Oil Prices, Credit Risks in Banking Systems, and Macro ... - MDPI

0 downloads 0 Views 1MB Size Report
Nov 4, 2016 - prices are major determinants of NPLs across GCC banks and the ..... macro-prudential measures and the strong financial regulation in the GCC region. .... hence follow the U.S. Federal Fund Rate in setting domestic policy.

International Journal of

Financial Studies Article

Oil Prices, Credit Risks in Banking Systems, and Macro-Financial Linkages across GCC Oil Exporters Saleh Alodayni Department of Economics, King Saud University, Riyadh 11587, Saudi Arabia; [email protected]; Tel.: +966-11-467-4177 Academic Editor: Nicholas Apergis Received: 21 August 2016; Accepted: 24 October 2016; Published: 4 November 2016

Abstract: This paper assesses the effect of the recent 2014–2015 oil price slump on the financial stability in the Gulf Cooperation Council (GCC) region. The first objective of this paper is to assess how oil price shock propagates within the macroeconomy and how the macro shocks transmit to GCC banks’ balance sheets. This part of the paper implements a System Generalized Method of Moments (GMM) and a Panel Fixed Effect Model to estimate the response of nonperforming loans (NPLs) to its macroeconomic determinants. The second objective of this paper is to assess any negative feedback effects between the GCC banking systems and the economy. The paper, therefore, implements a Panel VAR model to explore the macro-financial linkages between GCC banking systems and the real economy. The results indicate that oil price, non-oil GDP, interest rate, stock prices, and housing prices are major determinants of NPLs across GCC banks and the overall financial stability in the region. Credit risk shock tends to propagate disturbances to non-oil GDP, credit growth, and stock prices across GCC economies. A higher level of NPLs restricts banks’ credit growth and can dampen economic growth in these economies. The results support the notion that disturbances in banking systems lead to unwanted economic consequences for the real sector. Keywords: oil price slump; GCC nonperforming loans; macro-financial linkages JEL Classification: G21; Q43; G32

1. Introduction The recent 2014–2015 oil price slump has negatively affected the macroeconomic performance of oil exporting economies and their banking systems. With the current global macroeconomic conditions, international oil markets could enter a sustained period of low oil prices. While the macroeconomic consequences of low oil prices on oil exporting economies are well documented, the impact of the oil price slump on financial stability has not received as much attention. This paper, therefore, focuses on the effect of the oil price slump on the GCC (Gulf Cooperation Council) banking stability. The works of Espinoza and Prasad [1], Nkusu [2], Louzis et al. [3], and Klein [4] find evidence that supports the role of macroeconomic variables in determining the movements of nonperforming loans. While Espinoza and Prasad [1] study the macroeconomic determinants of nonperforming loans across GCC banks, they do not test the role of oil price in their model arguing that oil price does not vary across GCC countries and therefore brings less country specific information about these economies. While the argument sounds reasonable, it ignores the severe impact that oil price fluctuations might have on the entire GCC economies and banking systems.1 Therefore, this paper aims to explore the impact of oil prices

1

Please see Figure 1 for more details on possible scenario of the transmission channels of oil price slump to GCC banking systems.

