Main Determinants of the Shadow Banking
System: a panel data analysis
Author: Rosa Fuentes Valera Student number: 10827927
Supervisor: Tanju Yorulmazer
Master: MSc in Business Economics
Track: Finance
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Statement of Originality
This document is written by Student Rosa Fuentes Valera who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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Abstract
The purpose of this thesis is to determine the main factors which contribute to the growth of the Shadow Baking System. Apart from the regulatory determinants that were commonly used in previous literature, in this research we considered also macroeconomic and financial sector variables. Understanding of determinants of SBS could be very valuable for financial regulators and policy makers in order of prioritize their macro-prudential and system risk concerns and create an early warning system. The analysis is based on 14 variables from annual data of 25 countries spanning the period from 2002 to 2011.
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Contents
1. Introduction………. 05
2. Overview of Shadow Banking System and Literature review………... 07
2.1. Definition of Shadow Banking……….… 07
2.2. Systemic risk of the Shadow Banking System……… 08
2.3. Evolution of the Shadow Banking System………...10
2.4. Literature review………... 14
3. Empirical Analysis………..…… 23
3.1. Hypothesis………. 23
3.2. Methodology……….……….…..…. 31
4. Data and Descriptive Analysis……….………..… 34
4.1. Dependent Variable: Growth of Shadow Banking System……… 34
4.2. Explanatory variables………. 35 4.3. Descriptive Analysis……… 38 5. Empirical results……….. 41 6. Robustness test……….………..… 48 7. Conclusions ……….….……..………..51 List of Abbreviations References Appendix
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1. Introduction
In recent years, Shadow Banking System (SBS) was broadly recognized as a source of systematic risk and through its interconnectedness with the regular banking system is also identified as one of the many reasons why the financial system failed in the most recent global crisis (Financial Stability Board, 2011). Due the increasing interest in monitoring and regulation responses, some academic research describe in detail how are the interlinkages mechanisms between SBS and regular banking system which derived in the bankruptcy of some financial entities. Additionally, prior empirical research tests how regulatory and legal changes created opportunities for arbitrage that helped the rise of shadow banking and lead build-up of additional leverage and risks in the system.
Even though, much of these studies mentioned and suggested a few possible explanations about the rise of SBS like the excessive regulation for the banking sector, globalization and financial innovation, but no study has yet seriously examined other kind of factors that contributed to the emerged of SBS. In that sense, there remains unclear what are the main variables which lead the increase in the level of shadow banking activity, and if it exits a dissimilarity between countries. In that sense, the purpose of my thesis is to define the main determinants of Shadow Baking System by examining, in particular a set of macroeconomic, financial sector and regulatory variables. Understanding of determinants of SBS could be very valuable for financial regulators and policy makers in order of prioritize their macro-prudential and system risk concerns or create an early warning system.
Is important to mention, that shadow banks came to replicate core functions of the traditional banking system, in particular those of credit and maturity transformation, they took on many of the same risks but with far less capital. In the extent thereby banks are not anymore the most representative of the financial system risks, the approach of the academic literature fails to define possible relationships between the level of SBS assets with the economic environment, sector and banking regulation. To overcome these limitations, in this study we try to create a quantitative model of linkages between the level of Shadow banking assets and a set of variables.
6 To conduct this research we used a panel data regression of 25 countries with information of 14 variables for the period between 2002 and 2011, with data of shadow banking levels from the Global Shadow Banking Monitoring Report 2014 issued by FSB. For macroeconomic variables, we will use mainly the database from the International Monetary Fund, World Bank Database and Federal Reserve. In order to measure regulatory environment in the financial sector we will be use the World Bank’s Bank Regulation and Supervision Survey Database.
The study is structured as follows: Section 2 provides an overview of the Shadow Banking System and a review of the theoretical literature dealing with the determinants of banking crisis and an overview of previous empirical studies considering this topic; Section 3 provides information on the research methodology, which also includes the model and hypothesis of the coefficient of each independent financial variable potentially influencing the growth in the Shadow Banking System; Section 4 provides an explanation an sources of the data used in the research including the descriptive analysis of the explanatory variables; Section 6 describes the empirical results in all the specifications of our model; in Section 6 we performed a robustness test including a dummy variable related to a Systemic Banking Crisis; finally in Section 7 we presented the conclusion of this study and we talk about the drawbacks and suggestions for further research.
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2. Overview of Shadow Banking System and Literature Review
Since it is important to understand what we named as Shadow Banking System (SBS), basic concepts are discussed in this chapter. First the definition of the SBS is reviewed. After this, the source of systemic risk and interconnectedness with the traditional banking industry are explained, followed by a brief presentation of the evolution of this activity in the recent years. Finally, theoretical and empirical studies of determinants on banking and Shadow Banking System are presented.
2.1. Definition of Shadow Banking
The term “Shadow Banking System” was mention for first time in 2007 by the economist Paul McCulley at the annual financial symposium of the Kansas City Federal Reserve Bank. In McCulley’s speech, he describes shadow banks as unregulated entities which fund themselves using un-insured commercial paper and lacking of the traditional safeguards of traditional banks like explicit access to central bank liquidity or public sector credit guarantees. Such kinds of funding make them more vulnerable and prone to runs.
Even though that since then the term “Shadow Banking System” started to be used widely by the policy makers, analysts and press; so far there is not yet a sole definition in academic literature and even between regulatory authorities. For instance, the Financial Stability Board (FSB) defines shadow banking as the group of entities and activities structured outside the regular banking system that perform bank-like functions. While the Federal Reserve Bank of New York (FED) stipulate that SBS is a web of specialized financial institutions that channel funding from savers to investors through a range of securitization and secured funding techniques.
In order to understand how the different definitions adopted have different regulatory implications, Greene and Broomfield (2014) expounded how the FSB and FED approaches have diverged with respect how they identify a shadow banking entity. In the case of FSB, this institution has designated systemically significant entities taking account in which sector they are, while the FED has designated them considering their size. The authors
8 remark that these different approaches have regulatory implications in the extent that FSB conduct its shadow banking reform efforts in specific sectors and entities within those sectors. However, the FED classified different kind of entities into one single category which have to undergo bank-like prudential regulation.
