Economic Volatility: Does Financial Development ... - Murdoch University

Economic Volatility: Does Financial Development, Openness and Institutional Quality Matter In Case of ASEAN 5 Countries Hazman Samsudin 1,2* 1

PhD in Economics student at Faculty of Business and Government, University of Canberra, Canberra, ACT 2601 2 Tutor at Faculty of Management and Economics, University Malaysia Terengganu, Malaysia 21030 *email: [email protected] / [email protected] JEL Classification: C23, E02, F44, G20, O16.

1. Introduction In recent years there has been substantial attention on the link between economic volatility and financial development together with financial and trade openness policy as well as the role of institutional quality. Moreover, a series of financial crises have occurred and put the slowed down which saw the financial meltdown of the major economy of the world such as the Euro zone and previously East Asia 1997 financial crisis and it was said to associate with the rapid financial development together with the effect of openness instability as well as institutional quality factor. The state of financial conflict has raised the question of rationality behind the openness policy, the role of institutions as well as financial sector development and has fuel the fire on the topic and heat up the debate.

the persistent phase of growth with inflows of capital which then followed by economic collapse and capital flight.

Having said that, this study attempt to shed the light on the link between economic volatility with financial development and openness in both segments which is trade and financial along with the role of institutional quality in ASEAN-5 countries namely Indonesia, Malaysia, Philippines, Singapore and Thailand. In recent years ASEAN-5 have been subjected to rapid economic growth and several dramatic economic fluctuation has taken place which made a study on economic volatility on ASEAN-5 very tempting. ASEAN also have gone through several economic integration phase such as the establishment of ASEAN Free Trade Area (AFTA), ASEAN Comprehensive Investment Agreement (ACIA) and Chiang Mai initiatives and the increasing level of economic integration in trade and financial sector among them as well as international market such as China, Australia and New Zealand also have made this topic very interesting to discuss especially at how far the integration have affected their aggregate economic volatility. Moreover, the impact of financial and institutional sector reform especially during the privatization and liberalization era of the 80’s as well in the aftermath of 1997 crisis need to be asses the implication on economic volatility.

Furthermore, it’s been argued that government institutional also may be affected by political influence and it may be misused by political power to favor their cronies based institutions which will not bring the economy up to their optimum efficiency hence risking the economy towards crises moreover in the state of liberalization. For instance, Stigler (1971) as stated in Aggarwal and Goodell (2009), suggest that the supervision approach by official to banking regulation will make things worst rather than good because of interference with market forces which it indicate that strengthening institutions will only lead to more intervention thus slowing economic activities and risking for excessive volatility occurrences. In other words, strengthening institutional quality could lead towards paradox of enrichment1.

2. Selected literature review In witnessed the effect of globalization and financial contagion, many have reconsidered the pros and cons of financial liberalization due to the effects from capital controls removal where they are often associated with volatility (Schmukler, 2003). According to the same author capital controls removal often associated with economic volatility and according to Ang and McKibbin (2006), liberalization may increase economic volatility in financial system and hence trigger financial crises if carried out improperly. In addition, Stiglitz (2000) explained that the increasing recurrent of financial crises may have something to deal with financial liberalization since capital flows are cyclical in nature which will deteriorate economic swing. Moreover, Aghion et al. (2004) explained that, liberalization may destabilize economy where it will speed up

On the other hand, trade openness is also found to be unstable and may cause volatility hence leading to recession (Razin et al., 2003). Trade openness encourages specialization of production based on comparative advantage assumptions where it will make an economy more susceptible towards industry specific shocks (Kalemli Ozcan et al. 2003). Greater openness to world goods markets may also encourage domestic economic instability due to reliant on international environment such as exchange rate (Arora and Vamvakidis, 2004; Blankenau et, al., 2001; Rodrik, 1998) hence lead to sensitive susceptibility to external shocks.

Mean while, an increase in financial development could lead towards more economic volatility due to adverse selection and moral hazard which caused by increase in asymmetric problem where possibilities of failure to detect profitable investment still could be from abundance of financial instruments and sophisticated financial system especially when financial sector development is at intermediate level. Moreover, Acemoglu and Zilibotti (1997) illustrate that the interaction of investment indivisibility which followed by inability to diversify risk may magnify economic volatility. On the other hand, monetary shocks also could increase the chances of economic volatility moreover when monetary policies often changes which could refer to rapid intervention by government (Beck et al, 2000). While others such as Kiyotaki and Moore (1997) point out that the imperfection of capital market could intensify the effects of short run productivity shocks and make them more persistent. However, it is also been argue that a well developed financial system may have the ability of absorbing shocks easily through 1

Paradox of enrichment is the term used in population ecology to describe the collapse of population system when abundance of resources was given. In this sense, over strengthening institutional such as legal framework, may lead towards piling up more barriers in term of regulations which may negatively affect capital flow thus triggering volatility. Another example also could be rapid government intervention may lead towards frequent changing in regulations which is something might not preferred by investors.

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the capability of matching savers and investor easily at minimum amount of time thus avoiding capital flight which could meant for excessive volatility control. For instance, the main role of financial development is to lead and link between the deficit unit and the surplus unit which in turn may benefit the whole economy through the process of effectively turning saving into investment (Chinn and Ito, 2006 and Levine, 2005). According to Kose et al., (2006) this effects could also reduced volatility by means of providing access to capital which may assist in diversifying production base. Furthermore, financial system efficiency in processing information, monitoring and managing risk, supplying information about profitable ventures, diversify risks, and facilitate resource mobilization may lead towards effective investment which could decrease the chance of excessive volatility occurrences. Therefore, a well developed financial system assists in improving capital structure and the efficiency of resource allocation, promoting thereby long run economic growth (Kim et, al., 2009) and reduce economic volatility (Ahmed and Suardi, 2009). According to Chinn and Ito (2006), this is due to the nature of financial development in enhancing asymmetric information, lessens the cost of transaction and information, better corporate governance and facilitating risk management thus improving returns as well as reducing the cost of capital and investment respectively. Moreover, Kim et. al. (2009) added that financial institutions as well as financial market may also provide information on profitable ventures, diversify risks and facilitate resource mobilization. These effects on financial development has by large reduce economic volatility due to increase in confidence and certainties on return thus increase the level of investment and hence promote economic growth (Pindyck, 1991). On the other hand, openness also might have a negative relationship with economic volatility which means that, a more open an economy on international market, the lower the economic volatility will be. An open economic in both segment which is trade and financially could have better risk sharing and well diversified investment portfolio which could be vital in reducing the impact of economic shocks. This is also was in line with Bekaert et al., (2006) which also illustrate that financial opening does reduce volatility by improved risk sharing. On the other hand openness also may increase the amount of international portfolio investment flows which consecutively may increase the liquidity of domestic stock markets which refers to increase in total money supply where this could be vital for economic development and also with the presents of foreign entity might also facilitate access to international financial markets. Therefore open economy could facilitate volatility. As been discussed before, by allowing for financial opening specifically by lifting the restrictions on foreign portfolio flows are likely to improve stock market liquidity and also by permitting the presence of more foreign bank will increase the domestic banking system efficiency. According to Levine (2001), an increase in financial system efficiency for both banking and financial market may in turn equip them with capability to deal with an increase in volatility where they are more capable in absorbing economic shock. On the other hand, trade openness also may affect volatility negatively. Greater integration through openness could stabilize the consumer price which could bring the price level at optimum which matches with the international price level hence reducing the chances of inflationary shocks. In other words, openness in trade segment may improve resource allocation, lowers consumers’ prices and leads towards more efficient