Int. J. Financial Stud. 2016, 4, 23; doi:10.3390/ijfs4040023

www.mdpi.com/journal/ijfs

Int. J. Financial Stud. 2016, 4, 23

2 of 14

on GCC banks’ balance sheets and assess how oil price shock propagates within the macroeconomy. The first objective of this paper is to assess the oil price shock transmission channels, along with other macroeconomic shocks, to GCC banks’ balance sheets. This part of the paper implements a System Generalized Method of Moments (GMM) model of Blundell and Bond [5] and a Panel Fixed Effect Model to estimate the response of nonperforming loans (NPLs) to its macroeconomic determinants. The second objective of this paper is to assess any negative feedback effects between the GCC banking systems and the real economy. This second part of the paper implements a Panel VAR model to explore financial linkages between GCC banking systems and the real economy. The results find strong linkages between oil price fluctuations and NPLs and further negative feedback effects from instability in banking systems to the GCC macroeconomy. Declines in oil prices increase NPLs, as do the declines in non-oil GDP and stock. 2. Literature Review The global financial crisis triggered interest in the two-way linkages between financial system stability and macroeconomic performance. The work of Bernanke et al. [6] lays a theoretical model with financial acceleration that links incomplete financial markets and the real economy; and provide insights on how endogenously determined credit frictions propagate disturbance and spread to the macroeconomy. The theoretical foundation of the role of credit risk shocks and its implications on the real economy are also well grounded in the literature. The relevant literature to this paper are (i) the determinants of nonperforming loans, as a measurement for credit risk in the banking systems; and (ii) the feedback relationship between the financial instability in banking systems and the real economy. The literature on NPLs recognizes two major determinants of the variation in NPLs. The first strand of the literature assesses the macroeconomic determinants of NPLs, which influence the banks’ balance sheets and the debt-service capacity of the borrowers. The macroeconomic determinants of NPLs include business cycles, exchange rate pressure, unemployment rates, and lending rates. The second strand of this literature focuses on bank-specific determinants of NPLs, which vary across banks. The bank-specific determinants of NPLs include differences in risk managements, operation costs, and the sizes of the banks. A review of both these strands of literature is covered by Kaminsky and Reinhart [7], Espinoza and Prasad [1], Nkusu [2], and Klein [4]. The work of Keeton and Morris [8] is one of the early studies that discuss the causes of loan loss variation across banks. They study the insured commercial banks in the United States and the effect of loan loss variations across these banks on managerial risk preferences and the local economic conditions. Berger and DeYoung [9] use Granger causality techniques to examine the relationships among loan quality, cost efficiency, and bank capital across commercial banks in the United States. They find loan quality Granger causes cost efficiency and vice-versa. Furthermore, the study finds that a low level of cost efficiency is preceded by an increase in NPLs. Kaminsky and Reinhart [7] demonstrate that the instability of banking systems may trigger the beginning of a financial crisis. The study finds evidence from the 1990s crisis of emerging economies, which indicates that credit risks in banking systems typically lead to a currency crisis. The study finds that a currency crisis deepens the banking system crises and later spreads to the entire economy. This strand of the literature focuses on the adverse impact of credit risks on the stability of the financial sector. Jesus and Gabriel [10] find empirical evidence of a positive lagged relationship between rapid credit growth and NPLs. Their work examines the lending cycle and the required conditions and standards of the loans. The study empirically confirms that the banks, during the economic booms, tend to be more tolerant in both screening borrowers and collateral requirements. Marcucci and Quagliariello [11] study credit risks and the business cycles across different credit risk regimes in Italy. Their results confirm that the effect of business cycles on credit risks is more evident in weak financial conditions and hence there is a strong relationship between the severity of the financial crisis and the state of the economy. In another study, Marcucci and Quagliariello [12] further examine the default rates of borrowers on Italian banks and their cyclical behavior. The results

Int. J. Financial Stud. 2016, 4, 23

3 of 14

find default rates in the Italian banking system fall in economic booms and rise in economic recessions. The results confirm the intuitive relationship between credit risk and weak economic conditions. The paper of Espinoza and Prasad [1] is one of the few studies in the literature that examines the determinants of NPLs in the GCC region. They find that the NPL ratio increases as economic growth weakens and interest rates rises. However, Espinoza and Prasad [1] cover the GCC banks before the financial crisis of 2008 and do not include oil prices. As oil exporting economies, oil prices are major and relevant determinant of NPLs across this region. The main focus of this paper is to examine the effect of the oil price slump on the GCC banking stability. Nkusu [2] studies the link between NPLs and macroeconomic variables in advanced economies. The study finds that an adverse macroeconomic shock leads to a higher level of NPLs. Furthermore, the study shows that a sharp increase in NPLs leads to poor macroeconomic performance and weak economic growth. Louzis et al. [3] examine the determinants of NPLs in the Greek banking system. The study finds that macroeconomic determinants in Greece have a strong impact on NPLs across the banks. In particular, NPLs are largely explained by the GDP growth, the unemployment rate, the lending rate, and the public debt. The work of Klein [4] examines the NPLs in Central, Eastern and South-Eastern Europe (CESEE). The study looks at both bank-specific and macroeconomic factors and finds that the macroeconomic conditions have a stronger explanatory power across the CESEE region. Particularly, NPLs respond to GDP growth, unemployment and inflation across the region. Messai and Jouini [13] study the determinants of NPLs in Italy, Greece and, Spain which suffered the most from the 2008 subprime crisis. The study finds that the increase in GDP growth lowers the credit risk as does a decline in unemployment rates. 3. Oil Price Fluctuations and Oil Exporting Economies 3.1. The Economies of Gulf Cooperation Council Region Saudi Arabia, United Arab Emirates (UAE), Qatar, Kuwait, Bahrain, and Oman are GCC oil exporters and any fluctuations in international oil price could influence their GDP growth, government budgets, fiscal revenues, development programs and exports. As shown in Table 1, the fossil fuel exports in Saudi Arabia, Qatar, and Kuwait exceeded 80% of total exports. For UAE, Oman, and Bahrain, this ratio exceeded 60% of total exports. The oil revenues account for more than 50% of total government revenues in these economies. The high oil-dependency suggests a high level of vulnerability of GCC economies to external shocks that could threaten the financial markets and banking system stability. The speed with which the oil price shocks would transmit to the macro economy and the banking system, however, varies since it is helped by the high oil prices; GCC countries accumulated substantial financial buffers that could help to smooth the impact of severe fluctuations in international oil prices. The low debt-to-GDP ratio in most GCC countries also indicates that these economies have the capacity and the fiscal space to maintain a sustainable level of debt if needed. Table 1. GCC Countries.

