MACROECONOMIC AND FINANCIAL STABILITY: STRESS TESTING OF THE IMPACTS OF MACROECONOMIC SHOCKS ON CREDIT/ASSET QUALITY OF BANKING SYSTEM IN KUWAIT BASED ON MACRO ECONOMETRIC MODEL OF KUWAIT Asraul HOQUE* Abstract: The importance of financial sector came to the forefront with the onset of the global financial crisis in 2007-08, and it was understood that the costs are immense if the instability of the financial sector spreads to the real sector. Thus, maintaining macroeconomic stability and financial stability concurrently becomes now a major target and a challenge for policy makers. Financial policy makers, in turn, have given more emphasis on macro prudential analysis, which has focused on the ability of banks to withstand macroeconomic shocks based on stress tests. We carry out macroeconomic stress testing for Kuwaiti banking system to understand and assess the resilience and vulnerability of the banks as a whole in Kuwait. We build a macro model of Kuwait for the purpose, and then estimate a Credit Risk Satellite Model to carry out the stress testing. Simulation of macro model variables produces a range of future economic and financial variables such as GDP, PC, PI, CPI, M2, and CRD etc in the first stage. We estimate the Satellite Model that links, in the second stage of stress testing, NPL (non-performing loans as measure of credit risk) to the predicted macro model variables, thus mapping external shocks onto banks’ asset quality shocks.
Finally, we make various assumptions about macro shocks in terms of domestic policy shocks as well as external shocks to generate baseline and recession scenarios to assess if deliberate introduction of adverse economic conditions produce any vulnerability or instability in the banking system in Kuwait. We conclude that scenario of mild recession will not produce any banking crisis in Kuwait, but more adverse conditions would produce just a little vulnerability, but not a financial crisis.
JEL Classifications: C, E, G, and H
Key Words: Mathematical and Quantitative Methods, Macroeconomics and Monetary Economics, Financial Economics, and Public Economics.
1. Introduction: Macroeconomic and Financial Stability
A large section of the world population have been adversely affected by the global financial crisis that started in 2008 and still showing some of its impacts on the real sector. There appears to be an erosion of trust in the financial sector as a whole, and banking in particular, in advanced economies in Europe and America. Therefore, we should concentrate on integrating financial policies better with national economic policies, and ensuring that the finance industry functions as a means and not as an end for itself, noting that our main target is to work for sustainable growth of national economy with stable inflation and low unemployment. We all agree that sustainable growth, low inflation, steady employment growth, low levels of unemployment, and a balanced public finance are considered to be the main indicators of macroeconomic stability. We observe that financial sector stability had been in the back burner before the global financial crisis of 2007-2008. With the onset of the crisis, the importance of financial sector came to the forefront and it was understood that the costs will be
*Asraul Hoque, Financial Stability Office, Central Bank of Kuwait
immense if the instability in the financial sector spreads to the real sector. Therefore, in the period ahead, maintaining macroeconomic stability and financial stability concurrently remains a major target as well as a challenge for policy makers. The financial stability is of utmost importance to policy makers in attaining macroeconomic stability, a conviction shared by IMF, BIS and G20 with recent experience. Excessive volatility in macroeconomic aggregates, namely production, employment, fiscal budget, foreign trade and inflation, might impair the distribution of income and assets, and reduce the welfare of the society and thus achieving macroeconomic stability is crucial in sustaining the welfare of the society. Financial stability is crucial in providing an effective mechanism for the transfer of the economy’s resources from savers to producers. An effectively functioning financial system will ensure balanced distribution of risks and decrease the susceptibility of the economy to shock.
Otherwise, financial crises will disrupt real sector production and the economy will suffer sizeable loss of GDP and welfare, jeopardizing macroeconomic stability. We observe that the global crisis (in 2007-2008) was triggered by the excessive risk-taking in the financial sector and that paved the way to deteriorating macroeconomic indicators such as production, employment and budget balance in many countries (see, for example, Borio and Drehmann (2009), Brunnermeier (2009)).
The financial turmoil that originated with the sub-prime crisis in the United States of America in 2007-8 has emphasized the importance of credit risk for banking institutions. It also underlies the need for improved methodologies to better quantify banks’ vulnerabilities to different types of shocks with the use of stress tests. Well- functioning financial markets contribute to economic growth via more efficient allocation of resources and risk diversification. However, financial liberalization can also render the banking sector more fragile, which calls for adequate banking regulation and supervision. The Basel II framework requires banks to conduct stress tests on their potential future minimum capital requirements and consider the effect of recession scenarios. Therefore, macro stress testing should be viewed as a tool of macro-prudential regulation (see Alfaro and Drehmann (2009), Blaschke, Majnoni and Peria (2001), Boss (2002), Hoggarth and Whitley (2003), Pesola (2001), Shu (2002), Sorge (2004), and Cihak (2007), among others).
