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Econometric Analysis of SOFIX Index with GARCH Models

Author

Listed:
  • Plamen Petkov

    (Department of Statistics and Applied Mathematics, Tsenov Academy of Economics, 5250 Svishtov, Bulgaria)

  • Margarita Shopova

    (Department of Statistics and Applied Mathematics, Tsenov Academy of Economics, 5250 Svishtov, Bulgaria)

  • Tihomir Varbanov

    (Department of Statistics and Applied Mathematics, Tsenov Academy of Economics, 5250 Svishtov, Bulgaria)

  • Evgeni Ovchinnikov

    (Department of Statistics and Applied Mathematics, Tsenov Academy of Economics, 5250 Svishtov, Bulgaria)

  • Angelin Lalev

    (Department of Business Informatics, Tsenov Academy of Economics, 5250 Svishtov, Bulgaria)

Abstract

This paper investigates five different Auto Regressive Moving Average (ARMA) and Generalized Auto Regressive Condition-al Heteroscedacity (GARCH models (GARCH, exponential GARCH or EGARCH, integrated GARCH or IGARCH, Component GARCH or CGARCH and the Glosten-Jagannathan-Runkle GARCH or GJR-GARCH) along with six distributions (normal, Student’s t , GED and their skewed forms), which are used to estimate the price dynamics of the Bulgarian stock index SOFIX. We use the best model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data cover the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at analysing the SOFIX price and which distribution is most appropriate. The results, based on the BIC and AIC, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student’s t -distribution is preferred for modelling. From the results obtained, we can confirm that the CGARCH model specification supports a more appropriate description of SOFIX volatility than a simple GARCH model. We find that long-term shocks have a more persistent impact on volatility than the effect of short-term shocks. Furthermore, for the same magnitude, negative shocks to SOFIX prices have a more significant impact on volatility than positive shocks. According to the results, when predicting future values of SOFIX, it is necessary to include both a first-order autoregressive component and a first-order moving average in the mean equation. With the help of 5000 simulations, it is estimated that the chances of SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.

Suggested Citation

  • Plamen Petkov & Margarita Shopova & Tihomir Varbanov & Evgeni Ovchinnikov & Angelin Lalev, 2024. "Econometric Analysis of SOFIX Index with GARCH Models," JRFM, MDPI, vol. 17(8), pages 1-30, August.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:8:p:346-:d:1453575
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    References listed on IDEAS

    as
    1. Vladimir Tsenkov, 2011. "Efficient-Market Hypothesis and the Global Financial Crises – on the Example of SOFIX, DJIA and DAX Indexes," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 53-88.
    2. Josip ARNERIĆ & Blanka ŠKRABIĆ PERIĆ, 2018. "Panel GARCH Model with Cross-Sectional Dependence between CEE Emerging Markets in Trading Day Effects Analysis," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 71-84, December.
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