DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 53 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION Y.B. Suhartoko, Adji Pratikto*, Luciana Selvi Susanti Wira Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia Abstract The Economic crisis has impacted the disruption of the stability of a country's financial system, including ASEAN countries. In Indonesia, there is a Financial System Stability Committee (KSSK) whose duties are to coordinate monitoring and maintaining financial system stability. KSSK has the authority to set the criteria and indicators for assessing financial system stability conditions concerning the financial system's stability. The second authority is to evaluate the condition of financial system stability based on input from each member of the Financial System Stability Committee, along with supporting data and information. As an economic area with history, ASEAN countries certainly have a relationship, either strong or weak. This study conducted calculations of the financial stability index (Aggregate Financial Stability Index) built from the Morris framework (2010) consisting of sub-index Financial Development Index, Financial Vulnerability Index, Financial Soundness Index, World Economic Climate Index. The calculation results showed that in ASEAN 6, there were fluctuations in financial stability, and there were variations in the correlation of financial stability. Therefore, improving the financial stability in Indonesia needs to consider the existence of financial stability in other countries. Keywords: Aggregate Financial Stability Index, Financial Development Index, Financial Vulnerability Index, Financial Soundness Index, World Economic Climate Index 1. INTRODUCTION The economic crisis in mid-1997 that began in Thailand spread to neighboring countries in Asia resulted in economic instability. The impact of the 1997/1998 economic crisis was so far-reaching on the real and financial sectors. In addition, the economic and financial crisis requires a significant amount of recovery costs, although International Monetary Fund has taken over some policies setting in Indonesia. An infectious economic and financial crisis is inevitable because of economic globalization, where interdependence and depending on other economies is increasingly widespread. Economic instability will occur more often, so this condition must be tackled together as a preventive measure to prevent the crisis from happening again. The Government of Indonesia established the Financial System Stability Committee, abbreviated as KSSK, which organizes the prevention and handling of financial stability to improve the resilience of Indonesian economies. KSSK members consist of: 1. The Minister of Finance as the coordinator of concurrent members with voting rights; 2. Governor of Bank Indonesia as a member with voting rights; 3. Chairman of the Board of Commissioners of the Financial Services Authority as a member with voting rights; and *Coressponding author. Email address: adji.pratikto@atmajaya.ac.id https://creativecommons.org/licenses/by/4.0/ mailto:adji.pratikto@atmajaya.ac.id AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 54 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) 4. Chairman of the Board of Commissioners of the Deposit Guarantee Agency as a member with voting rights. One of KSSK's tasks is to coordinate in the framework of monitoring and maintenance of Financial Stability. Concerning the situation of financial stability, first, KSSK has authority to set the criteria and indicators for assessing the condition of financial stability. Second, assessing the condition of financial stability, based on supporting data and information, along with input from each member of the Financial System Stability Committee. Several factors that affect the financial stability and economic system stability assessment indicators also need to be reviewed. The authors used the Aggregate Financial Stability Index (AFSI) as a proxy for financial stability in this study. In connection with the authority of the KSSK, research is needed to conduct a calculation of the financial system stability index embodied in the Aggregate Financial Stability Index (AFSI). Concerning intra- ASEAN trade and financial relations, it is necessary to calculate AFSI and analyze its correlation between ASEAN-6 countries (Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam) to see the extent of the interdependence of financial stability intra- ASEAN countries. The results of this study can be utilized to look at the stability of each country's financial system and at least be a consideration to improve the financial stability among countries. Concerning the study, which countries have a strong financial stability correlation so that they can design the cooperation agreement to improve their financial stability. 