Also is important to mention that the FSB Shadow Banking monitoring perspective is mainly oriented to detect all non-bank credit intermediation activities where maturity, liquidity
and credit transformation create sources of systemic risk. Traditional Banks do these
transformations when they use short term deposits to fund longer term loans. While in the case of Shadow Banks, their short-term funds are obtained from money markets investors and used to buy different kinds of longer-term risky assets.
Considering both approaches, this research will be based on the definition and framework of FSB since this perspective is most useful to stablish more straightforward casual relation analysis between the different set of variables and each SBS’s sector. As well this approach is better for taking different regulatory actions by sector.
2.2. Systemic Risk of the Shadow Banking System
Generally, the term “shadow banking” suggests us something secretive or banned. According to Pozsar, Adrian, Ashcraft and Boesky (2010), shadow banks are financial intermediaries that provided additional sources of liquidity and funding for credit by conduct maturity, credit, and liquidity transformation; but having these additional sources is not necessarily in itself harmful for an economy. Actually, SBS could lead to a more efficient and competitive financial system providing low-cost credit to market participants and higher returns to investors. In fact, Shadow banks can be a complement of traditional banks by expanding access to credit and sharing risk.
Nevertheless, as has been deeply studied before, bank failures carry enormous costs for the entire economy and affect negatively the functioning of the national payments and clearing system (Bernanke 1983). For that reason banking regulation, effective supervision and a well-designed safety net, are necessary to maintain financial stability and reduce the
9 potential social costs of a bank bankruptcy. Due to SBS is not subject to the same rules and regulatory framework than banks, in the case of runs or failures these entities are not able to use the safety net provided by the government. In addition, through its interconnectedness with the traditional banking system, SBS could spread its vulnerabilities and that’s why SBS was broadly recognized as a source of systemic risk.
The interlinkages between banks are also described by Diamond and Rajan (2002) who through a theoretical model, try to explain how the failure of some insolvent banks can lead to the failure of other solvents banks and result in a crisis of the entire financial system. We consider that this model could also apply to shadow banks because the idea behind is if there are common influences like for example the market interest rate which determines assets values of banks and shadow banks, then the interest rate linkage causes this entities are linked.
At their notes about Shadow Banking, the FSB showed that are many ways in which shadow banking is interrelated with the regular banking system and how this interlinkages have an impact on financial stability which derived in the bankruptcy of some financial entities. Some examples of the relationships between banks and shadow banks are crossholdings of risky assets (investments in Asset-backed commercial papers issued with subprime debt) and liabilities (providing funding and liquidity or credit guarantees).
In that sense, the sources of systemic risk of shadow banks occur through financial contagion to banks caused by the effects of the fire sales of their assets and flight to quality of their sources of funding. A “fire sale” event happens when an entity is forced to liquidate their assets at a high discounted price to comply with its claims, because banks kept the same risky assets than shadow banks the value of their balances sheets suffered large losses with the accounting adjustment to market value. In the case of the “flight to quality” event, shadow bank’s investors of wholesale deposits run because they perceived shadow banks to be higher-risk investments and then purchase safer investments like government bonds. In the extent that shadow banks need to comply with their obligations they also withdrew
10 their deposits in banks and therefore they could not be any more a source of additional funding for them.
Finally, the recent crisis exposed the different ways of interconnectedness between banks and shadow banks. Before the crisis, liquidity and credit facilities granted by commercial banks to shadow banks provide some confidence to the market about the value of their assets. However, following the failure of the Reserve Primary Fund and Lehman Brothers in 2008, the confidence in these entities was deteriorated, investors questioned the value of their assets and many of them withdrew or not reinvested their funds. This investor’s run put in risk to the financial system because fire sales of assets reduced their market price and therefore due accounting rules the asset’s book value of banks and shadow banks also were reduced and experienced serious difficulties even if they didn’t have any solvency problem before the run. And also because shadow banks had to withdraw from other markets these sources of funding to banks were also impaired.
2.3. Evolution of the Shadow Banking System
According to the measures of FSB, the size of the Shadow Banking System Assets in 2002 was USD 26.4 trillion, just a decade later in 2013 this amount increase almost three times to USD 75.2 trillion. SBS has been growing very fast, since 2002 until the occurrence of the financial crisis in 2008, the amount of assets of the SBS has experienced a positive growth rate averaging 18.5, but this high growth rate came mainly from the small base taken as reference. During 2008, the year of the financial crisis, the volume of SBS assets decreased in USD 3.0 trillion which represented a reduction of -4.8% over the previous year. Despite this reduction, since 2009 until 2013 the shadow banking activity has been growing again but with a lower average rate of 5.2%. In general we can observe that shadow banking activity has experienced a significant growth trend which leads to an increase of the systemic risk in the financial system which requires our attention considering that the financial crisis has not changed much the SBS´s perspectives. (Figure 1)
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Source: FSB Global Shadow Banking Monitoring Report 2014
Regarding to the total Financial Assets, in the recent decade they have grown 1.6 times going from USD 119.0 trillion in 2002 to USD 304.6 trillion in 2013. In the case of Shadow Banks the proportion over the total financial assets was growing from 22.1% until reach a maximum level of 26.0% in 2007. However in 2008 this participation was reduced to 23.5% and in subsequent years after the crisis the SBS only grew to 24.6%. Accordingly this data, the SBS represent approximately a quarter of the financial system assets as a whole and about 54% of the amount of banks assets, which means the SBS has an important role in the financial system (Figure 2).
Source: FSB Global Shadow Banking Monitoring Report 2014
-10 -5 0 5 10 15 20 25 0 10 20 30 40 50 60 70 80 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 1. Size of Shadow Banking System and Growth Rate
SBS assets in trillion USD Growth Rate (%)
0 50 100 150 200 250 300 350 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 2: Financial Assets by intermediary
Banks Shadow Banks
Insurance companies and Pension funds Public financial institutions Central banks
12 In order to identify is there a different behavior of shadow banking activities between countries considered as Advanced Economies (AE) and countries considered as Emerging Market and Developing Economies (EMDE’S), we proceeded to classify our sample according to the definition provided by the World Economic Outlook from International Monetary Fund. Our results indicated that the average proportion of SBS’s assets in EMDE’S countries from 2002 to 2013 is 3.9%, in AE countries is 96.1%. Indeed SBS in EMDE’S countries seems to be growing since 2002 with an average growth rate of 26.8% (Figure 3). EMDE’S economies usually have a very different macroeconomic environment and a lower degree of development of financial markets. In that sense, many macroeconomic and financial variables used in this study, are significantly dissimilar for EMDE’S than for AE Countries in scope of this research. Giving these conditions it can be expected that the determinants of SBS will divergent by these circumstances in EMDE’S and AE countries.
