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Forecasting the Volatilities of Philippine Stock Exchange Composite Index Using the Generalized Autoregressive Conditional Heteroskedasticity Modeling

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  • Novy Ann M. Etac
  • Roel F. Ceballos

Abstract

This study was conducted to find an appropriate statistical model to forecast the volatilities of PSEi using the model Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Using the R software, the log returns of PSEi is modeled using various ARIMA models and with the presence of heteroskedasticity, the log returns was modeled using GARCH. Based on the analysis, GARCH models are the most appropriate to use for the log returns of PSEi. Among the selected GARCH models, GARCH (1,2) has the lowest AIC value and also has the highest LL value implying that GARCH (1,2) is the best model for the log returns of PSEi.

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  • Novy Ann M. Etac & Roel F. Ceballos, 2019. "Forecasting the Volatilities of Philippine Stock Exchange Composite Index Using the Generalized Autoregressive Conditional Heteroskedasticity Modeling," Papers 1904.00749, arXiv.org.
    • Handle: RePEc:arx:papers:1904.00749
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    References listed on IDEAS

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    1. C. C. Bautista, 2003. "Stock market volatility in the Philippines," Applied Economics Letters, Taylor & Francis Journals, vol. 10(5), pages 315-318, April.
    2. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    3. Karunanithy Banumathy & Ramachandran Azhagaiah, 2015. "Modelling Stock Market Volatility: Evidence from India," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 13(1 (Spring), pages 27-41.
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