2. LITERATURE STUDY Bank Indonesia defines a financial system consisting of financial institutions, financial markets, financial infrastructure, and non-financial and household companies, which interact in funding and or provision of economic growth financing (www.bi.go.id). (Schinasi, 2004) states that a stable financial system if it is able to facilitate (not inhibit) economic performance and eliminate financial imbalances that arise endogenously. A stable financial system as a system, always makes adjustments towards balance, after being exposed to the influence of shocks from within and from outside. It can carry out traditional functions related to efficient allocation of resources, to correct price distortions and ensure adequate payment systems and settlement systems, as functions that contribute to overall economic growth and well-being (Albulescu & Goyeau, 2010) In Jordan, Samer. A.M. Al (Al-Rjoub, 2021) uses Financial Stability Index (FSI) to prove that the banking sector has been consciously resilient against shocks and negative economic conditions in Jordan. FSI is intutitively attractive as it could enable policy makers to monitor the banking sector’s resilience to shocks and can help in anticipating the source of financial stress to the system. AFSI Calculation Model Aggregate Financial Stability Index (AFSI) is an aggregate index developed by (Albulescu, 2008) to analyze the stability of the Romanian financial system and in 2010, Morris built AFSI for the stability of jamaica's financial system. AFSI method is a separate technique that can be used to complement other methods. AFSI provides the possibility for users to compare the level of financial system stability in different periods and between different financial systems, observe the dynamics of changes in the stability level of a financial system, and allow forecasting related to the stability of a financial system. Another advantage of the AFSI method is that it uses a simple way of calculating and easy access to statistical data. In general, the data is quite available, more transparent, and very helpful in defining the stability of a country's financial system (Albulescu & Goyeau, 2010) https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 55 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) (Morris, 2010) states that the Aggregate Financial Stability Index (AFSI) has 4 (four) sub-indices as follows: Financial Development Index (FDI) Financial Development Index or development index shows that the greater the value of the index, the more financial is growing. This sub-index consists of four indicators. The first indicator is the percentage of total market capitalization to Gross Domestic Product (GDP) which is the percentage between the value of capital in the market or the value of the capital market against GDP. This indicator describes the development and size of the capital market. The larger this indicator indicates that investment is increasing. The next indicator is the percentage of domestic credit to GDP which describes the level of intermediation of financial institutions in this case commercial banks and People's Credit Banks (BPR) which are quite dominant. The higher this indicator shows that financial institutions are better at bridging between owners of excess funds (surplus units) and parties who need funds (unit deficit) and increasing domestic investment. The third indicator is the difference between the interest rate on the loan and the interest rate spread. This indicator illustrates the potential benefits of financial institution intermediation services. However, the larger this indicator also illustrates that financial institutions are increasingly inefficient. The last indicator is the bank concentration which is the assets of the three largest banks as part of all commercial bank assets. The concentration of banking in Indonesia is quite high after the 1998 crisis because of the number of banks that do mergers. According to Morris (Morris, 2010) the increase in this indicator illustrates the improvement of the efficiency of the banking sector. Financial Vulnerability Index (FVI) Financial Vulnerability Index shows that the lower the value of the index, the more vulnerable the financial system and vice versa. The Financial Vulnerability Index consists of eight indicators. The first economic indicator grouped into this sub-index is inflation. Inflation shows an increase in the price of goods in general. The increase in this indicator can be interpreted as a decrease in the value of money against goods that can decrease the level of public confidence in the currency so that the public tends to hold in the form of goods or other currencies. The second indicator is the percentage surplus or deficit of the government's balance of expenditure to GDP. In the event of a budget deficit to cover, the government can print money or debt. The debt can be sourced from the issuance of bonds or foreign loans. Some of each alternative has considerable risks. The third indicator is the percentage of the current account against GDP. The current account deficit can lead to reduced foreign exchange reserves and reduce its contribution to GDP. The fourth indicator is the Real Effective Exchange Rate (REER), which is domestic currencies' actual exchange rate performance against foreign currencies in general in the international economy. The fluctuating changes in this indicator show that the economy through exchange rate adjustments has undergone a significant correction (Albulescu & Goyeau, 2010). The fifth indicator is the percentage of private credit to total credit. This indicator illustrates the proportion of private sector funding through credit for investment and is also potentially bad credit. The sixth indicator is the percentage of loans against deposits. The increase in this indicator shows that it is easier and more efficient for financial institutions to carry out their intermediation functions. The seventh indicator is the percentage of deposits against the money supply. The increase in this indicator illustrates the tendency of people to save money in financial institutions rather than for consumption activities. The last indicator compares the percentage of reserves against deposits with the percentage of money held by the public against the money supply. This indicator