Search Results (3)

According to portfolio theory, diversifying investment to several stocks with negative correlations may reduce portfolio risk. In contrast, combining stocks with similar movement (co-movement) has no impact on portfolio risk reduction. This study aims to examine state-owned enterprise stock co-movement in Indonesia using orthogonal generalized auto-regressive conditional heteroscedasticity (O-GARCH) to help investors selectively choose stocks in a portfolio to reduce portfolio risks. Saturation sampling was used since all state-owned enterprise stocks listed on the Indonesian Stock Exchange were selected as samples. Based on monthly data from January 2013 to December 2021, the O-GARCH method was able to simplify the covariance matrix of the 17 SOEs. Of 17 SOEs, 11 had co-movement, as indicated by their similar principal components, whereas the remaining 6 stocks had a different principal component. Hence, investment managers or investors should not put the eleven stocks in the same portfolio as they have similar risk factors; instead, they may combine them with the six remaining SOE stocks which have a different co-movement. In addition, when the fiscal deficit is high and unconventional monetary policy is implemented in a crisis period, the SOE stock co-movement is higher. Thus, the SOE stock co-movement also depends on government-related matters and faces slightly different risks compared to its private-sector counterparts. Hence, the regulators formulating the policy on SOE stock holdings may use the results of this study by considering the potential merging of the SOE stocks with a similar stock return co-movement by taking account the timing in relation to fiscal deficit and crisis period. Full article

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. Full article

Carbon allowances traded in the EU-Emission Trading Scheme (EU-ETS) were initially designed as an economic motivation for efficiently curbing greenhouse as emissions, but now it mimics quite a few characteristics of financial assets, and have now been used as a candidate product in building financial portfolios. In this study, we examine the time-varying correlations between carbon allowance prices with other financial indices, during the third phase of EU-ETS. The results show that, at the beginning of this period, carbon price was still strongly corrected with other financial indices. However, this connection was weakened over time. Given the relative independence of carbon assets from other financial assets, we argue for the diversification benefits of including carbon assets in financial portfolios, and building such portfolios, respectively, with the traditional global minimum variance (GMV) strategy, the mean-variance-OGARCH (MV-OGARCH) strategy, and the dynamic conditional correlation (DCC) strategy. It is shown that the portfolio built with the MV-OGARCH strategy far out-performs the others and that including carbon assets in financial portfolios does help reduce investment risks. Full article