Macro prudential analysis has focused on the ability of banks to withstand macroeconomic shocks based on stress tests. Macroeconomic stress testing has thus become an important research area for financial stability analysis. Macroeconomic disturbances such as business fluctuations and adverse movements in interest rates, inflation rate, and exchange rates are revealed to have underlined some of the major systematic banking crises in 1990s. Based on the growing significance of the role of macroeconomic factors in causing banking crisis, there has been an increasing emphasis on the study of interactions between macroeconomic trends and banking fragility. Economic expansion, which is associated with an increase in corporate profits and household incomes, enables borrowers to be in a better position to service bank loans leading to reduction in bad loans. But when recession sets in, the converse usually occurs. This is what is known as cyclicality of bank lending. When asset quality deteriorates due to economic slowdowns there is likely to be a second round effect from the banking sector to the real economy. The pressure to maintain minimum
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capital adequacy due to enhanced credit risk shrinks credit supply and further amplifies the business cycles and bad loans in bank books. Stress tests permit a forward-looking analysis to assess the vulnerability of a banking system, in particular, to either a major fall in exchange rate, large increase in interest rate, a very sharp and prolonged contraction in the economy or combination of these shocks.
Plan Of the Paper: We discuss methodology of macroeconomic stress testing in section II. Section III describes the structure, estimation and scenario developments of the macro econometric model of Kuwait. We interpret empirical results related to baseline and stress scenarios in section IV. Section V provides a conclusion.
2. Methodology of Macroeconomic Stress Testing
Monitoring potential signs of heightened risks present in the financial system is important for central banks as they rely on such insights to be able to take both preventive measures and adequate action in crisis management. A key method supporting policymakers in the task of conserving financial stability is macro stress testing, because it performs quantitative analyses of financial fragility. Our primary focus here is on credit risk (non-performing loans - NPL or bad loans). This risk category has been the main topic of considerable analysis for a number of reasons, primarily because credit risk is still the pre-eminent risk category for banks. We estimate the impact of an increase in risk provisions on the risk-bearing capacity of Kuwaiti banks by means of scenario analysis in a macroeconomic model. Our scenarios are based on changes in key macroeconomic variables generated through shocks in monetary/fiscal policies and external shocks such as oil price. We compare the outcome of a number of scenarios to test the resilience of Kuwait banks to different macro shocks. The macro variables that may enter the NPL equation can be of the following categories (see Hoque (2013)):
1. Cyclical indicators: GDP, Non-oil GDP – these are expected to be negatively related to NPL. During periods of economic downturn, borrowers are less likely to be able to repay their debts.
2. Price stability indicators: CPI- this is expected to be positively related to NPL.
Higher inflation may indicate that an economy is operating above its potential growth level and may be overheating, creating uncertainty for future investment and growth/employment. Inflation reduces real income of fixed-income group of society, leading to default in debt repayment. It also generates uncertainty about future investment and aggregate demand and hence growth prospect.
3. Financial Market Indicators: interest rates (RSD). The interest rates are expected to be positively related to NPL because they represent the direct costs of borrowing.
Thus, higher the interest rate, the greater the cost of borrowing and the greater the possibility of loan default as firms and households are less able to service their debt.
4. External indicators: nondomestic factors that can impact domestic financial system are related to international trade links such as exchange rates, exports, imports, oil prices, current account balance, capital account balance and so on.
We first build the macro econometric model of Kuwait (based on Hoque (2011, 2012)). This would link stress events (such as shocks through fiscal or monetary policies, tight or easy policies) to macroeconomic variables such as GDP, Non-oil GDP, CPI (Consumer price index), PC (private consumption), PI (private investment), IMG (import of goods), M2 (broad money), CRD (credit to residents) and so on.