https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 56 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) reflects the preparation of financial institutions in anticipating massive withdrawals of deposits by the public. Financial Soundness Index (FSI) Financial Soundness Index shows that the greater the value of the index, the better the banking sector. FSI consists of five index building indicators. The first indicator is the percentage of bad loans against total banking credit. Increasing the index will disrupt the liquidity of the banking sector. The second indicator is the Capital Adequacy Ratio (CAR), describing the level of banking capitalization that is a condition of capital adequacy against weighted liquidity risks. The improvement of this indicator illustrates the readiness of banks to overcome liquidity risks. The third indicator is the percentage of capital against total assets. This indicator shows the proportion of capital to all assets owned by the banking sector. The higher this indicator indicates the more liquid and healthier the banking sector. The fourth indicator is Bank Return on Asset (ROA), which measures the rate of return of the banking sector. The larger this indicator reflects greater profits within the banking sector. The fifth indicator is bank Z-Score, which is the level of banking health that describes the possibility of banks can survive not going bankrupt. World Economic Climate Index (WECI) World economic climate index developed by the Center for Economic Studies & Research Institute "CESifo" shows the condition of the world economy using the perception of business condition related to investment opportunities. The increase in these indicators illustrates the increasingly better global economic climate. WECI shows that the greater the value of the index, the better global economic conditions. The data used is data in the annual period. The limited availability of data for some individual indicators led to adjustments, so that the data used is data from 2005 to 2017 which is the data with the most available time interval. 3. RESEARCH METHODS The data used in this study obtained from various sources in 2005-2018, that can be accessed through the CESifo website, International Monetary Fund (IMF) and World Bank. There are several steps to calculate AFSI. It is collecting and grouping data on each sub-index starting from 2005 to 2018. The next step is to normalize the indicator. The normalization method makes indicator values range from "0" to "1". The value "0" is the worst value and "1" is the best stability condition. So, the greater index shows the better condition of financial stability. The formula for empirical normalization methods is as follows: 𝐼𝑖𝑑𝑛 = πΌπ‘–π‘‘βˆ’Min (𝐼𝑖) Max (𝐼𝑖)βˆ’Min (𝐼𝑖) (1) 𝐼𝑖𝑑𝑛 = the value of an individual indicator that has been normalized 𝐼𝑖𝑑 = value of individual indicator i at t Min(Ii)= minimum value of individual indicator i during the observation period Max(Ii)= maximum value of individual indicator i during the observation period After normalizing the data, to obtain the value of sub-index by summing the normalization values of all individual indicator and divided by the total individual indicators in the sub-index. To get AFSI is done by summing the normalization value of all individual indicator with the total individual indicators of index constituents. Mathematically the four sub-indices and AFSI can be written as follows: https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 57 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) Financial Development Index (FDI) 𝐷𝑑̅̅ Μ… = βˆ‘ 𝐷𝑖𝑑4𝐼=1 4 (2) Notation in the equation above shows the value of the financial development index which is the average value of all its constituent indicators in period t. βˆ‘Dit is the sum of all index constituent indicators in period t. Financial Vulnerability Index (FVI) 𝑉𝑑̅̅ Μ… = βˆ‘ 𝑉𝑖𝑑8𝐼=1 8 (3) Financial vulnerability index is the average value of all its constituent indicators in the t- period. βˆ‘Vit is the sum of all index constituent indicators in period t Financial Soundness Index (FSI) 𝑆�̅� = βˆ‘ 𝑆𝑖𝑑5𝐼=1 5 (4) The equation above shows the value of the banking sector health index and is the average value of all its constituent indicators in period t. βˆ‘Sit is the sum of all index constituent indicators in period t. World Economic Climate Index (WECI) π‘Šπ‘‘Μ…Μ… Μ…Μ… = βˆ‘ π‘Šπ‘–π‘‘3𝐼=1 3 (5) The equation above shows the global economic conditions index value and is the average value of all WECI constituent indicators in period t. βˆ‘Wit is the sum of all index constituent indicators in period t. Aggregate Financial Stability Index (AFSI) AFSI = βˆ‘ 𝐼𝑖𝑑4𝐼=1 20 (6) βˆ‘Iit is the sum of all index constituent indicators in period t, where βˆ‘ 𝐼𝑖𝑑 = 4𝑖=1 βˆ‘ 𝐷𝑖𝑑 + 4 𝑖=1 βˆ‘ 𝑉𝑖𝑑 + 8 𝑖=1 βˆ‘ 𝑆𝑖𝑑 + 5 𝑖=1 βˆ‘ π‘Šπ‘–π‘‘ 3 𝑖=1 (7) Therefore AFSI = 4𝐷𝑑̅̅̅̅ 20 + 8𝑉𝑑̅̅ Μ… 20 + 5𝑆𝑑̅̅ Μ… 20 + 3π‘Šπ‘‘Μ…Μ… Μ…Μ… 20 (8) Or it can be written as follows: AFSI = (9)0,2 𝐷𝑑̅̅ Μ… + 0,4 𝑉𝑑̅̅ Μ… + 0,25 𝑆�̅� + 0,15 π‘Šπ‘‘Μ…Μ… Μ…Μ… The process of forming an index uses equally large weighting for each index building indicator. Van den End (2006) shows in the composition of the preparation of aggregate stability indexes the same weighting and different weights in econometric validation will produce small differences. So to make it simpler to use the same weighting method on each indicator. However, each sub-index has a different weight depending on the number of constituent indicators. https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 58 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) 4. RESULTS