production thus reducing volatility. Moreover it also encourages the technological transfer which may result in productivity improvements thus increase economic development which could mean a reduced impact on volatility. Mean while, trade openness also may increase industries specialization and according to Razin and Rose (1992), an increase in trade with increased specialization of intra industry would lead towards a declining in output volatility due to greater volume of intermediate inputs trade. Mean while, institutional quality also could negatively affect economic volatility. Institutional quality may reduce volatility by mean of increase in bureaucratic quality which could be vital in speed up any government work process, transparency thus ensuring return in investments, better legal framework making any investments decision easier with less uncertainties and less risk of contract repudiation could reduce the risk of capital flight. For instance, legal protections for creditors and the level of credibility and transparency of accounting systems are also likely to affect economic agents financial decisions (Beck and Levine, 2004), (Claessens et, al., 2002), (Caprio et, al., 2004), and (Johnson et, al., 2002) and also lowering volatility as they might reduce asymmetric information (Silva, 2002) as well as increase the level of investors’ confidence (pindyck, 1991). With given the relationship between economic volatility and it determinants are still ambiguous and the study focusing on this matter is still relatively thin, this study tends to dig more conclusions on this topic. 3. Area of study ASEAN-5 namely Malaysia, Indonesia, Thailand, Singapore and The Philippines are being chosen as the case of study. It is agreed that the study for all the members of ASEAN countries will be more comprehensive; however, data gathering process is not an easy task in a country such as Myanmar, Brunei, Laos, Cambodia and Vietnam in term of data availability. In addition, due to the fact that the GDPs of these countries would comprise nine-tenths of ASEAN’s overall GDP, a study on the main player of ASEAN members that is ASEAN-5 hope to be sufficient in order to capture the determinants of economic volatility in the said region therefore allowing for comparison study. Moreover, if ASEAN counted as a single market, it would be a market of 584 million people and 72 percent of it is accounted for by the population of the ASEAN-5. This came third in the world after China and India and, plus, ASEAN has a combined GDP of US$1,504 billion where 90 percent of it was a contribution of ASEAN-5 which is second to China’s in emerging Asia2. ASEAN 5 as an emerging economies did introduced continuous and prompt growth, with remarkable structural change and considerable enhancement in standards of living (Asian Development Bank, 1997) throughout the years. Furthermore, due to recent increase in economic integration and negotiation among them (for instance AFTA, AIC, Chiang Mai initiatives and etc) as well with international economies (for instance AANZFTA), an economic review on how it have shape the level of financial sector development and the level of openness as well as its institutional quality have affect their economic volatility looks very interesting to discuss. Therefore an economic review on this matter is essential especially in 2

Data are obtainable from ASEAN community in figures (ACIF) 2009

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assessing the effectiveness of recent economic integration and financial arrangements as well as policy decision. 4. Some issues in existing literature In reviewing the literature, it is found that the lack of past studies for individual ASEAN-5 countries. Most of the past studies are conducted based on cross country or panel data analysis. Therefore, the present study attempts to fill up this gap on the literature. The advantages of having individual country studies would give us a better finding because definitely for economic policies, historical and institutional factors are equally important. Other researchers such as Hasan et, al., (2009) also point out that most studies especially regarding of institutional and political influences employ cross country data which is hard to interpret due to the richness in historical experiences, norms and institutional contexts. In addition, the data on income or on inequality in different countries are not comparable, either because the purchasing power parity adjustments necessary for such comparisons are not reliable or because the methodologies underlying different countries’ numbers are too diverse to be pooled together, or both. Therefore, his study will fill the gap by undertaking individual country study where an individual country studies will allow for comparison studies in which where policies will work best. Furthermore, most of past studies highlighted the relationship between financial development, openness and institutional quality institutional quality with economic growth while less of them shed the light on economic volatility. It’s been argued that even if volatility is considered to have as a second ordered issue, their effects on growth could indirectly regards as first order welfare implications (Kose et. al., 2006). Therefore, a study on the effect of volatility has to be taken seriously moreover in recent years where there has been a substantial issue regarding economic volatility which involving the level of financial sector development and the level of openness as well as the crucial role of institutional quality. In addition, according to IMF (2003), they point out that financial liberalization should go along together with trade liberalization in assuring successful financial development where the main role from financial institutions are barely needed to reduce the trade transactions cost. It has been argued that with increasing globalization of trade and financial flows, a fully integrated economy cannot exist unless supported by a well functioning financial sector, and vice versa. International trade can flourish when essential trade related financial services and credit are available. On the other hand, trading opportunities help create demand for financial services and instruments, thus enhancing the development of financial system. However, as been mention above, China’s and India’s market rely heavily on trade openness but not complete financial openness and nevertheless their economies are performing well which given doubt on the hypothesis made by IMF (2003) and Rajan and Zingales (2003). Having said that, it is crucial to check the situation in the case of ASEAN-5 and therefore, this study will also test the simultaneous openness hypothesis. With those address issues in past literature, this study tend to fill the gap where this study will examine the relationship between economic volatility with financial development and openness in both segment simultaneously as well as the role of institutional quality in ASEAN 5 countries by utilizing time series data analysis for each country. 5. Derivation of Data

For the purpose of the study, aggregate economic volatility will be constructed by utilizing the principal component analysis. One of the benefits by employing this method is it can overcome the possibilities of multicollinearity and overparametrization as an overall indicator of aggregate economic volatility. However, before coming into the principal component analysis, it is better to define the terms volatility in the first place. The term volatility can be defined as the deviation of real time series data from its mean value overtime. Therefore, volatility in this study is measured by taking the five years rolling standard deviation of each proxy. An aggregate economic volatility can be captured through four perspectives which are the consumption growth volatility, output growth volatility, external volatility and internal volatility. Consumption growth volatility is proxy by the standard deviation of total consumption (Lσtc) where total consumption growth rate are the total of private consumption plus the government consumption. In most cases of developing countries, government consumption has been very influential and in mass volume, therefore would have an implication towards volatility. The standard deviation of the ratio of total consumption growth rate is simply to measure the effectiveness of consumption smoothing relative to output volatility. Output growth volatility on the other hand is proxy by standard deviation of GDP per capita (LσGDP per capita) where it may depicts the cyclical variability in net factor income flows which hold the effects of international risk sharing on national income due to market reforms. Mean while, external shock volatility is proxy by the standard deviation of term of trade (Lσtot) where it may represent the external shocks factor where the term of trade has been a factor in measuring social welfare and the growing trade activities in the region would have an implication on volatility. Internal shock volatility is proxy by standard deviation of government expenditure (LσGovex) where government expenditure would provide somewhat cyclical behavior and could have an instant effect on the response of private consumption to macroeconomic policy (Ahmed and Suardi, 2009) which can be used to capture the domestic shocks. Therefore an aggregate economic volatility is developed based on those perspectives of volatility. The summary of the principal component analysis on aggregate volatility is as in table 1. From the analysis, it indicates that most of the eigenvalues are able to capture more than 56% in case of Indonesia and Malaysia, Singapore 54%, Thailand 58% and 38% in Philippines. It is normally the first principal component (PC1) which will tell the most of data variation and each of the succeeding analysis will have the highest variance possible with the constraint it would be orthogonal to the previous component analysis. Therefore, the construction of aggregate economic volatility data is constructed based on the vector 1 for every country and the vector value for each variable will be scale accordingly3. The aggregate volatility data will then be derive after taking account the weight of each aggregate volatility component and denote with Lvol. Table 1: Aggregate volatility principal component summary Indonesia Eigenvalues

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PC1 2.259648

PC2 0.882693

PC3 0.643353

PC4 0.214306

The scaling of each variables vector value is done by doing simple average of total vector value

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% Of variance Cumulative % Variable Lσtc Lσgdp per capita Lσtot Lσgovex

0.5649 0.5649 Vector 1 0.520035 0.45364 0.577092 -0.43674

0.2207 0.7856 Vector 2 0.480896 0.42623 -0.21096 0.736589

0.1608 0.9464 Vector 3 0.426341 -0.75313 0.417478 0.277022

0.0536 1 Vector 4 -0.56262 0.212918 0.669458 0.43584

Malaysia Eigenvalues % Of variance Cumulative % Variable Lσtc Lσgdp per capita Lσtot Lσgovex

PC1 2.278689 0.5697 0.5697 Vector 1 0.483134 0.463977 0.577597 -0.46657

PC2 1.023897 0.256 0.8256 Vector 2 0.515294 0.486951 -0.31083 0.633038

PC3 0.499653 0.1249 0.9506 Vector 3 0.567277 -0.7243 0.305557 0.245415

PC4 0.197761 0.0494 1 Vector 4 -0.42338 0.151661 0.690219 0.566874

Philippines Eigenvalues % Of variance Cumulative % Variable Lσtc Lσgdp per capita Lσtot Lσgovex

PC1 1.53398 0.3835 0.3835 Vector 1 0.718816 0.212413 0.577401 -0.32372

PC2 1.164866 0.2912 0.6747 Vector 2 -0.03915 0.668927 0.20296 0.714011

PC3 0.887147 0.2218 0.8965 Vector 3 -0.11406 -0.64931 0.619656 0.425921

PC4 0.414007 0.1035 1 Vector 4 0.684661 -0.29293 -0.49137 0.45165

Singapore Eigenvalues % Of variance Cumulative % Variable Lσtc Lσgdp per capita Lσtot Lσgovex

