Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Forecasting Volatility of SOFIX Index with GARCH Models

Version 1 : Received: 20 June 2024 / Approved: 20 June 2024 / Online: 20 June 2024 (14:43:08 CEST)

How to cite: Petkov, P.; Shopova, M.; Varbanov, T.; Ovchinnikov, E.; Lalev, A. Forecasting Volatility of SOFIX Index with GARCH Models. Preprints 2024, 2024061434. https://doi.org/10.20944/preprints202406.1434.v1 Petkov, P.; Shopova, M.; Varbanov, T.; Ovchinnikov, E.; Lalev, A. Forecasting Volatility of SOFIX Index with GARCH Models. Preprints 2024, 2024061434. https://doi.org/10.20944/preprints202406.1434.v1

Abstract

This paper investigates five different GARCH models (GARCH, EGARCH, IGARCH, Component GARCH (CGARCH) and GJR-GARCH) along with six distributions (Normal, Student’s t, GED and their skewed forms), which are used to forecast the volatility for the Bulgarian Stock index SOFIX. We use a GARCH 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 covers 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 forecasting the volatility of SOFIX and which distribution is most appropriate. The results, based on BIC and AIC information criteria, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student t-distribution is preferred for forecasting. With the help of 5000 simulations, it is estimated that the chances of Index SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.

Keywords

SOFIX; forecasting; modeling; GARCH; EGARCH; IGARCH; Component GARCH; GJR-GARCH

Subject

Business, Economics and Management, Econometrics and Statistics

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