Source: FSB Global Shadow Banking Monitoring Report 2014 88% 90% 92% 94% 96% 98% 100% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 3: Shadow Banking by grade of country development
13 Similarly, with the objective to analyze the differences of SBS’s assets across geographical regions we proceeded to classify our sample in six regions: North America (NA), Asia (AS), Europe (EU), South America (SA), Oceania (OCE) and Africa (AF). The Figure 4 illustrated how in 2013 the NA region (42.8%) accounted the largest proportion of SBS’s assets followed by AS (29.6%) and EU (24.4%). Despite NA region holds the largest proportion of SBS’s assets since 2002, we observed that the tendency has been changing and the Asia and Euro regions gain greater participation over the years.
In 2008, trends in SBS´s assets show that the Asia region, have beaten global trends. The level of Asian shadow banking assets were remained growing even if in the other regions were impacted negatively because of the financial crisis.
Source: FSB Global Shadow Banking Monitoring Report 2014 0% 20% 40% 60% 80% 100% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 4: Shadow Banking by region or continent
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2.4. Literature review
The Shadow Banking System (SBS) has been identified as one of the main reasons why the financial system collapsed in the most recent global financial crisis (FSB, 2011). Therefore is great the interest in finding accurate regulatory responses to monitoring such kind of activities and entities. For that reason, most of the recent academic studies mainly focused in describing the interlinkages mechanisms of SBS with the traditional banking system and how these derived in the bankruptcy of some financial entities. Nevertheless, these works only mentioned or suggested a few possible explanations about which elements stimulate the rise of the SBS assets.
Additionally there are some empirical studies about how regulation stimulated growth of the SBS. However, to date no one has conducted an empirical study about other type of factors that contributed to the emerged of SBS. In that sense, as far we do not have empirical studies directly related to SBS´s determinants, this literate review is mainly based on previous studies of the determinants of regular banking system. Review of the existing theory on these topics is necessary in order to understand the relationship between the level of shadow banking, the performance of an economy, strengths/vulnerabilities of the financial system and the effects of banking regulation. We will make the analysis of the determinants of SBS by examining, in particular, three different kinds of variables: macroeconomic, regulatory environment and the degree of development of the financial industry.
Shadow banks in fact they have to modify their lines of behavior and activity according with changes in the economic, financial and regulatory environment in which they operate. These relationships should guidance the decisions of policy-makers and orientate their policies towards the development and monitoring of the financial system and the systemic risk associated with the SBS.
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a. Macroeconomic Determinants
The existing literature about the relationship between the behavior of the economy and the financial system consist in a broad amount of research papers which on one hand attempt to identify the macroeconomic factors that influence in the development of the financial system and on the other hand attempt to explain the effects of the development of the financial system over the economy. Moreover, these studies are mainly based on the context of financial/banking crises. In that sense, earlier works could be divided into two different flanks. The first flank seeks which macroeconomic variables explain or even predict financial crises. The second flank tries to estimate the adverse macroeconomic consequences of financial crises. For the purpose of our study, attention is paid to the literature belonging to the first flank.
After each financial crisis, increase the attention in finding the macroeconomic determinants of the financial system. Government authorities like Bank Supervisors and Central Banks make diagnostics of the financial system based on analysis of a set of macroeconomic indicators and relying their monitoring on them. Similarly some international agencies have created quantitative models which measure the impact of the economy on the health and performance of financial system. Despite all the theoretical fundamentals used for the researchers to determine the macroeconomic drivers of financial system are based only on data of the banking system, these studies will serve as starting point to evaluate if their findings could apply also to the shadow bank system.
By analyzing the macroeconomic environment of a sample of developed and developing countries in the period 1890-94, Demirgüc-Kunt and Detragiache (1998) find that crisis tend to occur when the economic environment experienced a low GDP growth rate, high real interest rate and high level of inflation. In their research they also find that adverse trade shocks have an effect in the increase of the likelihood of systemic problems but with a lower power. Nevertheless in the case of fiscal deficit and rate of depreciation of exchange rate the empirical results showed a not significant relationship.
16 Similarly Evans, Leone, Gill, and Hilbers (2000) recognize that the financial system is affected by the economic activity and some macroeconomic developments preceded financial crisis. In their work for the International Monetary Fund (IMF) about Macroprudential Indicators of Financial System Soundness, the authors considered a set of macroeconomic indicators1 that are necessary to take into account in financial stability assessments. The ratios considered by the authors are grouped in the following categories: economic growth, balance of payments, inflation, interest and exchange rates, lending and asset prices booms. However, they recognized that the list of indicators identified is large and could increase considering the features of specific sectors and markets and is need to select a smaller subset of indicators which allow comparing across countries or regions.
In the same way, previous theoretical studies have suggested how macroeconomic environment influence the development of the financial system. Mishkin (1992) stipulated that healthy economies require a financial system capable to channel funds to agents who have the most productive investment opportunities. Since an asymmetric information problem perspective, the author stated five primary factors in the economic environment which could cause a financial crisis. Three of these factors include the behavior in some macroeconomic variables like the interest rate, level of stock markets and aggregate price level2. For Mishkin an increase in real interest rates can worsen adverse selection problem and lead to a credit rationing in the extent that banks deny loans because the existence of a higher probability that they will lend to bad credit risk borrowers (good credit risk borrowers are not willing to pay such high interest rates). A decline in the stock market constitutes the second factor of financial instability identified by Mishkin. Its impact is through the decline in the net market value of firms which usually serves as collateral for bank’s loans. An erosion of the value of the firm reduces the protection against credit risks of banks, making them less willing to lend and also by the other side increase the incentives of the borrowers to make more risky investments. The third factor identified is unanticipated declines in price levels which similarly results in a decline in the net market
1
In 1998 the FMI included these indicators in its guidance note elaborated for the Financial System Surveillance.