PC1 2.166626 0.5417 0.5417 Vector 1 0.434509 0.597383 0.522493 0.425837

PC2 0.841703 0.2104 0.7521 Vector 2 -0.546 0.102872 -0.29661 0.776738

PC3 0.66755 0.1669 0.919 Vector 3 0.711217 -0.19362 -0.60893 0.293059

PC4 0.324121 0.081 1 Vector 4 0.085165 -0.7714 0.517906 0.3598

Thailand Eigenvalues % Of variance Cumulative % Variable Lσtc Lσgdp per capita Lσtot Lσgovex

PC1 2.322742 0.5807 0.5807 Vector 1 0.604046 0.320813 0.558549 -0.46929

PC2 0.865502 0.2164 0.7971 Vector 2 -0.08581 0.923306 -0.14897 0.343442

PC3 0.606705 0.1517 0.9487 Vector 3 0.208149 -0.18646 0.545735 0.789988

PC4 0.205051 0.0513 1 Vector 4 0.764487 -0.09909 -0.60664 0.194256

Note: Lσtc = log standard deviation of total consumption, Lσgdp per capita = log standard deviation of GDP per capita, Lσtot = log standard deviation of term of trade and Lσgovex = log standard deviation of government expenditure

The second variable which is aggregate financial development also goes through the same process of Principal component analysis. The aggregate financial development index are constructed based on the data of domestic credit to private sector divided by nominal GDP (Ldome), M2 over the nominal GDP (Lm2), the ratio of bank domestic asset to total assets of bank and central bank (Ldbacba), stock market capitalization (Lstmcap), total value stock traded (Lstval) and stock market turnover (Lsto). Basically this data follow the standard measurements of financial development as in Beck et. al., (2000) where the first three indicators reflect the banking sector development and the last three indicate the market sector development. Each of these variables captures different aspect of financial development. For instance, the first proxy represents overall development in private banking markets, where; it excludes credit granted to the public sector and credit issued by the central bank. The reason of excluding the loans issued to governments and public enterprise is, it is often argued that the private sector is able to utilize funds in a more efficient and productive manner which reflects the extent of efficient resource allocation. Second proxies also known as liquidity liabilities, in which it was largely used in measuring financial depth where it was designed

to depict the overall size of the formal financial intermediary sector and provide information regarding the degree of transaction service provided by the financial system. However, some have argued that these are not good proxies as they posses several weaknesses4. Nevertheless, for the purpose of this study, each proxy’s captures fairly different aspect of financial development, therefore neglecting this proxy should not be the case. The third proxy of bank based measurement of financial indicator is the bank assets which measure the degree of importance of each financial intermediary and domestic bank efficiency in turning society savings towards more profitable investment opportunities. As for the market based indicator which covers the development of non bank and equity sector, they are made up based on three proxies which is stock market capitalization (Lstmcap), total value stock traded (Lstval) and the stock market turnover (Lsto). The first proxy reflects the size of the equity markets it shows the share of domestic companies over GDP. Second and third proxies define the dynamism or the activeness of stock market (Beck et, al., 2000), where the total value stock traded shows the total market value of shares traded in term of GDP and stock market turnover ratio measure the transaction of stock towards the market size where it is often used as market liquidity measures. All of these measurements are specified in term of ratio to GDP. Therefore the aggregate financial development index is constructed based on these 6 variables. The summary of the principal component analysis is as in table 2. As clearly seen, in the case of Singapore there is only five principal components analysis took place. This is due to the one of the variable which is the bank domestic asset to total assets of bank and central bank (LDbacba) is problematic thus the variables is taken out of the construction of aggregate financial development 5. Nevertheless, for the rest of the country there has been no issue with the data. Based on the results, it indicate that more than 56% variation of the data are captured in the first principal component eigenvalues in Indonesia while 61% in Malaysia, 54% in Philippines, 55% in Singapore and 71% in Thailand. This shows that the combination values of each variable in vector 1 for every country are able to reflect more than half of total 6 variables. The vector values of each variable will be scaled to determine the weight it will carry in construction the aggregate financial development data which is denoted with Lfd. Table 2: Aggregate component summary

financial

development

principal

Indonesia Eigenvalues % Of Variance Cumulative % Variable LDome LM2 LDbacba LStmcap LSto LStval

PC1 3.334247 0.5557 0.5557 Vector 1 0.269068 0.462444 -0.14455 0.506153 0.432849 0.499304

PC2 1.57847 0.2631 0.8188 Vector 2 0.662642 0.055617 0.723949 -0.11256 0.056268 -0.13369

PC3 0.684444 0.1141 0.9329 Vector 3 -0.28049 -0.60489 0.315932 -0.04498 0.575911 0.349182

PC4 0.34908 0.0582 0.991 Vector 4 -0.06891 -0.26767 0.284605 0.566221 -0.63526 0.344155

PC5 0.036211 0.006 0.9971 Vector 5 -0.39407 0.174857 0.297423 0.534263 0.244322 -0.61688

PC6 0.017548 0.0029 1 Vector 6 -0.4998 0.561198 0.430964 -0.35082 -0.12063 0.334526

Malaysia Eigenvalues % Of Variance Cumulative % Variable LDome

PC1 3.651677 0.6086 0.6086 Vector 1 0.354346

PC2 1.266792 0.2111 0.8197 Vector 2 0.406205

PC3 0.517539 0.0863 0.906 Vector 3 0.724358

PC4 0.337203 0.0562 0.9622 Vector 4 0.424007

PC5 0.177745 0.0296 0.9918 Vector 5 -0.02001

PC6 0.049043 0.0082 1 Vector 6 -0.06753

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Some have argued that liquidity liabilities fail to capture the role of the financial system to direct funds from depositors to investment opportunities. Furthermore, the monetary aggregates of foreign fund in financial system are insufficient measures of financial development. It also potentially admits double counting. Further detailed see Ang and McKibbin (2006) and Kim et. al., (2009) 5 Lots of missing data in case of Singapore where data observation from 1972 until 1999 were missing. However the data was made available again in year 2000 onwards.

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LM2 LDbacba LStmcap LSto LStval

0.459608 0.335549 0.471863 0.383111 0.42565

0.24769 0.585863 -0.20813 -0.42914 -0.45048

-0.3338 -0.42036 0.22892 -0.33924 0.14035

-0.14468 -0.06994 -0.48917 0.706844 -0.23554

-0.66548 0.579068 -0.14107 -0.01503 0.448653

-0.39012 0.165097 0.649952 0.232648 -0.5826

Philippines Eigenvalues % Of Variance Cumulative % Variable LDome LM2 LDbacba LStmcap LSto LStval

PC1 3.209996 0.535 0.535 Vector 1 0.436796 0.085981 0.473467 0.517392 0.23496 0.504722

PC2 1.313401 0.2189 0.7539 Vector 2 0.18728 0.755346 0.263609 -0.01719 -0.48865 -0.29294

PC3 0.850685 0.1418 0.8957 Vector 3 0.258249 0.459556 -0.233 -0.35181 0.734764 -0.06461

PC4 0.433724 0.0723 0.968 Vector 4 -0.7939 0.238342 0.450062 0.096728 0.31702 -0.02248

PC5 0.147684 0.0246 0.9926 Vector 5 -0.27778 0.390802 -0.58327 0.204338 -0.1815 0.595999

PC6 0.04451 0.0074 1 Vector 6 0.002187 0.036526 -0.3306 0.74641 0.180811 -0.54731

Singapore Eigenvalues % Of Variance Cumulative % Variable LDome LM2 LStmcap LSto LStval Thailand Eigenvalues % Of Variance Cumulative % Variable LDome LM2 LDbacba LStmcap LSto LStval

PC1 2.768573 0.5537 0.5537 Vector 1 0.35013 0.522529 0.301967 0.494727 0.518107 PC1 4.236854 0.7061 0.7061 Vector 1 0.454667 0.426536 0.437682 0.438581 0.206424 0.429901

PC2 1.116991 0.2234 0.7771 Vector 2 0.733553 0.356684 -0.21382 -0.29425 -0.44986 PC2 0.910026 0.1517 0.8578 Vector 2 -0.04706 -0.23838 -0.04893 -0.21257 0.939694 0.101755

PC3 0.902719 0.1805 0.9577 Vector 3 0.077647 -0.07325 0.875441 -0.46966 -0.04037 PC3 0.444637 0.0741 0.9319 Vector 3 0.221646 0.49568 0.300205 -0.44753 0.119761 -0.63279

PC4 0.163767 0.0328 0.9904 Vector 4 0.573725 -0.76978 0.005406 0.209007 0.185909 PC4 0.226055 0.0377 0.9696 Vector 4 0.187796 0.384408 -0.78432 0.351812 0.169696 -0.2219