Next, we build Credit Risk Satellite Model. This links the generated/predicted macro variables to variables measuring banks asset qualities such as NPL (non-performing loans). In the first stage, the simulations of macro econometric model will produce a range of future economic and financial variables as outputs such as GDP, PC, PI, CPI M2 CRD, and other variables. The second stage of a stress-testing process would involve estimating the satellite or auxiliary models that link a measure of credit risk to the predicted macro model variables, thus mapping external shocks onto banks’
asset quality shocks. More specifically, NPL ratio is regressed against the nominal interest rate (RSD), consumer price index (CPI), nominal non-oil output, and some fiscal policy variable such as government expenditure (GE). The coefficients of the regression provide an estimate of the sensitivity of loan performance to these macroeconomic factors/shocks.
We thus link the implications of shocks used in macro model (such as shocks in monetary policy, that is, rise/fall in policy interest rate; fiscal policy, that is, rise/fall in government expenditure or external shock such as rise/fall in oil price) to loan performance through the satellite model. We just use the different simulated macro variables (future policy scenarios as a result of different types of shocks) in the equations of satellite credit-risk equations and calculate the simulated values of credit risk for different shocks applied in macro model. The result will show how the credit- risk model will expose the resilience and vulnerability of the banking industry.
3. Macro econometric Model of Kuwait
We now outline the structure of the macro econometric Model for policy evaluation in Kuwait. It has involves structural estimation, tracking performance, and policy evaluation in terms of different designs of fiscal, monetary as well as combinations of different policies. A thorough revision of the specifications of the model was carried out giving emphasis on economic rationale of the relationships in my updated model for 2013 (sample 1999-2011). It should be mentioned that estimated coefficients undergo changes even with the same specification when new information are added. The estimated coefficients have correct signs as expected from theory. They are highly significant around 1% to 5% level of significance and goodness of fit is quite satisfactory around .90 to .99. We estimate policy scenarios (policy evaluation/conditional forecasting) after assessing the reliability of the system estimates carried out using Three Stage Least Squares (3SLS) based on econometric diagnostics and standard statistical tests. We consider four sectors (real sector, labour market, foreign trade sector, and money market) of the economy in our model specification (see Appendix I for more details).
Real Sector: We consider 3 endogenous variables/equations in the sector- Non-oil GDP, PC, and PI.
Non-oil Output (GDPNOP): Non-Oil GDP (GDPNOP) represents Kuwait's production outside the oil industry. We have included three explanatory variables for
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the GDPNOP equation. Some of the explanatory variables have been included with a view to provide direct response to policy experiments (such as monetary, fiscal policy).
However, all the variables are relevant for explaining production function (Hoque and Mutairi, 1996). The explanatory variables are: GE - total government expenditure (boosting non-oil output by providing infrastructure through capital & development expenditure, direct/indirect facilities and subsidies as well as demand for its produced goods and services), RSD – short term interest rate (representing direct effect on deposit rate and indirectly cost of borrowing for investment expenditure and other borrowed funds), and LNKP – labour in the private sector (expected to exert upward impact – this variable represents a boosting effect for economic activities in the economy).
Private Consumption (PC): We specify private consumption (PC) as a function of non-oil output (GDPNOP) to represent income effect, and lagged consumption. Interest rate or other price variables did not have expected results. Increase in GDPNOP has a significant positive impact. Lagged consumption has a strong positive effect to boost consumption by generating a persistent habit from the past pattern.
Private Investment (PI): We specify private investment as a function of government expenditure (GE), and employment in the private sector (LNKP). Increase in GE exerts a positive influence on private investment by creating expansionary demand effects on goods and services. It appears that government expenditure does not have a crowding out effect (that is, replacing private investment by borrowing from banks and the public that dries up funds for loan facilities to private investment) in Kuwait but mutually boosts private investment by creating appropriate infrastructure and other facilities for private sector initiatives attracting them to new investment projects. However, increase in lending rate has no negative influence and its impact in terms of magnitude is very small.
The Labour Market
Non-Kuwaiti Labour in the Private Sector (LNKP): Employment in the private sector (LNKP) is found to be a function of GDPNOP (non-oil output), Kuwaiti labour force in the private sector (LKP), and lagged dependent variable. As expected, non-oil output has positive and significant influence on the level of employment in the private sector. Increase in Kuwaiti participation in private sector has a negative effect on LNKP, as expected.
The Foreign Trade Sector
This sector is critically important in reflecting at least three major characteristics of Kuwait's economy. These are: (a) dependence on export of oil to earn a significant part of its national income, (b) dependence on imports to satisfy most of its requirements of consumer and capital goods, and (c) limited domestic investment opportunities available to absorb surplus fund originating from its external transactions that result in a substantial outward flow of capital.