AND DISCUSSION The table of data grouping in the preparation of sub-indices can be seen in Table IV.1 as follows Table 1. Aggregate Financial Stability Index (AFSI) Financial Development Index (FDI) Source Market Capitalization / GDP Percent (%) World Bank National Currency Credit/GDP Percent (%) World Bank Interest Rate Spread Percent (%) World Bank World Bank Concentration Percent (%) World Bank Financial Vulnerability Index (FDI) Inflation, consumer prices Percent (%) World Bank General Balance, Deficit or Surplus/GDP Percent (%) World Bank Current Account / GDP Percent (%) World Bank Real Effective Exchange Rate (change) Percent (%) World Bank Non Governmental Credit / Total Credit Percent (%) World Bank Loan/Deposits Percent(%) World Bank Deposits /M2 Percent(%) World Bank (Reserves / Deposits) / (Note&coin / M2) Percent (%) World Bank Financial Soundness Index (FSI) Nonperforming Bank loans to gross loans Percent (%) World Bank Bank Capital Adequacy Ratio (CAR) Percent (%) World Bank Bank Capital to total assets Percent (%) World Bank Bank Return on Assets (ROA) Percent (%) World Bank Bank Z-Score Percent (%) World Bank World Economic Climate Index (WECI) World Inflation, Consumer Prices Percent (%) IMF World GDP Growth Percent (%) IMF Economic Climate Index Index Number CESifo To show the steps, the calculation step will be presented examples of empirical steps of AFSI calculation in Indonesia to provide a comprehensive picture of the calculation. An example of a calculation to be displayed is the FDI sub index. https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 59 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) Table 2. Market Capitalization of Listed Companies (% of GDP) Indonesia COUNTRY YEAR (n) MARKET CAPITALIZATION OF LISTED COMPANIES (% OF GDP) (𝐼𝑖𝑑) INDONESIAN 2005 28.4845 INDONESIAN 2006 38.0959 INDONESIAN 2007 48.9784 INDONESIAN 2008 19.3561 INDONESIAN 2009 39.8350 INDONESIAN 2010 47.7276 INDONESIAN 2011 43.6865 INDONESIAN 2012 46.6539 INDONESIAN 2013 37.9906 INDONESIAN 2014 47.3866 INDONESIAN 2015 41.0373 INDONESIAN 2016 45.6892 INDONESIAN 2017 51.2778 Min (𝐼𝑖) = 19.3561(Theminimum value of the individual indicator of the percentage of market capitalization against Indonesia's GDP during the observation period was 2008) Max (𝐼𝑖) = 51.2778(Themaximum value of individual indicators of the percentage of market capitalization against Indonesia's GDP during the observation period is 2017) Table 3. Calculation of Normalization of Market Capitalization of Listed Companies (% of GDP) Indonesia Year Formula Normalization Results 2005 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2005 = 28.4845 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2005 = 0.2860 2006 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2006 = 38.0959 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2006 = 0.5871 2007 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2007 = 48.9784 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2007 = 0.9280 2008 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2008 = 19.3561 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2008 = 0.0000 2009 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2009 = 39.8350 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2009 = 0.6415 2010 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2010 = 47.7276 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2010 = 0.8888 2011 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2011 = 43.6865 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2011 = 0.7622 https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 60 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) 2012 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2012 = 46.6539 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2012 = 0.8552 2013 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2013 = 37.9906 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2013 = 0.5838 2014 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2014 = 47.3866 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2014 = 0.8781 2015 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2015 = 41.0373 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2015 = 0.6792 2016 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2016 = 45.6892 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2016 = 0.8249 2017 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2017 = 51.2778 βˆ’ 19.3561 51.2778 βˆ’ 19.3561 𝐼𝑖𝑑𝑛 2017 = 1.0000 Table 4. National Currency Credit/GDP (%) Data Indonesia COUNTRY YEAR (n) NATIONAL CURRENCY CREDIT/GDP (%) (π‘°π’Šπ’•) INDONESIAN 2005 46.2049 INDONESIAN 2006 41.6594 INDONESIAN 2007 40.5802 INDONESIAN 2008 36.7702 INDONESIAN 2009 35.6418 INDONESIAN 2010 33.2846 INDONESIAN 2011 35.5566 INDONESIAN 2012 39.3252 INDONESIAN 2013 42.1045 INDONESIAN 2014 42.3979 INDONESIAN 2015 42.4149 INDONESIAN 2016 43.0875 INDONESIAN 2017 42.1166 Min (𝐼𝑖) = 33.2846(The minimum value of the National Currency Credit individualindicator against Indonesia's GDP during the observation period was 2010) Max (𝐼𝑖) = 46.2049(The maximum value of the National Currency Credit individualindicator against Indonesia's GDP during the observation period was 2005) Table 5. Calculation of Normalization of National Currency Credit Data on Indonesia's GDP Year Formula Normalization Results 2005 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2005 = 46.2049 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2005 = 1.0000 2006 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2006 = 41.6594 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2006 = 0.6482 2007 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2007 = 40.5802 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2007 = 0.5647 https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 61 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) 2008 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2008 = 36.7702 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2008 = 0.2698 2009 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2009 = 35.6418 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2009 = 0.1824 2010 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2010 = 33.2846 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2010 = 0.0000 2011 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2011 = 35.5566 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2011 = 0.1758 2012 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2012 = 39.3252 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2012 = 0.4675 2013 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2013 = 42.1045 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2013 = 0.6826 2014 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2014 = 42.3979 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2014 = 0.7053 2015 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2015 = 42.4149 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2015 = 0.7067 2016 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2016 = 43.0875 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2016 = 0.7587 2017 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2017 = 42.1166 βˆ’ 33.2846 46.2049 βˆ’ 33.2846 𝐼𝑖𝑑𝑛 2017 = 0.6836 Table 6. Interest Rate Spread (%) Indonesia COUNTRY YEAR (n) INTEREST RATE SPREAD (%) (π‘°π’Šπ’•) INDONESIAN 2005 5.9717 INDONESIAN 2006 4.5683 INDONESIAN 2007 5.8858 INDONESIAN 2008 5.1058 INDONESIAN 2009 5.2200 INDONESIAN 2010 6.2350 INDONESIAN 2011 5.4725 INDONESIAN 2012 5.8483 INDONESIAN 2013 5.3933 INDONESIAN 2014 3.8525 INDONESIAN 2015 4.3258 INDONESIAN 2016 4.7224 INDONESIAN 2017 4.5550 Min (𝐼𝑖) = 3.8525(minimum value of indonesia's individual interest rate indicator during the observation period is 2014) Max (𝐼𝑖) = 6.2350(maximum value of indonesia's individual interest rate indicator during the observation period is 2010) https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 62 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) Table 7. Calculation of Normalization of Indonesia's Interest Rate Spread Data Year Formula Normalization Results 2005 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2005 = 5.9717 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2005 = 0.8895 2006 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2006 = 4.5683 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2006 = 0.3005 2007 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2007 = 5.8858 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2007 = 0.8534 2008 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2008 = 5.1058 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2008 = 0.5261 2009 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2009 = 5.2200 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2009 = 0.5740 2010 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2010 = 6.2350 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2010 = 1.0000 2011 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2011 = 5.4725 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2011 = 0.6800 2012 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2012 = 5.8483 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2012 = 0.8377 2013 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2013 = 5.3933 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2013 = 0.6467 2014 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2014 = 3.8525 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2014 = 0.0000 2015 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2015 = 4.3258 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2015 = 0.1987 2016 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2016 = 4.7224 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2016 = 0.3651 2017 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2017 = 4.5550 βˆ’ 3.8525 6.2350 βˆ’ 3.8525 𝐼𝑖𝑑𝑛 2017 = 0.2949 Table 8. Data Bank Concentration (%) Indonesia COUNTRY YEAR (n) BANK CONCENTRATION (%) (𝐼𝑖𝑑) INDONESIAN 2005 42.8416 INDONESIAN 2006 42.3698 INDONESIAN 2007 42.3483 INDONESIAN 2008 42.9648 INDONESIAN 2009 44.1135 INDONESIAN 2010 42.3148 INDONESIAN 2011 41.3665 INDONESIAN 2012 40.6038 INDONESIAN 2013 38.4077 https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 63 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) INDONESIAN 2014 40.3356 INDONESIAN 2015 39.7019 INDONESIAN 2016 39.8376 INDONESIAN 2017 40.6930 Min (𝐼𝑖) = 38.4077(minimum value of individual indicators of Indonesian bank concentration during the observation period is 2013) Max (𝐼𝑖) = 44.1135(maximum indicator of individual bank concentration during the observation period is 2009) Table 9. Calculation of Normalization of Bank Concentration Indonesia Data Year Formula Normalization Results 2005 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2005 = 42.8416 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2005 = 0.7771 2006 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2006 = 42.3698 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2006 = 0.6944 2007 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2007 = 42.3483 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2007 = 06906 2008 