PC5 0.04795 0.0096 1 Vector 5 -0.06418 -0.04256 0.310917 0.635923 -0.70214 PC5 0.130725 0.0218 0.9914 Vector 5 -0.84016 0.447086 0.152843 0.230705 0.132535 -0.00964

PC6 0.051703 0.0086 1 Vector 6 0.028266 -0.40974 0.278208 0.620627 0.116877 -0.59588

Note: LDome = log domesticcredit to private sector, LM2 = log M2, LDbacba = bank domestic asset to total assets of bank and central bank, LStmcap = log stock market capitalization, LSto = log stock market turnover ratio and LStval = log total value stock traded

The financial openness data in this study will be proxy by the “De facto” and “De jure”. Financial openness measured by “De facto” is the financial globalization indicator constructed by Lane and Milesi Ferretti (2006) and this indicator is defined as the volume of a country's foreign assets and liabilities as a percentage of GDP. Therefore “de facto” might reflect the country history of financial openness. Mean while, “De jure” measurement of openness is the one develop by Chinn and Ito (2007) where they derive the index of capital account openness (KAOPEN). This measurement is build from four binary dummy variables where it reflects the cross border financial transactions restrictions which reported in the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). These binary variables are then reversed to make it equal to unity which reflects the perfect free market without restrictions. Therefore the “de jure” data is a dummy variable with range between 0 and 1 where the closer the value to 0 indicate that the country are practicing lots of restriction or protective policy while the closer to 1 indicate that the country is more open with less barriers. However, the dummy variables produced are by utilizing the principal components analysis which may suffer from measurements error where some variation of the underlying data may not be documented for. Furthermore, the data also may suffer from the enforcement issue. If let say a country have lifted up the barriers doesn’t mean it would imply greater capital account openness if the right to engaged is not fully utilized in international transaction thus it would over state the actual level of capital openness. Nevertheless, it’s been argued that the “de jure” measurements have a better grounded theory than “de facto” especially when “de jure” is more strongly associated with the decision to open up an economy towards capital flows. This is contrast with the

“de facto” measurements of openness where the measurements may bias towards other underlying factors of capital flows such as future prospect where it may increase the question of reliability of “de facto” as a proxy of capital account openness. Nevertheless “de facto” measurements are less vulnerable towards political factors influence since the decision to increase or reduce openness may be influenced by some interest groups. Having said that, despite the weaknesses of the measurements, it is actually the strength of the measurements and as been mentioned earlier, “de facto” measurements may reflect the country historically background, geographic and international politics which may be out of policy maker control thus less issue with influence of political factor compared to “de jure” measurements. Therefore, some researchers argue that the “de facto” measurements might be more relevant for pure test of financial openness hypothesis where it seems to reflect the true level of openness or outcome based measurements. On the other hand “de jure” measurements of openness may be close related towards the policy based financial openness measurements. Due to the both measurements might have their own strength and weaknesses as well as both measurements also depict financial openness in different perspective which is outcome based and policy based, both measurements of financial openness will be employ in this study. Both measurements are denoted as Ldejure and Ldefacto respectively. The third variable which is included in this study is the trade openness which is denote as Lto where it is defined as annual data on real GDP per capita, converted to US dollars at constant 2000 is measured by the ratio of total trade to GDP will be the proxy for trade openness. The measurement of trade openness is apparently less complicated and straightforward compare to financial openness measurements. The fourth variable is the institutional quality indicator. Actually there are numerous of institutional quality indicator have been made available and it can be divided in to two types which is the objective measurements and the subjective measurements. Objective measurements can be obtained by employing the Contract Intensive Money (CIM) which has been developed by Clague et. al., (1997)6. Besides its strength such as abundance of data in term of data duration and advantage over contamination of recent economic situation or performance knowledge bias by evaluators especially in subjective measurements, it also contained some weaknesses which is, it might be a little bit “noisy” as the decision of holding financial assets might be influenced by other factors such as norms, expectations based on global and domestic economic situation, interest rate and the rate of inflation. Having said that, this variable might not be sensitive towards growth rate when controlling for inflation and one of the most common measurement of financial development which is M2 to GDP where those variables is used in constructing aggregate financial development indicator in this study. Therefore, this study will employ the subjective over objective measurements due the reason stated above. For the purpose of this study, the subjective measurements of the institutions quality indicator provided by BERI (Business Environment Risk Guide) will be used over ICRG (International Country Risk Guide). This consideration is made based on the availability of longer time period where the data on ICRG only More information on this can be obtained from “contract intensive money: contract enforcement, property rights, and economic performance” by Clague et. al., (1997) 6

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available starting from 1984 while BERI starts from 1980. This is due to one of the aim of this study is to analyze the effect of openness and institutional quality on financial development as well as the economic volatility by emphasizing the time series data analysis which means the more the series of the data, the better the view of the relationship. Therefore, the data from BERI will be employed in this study to proxy for institutional quality factor which denoted with Lberi. Basically the data were made up based on several indicators such as the degree of privatization, bureaucracy delay, contract enforcement, communication and transportation, nepotism and corruption, and the level of legal framework. The degree of privatization refers to the seriousness of government in outsourcing or transferring any government activity through businesses, enterprise, agency, public service or public property to private sector in achieving better services to support businesses needs. It also could means as a medium of reducing red tape which usually exist in government sector and also as a useful sight of chances of force nationalism in a particular country. On the other hand, contract enforcement refers to the extent and the seriousness of government in honoring any contract that have been made. Communication and transportation also could make weigh in building up the institutions quality measurement where it could be as an indicator in assessing the “facilities for and ease of communication between headquarters and the operation, and within the country,” and also as an indicator of transportation quality which could be important for businesses as well as a reflection of government efficiency in allocating public goods and prioritize business activity. According to Knack and keefer (1995), it is likely that poorer country to have low indicator in this measurement. Mean while nepotism and corruption is a measurement of the wrong doings of government official such as bribe or illegal payment which might be something to do with licensing, exchange controls, taxation, policy protection and so forth. The measurement also reflect the level of prioritize on political connected organization or businesses, one sided decision due to owing for special payments to government official and other similar things. The last sub component of institutional quality is the level of legal framework. Legal framework can be viewed in two dimensions which is law as written and the actual practice such as dividend, royalties, remittances, repatriation of capital, hedging against devaluing currency and the like of it. Having said that, the differences between what was written and actual practice depict the level of willingness to accept the established institutions in making and implementing laws and adjudicate disputes. The higher the score for each component indicate the better the institutions in the particular country. Therefore, by combining these entire sub components will make an all rounder institutional quality measurement which may capture at every different aspect of governance that might influence economic volatility in a particular country. For the purpose of aggregating, this paper will follow the method of simple addition aggregating by Knack and keefer (1995) where all of the indices are given the same weight. According to the author, even when individual components of indices are employed, the result doesn’t change significantly and also when they are compiled with different weights. While the other issue on bias of employing BERI database over ICRG should not exist as all of the subcomponents are very close analogous with each other in term of the definitions and what is important is the selection was

made base on the needs of the study. Control variables on the other hand, are also being introduced in this study. The main reason for introducing the control variable is to avoid or reduced some of the econometrics problem such as endogeneity which may arise as a result of measurements error, autoregression with autocorrelated errors, simultaneity, omitted variables and sample selection errors. The first controlled variables are the real interest rate (Lint) which is suggested to have an impact on the financial sector. For example, Ang and McKibbin (2006) found that an increase in real interest rate would have a negative impact on the financial sector which also consistent with Arestis et al. (2002). Second controlled variable to be included is the per capita income (Lincpc) where it is necessary to control the causal relationship between income and financial deepening. It is become almost common to include this variable as this variable are often associated with their contribution towards the complexity of economic structure (Chinn and Ito, 2005). The third variable is the inflation rate (Linf) where it is defined as the rate of changes in CPI in which it is an essential indicator for macroeconomic stability measurements (Beck et al 2000). It is an important proxy for unpredictability in inflation as it will have an impact in decision making particularly in real assets saving (Chinn and Ito, 2006). The fourth, control variables is the exchange rate (Lex). It is expected that exchange rate could possibly trigger volatility as they could distort volume of trade and capital flows. However to what extent the distortion is still ambiguous depends on the nature of shock whether it is fiscal or monetary derives and depends on the exchange rate regime as well where at the end it could have positive or negative impact on volatility (Silva, 2002). In this study exchange rate is measured as the absolute value of the change in exchange rate which is defined as SDRs per unit of national currency. The last controlled variable to be included is the government expenditure to GDP (Lgovex) which also is at best as an indicator of macroeconomic stability. Government expenditure is vital in reflecting the impact of public expenditure in distorting private decisions which in turn will have an effect towards the financial sector. Most of the data are obtainable from World Development Indicator (WDI) except the data for institutional quality indicator which is obtainable from the Business Environment Risk Intelligence (BERI) and the openness indicator of “de facto” are obtainable from Lane and Milesi Ferretti (2006) and from Chinn and Ito (2007) for “de jure” indicator while the exchange rate data are obtainable from International Financial Statistic (IFS) online version. All of the data will be transformed in logarithm form in order to avoid precision error measurements and all data will have similar unit of measurements as well as to reduce widely varying quantities to much smaller ranges. By transforming into logarithm form also, the estimated coefficient also will be interpreted as elasticities. The data will cover from 1970 until 2011. 6. Methodology and Empirical Findings In order to capture the relationship between volatility with financial development and the effect of openness in a sound institutional quality, this model was further setup which can be viewed as follows: Lvol it    1 LFd it   2 Ldejure   3 Ldefactoit   4 Ltoit   5 Lberiit   6 Ctr   it