2
17 value of firms. In the extent that loans are fixed in nominal terms a decrease in price levels means the raise of liabilities’ real value without a corresponding increase in asset’s real value. The effect in moral hazard and adverse selection is the same as a decline in stock market. In the occurrence of any of these three macroeconomic factors, banks will respond reducing the supply of loans. However, in this scenario part of the demand for credit can be absorbed by Shadow banks in the extent that they are additional sources of credit and liquidity; hence they possibly would cover the unmet demand of loans. Moreover, with less regulatory costs Shadow banks could lend at lower interest rates and this may be attractive to borrowers with lower risk profile.
With the goal of study the effect of low interest rates on the financial sector; Verona, Martins and Drumond (2011) include to the shadow banks as a distinct class of financial intermediary into a DSGE model. The central result of their study is that a too low for too long interest rate policy could produce a boom-bust cycle however is not the origin of it. The authors modeled the behavior of SBS considering a persistently accommodative monetary policy; shadow banks under this environment have more optimism and perverse incentives which amplify the fluctuations in real and financial variables.
Another studies show how the financial system is influenced by the business cycle. During boom times asset prices increased very rapidly in a short period, deviating from their fundamentals and exhibit patterns different than predictions of standard models with perfect financial markets. Usually, these enormous increases in asset prices often are followed by crashes. Claessens and Ayhan (2013) presented the different theories which try to explain the causes of assets booms. The most relevant for our study is the theory related with the agency problem of “risk shifting”. This phenomenon could present when economic agents borrow at a low rates to invest in instruments with high rates of return but riskier, or when economic agents have limited liabilities but profits are not limited, which make them take on excessive risk. Likewise assets booms are also linked with the rational expectations theory which justifies the excessive increase in asset prices due investors’ expectations about future returns. Under both theories could explain how the SBS’ assets increase due the investor’s behavior.
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b. Financial System Sector Determinants
Besides the aforementioned macroeconomic variables, there are several factors about the financial system in general that might influence in the level of SBS. In that sense, in order to have a more complete perspective about the growth of the SBS, we should include variables that measure systemic risk within the financial sector. In that sense, Borio (2011) indicated that the recent financial crisis exposed the limitations of models employed for designing public policies based only in macroeconomic variables. The most widely used macroeconomic models did not reflect the financial sector very well, let alone allow assessments of systemic financial crises. The author revealed that macroeconomics models lacked of good financial stability assessments because financial distress risk measures just focus on valuation of the probability of failure of a single entity but not the financial system as a whole. Furthermore the author mentioned the best measure of systemic risk are leading indicators based on joint positive deviations from historical norms of the ratio of credit to GDP and of the ratio asset prices to GDP.
In the same way, Demirgüc-Kunt and Detragiache (1998) recognized that “macroeconomic environment is not the only factor behind systemic banking sector problems”, particular characteristics of the sector also have an influence in financial crisis. In their empirical study the authors find that an increase in the probability of banking crisis occur in the occasion of sudden capital outflows or when a large share of credit goes to the private sector.
Moreover, Shin (2009) emphasizes that credit supply is endogenous and depends, in particular, of decisions of financial intermediaries about their balance sheets in response to shifts in measured risks. Three key attributes merit special mention: equity, leverage and funding source. The level of equity gives the level of potential losses that banks are willing to absorb. Leverage is a reflection of the constraints placed on banks by its creditors on the level of exposure for each dollar of its equity. Finally, the funding source matters because the supply of credit to ultimate borrowers is larger when the banks borrow more from creditors outside the banking system.
19 Beirne and Friedrich (2014) evaluated the degree of effectiveness of macroprudential policies (MPPs) in managing foreign capital flows. They pointed out that nevertheless the positive effects of capital flows on economies in employment and economic growth, there is also evidence to suggest that foreign capital inflows can contribute to the formation of credit booms, lead to over-indebtedness, and facilitate maturity and currency mismatches. The finding of their research is that a high share of non-resident bank loans reduces the domestic effectiveness of most MPPs.
Another financial sector characteristic that is important to take into account is the degree of competition in the financial system, the indicator most commonly used to measure competition is the level of concentration of bank’s assets or bank’s deposits. Although, until date the academic literature is no clear about if a high level of competition contribute or not to the stability of the financial system. This has been shown by Beck, Demirgüç-Kunt and Levine (2003) which found that crises are less likely in more concentrated banking systems but at the same time their studies exposed that more competition, view as fewer entry regulations and activity restrictions, tend to reduce the probability that a country will suffer a systemic banking crisis.
In other hand, some economists studied the role of the degree of information systems’ development in shadow banking growth. Duca (2014) indicated that declines in informational and transactions costs in the financial system reduce the advantage of banks over shadow banks and consequently promotes growth of the SBS. This author used as a proxy the price deflator for information processing equipment which should have a negative relation with the level of SBS. Remembering to Adam Smith in 1776, he had already postulated, that when financial systems lower transactions costs, it facilitates trade and specialization fundamental inputs to technological innovation. In the era of digital banking, is possible to infer that reduction in transactions costs open the door to shadow banks. Another consideration to have is about the risk profile of banks, the most important measure of banking risk is the ratio of capital to assets. In order to regulate bank capital, the Basel Committee on Banking Supervision has designed an indicator named Bank regulatory
20 capital to risk-weighted assets, the idea behind risk weighting is that the probability of loss on some assets is greater than on others. According the International Monetary Fund the Risk-weighted assets (RWA) have at least three main purposes as a micro and macro prudential tool, because functions as a common measure for the assessment of the risk profile of a bank; ensures that capital allocation is proportionate with assets’ risks; and works as an early warning system of potential asset class bubbles. Considering the importance of RWA for risk assessment of the banking system, Das and Amadou (2012) test if investors find RWA as a reliable measure of risk, with that purpose they examined stock returns and market measures of risk. The author’s findings are that during the most recent financial crises, banks with lower risk-weighted assets have higher stock returns suggesting a correspondence between RWA and market risk. However this relationship is weaker in Europe where banks can use internal risk models to calculate RWA and become negative after the crisis.
Finally, is important to mention that there are initiatives such as the World Bank who has built a Global Financial Development Database to assess the relationship between features of the financial system and key financial sector policies. This database provides statistics on the size, access, efficiency and stability of the financial system including banks, nonbanks, equity markets, and bond markets. The goal of is using these measures to characterize and compare financial systems across countries and over time.
