IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v355y2019icp96-107.html
   My bibliography  Save this article

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

Author

Listed:
  • liu, Qi
  • Zhang, Guanlan
  • Ali, Shahzad
  • Wang, Xiaopeng
  • Wang, Guodong
  • Pan, Zhenkuan
  • Zhang, Jiahua

Abstract

Drought is one of the most frequent climate-related disasters occurring in North China Plain. The accurate and timely information of drought is vital for crop production and food security. In this study, the monthly precipitation data during 1965–2015 was used to calculate the Standardized Precipitation Index (SPI) with a time scale of 9 months (SPI-9) at five stations in Shandong Province, North China Plain. The GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model was adopted to eliminate the heteroskedasticity (ARCH effect) in the residuals of ARMA (Autoregressive and Moving Average) model, and the two models were combined into a composite model called ARMA-GARCH model. Both ARMA and ARMA-GARCH models were used to simulate SPI-9 drought index, and the results of comparison between two models showed that the ARMA-GARCH model performed better. Furthermore, the two models were used to predict SPI-9, the result showed that the accuracy of the ARMA-GARCH model is much higher than that of the ARMA model; for maintaining the stability of site-to-site correlation, the ARMA-GARCH model also outperformed the ARMA model. The research indicates that the ARMA-GARCH model could be used to more accurately simulate and predict SPI-9 drought index.

Suggested Citation

  • liu, Qi & Zhang, Guanlan & Ali, Shahzad & Wang, Xiaopeng & Wang, Guodong & Pan, Zhenkuan & Zhang, Jiahua, 2019. "SPI-based drought simulation and prediction using ARMA-GARCH model," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 96-107.
    • Handle: RePEc:eee:apmaco:v:355:y:2019:i:c:p:96-107
    DOI: 10.1016/j.amc.2019.02.058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300319301626
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2019.02.058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Schwert, G. William, 1987. "Effects of model specification on tests for unit roots in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 20(1), pages 73-103, July.
    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Yongmin & Wang, Ruizhi, 2022. "COVID-19 impact on commodity futures volatilities," Finance Research Letters, Elsevier, vol. 47(PA).
    2. Wilson Kalisa & Tertsea Igbawua & Fanan Ujoh & Igbalumun S. Aondoakaa & Jean Nepomuscene Namugize & Jiahua Zhang, 2021. "Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2047-2076, April.
    3. Abdol Rassoul Zarei & Mohammad Reza Mahmoudi, 2020. "Ability Assessment of the Stationary and Cyclostationary Time Series Models to Predict Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 5009-5029, December.
    4. Levent Latifoğlu & Mehmet Özger, 2023. "A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction," Sustainability, MDPI, vol. 15(19), pages 1-29, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kerry Patterson & Michael A. Thornton, 2013. "A review of econometric concepts and methods for empirical macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 2, pages 4-42, Edward Elgar Publishing.
    2. Dar-Hsin Chen & Leo Bin & Chun-Yi Tseng, 2014. "Hedging Effectiveness of Applying Constant and Time-Varying Hedge Ratios: Evidence from Taiwan Stock Index Spot and Futures," Journal of Risk & Control, Risk Market Journals, vol. 1(1), pages 31-49.
    3. Morema, Kgotso & Bonga-Bonga, Lumengo, 2018. "The impact of oil and gold price fluctuations on the South African equity market: volatility spillovers and implications for portfolio management," MPRA Paper 87637, University Library of Munich, Germany.
    4. Xu, Cheng Kenneth, 2000. "The microstructure of the Chinese stock market," China Economic Review, Elsevier, vol. 11(1), pages 79-97.
    5. Chen, Song Xi & Lei, Lihua & Tu, Yundong, 2014. "Functional Coefficient Moving Average Model with Applications to forecasting Chinese CPI," MPRA Paper 67074, University Library of Munich, Germany, revised 2015.
    6. Rodolfo Cermeño & María José Roa García & Claudio González-Vega, 2016. "Financial Development and the Volatility of Growth: Time Series Evidence for Mexico and United States," Monetaria, Centro de Estudios Monetarios Latinoamericanos, CEMLA, vol. 0(2), pages 195-232, july-dece.
    7. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    8. Shively, Gerald E., 2001. "Price thresholds, price volatility, and the private costs of investment in a developing country grain market," Economic Modelling, Elsevier, vol. 18(3), pages 399-414, August.
    9. Tomanova, Lucie, 2013. "Exchange Rate Volatility and the Foreign Trade in CEEC," EY International Congress on Economics I (EYC2013), October 24-25, 2013, Ankara, Turkey 267, Ekonomik Yaklasim Association.
    10. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    11. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    12. Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2024. "Testing Granger non-causality in expectiles," Econometric Reviews, Taylor & Francis Journals, vol. 43(1), pages 30-51, January.
    13. ?ikolaos A. Kyriazis, 2021. "Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 133-146.
    14. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    15. Chang, Chia-Lin & Hsu, Hui-Kuang, 2013. "Modelling Volatility Size Effects for Firm Performance: The Impact of Chinese Tourists to Taiwan," MPRA Paper 45691, University Library of Munich, Germany.
    16. Budi Setiawan & Marwa Ben Abdallah & Maria Fekete-Farkas & Robert Jeyakumar Nathan & Zoltan Zeman, 2021. "GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy," JRFM, MDPI, vol. 14(12), pages 1-19, December.
    17. Chia-Lin Chang & Michael McAleer, 2017. "A Simple Test for Causality in Volatility," Econometrics, MDPI, vol. 5(1), pages 1-5, March.
    18. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    19. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    20. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:355:y:2019:i:c:p:96-107. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.