Country

Saudi Arabia UAE Kuwait Qatar Bahrain Oman

General Government Gross Debt (% of GDP)

General Government Revenue (% of GDP)

2008–2012

2013

2014

2008–2012

2013

2014

8.7 18.7 9.5 30.8 26.5 5.5

2.2 15.9 6.4 32.3 43.5 5.1

1.6 15.7 6.9 31.7 43.8 5.1

43.1 37.1 69 40.4 24.2 45

41.4 41 71.8 52.2 24 49.1

37.3 37.7 68.7 47.4 24.1 47.2

Fuel Exports (% of Merchandise Exports) 2008–2012 88.65 64.81 94.85 87.89 69.6 79.44

2013

2014

87.42 94.22 88.68 82.54

87.81 83.53

Sources: Middle East and Central Asia October 2015 Regional Economic Outlook (IMF) and Development Indicators (World Bank). UAE: United Arab Emirates.

UAE Kuwait Qatar Bahrain Oman

18.7 9.5 30.8 26.5 5.5

15.9 6.4 32.3 43.5 5.1

15.7 6.9 31.7 43.8 5.1

37.1 69 40.4 24.2 45

41 71.8 52.2 24 49.1

37.7 68.7 47.4 24.1 47.2

64.81 94.85 87.89 69.6 79.44

94.22 88.68 82.54

87.81 83.53

Sources: Stud. Middle East and Central Asia October 2015 Regional Economic Outlook (IMF) and Development4 of 14 Int. J. Financial 2016, 4, 23 Indicators (World Bank). UAE: United Arab Emirates.

3.2. The The Effect Effect of of Oil Oil Price Price Fluctuations Fluctuations on on Banking Banking Systems 3.2. Systems in in Oil Oil Exporting Exporting Economies Economies Figure 11 lays of oil and its its Figure lays out out the the potential potential dynamic dynamic of oil price price slump slump on on oil oil exporting exporting economies economies and transmission channels to the banks’ balance sheets. As discussed earlier, fluctuations in international transmission channels to the banks’ balance sheets. As discussed earlier, fluctuations in international oil price A sustained decline in in oil oil price influence influence the the GCC GCC economic economic growth growth and and their their banking banking systems. systems. A sustained decline oil prices, however, could lead to a decline in the liquidity and deposits of the GCC banking system. prices, however, could lead to a decline in the liquidity and deposits of the GCC banking system. The The banks particularly exposed to investments non-oilsectors sectorsthat thatinclude includereal realestate, estate, stock stock GCCGCC banks are are particularly exposed to investments in in non-oil market, and and loans market, loans to to households households and and corporate corporate sectors. sectors.

Figure 1. Possible scenario of the transmission channel of oil price slump to banking systems. *: possible effects Figure 1. Possible scenario of the transmission channel of oil price slump to banking systems. *: possible on GCC economies. effects on GCC economies.

Oil revenues influence the size of businesses and the depth of GCC financial and banking Oil revenues influence the size of businesses the depth of GCC financial and banking systems. systems. GCC governments’ expenditures on and construction and infrastructure programs drive GCC governments’ expenditures on construction and infrastructure programs drive domestic non-oil domestic non-oil GDP growth. GCC banks are particularly exposed to corporate sectors and GDP growth. GCC banks are particularly exposed to corporate sectors and households in these sectors. households in these sectors. The channels of this exposure to non-oil GDP sectors are either through The channels of this exposure non-oil GDP sectorsprojects, are either in stock financing investments in stocktomarkets, real estate or through throughfinancing collateralinvestments requirements. markets, real estate projects, or through collateral requirements. Figure 2 shows the exposure of GCC banks to real estate and construction loans. With more than 30%, Bahraini and Kuwaiti banks have the highest exposure rates to real estate and construction sectors. Given the above scenarios, this paper considers oil price, non-oil GDP, lending interest rate, stock price, housing prices, and credit growth to examine the credit risk implications of the recent oil price slump on GCC banking systems.

Figure 2 shows the exposure of GCC banks to real estate and construction loans. With more than 30%, Bahraini and Kuwaiti banks have the highest exposure rates to real estate and construction sectors. Given the above scenarios, this paper considers oil price, non-oil GDP, lending interest rate, stock price, housing prices, and credit growth to examine the credit risk implications of the recent oil Int. J. Financial Stud. 2016, 4, 23 5 of 14 price slump on GCC banking systems.

Figure Sharesofofreal realestate estatein inGCC GCC (Gulf (Gulf Cooperation Cooperation Council) [14]). Figure 2. 2.Shares Council)banking bankingloans loans(see (see [14]).