(1) 6

Where, Lvol is volatility, Lfd is financial development, Ldejure and Ldefacto financial openness and Lto is trade openness, Lberi is institutional quality, Ctr is set of control variables and ε is standard error while α and β is the estimated parameter in the model. For the purpose of this study the Autoregressive Distributed Lag (ARDL) bound test to cointegration will be employed to estimate equation (1). This is because the ARDL method of estimation will still efficient even if there is a mix level of stationarity of I(1) or I(0) among the regressors7. Other advantage of bounds test approach is the method can be applied for a small sample study8. These are the advantages of using Pesaran et al.’s (2001) method cover common practice of cointegration analysis like Engle and Granger (1987) and Johansen and Juselius (1990). Another important advantage of the bounds test procedure is that estimation is possible even when the explanatory variables are endogenous. However, even though the method allow for mix stationarity level to be mix in the same model, the regressand should not be at I(0) level of stationarity and no other variables at I(2) level of stationarity should incorporated in the same model or otherwise it may lead towards spurious regression and the presence of long run cointegration may not be detected. Therefore, prior to cointegration test a unit root test is essential in order to avoid such problems and Augmented Dickey Fuller (ADF) along with Phillip and Perron (PP) test will be employ. The result is as in table 3 and 4. Table3: ADF and PP test at level – I(0) Variables

Lvol

Indonesia -2.59624

Augmented Dickey Fuller (ADF) Malaysia Philippines Singapore -3.15262 -2.14247 -2.30734 -1.34256 -2.16811 -2.3935 -1.66628 -2.72008 -1.53723 -3.81441** -1.46612 -2.04718 -1.3969 -0.02801 -2.77342 -4.07997** -2.90527 -2.357 -2.54417 -0.43979 -3.16884 -1.68713 -2.56508 -2.10582 -2.51092 -1.96463 -1.13087 -4.74724*** -3.95455** -6.05135*** -2.77918 -2.76329 -3.42105* Philip and Perron (PP) Malaysia Philippines Singapore -2.5401 -2.47118 -2.48863

Lfd Ldejure Ldefacto Lto Lberi Lex Lgovex Lincpc Linf Lint

-1.57466 -2.21487 -2.17042 -3.51358* -1.99465 -2.4238 -1.94083 -1.29061 -4.58039*** -2.82227

-1.22689 -1.08006 -3.81441** -0.80461 -4.51312*** -2.20817 -1.84332 -2.30131 -3.89111** -2.58444

Lvol Lfd Ldejure Ldefacto Lto Lberi Lex Lgovex Lincpc Linf Lint Variables

Indonesia -2.28527 -1.57466 -2.34923 -2.1445 -3.60794** -1.99465 -2.35555 -2.23115 -1.19669 -4.71221*** -2.71115

-2.29742 -2.74688 -1.40117 -0.15002 -3.80175** -0.97397 -2.80955 -1.79413 -6.49581*** -2.09096

Thailand -2.01359 -2.52508 -2.5461 -2.56916 -1.44104 -1.19917 -2.24478 -2.00501 -4.63075*** -3.67186**

Thailand -2.61795

-2.4204 -1.53723 -2.22978 -2.37239 -2.43786 -2.20887 -2.10582 -1.16165

-1.85492 -2.5061 -2.66821 -1.47206 -1.4884 -1.64334 -1.66903

-4.58254***

-4.60334***

-2.60335

-2.88976

Note: *, ** and *** indicate significance level at 10%, 5% and 1%

Lvol

Indonesia -5.55784***

Augmented Dickey Fuller (ADF) Malaysia Philippines Singapore -5.02257*** -4.46507*** -6.41748***

Lfd Ldejure Ldefacto Lto Lberi Lex Lgovex Lincpc Linf

-5.52675*** -8.59547*** -6.80268*** -8.30516*** -6.40976*** -6.65154*** -7.58525*** -5.39917*** -8.7251***

-5.9065*** -4.66648*** -6.14235*** -5.10467*** -6.21031*** -4.93398*** -6.16589*** -5.04941*** -7.03688***

7

-6.93325*** -6.86955*** -7.06358*** -5.37681*** -4.00312** -4.84474*** -5.41127*** -4.28522*** -8.36367***

-5.4623*** -4.66728*** -5.60834*** -5.82823*** -5.53684*** -3.90273** -5.19302*** -5.15183*** -6.69856***

Indonesia -6.10007***

Lfd Ldejure Ldefacto Lto Lberi Lex Lgovex Lincpc Linf Lint

-5.44523*** -8.65081*** -6.80649*** -8.6608*** -6.34989*** -6.69909*** -7.49917*** -5.39917*** -10.1573*** -7.39224***

-6.15592*** -3.89959** -10.9251*** -5.07113*** -29.314*** -4.78617*** -6.16988*** -5.00529*** -8.27553*** -11.263***

-6.13653***

-6.93203*** -6.87036*** -7.47337*** -5.3687*** -3.96656** -4.83897*** -5.52937*** -4.27414*** -20.3093*** -14.4609***

-4.76334***

-7.50976*** -5.74458*** -5.61356*** -5.82233*** -5.61086*** -3.51264* -5.14546*** -4.67511*** -17.4313*** -6.41287***

-4.07549** -7.44433*** -6.76937*** -6.07186*** -5.2139*** -5.3666*** 0.7464*** -8.91351*** -5.23729***

Thailand -3.70417**

Note: *, ** and *** indicate significance level at 10%, 5% and 1%

From the table, it shows that only few of regressors variables exhibit I(0) stationarity while the regressand and all of other variables cannot reject the presence of unit root at level. However, after conducting the unit root test in first difference the result shows that all of the variables have become stationary at first difference and most of them significant at 1% significance level I(1). In other related issue, it is also observed that the unit root test for financial openness measured by dejure was not included in the test for Thailand. This is because the data show no variation in the trend between 1970 until 2004 which made any regression with the variable seems impossible and also raises the doubt over the data reliability for Thailand where it is well known that Thailand took an important step in lifting up the barrier on its capital account, FDI restrictions and foreign borrowing in late 80’s until before the 1997 crisis 9. Due to this situation, the variable needs to be taken out from the model for Thailand. After understanding the underlying background of each variables data and under this circumstance especially when facing with mix stationariy variables, by employing the ARDL bound test for cointegration would be the most efficient way in determining the long run relationship among the variables moreover in small sample dataset. However, prior to the procedure, the underlying order of auto regression (AR) need to be estimate as one of the important issues of ARDL cointegration test is the lag length determination where randomly choosing the lag length may lead towards inefficiency or biased estimates parameter. In determining the order of AR, Aikake’s Information Criteria (AIC) is preferred rather than the Schwarz Bayesian Criteria (SBC)10. The lag length criteria, the list of variables and the number of optimum lag length criteria is presented in the appendix section of 1A. After obtaining the optimum lag length based on equation (1), the Unrestricted Error Correction Model (UECM) is developed as follows. Lvolt   0  1Lvolt 1   2 Lfdt 1  3 Ldejuret 1   4 Ldefactot 1  5 Ltot 1

Table 4: ADF and PP test at 1st difference – I(1) Variables

Lvol

-5.08477*** -6.50074*** -5.79677*** Philip and Perron (PP) Malaysia Philippines Singapore -5.02185*** -4.46507*** -6.51351***

