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Modelling and forecasting Nifty 50 using hybrid ARIMA-GARCH Model

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  • Parminder Kaur
  • Ravi Singla

Abstract

This study proposes an estimation technique for developing the best fit ARIMA-GARCH model to predict the closing values of Nifty 50. The study put forward different methods to resolve the issue of non-stationarity in mean as well as variance of the series before starting the estimation process. This study has applied autoregressive integrated moving-average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH), exponential GARCH (EGARCH) and threshold GARCH (TGARCH) model along with other estimation procedures on the daily closing prices of Nifty 50 from Jan 1, 2009 to Dec 30, 2019. Finally, the study identifies ARIMA(2,1,2)-EGARCH(1,1,1) as best model to predict the closing prices of Nifty 50. The findings indicate that the static forecast provides better results as compared to the dynamic forecast. These research findings will add to the tool kit of domestic as well as international portfolio managers and investors to frame suitable NIFTY trade strategies with least possible risks.

Suggested Citation

  • Parminder Kaur & Ravi Singla, 2022. "Modelling and forecasting Nifty 50 using hybrid ARIMA-GARCH Model," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 14(1), pages 7-20, June.
    • Handle: RePEc:rfb:journl:v:14:y:2022:i:1:p:7-20
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    1. repec:cup:cbooks:9781107034662 is not listed on IDEAS
    2. Zhengde Xiong & Lijun Han, 2015. "Volatility spillover effect between financial markets: evidence since the reform of the RMB exchange rate mechanism," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-12, December.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Ravi Singla & J. S. Pasricha, 2012. "Asset Pricing In The Indian Capital Market: A Study Of Positive And Negative Return Periods," Journal of Academic Research in Economics, Spiru Haret University, Faculty of Accounting and Financial Management Constanta, vol. 4(1 (March)), pages 90-101.
    5. Brooks,Chris, 2014. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781107661455, December.
    6. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    7. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. 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.
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