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

## TL;DR

This paper evaluates various GARCH models to accurately forecast the volatility of the Philippine Stock Exchange Index, finding GARCH (1,2) as the most suitable model based on statistical criteria.

## Contribution

It identifies GARCH (1,2) as the best model for PSEi volatility forecasting, demonstrating the effectiveness of GARCH modeling in this context.

## Key findings

- GARCH models outperform ARIMA in volatility forecasting.
- GARCH (1,2) has the lowest AIC and highest LL among tested models.
- GARCH (1,2) is the most appropriate model for PSEi log returns.

## 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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Source: https://tomesphere.com/paper/1904.00749