Import of Goods (IMG): Import of goods (IMG) is found to be a function of non-oil output (GDPNOP), nominal effective exchange rate (NEERM), and stock price index (SPI). Import of goods is found to be positively related to non-oil output, as expected.
The positive influences are coming from expenditure variables that represent the income effect on demand (represented by GDPNOP). Price effect is coming through effective exchange rate, which may have both substitution effect (negative) and income effect (positive). An increase in NEERM (implying appreciation of KD against dollar and other major currencies) reduces de facto price in KD for the same number of units and hence exerts positive influence on increasing import. It also generates some other effect because appreciation releases some extra KD to encourage substitution effect in purchasing other goods. The net result is that an increase in effective exchange rate reduces total expenditure on import of goods given that the price elasticity of import is significantly low. In other words, substitution effect dominates income effect in case of the impact of NEERM on IMG. The argument is further reinforced by a dual scale graph between IMG and NEERM (showing a negative relationship). SPI has obviously positive impact on IMG.
The Money/Finance Market
Consumer Price Index (CPI): Consumer price index (base year=2000) is specified as a function of broad money (M2), and its own lag. It is an established fact that there is a direct positive relationship between broad money and CPI. Lagged CPI has strong positive impact because people expect price to increase when it has in the past.
Credit to Residents by Local Banks (CRD): CRD is found to depend on non-oil output (GDPNOP), and its own lag. It is found that the level of credit to residents is positively influenced by the level of non-oil activity in the economy (represented by GDPNOP). As expected, past credit facilities has a strong and sizeable impact on current CRD. We could not observe any negative impact on CRD by lending rates or fees & charges revenue.
Narrow Money (M1): M1 is specified as a function of non-oil output (GDPNOP), short term deposit rate (RSD), and lagged narrow money. Non-oil output represents activity in the economy and hence its increase will increase the transaction demand for money. We find a negative relation between money stock and interest rate as expected from theory. As usual, lagged demand for narrow money has a strong positive influence on current demand.
Broad Money (M2): Broad money (M2) is found to depend on non-oil output (GDPNOP), and lagged broad money. The scale variable (like GDPNOP) has usual positive influence. Lagged stock of M2 has positive influence. Interest rate did not have any impact on broad money.
Private Sector Deposits (Dinar) in Local Banks (PSDK): Private sector dinar deposit in local banks (PSDK) is specified as a function of non-oil output (GDPNOP), short term deposit rate (RSD), and its own lag. Increase in non-oil output is showing positive influence as expected from economic theory. We also see a positive relation between RSD and PSDK. As expected, we see positive impact from past private sector deposit in local currency.
Tracking Performance of the Model
We find that all stochastic variables have mean error between 1% and 5% during the sample period. Overall, the system exhibits remarkable tracking performance from the range of almost perfect tracking (that is, the gap between actual and predicted value is zero) to highly desirable tracking for the historical time period.
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SIMULATION/POLICY EVALUATION Introduction: We now report the results of different policy experiments based on econometric simulation. Briefly speaking, simulation is a numerical technique (based on an estimated model) used to guide future policy options based on the direction of market determined variables when policy instruments are changed. Policy Design: We consider the following three experiments.
In each experiment, we simulate the model 5 years into the future to the year 2017, beginning in 2012. Our objective is to forecast the effects of alternative economic policies based on instruments in fiscal and monetary variables, external factors such as oil price in US$, exchange rate and quantity of crude oil production. The detailed policy designs will be described in each experiment in following three tables.
IV. Empirical Results: Interpretations of Baseline and Stress Scenarios Summary of forecasts using the macro model
Table 1: Baseline Scenario: Design of Experiments for the years 2012 to 2017: (1) Crude oil price per barrel of US$ 110, 113, 115, 118, 126, 135 for the years 2012…2017 respectively; (2) Crude oil production in million barrels/day: 2.5, 2.6, 2.7, 2.8, 2.8, 2.8 for the years 2012…2017 respectively; (3) Government Expenditure increases by 15% per year up to 2015 and then increases by 5% for the years 2016 and 2017 ; (4) short- term Interest rates are 1.5, 1.8, 2.0, 2.1, 2.2, 2.3 for the years 2012… 2017 respectively ; (5) Exchange rate is US$3.5 per KD. Expressed in million KD unless otherwise specified.
Percentage changes from previous year indicated in blue. Non-performing loans as a ratio of total bad loans to total loans are shown in red in last row of the following table.