DataDescription Description 4. 4.Data This paper considersa apanel paneldata dataof of GCC GCC individual Fitch’s database This paper considers individualbanks’ banks’balance balancesheets sheetsfrom from Fitch’s database spanning 2000–2014 and macroeconomic data from the IMF. These include nonperforming loans ratio spanning 2000–2014 and macroeconomic data from the IMF. These include nonperforming loans ratio (NPL), international oil price, real non-oil GDP, lending interest rate, three-year average of credit (NPL), international oil price, real non-oil GDP, lending interest rate, three-year average of credit growth, stock prices, and housing prices. There are no indexes for GCC housing prices; however, this growth, stock prices, and housing prices. There are no indexes for GCC housing prices; however, this paper utilizes CPI components of Housing, Water, Electricity and Other Fuels as a proxy for the paper utilizes CPI components of Housing, Water, Electricity and Other Fuels as a proxy for the housing housing price indexes. In the GCC region, the water and electricity are subsidized and the movements price indexes. In the GCC region, the water and electricity are subsidized and the movements in this in this component of the CPI are mostly due to movements in housing prices. The paper component of thethat CPIitare mostly movements prices.prices, The paper that it acknowledges may not bedue theto optimal proxy in forhousing GCC housing but itacknowledges might be the best may not beproxy the optimal proxy forThe GCC might be theare best feasibleinproxy feasible for these prices. listhousing of all theprices, banks but usedit in this paper reported Tablefor A1these in prices. The list of all the banks used in this paper are reported in Table A1 in Appendix A. The variables Appendix A. The variables and data sources are reported in Table A2 in Appendix A under data and data sources are reported in this Table A2 in Appendix Athat under descriptions. Overall, however, descriptions. Overall, however, paper acknowledges the data sample size (38 banks) and the time this paper acknowledges thatbanks the sample size (38 banks) and time span (2000–2014) of precise the GCC span (2000–2014) of the GCC considered for this paper arethe relatively small for obtaining banks considered for this paper are relatively small obtainingand precise or a precise causal estimates or a precise causal effect between oil pricefor fluctuations GCCestimates banking stability. effect between oil price fluctuations and GCC banking stability.

5. Methodology 5.1. Methodology: Dynamic Panel Models This part of the paper examines the transmission channels of oil price fluctuations to GCC banks’ balance sheets and their macroeconomic determinants. This paper employs a dynamic system GMM and Fixed Effect models to estimate the response of nonperforming loans to different macroeconomic shocks, particularly to oil price fluctuations. NPLi,t = γ1 NPLi,t−1 + γ2 OilPricet−1 + γ3 Credit Growthi,t−1 + X C ji,t−1 β + λi + ei,t

(1)

Int. J. Financial Stud. 2016, 4, 23

6 of 14

NPLi,t is the log of NPL of the ith bank at time t, where i = 1, . . . , N and t = 1, . . . , T, Credit Growthi,t is the 3-years average total gross loans of the ith bank at time t, where i = 1, . . . , N and t = 1, . . . , T. OilPricet is the international oil price for each ith bank at time t where t = 1, . . . , T. X C j,t is a vector of exogenous variables of the jth country associated with the ith bank at time t, where j = 1, . . . , J and t = 1, . . . , T. λi is the panel-level fixed effect, and ei,t are i.i.d residuals. The analysis of this part considers two alternative econometric techniques to estimate the dynamic panel model: (i) Fixed Effect model; and (ii) Dynamic System GMM Model. The former approach removes the unobserved heterogeneity across the banks but has a limitation once the lagged dependent variable is included. The fixed effect model with lagged dependent variable suffers “Dynamic Panel bias”. This is a result of the correlation between the error term and the lagged dependent variable after the demeaning process. To avoid the issue of panel dynamic bias, the latter econometric technique implemented is a Dynamic System GMM model of Blundell and Bond [5]. The collapsing method of Holtz-Eakin et al. [15] is implemented to reduce the number of instruments in the model. Roodman [16,17] provides an excellent review of the Dynamic System GMM Models. In this paper, the Dynamic System GMM Models are estimated following the techniques provided by Roodman’s work. The Econometric Results of Dynamic Panel Models As a macroeconomic determinant of NPLs in the GCC region, a decline in oil price contributes to a higher level of NPLs as well as the declines in Non-oil GDP, and stock prices. The results in Table 2 of the system GMM model (3) show that a one-percentage point decline in oil price growth leads to a statistically significant increase in NPLs by 0.458%. A one-percentage point decline in Non-oil GDP leads to a statistically significant increase in NPLs by 0.708%. A one-percentage point increase in interest rate leads to a statistically significant increase in NPLs by 0.0219%. A one-percentage point decline in stock prices leads to a statistically significant increase in NPLs by 0.397%. A one-percentage point decline in housing prices leads to a statistically significant increase in NPLs by 0.860%. The results indicate that bank-specific credit growth rates are an insignificant determinant of NPLs in the region. Perhaps, this insignificant explanatory power of bank-specific credit growth reflects the macro-prudential measures and the strong financial regulation in the GCC region. The results are qualitatively and quantitatively robust using logit transformation of NPLs in Table 3. Table 2. Econometric results of Fixed Effect and System GMM Models. (1)

