have proven to be much more challenging. This is because drought is unlike any other natural hazard and cannot be characterised by a single weather event. There is also a high degree of spatial variability in this phenomenon across the vast expanse of the Australian continent. Therefore, by characterising regionally specific expressions of drought, we may improve drought predictability. In this study, we analyse the timing of onset and termination of meteorological droughts across Australia from 1900 to 2015, as well as their local and regional climate controls. We show that meteorological drought onset has a strong seasonal signature across Australia that varies spatially, whereas termination is less seasonally restricted. Using a Random Forest modelling approach with predictor variables representative of large-scale ocean-atmosphere phenomena and local climate, up to 75% of the variance in the Standardised Precipitation Index during both onset and termination could be explained. This study offers support to continued development in longlead forecasting of local and large-scale ocean/atmosphere conditions to improve drought prediction in Australia and elsewhere.

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“…The autoregressive moving‐average (ARMA) model is a commonly used random time‐series analysis method that can make short‐term predictions for the series with certain regular changes, 32 and the model had been widely applied and verified in the fields of economics, 33,34 wind speed, 35 and drought 36 . VIs were indicators of crop dynamic growth, and the long‐term VIs sequence of annual crops had good periodic changes 37 .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy of ARMA and multi‐index methods for predicting winter wheat maturity date

Wang

Shen

et al. 2021

J Sci Food Agric

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BACKGROUND: Accurate and timely prediction of regional winter wheat maturity date can provide essential information to improve the management of agriculture and avoid declines in the yield and quality of crops. In this paper, we propose the use of an autoregressive moving-average model to predict vegetation indices on 1, 9, and 17 May each year, and applied them to the methods of evaluating crop maturity based on vegetation indices. Growing degree days and a widely applied local empirical method were selected to explore and compare the feasibility of several methods. We analyzed winter wheat harvested from the Guanzhong Plain during 2003-2013 and used leave-one-out cross-validation to compare and verify the performance of the maturity prediction methods.RESULTS: The results demonstrated that (i) the vegetation index methods and growing degree days methods predicted maturity with higher accuracy than did the widely applied local empirical method, and (ii) the two-step filtering method based on future meteorological data from The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble exhibited the highest prediction accuracy on 1 May and had the lowest error fluctuation range on 17 May.CONCLUSION: These results provide new insights for predicting regional crop maturity, deploying agricultural harvesting equipment in various regions, and avoiding decreases in crop yields caused by adverse weather.

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SPI-based drought simulation and prediction using ARMA-GARCH model

Cited by 37 publications

References 27 publications

COVID-19 impact on commodity futures volatilities

COVID-19 impact on commodity futures volatilities

Characterising the seasonal nature of meteorological drought onset and termination across Australia

Evaluating the accuracy of ARMA and multi‐index methods for predicting winter wheat maturity date

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