Lint Variables

  6 Lberit 1   7 L inf t 1  8 Lgovext 1  9 Lext 1  10 L int t 1 -3.67015**

i 1

-4.31074*** -7.22065*** -6.76839*** -5.54748*** -5.19287*** -5.23091*** -4.48715*** -7.77897***

There is a possibilities of mix level of stationarity in this study as such variables as inflation rate, interest rate and financial openness indicator are all may subject to rapid intervention of government as a tool of monetary policy and international policy 8 This study contains variables with 30 to 40 years of observation thus employing ARDL method seems imminent.

p

o

 11Lincpct 1   12i Lvolt i   13i Lfdt 1

Thailand

(2)

i 1

q

r

s

  13i Ldejuret  i   14i Ldefactot i   15i Ltot i i 0

i 0

i 0

t

u

v

w

i 0

i 0

i 0

i 0

  16i Lberit  i   17i L inf t i   18i Lgovext  i   19i Lext i x

y

i 0

i 0

   20i L int t  i    21i Lincpct  i  t 9

Other researchers such as Badi et. al., (2009) also pointed out this problem relating to Chinn and Ito index. 10 This due to AIC tends to move from lowest possible lag order at slow rate as the sample size increases which may wander the chances of omission of relevant variables biased from the regression. Having said that, overestimation of the order of AR seems preferable.

7

As usual the β is the estimated coefficient, Δ is the difference operator and µ is the white noise disturbance term. From the UECM, the long run elasticities can be obtained from the independent variable coefficient of the first lag divided by the dependent variable coefficient of the first lag. In conducting the ARDL bound test, it involve several step where the first step is to estimate equation (2) by using the Ordinary Least Square (OLS) technique then proceeded with the calculation of Fstatistic (Wald test) to determined the existence of long run relationship between aggregate economic volatility and its determinants. The Wald test is done by imposing a restriction on both dependent and independent variables coefficients where the null and alternative hypothesis of equation (2) can be view as following. H0 : β1 = 0 …...βi = 0 (No long run relationship) H1 : β1 ≠ 0 …...βi ≠ 0 (Exist long run relationship) Then, the estimated Wald test F stat is compared with the critical values provided by Paseran et. al. (2001) and Narayan (2005). If the calculated F stat is lower than the critical values than the null hypothesis of no cointegration will not be rejected where it assume that the regressors are cointegrated of order zero I(0). On the other hand, if the calculated F stat exceed the upper critical value than the null hypothesis of no cointegration can be rejected where it assume that the regressors are cointegrated at order one I(1). However, if the calculated F stat falls between the lower and upper bound, then no conclusion can be made. The results of ARDL couintegration based on equation (2) are as follows. Table 5: Long run coefficients of the UECM results based on equation (2) I. Estimated Model Variable Indonesia 16.28770 Constant Lvol t-1 Lfd t-1 Ldejure t-1 Ldefactot-1 Lto t-1 Lberi t-1 Linf t-1 Lgovex t-1 Lex t-1 Lint t-1

(1.303177) -2.501958*** (-7.669844) -0.954951** (-3.330524) -1.535116 (-0.394802) 6.636988*** (5.847089) 18.35930*** (5.392568) 11.80365* (2.319463) -5.119772** (-3.424439) 13.52572** (3.040839) -2.376610** (-3.593044) 3.593907*** (5.863104)

II. Goodness of fit R2 0.978044 Adj R2 0.840817 Std error 0.211767 7.127206** F-Statistic -0.548228 AIC III. Diagnostic checking 0.641970 Normality [0.725434] test

ARCH test RESET test

Philippines

Singapore

Thailand

-46.68303 (-0.655644) -0.865959** (-2.579258) -2.841308 (-1.979420) -3.009972** (-3.036930) -1.148548 (-0.666615) -14.77133*** (-4.909498) -4.198213 (-0.264818)

-23.61783*** (-4.212776) -0.971475*** (-3.529380) 0.750217* (1.947015) -0.895013** (-2.944416) 2.427616** (3.105067) -1.767194** (-2.333654) 4.259750** (2.911652) -0.686878 (-1.527405)

-102.7617* (-2.538747) -0.608958** (-3.768341) -1.237306* (-2.565102) 2.478401** (3.362056) 1.986597*** (4.074617) -2.953602* (-2.307630) 24.91888** (2.626151) 0.740029* (2.205309)

-116.4875** (-2.976877) -0.708897* (-1.896355) -7.343235** (-2.307098)

-3.479213* (-2.031231)

7.282700** (2.395190) -0.531025 (-1.690097) 9.648564** (2.862000)

-1.199344 (-0.677330) -8.373137** (-3.069667) 11.81651 (1.478062)

-16.21248** (-3.458740)

1.194607** (2.644559) 6.053503** (2.616684)

Lincpc t-1

Serial correlation

Malaysia

0.957668 0.771407 0.329371 5.141534** 0.536826

0.848506 0.528687 0.206152 2.653076* -0.111169

0.950942 0.735089 0.113458 4.405495* -1.594675

0.901766 0.643903 0.357317 3.497067** 0.924525

4.192683 [0.122905]

0.448969 [0.798928]

1.340146 [0.511671]

0.213577 [0.898716]

2558.877*** [0.0004] 1.743601 [0.1732]

5.261774 [0.1638] 1.779339 [0.1819]

4.494374 [0.3503] 0.886418 [0.4899]

3.545274 [0.2278] 0.757633 [0.5304]

3.013701 [0.1964] 0.857392 [0.4771]

4.103269 [0.3446]

3.384173 [0.2364]

1.073603 [0.3920]

0.716366 [0.6272]

1.784380 [0.2465]

Note: *, ** and *** indicate the significant level at 10%, 5% and 1% respectively. Figure in the square brackets [] quoted the probability values and figure in round bracket () indicate the t test value.

The results indicate that the goodness of fit measurements of the model remain superior for all of the countries under observation

especially the reported value of R2, adjusted R2 and the standard error. The F stat also indicates that there is a significant relationship among the variables at 10% and 5%. In sum, all of the variables fit the model well for the entire set of country under observation. On the other hand the diagnostic checking indicate that the model have been correctly specified under the RESET test for all of the country under observation. The model also has passed the normality test measured by the Jarque Bera test where both level of skewness and kurtosis have been checked while the ARCH test checked for the presence of heteroscedasticity in the model and none have been detected. However, under the serial correlation test which is reported by the Breusch Godfrey LM test indicate that there is a possibility of the serial correlation between the error term and the specified model in case of Indonesia. Moreover, the reported CUSUM test also shows that the series has exceeded the minimum bound for Indonesia which shows that the model might suffer from instability of long run coefficients issue11. Therefore, any results interpretation regarding Indonesia has to be carried out properly as there is some issues with the diagnostic checking as noted. Nevertheless for the rest of the country under observation, there has been no issue with the diagnostic checking test and they have passed those tests easily thus any interpretation out of it can be considered as reliable as reported in table 5. Table 6 summarize the long run relationship based on equation (2) where the computed F stat are generated using the Wald test which then will be compared with the asymptotic critical values generated by Paseran et. al. (2001) and Narayan (2005) for specific sample size. Table 6: Results of the ARDL bounds test Country

Indonesia Malaysia Philippines Singapore Thailand Unrestricted intercept and no trend Significance level

Computed F-statistic 10.30195** 7.121457** 5.125420** 6.791438** 4.176731**

Critical values (Table CI(iii) case III – Paseran et al. (2001) Lower Upper bound bound (k = 7)

Lower Upper bound bound (k=8)

Lower Upper bound bound (k=9)

2.96 4.26 2.79 4.1 2.65 3.97 2.32 3.5 2.22 3.39 2.14 3.3 2.03 3.13 1.95 3.06 1.88 2.99 Note: *,** and *** indicate significant level at 10%, 5% and 1% based on Wald test. Number in bracket () indicate the value of degree of freedom while the critical value table is obtained based on paseran et. al., (2001) and Narayan (2005) unrestricted intercept and no trend table CI(iii) case III.

1% 5% 10%

The calculated F stats for Malaysia, Philippines, Singapore and Thailand exceed the upper critical value at 7 degree of freedom and Indonesia at 9 degree of freedom which indicates that the null hypothesis of no cointegration among the observed variables can be rejected at least at 5% for all cases. Therefore, it can be concluded that there is a consistent long run relationship between volatility, financial development, openness and institutional quality in ASEAN 5 countries. Table 7 shows the long run elasticities and short run causality for ASEAN 5 countries of all of the regressors. Table 7: Short run causality and long run elasticities I. Long run estimated coefficient Variable Indonesia Malaysia Lfd -0.38168** -3.28111 Ldejure -0.61357 -3.47588** Ldefacto 2.652718*** -1.32633 11

Philippines

Singapore

Thailand

0.772245* -0.92129** 2.498897**

-2.03184* 4.069905** 3.262289***

-10.3587** -1.69185

A graphical presentation of the CUSUM test is reported in appendix 1A.