Variable 2011 2012F 2013F 2014F 2015F 2016F 2017F
44409 49871 54110 58491 63112 67782 72662
Gross Domestic
Product 12.30 8.50 8.10 7.90 7.40 7.20
28674 30136 31916 33830 35927 38190 40634
Oil GDP
5.10 5.90 6.00 6.20 6.30 6.40
15735 19735 22194 24661 27185 29592 32028
Non-oil GDP
25.40 12.46 11.11 10.23 8.85 8.23
10125 11173 12235 13379 14497 15693 16966
Private Consumption
10.35 9.51 9.35 8.36 8.25 8.11
6060 6639 7228 7842 8484 9140 9831
Goods Imports
9.55 8.87 8.49 8.19 7.73 7.56
148 155 162 168 174 180 186
Consumer Price Index
(2000=100) 4.50 4.30 3.98 3.72 3.50 3.20
25611 28323 31203 34326 37714 41365 45001
Credit to Residents
10.59 10.17 10.01 9.87 9.68 8.79
6208 7026 7934 8784 9595 10416 11269
Narrow Money (M1)
13.17 12.92 10.71 9.23 8.56 8.19
27345 31520 35794 40157 44582 49446 54519
Broad Money (M2)
15.27 13.56 12.19 11.02 10.91 10.26
24248 27805 31673 35179 39601 44317 49356
Private Sector
Deposits in KD 14.67 13.91 11.07 12.57 11.91 11.37
Non-Performing Loans (NPL )
.0640 .0545 .0452 .0310 .0140 .0110 .0054 F = Forecast
INTERPRETATION OF BASELINE SCENARIO
This scenario is simulated to provide a benchmark for comparison with other policy experiments of recession scenarios having different degrees of policy shocks and external shocks. It shows the future path of dependent variables when most independent variables (except price of crude oil and crude oil quantity) are allowed to grow at a rate consistent with their observed trends in the past. The price of crude oil is assumed to be US$110, $113, $115, $118, $126, and 135 dollars per barrel for the years 2012 through to 2017 respectively. We assume that government expenditure increases by 15% per year during 2012 through to 2015 and then increases by 5% for the years 2016 and 2017. This is quite consistent with the past expenditure pattern.
Specifically, the average increase of government expenditure during 2007-2011 was 18
% per year. If the government decides to adopt active expansionary fiscal policy, it could easily carry that out (if rapid growth is warranted in Kuwait) by raising government expenditure by higher rates beyond 18%. This would not be handicapped by budget deficit or debt crisis in Kuwait. Finally, as to short term interest rates, they would undergo the following changes over the period 2012 through to 2017: 1.5%, 1.8%, 2.0%, 2.1%, 2.2%, and 2.3%, which is consistent with Kuwait’s monetary policy stance as well as its major trading partners.
It is observed that the economy shows a positive upward movement (although rate of growth is bit dampened and slow over the years) in terms of major indicators.
Non-oil GDP shows growth in percentage terms although at a slower speed over the years during 2013-17. We note that GDP and CPI growth rates over 2012 and 2013 are quite similar to that of the IMF outlook for Kuwait for 2012 and 2013. Most other variables grow more or less in the same pattern, that is, rate of growth is slowing down a bit towards the end. It is expected that this overall positive development in the real economy, would imply dampened credit risk in the banking sector. This is, indeed, what we observe in the prediction of Non-performing loans, presented in the last row of table 1 (Baseline Scenario). Non-performing loans as a ratio of total loans is decreasing over the years during 2012-2017 compared to the observed ratio of 0.0640 in 2011. This is much lower than Capital Adequacy Ratio we observe in Kuwait’s banking system indicating a clear picture of absence of any credit risk in the banking system of Kuwait.
Summary of forecasts using the macroeconomic model
Table 2: Stress Scenario 1 (Bad – mild recession): Design of Experiments for the years 2012 to 2017: (1) Crude oil price per barrel of US$ 80, 85, 90, 94, 96, 98 for the years 2012…2017 respectively; (2) Crude oil production in million barrels/day at 2.5 for the years 2012 to 2014, and then 2.6 from 2015 to 2017; (3) Government Expenditure decreases by 7.5% per year; (4) short term Interest rates are 1.7, 1.9, 2.1, 2.3, 2.4, 2.5 for the years 2012…2017 respectively; (5) exchange rate is US$ 3.5 per KD for 2012 to 2014, then US$3.6 per KD for the years 2015…2017. Expressed in million KD unless otherwise specified. Percentage changes from previous year indicated in blue.
Non-performing loans as a ratio of total bad loans to total loans are shown in red in last row of the following table.