(2)

(3)

(4)

System GMM

Fixed EM

System GMM

Fixed EM

NPLt −1

0.817 *** [0.0878]

0.701 *** [0.0508]

0.814 *** [0.0800]

0.691 *** [0.0488]

Oil Price Growtht −1

−0.00512 *** [0.00187]

−0.00679 *** [0.00139]

−0.00458 *** [0.00165]

−0.00586 *** [0.00145]

NOGDP Real Growtht −1

−0.00835 * [0.00420]

−0.0131 *** [0.00323]

−0.00708 * [0.00374]

−0.0103 *** [0.00307]

Interest Ratet −1

0.0231 ** [0.00866]

0.0514 ** [0.0201]

0.0219 ** [0.00901]

0.0512 ** [0.0195]

Credit Growtht −1

0.00111 [0.00485]

−0.00245 [0.00445]

0.00397 [0.00490]

−0.00210 [0.00444]

Stock Price Growtht −1

−0.00389 *** [0.000800]

−0.00290 *** [0.000806]

−0.00397 *** [0.000785]

−0.00310 *** [0.000808]

Variables 2

2

Variable_growtht = log



Varible_levelt Varible_levelt−1



.

Int. J. Financial Stud. 2016, 4, 23

7 of 14

Table 2. Cont. Variables 2

(1)

(2)

(3)

(4)

System GMM

Fixed EM

System GMM

Fixed EM

−0.00860 ** [0.00361]

−0.00756 ** [0.00292]

Housing Prices Growtht −1 Constant

0.156 [0.194]

0.214 * [0.124]

0.158 [0.175]

0.235 * [0.123]

Observations

467

467

463

463

R-squared

0.601 38

0.600

Number of Banks

38

38

38

No. of instruments

33

34

Hansen test p-value

0.180

0.166

A-B AR(1) test p-value

0.000641

0.000601

A-B AR(2) test p-value

0.164

0.156

Standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 3. Econometric results of Fixed Effect and System GMM Models—Logit transformation of NPLs. 3 (1)

(2)

System GMM

Fixed EM

LogitNPLt −1

0.866 *** [0.0782]

0.700 *** [0.0486]

Oil Price Growtht −1

−0.00394 ** [0.00176]

−0.00620 *** [0.00154]

NOGDP Real Growtht −1

−0.00685 * [0.00369]

−0.0111 *** [0.00325]

Interest Ratet −1

0.0135 [0.00818]

0.0535 ** [0.0202]

Credit Growtht −1

0.00350 [0.00380]

−0.00152 [0.00454]

Stock Price Growtht −1

−0.00385 *** [0.000850]

−0.00325 *** [0.000830]

Housing Prices Growtht −1

−0.00896 ** [0.00362]

−0.00786 ** [0.00302]

Constant

−0.471 * [0.244]

−1.152 *** [0.175]

Observations

463

463

Variables

R-squared

0.613

Number of Banks

38

No. of instruments

34

Hansen test p-value

0.211

A-B AR(1) test p-value

0.00118

A-B AR(2) test p-value

0.140

Standard errors in brackets. *** p < 0.01, ** p < 0.05, * p < 0.1.

3

h

LogitNPLt = log



NPLt 1−NPLt

i

.

38

Int. J. Financial Stud. 2016, 4, 23

8 of 14

5.2. Methodology: Panel Vector Auto Regressions (PVAR) Model In the second part of this paper, a Panel Vector Auto Regressions (PVAR) model is implemented to assess the feedback effects between the banking systems and the real economy. To assess the feedback effect of disturbances in the banking system, the analysis focuses on the impulse responses to various structural shocks, particularly to credit risk shock and macroeconomic shocks. To avoid the earlier discussed issue of panel dynamic bias, the model follows Helmert transformation to demean the variables as in Love and Zicchino [18]. Canova and Ciccarelli [19] and Love and Zicchino [18] provide a comprehensive review of Panel VAR models. The Panel VAR used in this part is specified as: Yi,t = Yi,t−1 A + X C ji,t B + X I t D + λi + ei,t .

(2)