Therefore the size of financial system assets is partly conditioned by both the structural and the institutional environments in which operates. In that sense, is important to consider some of these variables in our study of the determinants of shadow bank system.
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c. Regulatory Determinants
Increasingly, the research community is turning to studies which expound why SBS needs to be monitoring. Adrian and Ashcraft (2012) explain the fundamental reasons for the existence of SBS, how is the operative of shadow banking institutions and activities, why shadow banks need to be regulated and also include some suggestions about how SBS should be regulated.
Furthermore, most of the existing articles and literature about SBS concludes that its growth in the last decade was motivated by regulatory and tax arbitrage3 and this represented the answer of the finance sector to regulation, in particular to capital requirements. As Brunnermeier (2009) and Pozsar (2010) indicated an important contributory factor behind the creation of some shadow banking entities was a desire by banks to reduce the amount of regulatory capital they held against credit exposures.
Luck and Schempp (2014) explain in more detail about how regulatory arbitrage induces the coexistence of regulated commercial banks and unregulated shadow banks. There are additional theoretical studies as the paper of Goodhart, Kashyap, Tsomocos and Vardoulakis (2012) which illustrate the regulatory arbitrage through shadow banks to a group of financial regulations such as loan-to-value limits, capital and liquidity requirements, dynamic loan loss provisioning and margin requirements on repurchase agreements. Moreover, Duca (2014) indicated that the implementation of Basel I in 1990 which raised the capital requirement to 8 percent, encouraging the rise of shadow banking by inducing to banks in make more securitization to release capital.
In addition some empirical research tested how regulatory and legal changes created opportunities for arbitrage that helped the rise of shadow banking and lead build-up of additional leverage and risks in the system. For instance Acharya, Schnabl, and Suarez (2011) find that the rapid expansion of the ABCP market in 2004 appears to be driven by changes in regulatory capital rules. Furthermore, Funke, Mihaylovski and Zhu (2015)
3 Regulatory arbitrage implies that some regulatory requirements represented an economic disadvantage of
22 through a nonlinear stochastic general equilibrium model (DSGE) find the relation between interest rates regulation in the commercial banking sector and shadow banking in China. The authors’ findings were the imposition of a regulation in the interest rates leads to an increase in loans provided by the shadow banking system.
In the other hand, Duca (2014) studied and tested some determinants of the use of shadow banking funding which are related with information costs and regulatory arbitrage. According to his studies the shadow bank funding of nonfinancial corporations in the long-run equilibrium share is negatively related to information costs and positively related to bank reserves and capital requirements. While in the case of short-run share shadow bank funding is positively related with liquidity premium and negatively related with deposit rate ceilings and other regulatory changes that benefit shadow banks relative to regular banks. With the aiming of assess systematically the status of banking systems in countries and to make recommendations for reform, the World Bank created the first cross-country database on bank regulation and supervision. The Bank elaborated an extensive survey of bank regulation and supervision around the world and created a database. The database include information related with permissible bank activities, capital requirements, the powers of official supervisory agencies, information disclosure requirements, external governance mechanisms, deposit insurance, barriers to entry, and loan provisioning.
With this information Barth, Caprio and Levine (2012) constructed summary indices of key bank regulatory and supervisory policies to facilitate cross-country comparisons and analyses of changes in banking policies over time. The authors find that many countries made capital regulations more stringent and granted greater discretionary power to official supervisory agencies over the past 12 years.
In order to develop this research we will test some of variables related with regulatory requirements as determinants of the level of shadow bank system.
23 The main direction of interest of this study is developing a comprehensive model of the relation between the growth of SBS and the different categories of variables presented in the literature review. First we present the formal hypotheses proposed about the relation between the SBS and all our explanatory variables, followed by a description of the econometric methodology used for the empirical analysis.
3.1. Hypothesis
This section postulates multiple hypotheses that are tested in the empirical section. Testing hypotheses will help us to formulating an answer to the central research question of which are the main determinants of the Shadow Banking System. The hypotheses are based on theory and previous findings in theoretical and empirical research papers.
3.1.1. Macroeconomic Variables
Considering all the literature explained previously, for the purpose of incorporating them into our study we collect the following macroeconomics indicators which have a main impact in the operation of the financial system:
Economic growth rate: the first hypothesis focuses on the SBS reaction to the level of
growth in an economy. A positive economic growth clearly affect the investment opportunities, improving the quality of shadow bank’s assets and thus increasing the distance-to-default of their domestic borrowers (like traditional banks) because the improvement in their debt serving capacity. In other hand, in face of an adverse aggregate shock, shadow banks' assets tend to contract, constraining the supply of liquidity and collateral for the traditional banking system. For banks this financing's constraints lead them to tightening the supply of credits which cause a reducing in the economic activity.
Hypothesis 1: Positive economic growth rate result in a significant SBS growth.
Inflation Rate: this rate affects the assets prices exposing shadow banks to market risk.
Higher inflation rate is expected to have a negative impact on shadow bank’s assets and the value of collaterals could decline below the loan amount they guarantee. In contrast high
24 levels of inflation can bring down costs and prices, providing a boost in spending power to households and businesses, which will favor the SBS growth.
Hypothesis 2: Higher inflation rates tend to reduce SBS growth.
Real Interest Rates: interest rate risk it is a major risk to all investors (including shadow
banks) in fixed rate instruments. As interest rates rise, the value of this kind of investment instruments fall. This rationale is also applied to a portfolio of long term loans at fixed interest rates because when occur an increase in the interest rates banks or shadow banks are not able to adjust rapidly the interest rate that they charge to their borrowers, making reduce their profits or incur in losses.
Likewise, even when shadow banks could transfer the interest rate to their borrowers, a context of high real interest rates will lead them to have more nonperforming loans reducing the value of their assets.
Hypothesis 3: Higher real interest rates tend to reduce SBS growth.
Stock market capitalization: stock market size is the overall assessment of the market
perceptions of companies’ value. It takes into account things that don't appear on their balance sheets like intangible assets, growth prospects, reputation, etc. A reduction in the market value of the firms reduce the value of the collaterals used for shadow banks as protection against credit risks, making them less willing to lend. Conversely, an increase stock market capitalization means an increase in the value of the firms and also could imply a reduction in trading costs (which translates to lower investment barriers) making shadow banks more willing to lend.
Hypothesis 4: Higher market capitalization tends to increase SBS growth.