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2008 = 42.9648 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2008 = 0.7987 2009 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2009 = 44.1135 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2009 = 1.0000 2010 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2010 = 42.3148 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2010 = 0.6848 2011 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2011 = 41.3665 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2011 = 0.5186 2012 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2012 = 40.6038 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2012 = 0.3849 2013 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2013 = 38.4077 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2013 = 0.0000 2014 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2014 = 40.3356 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2014 = 0.3379 2015 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2015 = 39.7019 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2015 = 0.2268 2016 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2016 = 39.8376 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2016 = 0.2506 2017 𝐼𝑖𝑑𝑛 = 𝐼𝑖𝑑 βˆ’ Min (𝐼𝑖) Max (𝐼𝑖) βˆ’ Min (𝐼𝑖) 𝐼𝑖𝑑 2017 = 40.6930 βˆ’ 38.4077 44.1135 βˆ’ 38.4077 𝐼𝑖𝑑𝑛 2017 = 0.4005 Furthermore, the Financial Vulnerability Index (FVI) sub-index, the Individual Indicator of the Financial Soundness Index (FSI) sub-index, and the individual indicator of the World Economic Climate Index (WECI) sub-index follow the same steps as FDI. The overall calculation results for AFSI are presented in the following table. https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 64 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) Table 10. Aggregate Results Of Each Individual Indicator Of FDI, FVI, FSI, WECI, and AFSI Aggregate Index COUNTRY YEAR FDI FVI FSI WECI AFSI INDONESIAN 2005 0.7381 0.3264 0.3492 0.7129 0.4724 INDONESIAN 2006 0.5575 0.5187 0.4287 0.7928 0.5451 INDONESIAN 2007 0.7592 0.3746 0.3162 0.7900 0.4992 INDONESIAN 2008 0.3986 0.4392 0.1070 0.5794 0.3691 INDONESIAN 2009 0.5995 0.4069 0.3043 0.0679 0.3689 INDONESIAN 2010 0.6434 0.5800 0.3578 0.6636 0.5497 INDONESIAN 2011 0.5341 0.5462 0.3807 0.6282 0.5147 INDONESIAN 2012 0.6363 0.4369 0.4607 0.4766 0.4887 INDONESIAN 2013 0.4783 0.4431 0.5042 0.4788 0.4707 INDONESIAN 2014 0.4803 0.4563 0.5111 0.5225 0.4847 INDONESIAN 2015 0.4528 0.5083 0.5668 0.4393 0.5015 INDONESIAN 2016 0.5498 0.5006 0.6818 0.3706 0.5362 INDONESIAN 2017 0.5947 0.4896 0.7480 0.5360 0.5822 MALAYSIA 2005 0.6128 0.5552 0.2901 0.7129 0.5241 MALAYSIA 2006 0.7185 0.6562 0.2458 0.7928 0.5866 MALAYSIA 2007 0.7338 0.5845 0.2051 0.7900 0.5503 MALAYSIA 2008 0.4592 0.5664 0.2551 0.5794 0.4691 MALAYSIA 2009 0.7843 0.3776 0.3912 0.0679 0.4159 MALAYSIA 2010 0.6913 0.4629 0.4018 0.6636 0.5234 MALAYSIA 2011 0.3615 0.5121 0.6123 0.6282 0.5244 MALAYSIA 2012 0.3828 0.4361 0.4968 0.4766 0.4467 MALAYSIA 2013 0.4445 0.4976 0.3428 0.4788 0.4455 MALAYSIA 2014 0.3983 0.5561 0.3912 0.5225 0.4783 MALAYSIA 2015 0.4099 0.4896 0.3685 0.4393 0.4359 MALAYSIA 2016 0.4010 0.5451 0.4361 0.3706 0.4629 MALAYSIA 2017 0.4735 0.6101 0.6421 0.5360 0.5797 PHILIPPINES 2005 0.2445 0.4175 0.7198 0.7129 0.5028 PHILIPPINES 2006 0.2639 0.5249 0.7686 0.7928 0.5738 PHILIPPINES 2007 0.5939 0.4475 0.6315 0.7900 0.5741 PHILIPPINES 2008 0.3810 0.3964 0.1014 0.5794 0.3470 PHILIPPINES 2009 0.5751 0.3510 0.3027 0.0679 0.3413 PHILIPPINES 2010 0.6173 0.4184 0.5177 0.6636 0.5198 PHILIPPINES 2011 0.3240 0.5034 0.6386 0.6282 0.5201 PHILIPPINES 2012 0.3475 0.5647 0.8153 0.4766 0.5707 PHILIPPINES 2013 0.4981 0.6074 0.4935 0.4788 0.5378 PHILIPPINES 2014 0.6684 0.5846 0.4038 0.5225 0.5468 PHILIPPINES 2015 0.6713 0.5853 0.3530 0.4393 0.5225 PHILIPPINES 2016 0.6514 0.4353 0.2883 0.3706 0.4320 PHILIPPINES 2017 0.8379 0.5373 0.2909 0.5360 0.5356 https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 65 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) SINGAPORE 2005 0.4082 0.2599 0.7430 0.7129 0.4783 SINGAPORE 2006 0.1953 0.3478 0.7562 0.7928 0.4861 SINGAPORE 2007 0.3037 0.4163 0.0307 0.7900 0.3534 SINGAPORE 2008 0.3908 0.5315 0.2768 0.5794 0.4469 SINGAPORE 2009 0.5254 0.3054 0.6022 0.0679 0.3880 SINGAPORE 2010 0.6052 0.4220 0.7051 0.6636 0.5657 SINGAPORE 2011 0.5685 0.5944 0.3599 0.6282 0.5357 SINGAPORE 2012 0.6757 0.5995 0.6052 0.4766 0.5977 SINGAPORE 2013 0.6175 0.5661 0.2638 0.4788 0.4877 SINGAPORE 2014 0.6672 0.5717 0.2353 0.5225 0.4993 SINGAPORE 2015 0.5713 0.4663 0.2920 0.4393 0.4397 SINGAPORE 2016 0.5596 0.5052 0.3483 0.3706 0.4566 SINGAPORE 2017 0.7073 0.4899 0.4368 0.5360 0.5270 THAILAND 2005 0.3522 0.4158 0.5118 0.7129 0.4717 THAILAND 2006 0.1443 0.4933 0.2753 0.7928 0.4139 THAILAND 2007 0.3771 0.5654 0.2650 0.7900 0.4863 THAILAND 2008 0.2094 0.4969 0.3169 0.5794 0.4068 THAILAND 2009 0.4845 0.2751 0.4492 0.0679 0.3294 THAILAND 2010 0.4804 0.4655 0.4844 0.6636 0.5029 THAILAND 2011 0.4239 0.5954 0.3625 0.6282 0.5078 THAILAND 2012 0.5973 0.5013 0.4281 0.4766 0.4985 THAILAND 2013 0.5964 0.4601 0.4809 0.4788 0.4954 THAILAND 2014 0.8659 0.4494 0.5816 0.5225 0.5767 THAILAND 2015 0.6276 0.4809 0.6395 0.4393 0.5437 THAILAND 2016 0.6185 0.4900 0.7246 0.3706 0.5564 THAILAND 2017 0.6392 0.5205 0.7993 0.5360 0.6163 VIETNAMESE 2005 0.5000 0.3195 0.6600 0.7129 0.4997 VIETNAMESE 2006 0.4877 0.4405 0.7018 0.7928 0.5681 VIETNAMESE 2007 0.5518 0.4148 0.7571 0.7900 0.5840 VIETNAMESE 2008 0.4084 0.6199 0.5221 0.5794 0.5471 VIETNAMESE 2009 0.4213 0.3509 0.3230 0.0679 0.3155 VIETNAMESE 2010 0.4051 0.3510 0.3860 0.6636 0.4175 VIETNAMESE 2011 0.4230 