8

Lto 7.337973*** -17.0578*** -1.81908** Lberi 4.717765* -4.84805 4.384827** Linf -2.04631** -0.70705 Lgovex 5.406054** -18.722** Lex -0.9499** 1.229684** Lint 1.436438*** 6.990519** Lincpc II. Short run causality test (Wald test/ F-statistic) Variable Indonesia Malaysia Philippines ∆Lfd 32.68050*** 2.362106 4.069613* ∆Ldejure 17.88078** 0.706129 4.239442* ∆Ldefacto 0.103051 0.194406 3.688662* ∆Lto 28.16926*** 16.46085*** 2.545739 ∆Lberi 7.800459** 0.665836 0.484104 ∆Linf 10.94070** 0.525235 3.020265 ∆Lgovex 11.09668** ∆Lex 5.062615* ∆Lint 3.909624 3.401333* 6.386439* ∆ Incpc

-4.85026* 40.92052** 1.21523*

-11.8115** 16.66887

-5.71339*

10.27328** -0.74909 13.61067**

Singapore

Thailand

3.067862 8.777534** 0.021089 3.324536 6.359163** 3.067650

1.145910

0.389797

1.680050 0.015732 4.144343*

0.170328 8.132005** 0.402449

Note: *, ** and *** indicate the significant level at 10%, 5% and 1% respectively. The ∆ operator indicate the first difference operator.

From the reported table, it is clear that financial development (Lfd) is significant and negative in the cases of Indonesia, Singapore and Thailand as expected while positive in Philippines. This imply that the aggregate economic volatility falls by 0.38% in Indonesia, 2.03% in Singapore and 10.36% in Thailand when there is a 1% increase in aggregate financial development while in Philippines an increase in aggregate financial development may rise aggregate economic volatility by 0.77%. This suggest that financial development are able to reduce economic volatility in long run by means of international risk sharing and efficient fund management as already known that those countries have taken an important step in restructuring their financial sector 1980’s followed by another significant reform in the aftermath of 1997 financial crisis such as establishing appropriate institutional frameworks, abolishment of nonviable financial institutions from the system and strengthening viable institutions through consolidation, recuperating regulations and supervision in banking sector as well as promoting transparency in financial market operations. However, different case for Philippines even though after series of financial reform but still fail to establish financial sector development as a mean for mitigating economic volatility. One possible explanation for this might be inadequate financial policy measurements which then followed by weak institutional quality where it is well known that Philippines have been listed among the most corrupted country which also have reported under the Business Environment Risk Intelligence (BERI) and International Country Risk Guide (ICRG) where the score of institutional quality was low at about 3 out of possible 100 from 1980 until 2011 and 2.2 out of possible 100 from 1984 until 2008 respectively. It is also well known that the country has saw the assassination of their top leader previously which indicate that the level of institutional quality in Philippines is low where all this factor might explain the insignificant of financial sector development measurements in Philippines. Hence this situation might have led towards more volatile state of economy and measurement of financial development had become a contribution towards economic fluctuation on longer term in case of Philippines. The financial openness (Ldejure) indicates that it is a significant determinant in Malaysia, Philippines and Singapore. In Malaysia and Philippines shows that any policies regarding capital account openness have succeed in reducing volatility by 3.5% and 0.9% in long run while in Singapore financial openness policy may trigger volatility by 4.1%. This shows that openness measured by policy (de jure) are able to reduce volatility in Malaysia and Philippines where it might have create

better risk sharing and well diversified investment portfolio which could be vital in reducing the impact of economic shocks as well as by permitting the presence of more foreign bank will increase the domestic banking system efficiency. For instance, Chiang Mai initiative took place in the aftermath of 1997 crisis might have help contributing towards lowering volatility as well as an ASEAN Comprehensive Investment Agreement (ACIA) which was later on signed in February 2009 which main objectives were to create a free and open investment regime thus realizing economic integration. The ACIA streamlines the existing ASEAN investment agreements, with a view to attracting more foreign investment into ASEAN and increasing intra-ASEAN investment. However, in Singapore any in changes in financial openness policy may trigger their economic volatility which might be due to the fact that Singapore is the third largest financial centre in Asia after Japan and Hong Kong as well as Singapore has become a financial instruments trading hub for ASEAN countries. Therefore, a small open economy of Singapore economic activity depends much on international financial trading which make their economic activity level more sensitive towards the changes on financial openness policy. However in term of outcome of the financial openness policies (Ldefacto), it indicates that it may have a positive significant effect in triggering more volatility in long run in case of Indonesia by 2.7%, 2.5% in Philippines and 3.3% in Singapore. This shows that the outcome of rising financial openness may only trigger more economic volatility in all cases as openness may increase financial activity level moreover in profit taking activity by investors as well as increasing the volume of financial instrument turnover. This shows the cyclical nature of capital flows where large sum of fund coming in into an economy will then followed by capital outflows. This was parallel with Singapore where the aggregate economic volatility have risen by 33% from 1976 until 2011 along with financial openness (ldefacto) which have grow more than 7 times since 1970 which match with the role of Singapore as a financial hub especially in ASEAN region. In the case of Philippines and Indonesia, the positive relation between financial openness (Ldefacto) and aggregate volatility (Lvol) is due to the fact that both country level of openness have sharply decrease since the 1997 East Asia financial crisis by 82% in Philippines and 78% in Indonesia since 1997 crisis due to more careful step by investor in investing especially in Indonesia and Philippines and it is also one of the measurements taken in order to calm down their economic volatility by both country. Therefore, in Indonesia economic volatility has reduced as much as 26% and 20% respectively since 1997 and this result was parallel with recent economic occurrences. On the other hand, trade openness (Lto) also is a significant determinant on economic volatility for all of ASEAN 5 countries in long run. It demonstrates that there is a negative relationship between trade openness and volatility except in the case of Indonesia. As for Malaysia it shows that an increase in trade openness is able to reduce economic volatility as large as 17.1%, 1.8% in Philippines 4.9% in Singapore and 11.9% in Thailand. This shows that trade openness may deviate escalating inflation which may trigger volatility by substitution effects as well as increase in product specialization where by specializing may reduce the cost of product in long term thus eliminating the chances of excessive volatility driven by inflation. In other words, openness in trade segment may improve resource allocation, lowers consumers’ prices and leads towards more efficient production thus reducing volatility. Moreover it also 9

encourages the technological transfer which may result in productivity improvements thus increasing economic development which could mean a reduced impact on volatility. This is in line with increasing number of bilateral trade arrangements especially among ASEAN countries and with other country such as Australia under the free trade agreement with Australia and New Zealand (AANZFTA) on August 2008 which is expected to favor both regions investment in term of trade and finance. However, in Indonesia an increase in trade openness is estimated to increase economic volatility by 7.3%. As already know that Indonesia is among most affected country hit by the 1997 crisis which have affected their trade industries and saw a sharp decrease in trade activities as much as 11% due to the crisis. It is suggested that the Indonesian government to increase their reserves during the favorable swing which then can be used when the event of bad volatility set in to smoothen out the impact on trade sector. Institutional quality (Lberi) is found to be positive significant determinants in Indonesia, Philippines and Singapore. From the analysis, it shows that by strengthening institutional quality it may have increase the level of economic volatility by 4.7% in Indonesia, 4.4% in Philippines and 41% in Singapore in long run. This result was surprisingly contradict with the early hypothesis which by strengthening institutional quality should have a reduce impact on volatility. Among possible explanation for this situation is might due to by strengthening institutional quality might encourage streaming of economic resources towards political linkages institution including towards incompetent institutions which in turn may drag their economy towards volatile state of economy especially in the case of Indonesia and Philippines as both of them are among countries which always associate with political instability. However, in case of Singapore this explanation might not be applied as they are among less corrupted economy and one possible explanation is sometimes by rapid changes in upgrading institutional quality especially the set of legal framework might not be favorable by some investors thus affecting the flow of capital inwards and outwards the country hence triggering more volatility especially when Singapore itself is one of the most active country involved in international trade and finance. The set of control variables also show a significant relationship in those countries which reflect important role of fiscal and monetary policy as well as income factor. Fiscal policy is reflected by government expenditure (Lgovex) and it is negatively significant in Malaysia and positive in Indonesia which means that fiscal policy in Malaysia are able to control excessive volatility by 18.7% while in Indonesia their fiscal policy have led towards more volatility as much as 5.4% in long run. On the other hand, monetary policy indicators such as inflation (Linf) in Singapore act as a contributing factor towards volatile state by 1.2% and the decreasing level of inflation in Indonesia are able to reduce volatility by 2% in longer term. Exchange rate (Lex) policy on the other hand seems to reduce volatility in Indonesia by 0.9% and Singapore by 5.7% which shows effective monetary policy in attracting more foreign funds inwards the country with less capital flight. However in Thailand, the effect of Asian financial crisis on their exchange rate have given massive impact where the results reveal that exchange rate have added towards more volatility by 10% in long run. Interest rate (Lint) in Indonesia and Philippines seems to have positive impact as much as 1.4% and 1.2% respectively which shows that rapid intervention in monetary policy in long run may trigger more volatility as much of investments decision