Yi,t is a vector of endogenous variables at time t, where i = 1, . . . , N and t = 1, . . . , T. X C ji,t is a vector of exogenous variables of the jth country associated with ith bank at time t where j = 1, . . . , J and t = 1, . . . , T, X I t is a vector of exogenous international variables for each ith bank at time t where t = 1, . . . , T. λi is the panel-level fixed effect, and ei,t are i.i.d residuals. The identification scheme in this part of the paper is a recursive Cholesky decomposition. Oil price is modeled as an exogenous variable in the identification of this paper. The domestic variables are ordered as [Interest Rate, Non-oil GDP, Credit Growth, NPLs]. The macro variables are set first as Interest Rate, and then Non-oil GDP. The interest rate is set first as GCC central banks adopt fixed exchange rate regimes and hence follow the U.S. Federal Fund Rate in setting domestic policy interest rate. The bank-specific variables are ordered as Credit Growth, then NPLs. Credit Growth responds contemporaneously to Interest Rate and Non-oil GDP, but with a lag to NPLs. NPLs respond contemporaneously to all the variables in model. Results of Panel Vector Auto Regressions (PVAR) Model The results of the PVAR model are reported in Figures 3–6 and Tables 4–6. Figure 3 indicates credit risk shock, as a shock to nonperforming loans tends to restrict credit growth across the banks and dampens economic growth in GCC economies. The interest rate declines in response to credit risk shock. The results confirm significant negative feedback between the banking system instability and the real economy. A positive Non-oil GDP shock expands the credit growth across the banks and lowers NPLs. However, Non-oil GDP shock increases the interest rate (see Figure 4). An interest rate shock increases the cost of borrowing and hence leads to a higher level of NPLs and could slowdown the GCC economic growth. A positive shock to credit growth across GCC banks leads to higher economic growth and lowers the NPLs across the region. The variance decompositions are reported in Tables 4–6. The variance decomposition of Non-oil GDP (see Table 5) across GCC economies indicates that oil price shock explains about 35% of Non-oil GDP variation, while NPLs explains almost 30% of the Non-oil GDP variation. The variance decomposition of GCC credit growth (see Table 6) indicates that Non-oil GDP shock explains about 17% of credit growth variation, interest rate shock explains about 11% of credit growth variation, and NPL shock explains about 40% of credit growth variation.

and the real economy. A positive Non-oil GDP shock expands the credit growth across the banks and lowers NPLs. However, Non-oil GDP shock increases the interest rate (see Figure 4). An interest rate shock increases the cost of borrowing and hence leads to a higher level of NPLs and could slowdown the GCC economic growth. A positive shock to credit growth across GCC banks leads to higher Int. J. Financial Stud. 2016, 4, 23 9 of 14 economic growth and lowers the NPLs across the region.

Int. J. Financial Stud. 2016, 4, 23

Figure 3. Impulse credit shock. Figure 3. Impulseresponses responses toto credit riskrisk shock.

Figure 4.4.Impulse toNon-oil Non-oilGDP GDP shock. Figure Impulseresponses responses to shock.

9 of 14

Int. J. Financial Stud. 2016, 4, 23

Int. J. Financial Stud. 2016, 4, 23

Figure 4. Impulse responses to Non-oil GDP shock.

Figure 5.5.Impulse toCredit CreditGrowth Growth shock. Figure Impulseresponses responses to shock.

10 of 14

10 of 14

Figure 6.6.Impulse toInterest InterestRate Rate shock. Figure Impulseresponses responses to shock.

The variance decompositions are reported in Tables 4–6. The variance decomposition of Non-oil GDP (see Table 5) across GCC economies indicates that oil price shock explains about 35% of Non-oil GDP variation, while NPLs explains almost 30% of the Non-oil GDP variation. The variance decomposition of GCC credit growth (see Table 6) indicates that Non-oil GDP shock explains about 17% of credit growth variation, interest rate shock explains about 11% of credit growth variation, and NPL shock explains about 40% of credit growth variation. Table 4. The forecast error variance decomposition of interest rates in the GCC region.

Int. J. Financial Stud. 2016, 4, 23

11 of 14

Table 4. The forecast error variance decomposition of interest rates in the GCC region. Interest Rate Steps

Oil Price Growth

Interest Rate

NOGDP Growth

Credit Growth

NPLs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

17.684 19.572 19.662 18.993 18.294 17.722 17.308 17.024 16.835 16.709 16.623 16.561 16.514 16.477 16.445

82.316 76.514 73.801 71.975 70.611 69.608 68.897 68.406 68.068 67.833 67.664 67.536 67.436 67.355 67.287

0.000 3.841 5.558 6.929 7.846 8.477 8.898 9.181 9.373 9.507 9.606 9.681 9.741 9.791 9.832

0.000 0.009 0.197 0.362 0.488 0.562 0.600 0.616 0.621 0.621 0.620 0.619 0.618 0.617 0.617

0.000 0.063 0.782 1.741 2.760 3.630 4.297 4.774 5.103 5.329 5.487 5.602 5.690 5.761 5.819

Table 5. The forecast error variance decomposition of Non-oil GDP in the GCC region. NOGDP Growth Steps

Oil Price Growth

Interest Rate

NOGDP Growth

Credit Growth

NPLs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

61.290 40.684 38.172 37.404 37.233 36.857 36.341 35.847 35.458 35.180 34.987 34.848 34.743 34.657 34.584

0.803 0.605 0.607 0.985 1.762 2.733 3.692 4.527 5.211 5.763 6.210 6.579 6.889 7.156 7.387

37.907 24.190 23.391 22.538 22.240 22.128 22.030 21.936 21.842 21.757 21.682 21.616 21.560 21.511 21.468