Volatility in Exchange Rates: exchange rates affect the borrower’s capacity to comply with
their financial obligations hence the exchange rate volatility influences the level of uncertainty that shadow banks face on their future profits. Higher exchange rate volatility
25 affects the value of shadow banks portfolios and cause difficulties in the process of planning or takes investment decisions restraining their capacity as liquidity and credit sources. Hypothesis 5: Higher volatility in exchanges rates tends to reduce SBS growth.
3.1.2. Financial Sector Variables
To summarize part of the researches explained in literature review section, a set of four financial sector variables were selected as potential determinants:
Domestic Credit to private sector: this variable is considered as a measure of financial
depth of a market, in the extent that if the level of credit to local business is high it means there is a degree of developing in the domestic financial system. In this context, the participation of shadow banks in the financial system of a country can be fueled through different channels. For instance, shadow banks might want to provide more funds for private sector investment or might want to offer to traditional banks new financial instruments like loan coverages or liquidity facilities.
It is well recognized that the financial sector plays the primary role in the developing of a country; nevertheless credit expansion could also be caused by poor credit risk analysis which in the long run will create conditions for a future financial distress.
Hypothesis 6: Higher levels of the ratio of Domestic Credit to private sector tend to impulse SBS growth.
External Funding: Loans from nonresident banks serves as an indicator of how the domestic
financial system is open to external financing. Capital flows have positive effects in an economy by promoting investment and growth through the increase in the supply of credit to ultimate borrowers. For shadow banks the advantages of external funding are that allows
26 them to use financial resources at a lower interest rate and to obtain additional liquidity sources.
Hypothesis 7: Higher levels of external funding tend to impulse SBS growth.
Concentration: we are going to use banks' assets concentration as the measure of the level
of competition in the banking system. A high concentration will lead to low levels of competition that will be reflected in higher levels of credit interest rates which is will be negative for investors. In this environment large banks will focus on the most profitable niches and may neglect those that are less profitable, however new financial institutions as shadow banks could be established to fill some niches within the industry that are not been captured by the banking system.
Hypothesis 8: Higher levels of concentration tend to impulse SBS growth.
Regulatory Capital to RWA: due to the nature of its operations banks generally bias toward
greater leverage and greater risk than may be desirable from the point of view of public policy. In that sense this indicator gives a measure of the risk profile of the banking system. The smaller the ratio, the greater the bank’s leverage and smaller the loss of asset value required to reduce capital to zero or below. For investors as shadow banks a lower bank’s Regulatory Capital to RWA means that the financial system is more risky, making them reduce their positions or support to the banking system.
Hypothesis 9: Lowe levels of Regulatory Capital to RWA tend to reduce SBS growth.
3.1.3. Regulatory Variables
Despite the existence of international organizations who watch over the stability of the financial system such as the Bank for International Settlements or the International
27 Monetary Fund, among others; there is no a centralized database of the financial system regulation which allow us a comparison across time and countries. Therefore, in order to obtain some measures about the regulatory environment in the financial sector of our sample, we will use the four surveys conducted by the World Bank in the years 1999, 2003, 2007 and 2012 which contain information about regulatory regimes of approximately 180 Countries.
In the extent that the content of the survey is not homogenous, in addition to the survey responses posted by the World Bank about the level of the minimal capital ratio and the exigence of a minimal liquidity requirement, we used the work done by Barth, Caprio, and Levine (2012). These authors in order to summarize and standardize some aspects of key bank regulatory and supervisory regimes, they constructed a series of indexes based upon the responses in the four surveys.
Also is important to mention that some important variables that we wish to consider in our research regarding provisions or non-performing loans, we had to let aside because the information provided for the countries was incomplete and we do not have enough data to perform an accurate analysis. Considering the explained above we proceed to describe the hypotheses related with the five regulatory variables used in our empirical analysis.
Minimal Capital Ratio: This variable is the minimal capital ratio implemented in the target
country. In 1998 with Basel I the principle that regulatory capital requirements should be adjusted to the risks taken by banks was accepted internationally. Since that year there have been some modifications to the definition of capital and measurement of risks. In 2004 was published Basel II which recommended a regulatory capital equal to at least 8 percent of their risk-weighted assets. Currently the implementation of Basel 3 is still in progress. Nevertheless these international standards acts as general guidelines for the government agencies of each country which decided to adopt them.
28 The setting of a higher level of the minimal capital ratio could drive more and more financial companies from the traditional banking sector to the shadow banking sector, where they can benefit from cost advantages due to less strict regulations.
Hypothesis 10: Higher levels of minimal capital ratio for banks tend to impulse SBS growth.
Minimal Liquidity Requirement: in line with the minimal capital requirement, the reaction
of the market-based financial intermediaries to the setting of a minimal liquidity requirement will be a major incentive for the creation of shadow banks. Liquidity requirements lead to a scarcer and more expensive credit supply from banks, giving to shadow banks cost advantage based on this regulatory advantage.
Hypothesis 11: A minimal liquidity requirement for banks tends to impulse SBS growth.
Securities Activities Index: with this variable we measure the extent to which banks could
engage in securities activities. Since banks have incentives to pursue excessively risky strategies because of limited liability and deposit insurance. A central aim of bank regulation is to ensure that banks do not take so many risks which lead them to failure, which can have very high social costs. For that reason many countries have restricted the securities activities that banks can perform, for instance Glass–Steagall Act in United States limited commercial bank securities activities and affiliations within commercial banks and securities firms. However, restrictions on banks performing securities activities will be force them to take advantage of regulatory loopholes to create products and affiliated companies that drive to the rising of shadow banks.
Hypothesis 12: Higher restrictions for banks in securities activities (high index) tend to impulse SBS growth.
Capital Regulatory Index: is an index which measure two aspects of capital regulatory
requirements. The first one is about the “Overall capital stringency” which evaluates if capital requirements reflect risk elements and deducts market value losses. The second one
29 is about “Initial Capital Stringency” which evaluates if certain funds may be used to initially capitalize a bank and whether they are officially.
Capital regulation can prevent excessive risk taking by banks but when equity requirements per unit are not the same for all entities which perform the same kind of activities, such capital requirements are not effective. Such capital requirements cause underinvestment which is channeled to the evolving shadow banking system.
