Abstract
The purpose of this study is aim to estimate the time-varying systematic risk or beta by using multivariate GARCH models. Since there are several researches found that estimating systematic risk by market model using traditional regression approach violated classical assumptions in both stationary assumption and independent identically distributed of the innovations. Then, the study focuses on using multivariate GARCH to improve the beta estimation since GARCH model is the popular model used in volatility clustering data. There are three type of Multivariate GARCH used in this study to compare the forcasting ability of each model of Multivariate GARCH. The results show from the plots of beta that Multivariate GARCH model can catch up volatility of risk quicker and better than Ordinary Least Square model and from model performance evaluation, vech model Multivariate GARCH confirms the superiority in capturing Time-Varying Systematic risk among the other Multivariate GARCH.
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Kridsadarat, M. (2013). Estimating Time-Varying Systematic Risk by Using Multivariate GARCH. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S., Suriya, K. (eds) Uncertainty Analysis in Econometrics with Applications. Advances in Intelligent Systems and Computing, vol 200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35443-4_16
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DOI: https://doi.org/10.1007/978-3-642-35443-4_16
Publisher Name: Springer, Berlin, Heidelberg
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