Predicting Co-Movement of Banking Stocks Using Orthogonal GARCH

1.

COMMENT:

“The entire paper needs to be thoroughly proof-read, to rid it off some of the remaining grammar and typo and spelling errors.

RESPONSE:  

We are thankful for the comments given and we have done grammarly proofread due to the limited time provided. However we are willing to use the English editing service provided by the journal should the paper is accepted. In figure 2, we have changed the screen by scree.

2.

COMMENT: “On page 2, line 72, what does 'right degree of co-movement' mean?”

RESPONSE: 

We have added the explanation as follows: “The results indicated that the OGARCH produced the right degree of co-movement compared to other methods. It means OGARCH is able to produce a positive definite covariance to overcome the estimation problems using other GARCH model.”

3.

COMMENT: “Line 80, the meaning of the 1st sentence is very unclear. Similar comment goes for line 100 (1st line on page 3).” 

RESPONSE: 

We have revised the sentence as below:

Line 80: By implementing the OGARCH method, this research is expected to produce a more straightforward approach to predicting the SOE stocks' co-movement in Indonesia's banking sector, since the capital market players is not yet familiar with this method. Instead, they use the heuristic methods such as correlation coefficient in constructing stock portfolios.

Line 100: This research also provides a novelty in predicting the co-movement of SOE stocks in the banking sector in Indonesia since most researchers still use heuristic measures such as multivariate GARCH which posses some estimation problems.

4.

COMMENT:

In line 84, there is a mention of 'dominant players in...'. The author(s) will need to include some statistics to justify/substantiate/back that statement.

RESPONSE: 

We agree with the reviewer suggestions to back the statement. The revision is as follows:

“The objects studied are the state-owned banks because they are the dominant players in the Indonesian banking Industry. According to the industry profile report December 2019, during the 2018-2019 period, banking SOE account for 43 to 44% market share in terms of total assets compared to all banking industry players in Indonesia.  “

5.

COMMENT: it will be good to have a Descriptive Statistics table showing basic order statistics e.g. mean, standard deviation, kurtosis, number of observations, etc

RESPONSE: 

We agree with the reviewer suggestions to provide descriptive statistics as follows:

Table 1 presents the statistic descriptive of all SOE banking stocks in this study: BBNI, BBRI, BBTN and BMRI.

Table 1. Descriptive statistics.

Source: Bloomberg, processed.

In Table 1, BBRI shows the highest average daily stock return (0.0877%) compared to other banking stock. This might relate to its strong fundamental conditions as indicated by BBRI being the most SOE banking stocks hold by foreign investors. BBRI has the widest outreach with its coverage scattered all over Indonesia. It focuses on SMEs lending compared to other SOE banking stocks. In contrast, BBTN has the lowest average daily return (0.0499%) over the research period. It also has the highest deviation (2.35%) compared to other SOE banking stocks. Focusing on mortgage loans differentiate BBTN substantially from other SOE banks. The focus strategy might contribute to its low return and high daily stock fluctuation.

6

COMMENT: There should be more indepth discussion on the outcomes of the IRF analysis. Why do BBNI, BBRI, and BMRI behave similarly? More importantly, what are the characteristics or attributes of the BBTN stock that makes it behave differently?

RESPONSE: 

We have added the arguments as follows:

BBTN focused on mortgage loans which is evidence from its loan type composition where mortgage loans dominates other loans. Thus, the pricing of loans and its return is mainly affected by the property market and economic cycle. When focused mainly on mortgage loans, it becomes concentrated and no longer diversified. Pricing and return are lower than other diversified banks but it is backed by a definite collateral, provided that the property market is in a stable condition (does not collapse).

7

COMMENT: Aside of suggesting on composition of investors' portfolios, the author(s) do not go further to discuss any policy relevance. This is an important omission, otherwise it is simply a number crunching exercise

We have provided a further discussion as follows:

The policy implication of this research is related to the importance of addressing the specific characteristics of each government- owned bank under consideration when aiming to form a bank holding company. It is advisable to place banks with similar characteristics in terms of co movement into one holding company instead of placing banks with different co movement into one holding company.

8

COMMENT: The author(s) should discuss how specifically this study adds to knowledge.

We have provided a further discussion as follows:

The theoretical implication of this research is providing empirical evidence from Indonesian banking sector in the ability of OGARCH in remedying the inherent estimation problems found in multivariate ARCH modelling.

9

COMMENT: The author(s) should relate their findings to already existing articles which address a similar issue for ASEAN countries i.e. at a country/regional level.

We have provided a further discussion as follows:

In the context of ASEAN countries, this research contribute to the knowledge related to portfolio construction involving government-owned banking stocks since there is a similarity of  banking industry structure in some ASEAN countries where a few government-owned banks dominate the banking industry

COMMENT: They should use daily data, because daily data reflect quickly and effectively information about the market.

RESPONSE: 

We are thankful for the comments given and we have done daily data for all of our analysis