0.000 6.571 6.058 5.844 5.839 5.867 5.868 5.830 5.779 5.730 5.688 5.653 5.625 5.601 5.580

0.000 27.950 31.772 33.228 32.927 32.415 32.068 31.861 31.710 31.570 31.434 31.303 31.183 31.076 30.980

Table 6. The forecast error variance decomposition of Credit Growth in the GCC region. Credit Growth Steps

Oil Price Growth

Interest Rate

NOGDP Growth

Credit Growth

NPLs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.886 0.727 0.714 0.707 0.686 0.672 0.675 0.693 0.716 0.738 0.756 0.769 0.779 0.787 0.793

11.749 12.070 11.661 11.502 11.512 11.580 11.636 11.665 11.678 11.689 11.706 11.728 11.754 11.779 11.802

9.001 18.193 18.519 18.147 17.827 17.674 17.621 17.604 17.591 17.579 17.569 17.562 17.557 17.555 17.553

78.364 46.133 33.545 28.448 26.525 25.966 25.881 25.891 25.892 25.880 25.866 25.854 25.843 25.832 25.821

0.000 22.877 35.560 41.195 43.449 44.108 44.186 44.148 44.124 44.114 44.103 44.086 44.067 44.048 44.031

Int. J. Financial Stud. 2016, 4, 23

12 of 14

6. Conclusions While the macroeconomic implications of oil price fluctuations on GCC economies are significant and well studied, its implications on GCC banking systems has received less attention. This paper aims to understand the impact of the recent oil price slump on GCC banks’ balance sheets and examine any negative feedback effects between the GCC banking systems and the macroeconomy. The results show that macro economic variables, including the oil price, Non-oil GDP, interest rate, stock prices, and housing prices are major determinants of NPLs across GCC banks, and, therefore, of financial stability in the region. The Credit risk shock adversely impacts non-oil GDP, and credit growth across GCC economies. A higher level of NPLs restricts banks’ credit growth and can dampen economic recovery in these economies. These results support the notion that disturbances in banking systems lead to adverse economic consequences in the real sector. The results are qualitatively robust across different specifications. Counter-cyclical policies that limit the GDP slowdown can promote financial stability across the GCC region. Policy makers with financial stability objectives need to monitor the developments in international oil markets and smooth the potential effects to GCC banking systems. GCC countries implement fixed exchange rate regimes, and, therefore, exchange rates do not impose serious credit risks in the region. The GCC economies, however, accumulated a large amount of oil stabilization buffers and have the fiscal space to limit any negative feedback to the real economy. Acknowledgments: The work of this paper was originally developed while Saleh Alodayni was an intern at the International Monetary Fund (IMF)—Summer of 2015—in Washington, D.C. under the supervision of Inutu Lukonga. We thank all the IMF staff, especially the staff of the Middle East and Central Asia’s Regional Studies Division. We thank Raphael Espinoza for his technical help and all the participants in the Middle East and Central Asia discussion Form. Conflicts of Interest: The author declares no conflict of interest.

Appendix A. Data Description. Table A1. List of the GCC Banks Sample—Fitch. Country

Category

Name

Bahrain Bahrain Bahrain Bahrain Bahrain Kuwait Kuwait Kuwait Kuwait Oman Oman Oman Oman Oman Qatar Qatar Qatar Qatar Qatar Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia

Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Islamic Banks Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank

Ahli United Bank BSC Arab Banking Corporation BBK B.S.C. Gulf International Bank B.S.C. National Bank of Bahrain Ahli United Bank (Kuwait) Commercial Bank of Kuwait Gulf Bank National Bank of Kuwait Bank Dhofar S.A.O.G Bank Muscat HSBC Bank Oman SAOG National Bank of Oman Oman Arab Bank SAOC Ahli Bank Q.S.C Commercial Bank of Qatar Doha Bank Qatar Islamic Bank Qatar National Bank Arab National Bank Bank Aljazira Banque Saudi Fransi National Commercial Bank Riyad Bank

Int. J. Financial Stud. 2016, 4, 23

13 of 14

Table A1. Cont. Country

Category

Name

Saudi Arabia Saudi Arabia Saudi Arabia Saudi Arabia United Arab Emirates United Arab Emirates United Arab Emirates United Arab Emirates United Arab Emirates United Arab Emirates United Arab Emirates United Arab Emirates United Arab Emirates

Commercial Bank Commercial Bank Commercial Bank Investment Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank Commercial Bank

SAMBA Financial Group Saudi British Bank Saudi Hollandi Bank Saudi Investment Bank Abu Dhabi Commercial Bank Bank of Sharjah Commercial Bank International First Gulf Bank P.J.S.C. Mashreqbank National Bank of Fujairah National Bank Of Umm Al-Qaiwain National Bank of Abu Dhabi PJSC Union National Bank

Table A2. Variable description and data sources. Variable

Definition

Units

Description

Sources

NPL

Non-performing Loans

Ratio

Non-performing Loans ratio (Bank level)

Fitch

Oil Price

International Oil price

U.S. Dollar

Crude Oil Price

IMF

Non-oil GDP

Non-oil sector

Non-oil GDP (2005 )

National authorities; staff reports

Interest Rate

The lending Rate

%

The lending Rate

CreditGrowth

Gross Loans

U.S. Dollar

Three-year Average of Total Gross Loans

National authorities Fitch

StockPrices

Stock price index

Index

Average Stock market price index

Bloomberg

HousingPrices

Housing price index

Index (2005)

CPI components of Housing, water, electricity & other fuels

National authorities

References 1.