0.5490 0.4590 0.6282 0.5132 VIETNAMESE 2012 0.4421 0.5688 0.4496 0.4766 0.4998 VIETNAMESE 2013 0.5005 0.4895 0.4404 0.4788 0.4778 VIETNAMESE 2014 0.4707 0.4892 0.3030 0.5225 0.4440 VIETNAMESE 2015 0.5265 0.4239 0.2825 0.4393 0.4114 VIETNAMESE 2016 0.4803 0.4642 0.2591 0.3706 0.4021 VIETNAMESE 2017 0.5426 0.4935 0.2413 0.5360 0.4466 The Aggregate Financial Stability Index(AFSI) calculated in the study showed a decline in the aggregate index value of some countries when shocks to financial system stability such as the crisis occurred in 2008. If a country's aggregate index is high then the stability of the country's financial system is more stable, but if a country's aggregate index is low then the stability of its financial system is unstable. https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 66 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) Figure 1. Aggregate Financial Stability Index(AFSI) Data Indonesia 2005-2017 (Year 1 = 2005) Indonesia's Aggregate Financial Stability Index(AFSI) from 2005 to 2017 experienced fluctuating movements with trends that tend to increase due to the influence of individual indicators constituents of the Aggregate Financial Stability Index(AFSI). From chart.6, aggregate financial stability index (AFSI)was lowest in2009 at 0.3689 and highest in 2017 at 0.5822. Indonesia's Aggregate Financial Stability Index (AFSI)is below average in2005, 2008, 2009, 2012, 2013, and 2014. Then, Indonesia's Aggregate Financial Stability Index(AFSI) showed a trend of increasing value and was in the fourth highest position compared to Singapore and Vietnam. Figure .2. Aggregate Financial Stability Index(AFSI) Data Malaysia 2005-2017 Aggregate Financial Stability Index(AFSI) Malaysia from 2005 to 2017 experienced fluctuating movements with trends that tend to decrease due to the influence of individual https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 67 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) indicators constituent aggregate financial stability index(AFSI). From the chart.7 above, the Aggregate Financial Stability Index (AFSI)was the lowest in2009 at 0.4159 and the highest in 2006 at 0.5866, with the average for the 13-year observation period of 0.4956, Malaysia's Aggregate Financial Stability Index (AFSI)below theaverages of 2008, 2009, 2012, 2013, 2014, 2015 and 2016 showing instability. Then, the Aggregate Financial Stability Index (AFSI)Malaysiashowed a slight trend of decline in value and was in the second highest position compared to Indonesia, Singapore, Thailand, and Vietnam. Figure 3. Aggregate Financial Stability Index(AFSI) Data Philippines 2005-2017 The Philippine Aggregate Financial Stability Index(AFSI) from 2005 to 2017 experienced fluctuating movements with trends that tend to increase due to the influence of individual indicators constituents of the Aggregate Financial Stability Index (AFSI). From the chart above, the Aggregate Financial Stability Index (AFSI) was the lowest in 2009 at 0.3413 and the highest in 2007 at 0.5977, with the average for the 13-year observation period of 0.5019. The Philippine Aggregate Financial Stability Index (AFSI) below the averages of 2008, 2009, and 2016 showing instability. Then, the Philippine Aggregate Financial Stability Index (AFSI) showed a slightly increasing trend in value and was at the highest position compared to Indonesia, Malaysia, Singapore, Thailand, and Vietnam. https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 68 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) Figure 4. Aggregate Financial Stability Index(AFSI) Singapore 2005-2017 Aggregate Financial Stability Index (AFSI) Singapore from 2005 to 2017 experienced fluctuating movements with trends that tend to increase due to the influence of individual indicators constituent. From the chart above, the Aggregate Financial Stability Index (AFSI) was the lowest in 2007 at 0.3534 and the highest in 2012 at 0.5977, with the average for the 13-year observation period of 0.4817. Singapore's Aggregate Financial Stability Index (AFSI) below averages of 2005, 2007, 2008, 2009, 2015 and 2016 showing instability. Then, the Aggregate Financial Stability Index (AFSI) Singapore showed a slightly increasing trend in value and was at the second lowest position compared to Indonesia, Malaysia, Singapore, and Thailand. Figure 5. Aggregate Financial Stability Index(AFSI) Thailand 2005-2017 Thailand's Aggregate Financial Stability Index(AFSI) from 2005 to 2017 experienced fluctuating movements with trends that tend to increase due to the influence of individual https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 69 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) indicators constituents of the Aggregate Financial Stability Index (AFSI). From the chart above, the Aggregate Financial Stability Index (AFSI) was the lowest in 2009 at 0.3294 and the highest in 2017 at 0.6163, with an average for the 13-year observation period of 0.4927. Thailand's Aggregate Financial Stability Index (AFSI) is below average in 2005, 2006, 2007, 2008, and 2009. Then, the Aggregate Financial Stability Index (AFSI)of Thailand showed a trend of increasing value and was at the highest position compared to Indonesia, Thailand, and Vietnam. Figure 6. Aggregate Financial Stability Index(AFSI) Vietnam 2005-2017 Vietnam's Aggregate Financial Stability Index (AFSI) from 2005 to 2017 experienced a fluctuating movement with a trend that tends to decline due to the influence of individual indicators. From the chart above, the Aggregate Financial Stability Index (AFSI) was the lowest in 2009 at 0.3155 