depends on the level of interest rate. Mean while, income per capita (Lincpc) shows that it is a positive significant contribution towards volatility in Malaysia and Thailand as much as 7% and 13.6% respectively. This shows that an increase in income per capita trigger more volatility in both country in long run as more international transaction taking place which might due to preference of international products and investment abroad. On the other hand, the short run causality shows that aggregate financial development indicator (Lfd) is positive significant indicator in Indonesia and Philippines. The financial openness indicator measured by de jure also indicates that it is a positive significant in Indonesia, Philippines and Singapore while de facto only significant in Philippines. On the other hand trade openness (Lto) is significant in Indonesia, Malaysia and Thailand while institutional quality (Lberi) is significant determinant only in Indonesia and Singapore. Mean while the set of control variables such as inflation rate (Linf), government expenditure (Lgovex) and exchange rate (Lex) only significant in Indonesia while interest rate (Lint) is significant determinant is Philippines only. The effect of income per capita (Lincpc) is affecting volatility only in Malaysia and Thailand in short run. As a sum all of the short run causality is positive related towards volatility which shows that any small changes could lead towards triggering economic volatility in short run as investors and savers have become more skeptical about ASEAN 5 market moreover after the 1997 East Asia financial crisis lesson. 7. Conclusion In sum, this paper examines the existence of cointegration relationship between the aggregate economic volatility and financial development, simultaneous openness in both financial and trade segment as well as the quality of institutions in ASEAN 5 economies namely Indonesia, Malaysia, Philippines, Singapore and Thailand during the period of 1970 until 2011. In achieving the conclusion, ARDL bound test developed by Paseran et. al., (2001) were utilized and the empirical results reveal the existence of long run relationship between aggregate economic volatility and its determinants in ASEAN 5 economies. However, there is a model stability issue in case of Indonesia where any interpretation of the results need to be carried out properly and with cautious. Among the vital information reveal from the analysis is that the aggregate financial development is a significant and negatively related to economic volatility in Indonesia, Singapore and Thailand while positive in Philippines. In other words, aggregate financial development is able to reduce aggregate economic volatility except in Philippines where it will lead to more economic volatility. Therefore, aggregate financial development will lead towards less volatility in most cases in ASEAN 5 economies. However, in reviewing the Europe and US financial crisis, some have point out that the use of complex currency as well as credit derivatives structures are among the contributor towards the crisis which means that as financial sector getting develop, the more complexness of financial structure will be become. Having said that, ASEAN 5 countries have to learn from the Euro zone conflict and become more cautious or otherwise the reducing effect of financial development on economic volatility will vanish. Such effort as the ASEAN Comprehensive Investment Agreement (ACIA) and Chiang Mai initiative are all proven to be crucial and it is suggested that more agreements which may benefit financial 10

development should be initiated. On the other hand, both type of openness which is financial and trade are also considered to have a significant impact on economic volatility. Financial openness provides a mix conclusion where it depends on the type of measurements. If it is measured from the policy (de jure) effect point of view, it seems that it may have a reduce impact on volatility in most cases. Among other researchers who come with similar conclusion is Bekaert et al (2006) where they demonstrate that countries with more open capital account tend to reduce consumption growth volatility moreover after equity market opening and financial opening also tend to reduce the ratio of consumption growth volatility to GDP growth to volatility due to improved risk sharing. However, if it is from the financial openness outcome (de facto) view, then financial openness may lead towards more volatile state of economy which is parallel with the finding such as Buch et al., (2005) which suggest that the link between financial openness and volatility has not been stable over time which could only trigger crises. Therefore, financial liberalization, if carried out inappropriately, may encourage destabilization in the financial system and trigger financial crises (Ang and McKibbin, 2006). Having said that, if financial openness is view from “de facto” perspective, then the IMF hypothesis does not hold and this may be the answer of rapid economic development in China and India. Trade openness on the other hand is significant and negatively related in most of ASEAN 5 countries except for Indonesia where trade openness has been a means in relaxing economic volatility in most of ASEAN 5 countries which also indicate the success of ASEAN Free Trade Agreements (AFTA) in increasing economic integration. According to Razin and Rose (1992), trade openness may encourage specialization thus increasing the trade volume and with increased specialization of intra industry would lead towards a declining in output volatility due to greater volume of intermediate inputs trade. However, this finding also is contradicted with Kose et. al., (2006) where they observed that only trade openness is significantly positive towards volatility but less robust on financial openness. However, the sample is based on 92 cross country analysis while this study is based on time series analysis for every single country. Some researchers might argue that definitely for economic policies, diversity in historical experiences and institutional factors are equally important as well as the cultural norms contexts which is difficult to interpret with cross country analysis (Hasan et, al., (2007) and this might explain the differences in the results as well as some difference variables involve. On the other hand, it is also suggested that any empirical analysis should weight financial and trade openness simultaneously in the same model as been suggested by IMF (2003) and Rajan and Zingales (2003) where financial and trade openness should go hand by hand as the two sector barely need each other in other in order to developed and also in real world the interdependent among the two is very high. Institutional quality also proven to be a positive significant effect in this study for Indonesia, Philippines and Singapore while in the other countries, institutional quality has no significant impact. However the effect of institutional quality is not consistent with other researchers finding and is contradict with most of other findings. Among of possible theoretical explanation is strong institutional quality often relates with absolute control of power where absolute control might leads towards misused of power and less debate or arguments of

policy. For instance, they could misuse the powers for political benefit or even personal benefit. This in turn may reduce the welfare and economic maximization thus triggering capital flight and encourages volatility. As a fact which already known that both Indonesia and Philippines have experienced to live with this circumstances under the Suharto and Sukarno regime in Indonesia and Fidel Ramos in Philippines which they always associated with controversy or could also due to paradox of enrichment or it could also due to reverse causality effect. This means that whenever the country confronted with excessive volatility, they tend to strengthen their institutional quality in order to overcome the situation by attracting more investments to balance the gap. The reverse causality possibilities between institutional quality and volatility are up for further future research. The other control variables also prove to be a significant determinant of economic volatility such as fiscal and monetary policy where this kind of policy should go along together with liberalization as well as financial sector deepening. 8. REFERENCES 1.

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Malaysia 2 1 3 3 1 2 1 1 2 1 1

Philippines 1 1 1 1 1 2 3 1 2 1 2

6

4

2

0

-2

-4

-6 2008

2009

2010

CUSUM

2011

5% Significance

Malaysia 8 6 4 2 0 -2 -4 -6 -8 2006

2007

2008 CUSUM

2009

2010

5% Significance

Philippines 10.0 7.5 5.0 2.5 0.0 -2.5 -5.0 -7.5 -10.0 2003

2004

2005

2006

2007

CUSUM

2008

2009

2010

2011

5% Significance

Singapore 8 6 4 2 0 -2 -4 -6 -8 2007

2008

2009 CUSUM

2010

2011

5% Significance

10.0

Optimum lag length based on Aikake’s Information Criteria (AIC) Indonesia 1 1 2 1 1 1 3 3 1 1 1

Indonesia

Thailand

Appendix 1A

Variables Lvol Lfd Ldejure Ldefacto Lto Lberi Linf Lgovex Lex Lint Lincpc

Graphical report of CUSUM test

Singapore 1 3 1 1 1 3 2 1 2 3 3

Thailand 3 3 1 1 1 1 3 1 1 2

7.5 5.0 2.5 0.0 -2.5 -5.0 -7.5 -10.0 2004

2005

2006

2007

CUSUM

2008

2009

2010

2011

5% Significance

12