Hypothesis 13: Higher Capital regulatory requirements (high index) for banks tend to impulse SBS growth.
Supervisory Power index: an index which measure if the supervisory authorities have the
authority to take specific actions to prevent and correct problems. The protection of depositors has been traditionally an important mandate of banking supervisors in that sense supervision of banks has a great importance. Countries with strong supervisory frameworks make banks less inclined to take on excessive risk. Nevertheless to prevent potential detrimental effects of the supervision (like fines or penalties) banks circumvent these rules by shifting parts of their activities to the less regulated shadow banking sector. Hence more stringent supervision of traditional banks pushes credit intermediation into the shadow. Hypothesis 14: Higher Supervisory power (high index) tends to impulse SBS growth.
3.1.4. Control Variables
Specific characteristics, as well as differences between countries should result in particular proposals for creating economic policies. Features like the level of income and economic
30 development have been considerate as control variables to explore if these dissimilarities have any effects over the level of growth of the SBS in the countries under study.
Economic category of the country: to use economic conditions (advance or development
country) as a control variable we will use the classification of the World Economic Outlook from International Monetary Fund. When economic is more developed, financial and capital markets are also more developed, in this markets shadow banks might have the opportunity to make more activities and sell more sophisticated products, which leads to higher returns. Hypothesis 15: In more development economies the SBS growth is higher.
Income level of the country: another way to classify countries is by their levels of income, in
order to use this feature as a control variable we use classification realized by the World Bank in the World Economic Outlook but just considering two types: countries with high level of income (without take into account if they are OECD countries or not as the World Bank does) and countries with medium level of income (we group the countries with upper middle and lower middle levels of income). A high-income economy is defined by the World Bank as a country with a gross national income per capita above US$12,735. Similarly as the previous variable we expected the relation between the growth of the SBS and the level of income of a country is positive. When the residents of a country perceive higher incomes they will more likely to invest their superplus in alternative instruments with higher expected returns as the provided by shadow banks, in that sense SBS could increase the number of operations.
Hypothesis 16: In Countries with higher level of incomes the SBS growth is higher.
3.2. Methodology
In order to test the hypothesis stated in the previous section we use a panel data regression of 25 countries which include 14 Advanced Economies and 11 Emerging Economies. These
31 economies represent approximately 80% of global GDP and have 90% of global financial assets. The countries are Argentina, Australia, Brazil, Canada, Chile, China, Hong Kong, Indonesia, India, Japan, Korea, Mexico, Russia, Saudi Arabia, Singapore, Switzerland, Turkey, United Kingdom, United States, South Africa, Germany, France, Italy, Netherlands and Spain. Because data availability our study is limited to the period between 2002 and 2011. Moreover, it should be noted that the panel is unbalanced, because the limited availability of the data.
We use panel data as the estimation technique because it has the advantage over other forms in providing robust results for a relatively short time series across several different observations in a cross section. Additionally Stock and Watson (2012) states that a panel data helps to control for some types of omitted variables that differ across entities but are constant over time or that vary through time but do not vary across entities. As well the authors noted that the assumption in the cross-sectional analysis about the independency of each observation, in panel data no necessarily holds because this model allows autocorrelation of the observations within the entity. In other words the error term could correlated over time within an entity which make sense when we work with economic data. As Hsiao (1995) exposed in their paper “Analysis of Panel Data”, this tool provide more accurate inference of model parameters improving the efficiency of econometric estimates. In the extent there are more sample variability than cross-sectional data or time series and the degrees of freedom increase reducing the collinearity between the variables. The author also mentioned other significant advantages of estimations with panel data such as test more complicated behavioral hypotheses, control the impact of omitted variables, simplify computation and statistical inference, generate more accurate predictions for individual outcomes, among others.
The econometric analysis of panel data models confronts the following issues: First, the inclusion of time fixed effects. Second, whether if the individual effects are fixed or random. Third if there are presences of autocorrelation and/or heteroscedasticity.
32 Regarding to the first point of whether or not to include time effects, we need to assess if part of our time series variation in the dependent variable is explained by overall time trends or other time series patterns. Moreover, according to Torres-Reyna (2007) is possible to perform a statistical test with the purpose to determinate if time fixed effects are needed in our panel data model. We must add time-series dummies and execute a joint test with the null hypothesis that the coefficients for all years are jointly equal to zero, if we do not reject the null hypothesis then no time fixed effects are needed.
To choose which model of panel data use between fixed or random effects we run a Hausman test which test if the error terms (µi) are correlated with the regressors and where the null hypothesis is that they are not correlated and we should preferred random effects model vs. the alternative the we should preferred fixed effects model. If the p-value for the Hausman test, where you compare random vs fixed-effects, is < .05 then the random-effects estimator is no good. In the other hand if the p-value is > .05, the fixed-effects estimator is consistent; however, the random-effects estimator is more efficient. If the estimates using random effects are not significantly different from the fixed-effects estimator then you can retain the random-effects estimator. An advantage of random effects is that we can include time invariant variables to play a role as explanatory variables (i.e. category of the country) but we should to specify other individual characteristics that could influence the predictor variables. The problem with this is that some variables may not be available therefore leading to omitted variable bias in the model. In the fixed effects model these variables are absorbed by the intercept.
Finally, one limitation of panel data sets is the distortions in the errors’ measurements due the occurrence of autocorrelation and heteroscedasticity; this imprecision may cause some bias in the inference. According to Stock and Watson (2012) in panel models where the errors are heteroskedastic or are autocorrelated, the so-called panel cluster standard errors are appealing because they are robust to heteroskedasticity in the cross-section and quite general forms of serial correlation over time.
33 After considering the exposed previously, to estimate the effect of macroeconomic, financial sector and regulatory variables over the SBS Growth and to observe whether the effects of these variables are different according the degree of development or level of income of the countries under study, the follow general equation was used.
LnYit =β0 + β1X1,it-1 +…+βnXn,it-1 + δ1S1,it-1 +…+δkSk,it-1 + γ1Z1,it-1 +…+γqZq,it + λ1W1,it-1 +…+λkWk,it-1
+ αi +µit + ɛit
Where:
(i) LnYit: is the growth in the level of shadow banking activities. This variable will be
model as a function of a set of independent variables that can be grouped in four different blocks:
(ii) β1X1,it-1 +…+βnXn,it-1: set of macroeconomics variables, lagged by one period.