2.

3.

4.

5. 6.

7. 8. 9. 10.

Espinoza, R.A.; Prasad, A. Nonperforming Loans in the GCC Banking System and Their Macroeconomic Effects. IMF Working Papers. 2010. Available online: http://www.imf.org/external/pubs/cat/longres. aspx?sk=24258.0 (accessed on 30 June 2015). Nkusu, M. Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies. IMF Working Papers. 2011. Available online: http://www.imf.org/external/pubs/cat/longres.aspx?sk=25026.0 (accessed on 30 June 2015). Louzis, D.P.; Vouldis, A.T.; Metaxas, V.L. Macroeconomic and bank-specific determinants of non-performing loans in Greece: A comparative study of mortgage, business and consumer loan portfolios. J. Bank. Finance 2012, 36, 1012–1027. [CrossRef] Klein, N. Non-performing loans in CESEE: Determinants and impact on macroeconomic performance. IMF Working Papers. 2013. Available online: http://www.imf.org/external/pubs/cat/longres.aspx?sk=40413.0 (accessed on 30 June 2015). Blundell, R.; Bond, S. Initial conditions and moment restrictions in dynamic panel data models. J. Econom. 1998, 87, 115–143. [CrossRef] Bernanke, B.S.; Gertler, M.; Gilchrist, S. Chapter 21 The financial accelerator in a quantitative business cycle framework. In Handbook of Macroeconomics; Elsevier: Amsterdam, The Netherlands, 1999; Volume 1, Part C; pp. 1341–1393. Kaminsky, G.L.; Reinhart, C.M. The twin crises: The causes of banking and balance-of-payments problems. Am. Econ. Rev. 1999, 89, 473–500. [CrossRef] Keeton, W.R.; Morris, C.S. Why Do Banks’ Loan Losses Differ? Econ. Rev. 1987, 72, 3–21. Berger, A.N.; DeYoung, R. Problem loans and cost efficiency in commercial banks. J. Bank. Finance 1997, 21, 849–870. [CrossRef] Jesus, S.; Gabriel, J. Credit cycles, credit risk, and prudential regulation. Int. J. Cent. Bank. 2006, 2, 65–98.

Int. J. Financial Stud. 2016, 4, 23

11. 12. 13. 14. 15. 16. 17. 18. 19.

14 of 14

Marcucci, J.; Quagliariello, M. Asymmetric effects of the business cycle on bank credit risk. J. Bank. Finance 2009, 33, 1624–1635. [CrossRef] Marcucci, J.; Quagliariello, M. Is bank portfolio riskiness procyclical?: Evidence from Italy using a vector autoregression. J. Int. Financial Mark. Inst. Money 2008, 18, 46–63. [CrossRef] Messai, A.S.; Jouini, F. Micro and macro determinants of non-performing loans. Int. J. Econ. Financial Issues 2013, 3, 852–860. Lukonga; et al. IMF Staff Discussion Note. In preparation. Holtz-Eakin, D.; Newey, W.; Rosen, H.S. Estimating vector autoregressions with panel data. Econometrica 1988, 56, 1371–1395. [CrossRef] Roodman, D. How to Do xtabond2: An Introduction to Difference and System GMM in Stata. Stata J. 2009, 9, 86–136. [CrossRef] Roodman, D. XTABOND2: Stata Module to Extend Xtabond Dynamic Panel Data Estimator; Statistical Software Components; Boston College Department of Economics: Boston, MA, USA, 2015. Love, I.; Zicchino, L. Financial development and dynamic investment behavior: Evidence from panel VAR. Q. Rev. Econ. Finance 2006, 46, 190–210. [CrossRef] Canova, F.; Ciccarelli, M. Panel Vector Autoregressive Models: A Survey. (The Views Expressed in This Article are Those of the Authors and Do Not Necessarily Reflect Those of the ECB or the Eurosystem). In VAR Models in Macroeconomics—New Developments and Applications: Essays in Honor of Christopher A. Sims (Advances in Econometrics, Volume 32); Fomby, T.B., Kilian, L., Murphy, A., Eds.; Emerald Group Publishing Limited: Frankfurt am Main, Germany, 2013; pp. 205–246. © 2016 by the author; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

Suggest Documents


Final Fantasy OVA Episode 4 English Subbed | Replace (2018) | com.aspieapps.free.emulator