and the highest in 2007 at 0.5840, with the average for the 13-year observation period of 0.4713. Vietnam's Aggregate Financial Stability Index (AFSI) below averages in 2009, 2010, 2014, 2015, 2016 and 2017 showing instability. Then, Vietnam's Aggregate Financial Stability Index (AFSI) showed a downward trend in value and was at the lowest positions compared to the other 5 ASEAN countries (Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam). Based on the calculation of AFSI 6 ASEAN countries, we calculate the AFSI data correlation between countries, to find the relations between each country's AFSI. This simple statistical calculation is important to see how close the financial stability relationship between countries is. https://creativecommons.org/licenses/by/4.0/ AFEBI Economic and Finance Review (AEFR) Volume 7, No 1 (2022) 70 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) Table 11. Aggregate Financial Stability Index (AFSI) Correlation Matrix Based on Table IV.11, several states can be stated regarding the stability relationship of the intra-country financial system as follows. β€’ AFSI Indonesia is strongly correlated, significant and in line with AFSI Malaysia, Philippines and AFSI Thailand, If AFSI Malaysia, Philippines and AFSI Thailand experience an increase or decrease in stability, then Indonesia also experienced the same. If Malaysia, the Philippines and Thailand increase the stability of their financial systems, then the stability of Indonesia's financial system also increases. β€’ AFSI Indonesia is quite strong, insignificant and in line with AFSI Singapore, (AFSI Indonesia increases / stabilizes when AFSI Singapore increases / stabilizes, AFSI Indonesia decreases when AFSI Singapore decreases). β€’ AFSI Indonesia is very weak, insignificant and in line with AFSI Vietnam. (AFSI Indonesia increases/stabilizes when Vietnamese AFSI increases/stabilizes, and AFSI Indonesia decreases when VIETNAMESE AFSI decreases, but very weak relations). β€’ AFSI Malaysia is strongly correlated, insignificant and in line with Philippine. β€’ AFSI Malaysia correlates very weakly, insignificantly, and unidirectionally with AFSI Singapore and AFSI Thailand, (When AFSI Singapore or AFSI Thailand increases/stabilizes, then AFSI Malaysia increases/stabilizes, and vice versa but very weak in relationship). β€’ AFSI Malaysia is strongly, significantly and in line with AFSI Vietnam. If AFSI Malaysia experiences instability, then AFSI Vietnam can be affected by such instability (AFSI Malaysia increases / stabilizes when AFSI Vietnam increases / stabilizes, AFSI Malaysia decreases when AFSI Vietnam decreases). β€’ Philippine AFSI is strongly correlated, insignificant and in line with AFSI Singapore and AFSI Vietnam, (If AFSI Singapore or AFSI Vietnam increases/ stabilizes, then Philippine AFSI increases/stabilizes. If AFSI Singapore or AFSI Vietnam decreases, then THE Philippine AFSI decreases). https://creativecommons.org/licenses/by/4.0/ DETERMINATION OF FINANCIAL STABILITY INDEX BETWEEN SOUTHEAST ASIAN COUNTRIES (ASEAN 6) AND ITS INTRACORRELATION 71 Published by AFEBI Economic and Finance Review This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/) β€’ Philippine AFSI is strongly correlated, insignificant and in unidirectional with Thailand's AFSI, (Philippine AFSI increases/stabilizes when Thai AFSI increases/stabilizes, Philippine AFSI decreases when Thai AFSI decreases). β€’ AFSI Singapore correlates quite strongly, insignificantly and in unidirectionally with AFSI Thailand, (AFSI Singapore increases/stabilizes when THAI AFSI increases/stabilizes, AFSI Singapore decreases when THAI AFSI decreases). β€’ AFSI Singapore is very weak, insignificant and in the same direction as AFSI Vietnam. (AFSI Singapore increases/stabilizes as VIETNAM AFSI increases/stabilizes, and AFSI Singapore decreases when AFSI Vietnam declines, but very weak relations). β€’ Thai AFSI correlates very weakly, insignificantly and in the opposite direction with AfSI Vietnam, (Thai AFSI increases/stabilizes when Vietnamese AFSI decreases, and Thai AFSI decreases when Vietnamese AFSI increases/stabilizes, but very weak relations). 5. CONCLUSIONS Overall, in the observation period, the asean-6 country's financial system stability index is volatile or unstable. In addition, the correlation between AFSI in 5 ASEAN countries showed results that varied even insignificant. Based on these findings, financial sector stability control authorities need to make efforts to further improve the stability of the financial system and monitor against escalation of instability in other countries' financial systems, especially those with strong and significant correlations. The weight of AFSI calculation using the proportion of sub-index indicators to total sub-indices needs to be reviewed again by looking at their contribution both theoretically and statistically sub index and AFSI as a whole. References Al-Rjoub, S. A. M. (2021). A financial stability index for Jordan. Journal of Central Banking Theory and Practice, 10(2). https://doi.org/10.2478/jcbtp-2021-0018 Albulescu C.T. 2008. Assessing Romanian Financial Sector Stability by Using an Aggregate Index, in β€œOeconomia”, Tom XVII, Volume 2, pp.67-87 Albulescu, C.T, Goyeau D. 2010. Assessing and Forecasting Romanian Financial System’s Stability Using an Aggregate Index. Journal of Economic Literature Classification: C43, C51, C53, G17 : 1-31 Morris VC. 2010. Measuring and Forecasting Financial Stability: The Composition of an Aggregate Financial Stability Index for Jamaica. 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