(iii) δ1S1,it-1 +…+δkSk,it-1: set of financial sector variables, lagged by one period.
(iv) γ1Z1,it-1 +…+γqZq,it: set of regulatory variables, lagged by one period.
(v) λ1W1,it-1 +…+λkWk,it-1: set of controls variables, lagged by one period.
(vi) αi: to control for any additional country specific time-invariant characteristics.
(vii) µit:is the between-entity error.
(viii) ɛit:is the within-entity error.
4. Data & Descriptive Analysis
Mostly the data was obtained from international sources that provide accurate, compatible and complete information for many countries and time periods. These databases offer data in a standard format which make it easy to compare and understand. However, some problems occurred during the collection of the data, for instance there was no available information about an important variable for the research or there was missing information
34 for some countries, in this cases we try to obtained the missing data from public authorities of the country.
4.1. Dependent Variable: Growth of Shadow Banking System
A key element in our study is the construction of the Growth of Shadow Banking System Assets. We used the information of shadow banking assets level from the Global Shadow Banking Monitoring Report 2014 issued by FSB, this report includes quantitative information derived from Flow of Funds/Sectorial Balance Sheet data of national financial systems. Flow of funds accounts delivers information about the financial assets of other financial intermediaries, but to make more accurate this measure the FSB exclude entities which not provide credit intermediation. Data for an active firm therefore is available since 2002 to 2013, but we restrict our study to the period 2002-2011 due the data availability of the dependent variables.
We use this measure for our model for several reasons. First, FSB is using a consistency basis to measure SBS within the countries. Second, is hard to determine precisely which financial activities should be included in the calculation, so the entity approach used by FSB give one of the best proxies of the level of SBS in each country. Finally, the complete data of level of SBS for all countries is difficult to obtain however the measure used by FSB incorporated data of the most representative economies (80% GDP of the global economy) and include different types of countries (Advanced/Development Economies and High/Low Income countries).
As Stock and Watson (2012) suggested in the economic analysis the use of natural logarithms allow regression models to estimate log-linear relationships. In that sense the growth in the level of shadow banking activities is analyzed using the variable LnYit,
measured by the change of the financial assets of the Other Financial Intermediaries in natural logarithms. This variable will be model as a function of a set of independent variables that we grouped in three different blocks: macroeconomics, sector and regulatory variables.
35
4.2. Explanatory variables
4.2.1. Macroeconomic Variables:
In the case of the data of macroeconomic variables, we will use mainly the database from the International Monetary Fund, World Bank Database and Federal Reserve.
Economic growth rate (GDPGrowthit): is the annual percentage growth rate of GDP at
market prices based on constant local currency for country i at time t. Aggregates are based on constant 2005 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data is sourced from the World Bank database.
Inflation Rate (Inflationit): is the annual percentage change in the cost to the average
consumer of acquiring a basket of goods and services for country i at time t. Inflation as measured by the consumer price index. Data is sourced from the World Bank database. Domestic Real Interest Rates (RealIntRateit): is the lending interest rate adjusted for
inflation as measured by the GDP deflator for country i at time t. Data is sourced from the World Bank database.
In the case of European countries as Germany, Spain and France we use the interest rate of the Deposit Facility provided for the euro area by the Governing Council of the European Central Bank. Deposit facility rate is the interest rate when Eurosystem counterparties make overnight deposits at a national central bank. Turkish real interest rate was obtained from the Statistical Indicators Annual Report from the Turkish Statistical Institute. Finally data from the Discount Rate for Saudi Arabia was obtained from the database of the Federal Reserve.
36 Stock Market Capitalization (SMCapGDPit): is the total value of all listed shares in a stock
market as a percentage of GDP for country i at time t. Data is sourced from the Global Financial Development Database of the World Bank.
Volatility in Exchange Rates (VolExcRateit): is the annual volatility of the Official
exchange rate in terms of local currency units relative to the U.S. dollar, for country i at time t. This variable was calculated from data of nominal exchanges rate provided by the International Monetary Fund. In the case of Hong Kong we obtained daily data of exchange rates from Bank of England database. For Argentina and Turkey we obtained monthly data from the Federal Reserve Economic database.
4.2.2. Financial Sector Variables:
Correspondingly, to procure the variables related to financial sector we will be use the World Bank’s Global Financial Development Database.
Credit to private sector (CredPrivSecit): is the domestic credit to private sector provided
by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. This information is for country i at time t. Data is sourced from the Global Financial Development Database of the World Bank.
Regulatory Capital to RWA (RegCapRWAit): The capital adequacy of deposit takers. It is a
ratio of total regulatory capital to its assets held, weighted according to risk of those assets, for country i at time t. Data is sourced from the Global Financial Development Database of the World Bank.
Concentration (Concentrationit): is the assets of three largest commercial banks as a
share of total commercial banking assets, for country i at time t. Total assets include total earning assets, cash and due from banks, foreclosed real estate, fixed assets, goodwill, other intangibles, current tax assets, deferred tax assets, discontinued
37 operations and other assets. Data is sourced from the Global Financial Development Database of the World Bank.
Loans from nonresident banks (net) to GDP (LNResGDPit): Ratio of net offshore bank
loans to GDP, for country i at time t. An offshore bank is a bank located outside the country of residence of the depositor. Data is sourced from the Global Financial Development Database of the World Bank.
4.2.3. Regulatory Variables:
In order to measure regulatory environment in the financial sector we will be use the World Bank’s Bank Regulation and Supervision Survey Database.
Minimum capital-asset ratio requirement (%) (MinCapRatioit): is the level of the
minimum capital requeriment, for country i at time t.
Minimum Liquidity Requirement (MinLiqReqit): is a binary variable which is 1 for
countries which have a minimum liquidity requirement and 0 otherwise.
Securities Activities Index (Secur_actit): is an index which measure if banks may engage
in securities activities, for country i at time t. The quantification of the index is from 1 to 4 and higher values indicate more restrictive.
Capital Regulatory Index (Cap_regit): is an index which measure capital regulatory
requirements, for country i at time t. The quantification of the index is from 1 to 10 and higher values indicate more stringency.
Official Supervisory Power (Sup_Powerit): is an index which measure the supervisory
power of the banking authorities, for country i at time t. The quantification of the index is from 1 to 14 and higher values indicate greater power.
