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Abstract

This paper empirically investigates the various approaches to model time-varying systematic risk in Indonesia and Thailand by using time-series data from 2009 to 2017. Indonesia and Thailand were used as examples because of their growing economics since the turn of 20th century. As recent empirical studies have been conducted on stock markets in developed countries, there is an increasing need for testing in emerging markets, which have grown and become increasingly popular with international investors, such as Indonesia and Thailand. This study examines dynamic beta models using GARCH (1,1), EGARCH, TARCH, Schwert-Seguin, and the Kalman-Filter group to empirically find the most optimal time-varying beta model. This study uses the Fama-French Five Factors asset pricing model to include other factors that might influences value of systematic risk for each portfolio in both countries. This model can capture five factors that can affect returns, namely market factors (CAPM), size, book to market equity, profitability, and investment. By incorporating volatility and state space estimation, this study compares all tested models based on information criteria (AIC, SIC, and HIC). The results of this research proves that GARCH (1,1) in Indonesia and TARCH in Thailand outperforms other models in capturing the systematic risk. This study will be useful for future economic studies in Indonesia, Thailand and their neighboring countries.

Keywords

EGARCH Fama-French five factors GARCH (1,1) Kalman Filter Schwert and Seguin TARCH Time-varying beta

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