Predicting co-movement of banking stocks using orthogonal GARCH

Apriani Dorkas Rambu Atahau, Robiyanto Robiyanto and Andrian Dolfriandra Huruta

This study investigates the application of orthogonal generalized auto-regressive conditional heteroscedasticity (OGARCH) in predicting the co-movement of banking sector stocks in Indonesia. All state-owned banking sector stocks in Indonesia were studied using daily data from January 2013 to December 2019. The findings indicate that the OGARCH method can simplify the covariance matrix. Most state-owned banking stocks in the banking sector have a similar principal component influencing their conditional variance. Nonetheless, one stock has different principal components. The findings imply that combining the state-owned banking stocks with different principal components effectively reduces the risk of state-owned banking stock portfolios.

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Year of publication:	2022
Authors:	Atahau, Apriani Dorkas Rambu ; Robiyanto, Robiyanto ; Huruta, Andrian Dolfriandra
Subject:	OGARCH \| principal component analysis \| state-owned enterprises \| banking sector returns \| Bank \| Öffentliches Unternehmen \| Public enterprise \| Kapitaleinkommen \| Capital income \| ARCH-Modell \| ARCH model \| Hauptkomponentenanalyse \| Principal component analysis \| Prognoseverfahren \| Forecasting model \| China

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Type of publication:	Article
Type of publication (narrower categories):	Aufsatz in Zeitschrift ; Article in journal
Language:	English
Other identifiers:	10.3390/risks10080158 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013368248