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Variable 2011 2012F 2013F 2014F 2015F 2016F 2017F
44409 38955 41232 43463 45573 46866 48047
Gross Domestic Product
-12.28 5.84 5.41 4.85 2.83 2.51
28673 22057 23360 24664 25979 26506 27034
Oil GDP -23.07 5.90 5.58 5.33 2.02 1.99
15735 16898 17872 18799 19594 20360 21013
Non-oil GDP 7.39 5.71 5.19 4.23 3.91 3.21
10125 11026 11935 12879 13874 14809 15459
Private Consumption 8.90 8.25 7.91 7.73 6.74 4.39
6060 6485 6934 7341 7751 8152 8502
Goods Imports 7.01 6.92 5.87 5.59 5.17 4.29
148 153.7 159 164.3 169.2 174.1 178.1
Consumer Price Index (2000=100)
3.91 3.52 3.37 3.01 2.91 2.32
25611 27841 30121 32491 34963 37246 38806
Credit to Residents 8.71 8.19 7.87 7.61 6.53 4.19
6208 6847 7543 8251 8958 9686 10454
Narrow Money (M1) 10.29 10.17 9.39 8.57 8.13 7.93
27345 30686 34092 37818 41611 45601 49800
Broad Money (M2) 12.22 11.10 10.93 10.03 9.59 9.21
24248 26966 29902 33003 36095 39383 42844
Private Sector Deposits in KD
11.21 10.89 10.37 9.37 9.11 8.79
Non-Performing Loans (NPL)
.0640 .0888 .1017 .1147 .1273 .1395 .1496 F = Forecast
INTERPRETATION OF RESULTS OF STRESS SCENARIO 1 (Mild Recession) Stress Scenario 1, shown above, is designed to show the impacts of a deliberate choice of policy variables/instruments resulting in inferior picture compared to baseline scenario. Crude oil prices for this scenario are assumed to be US$80, $85, $90,
$94, $96 and $98 for the years 2012 through to 2017 respectively, while crude oil production stands at 2.5 million barrels/day for the years 2012 to 2014, and then 2.6 million barrels/day from 2015 to 2017. The impacts of oil price decline is reflected in oil GDP and GDP only, as expected. These two variables show deteriorating percentage growth during simulation period, relative to baseline scenario.
Fiscal policy variable (represented by government expenditure, GE) is assumed to decrease by 7.5% per year during 2012-2017. Monetary policy variable (represented by short-term interest rate, RSD) shows the following rates during the simulation period: 1.7%, 1.9%, 2.1%, 2.3%, 2.4%, and 2.5% for the years 2012… 2017 respectively. Exchange rate is assumed to be US$3.5 per KD for 2012 to 2014, then US$ 3.6 per KD for the years 2015… 2017. The joint policy instruments assumed represent a contraction (deteriorating) scenario relative to baseline scenario. If the contraction is observed in the simulated dependent variables by applying the chosen policy, then policy makers can safely conclude that our macro model is working as stipulated and designed. The simulated results are exactly reflecting that stipulation.
As such, all the variables are showing downward growth compared to baseline scenario growth, although not in a very systematic pattern. This is what we would expect in a mild recession when a deliberate contraction in the economy is orchestrated by tighter fiscal and monetary policy to stabilize the economy, faced with an overheated economic scenario. Non-performing loans (NPL) presented in the last row of table 2 for Scenario 1 is showing a bit inferior result compared to baseline scenario. This is expected because of our assumption of mild recession by adopting tighter domestic fiscal and monetary policy, as well as adverse external conditions. NPL as a ratio of total loan is increasing through to 2017 reaching about 0.149, but still the ratio is much lower than Capital Adequacy Ratio observed in Kuwait’s banking sector (was about 0.178 in 2011). This indicates that Kuwait’s banking sector is quite resilient and not vulnerable in the face of mild recession that we experimented through our macro model.
Summary of forecasts using the macro model
Table 3: Stress Scenario 2 (Worse – deeper recession)
Design of Experiments for the years 2012 to 2017: (1) Crude oil price per barrel of US$ 80, 75, 70, 65, 60, 55 for the years 2012…2017 respectively; (2) Crude oil production in million barrels/day is 2.5 for 2012 to 2014, and 2.6 for 2015 to 2017; (3) Government Expenditure decreases by 10% per year; (4) short term Interest rates are 1.9, 2.2, 2.4, 2.6, 2.8, 3.0 for the years 2012… 2017 respectively; (5) exchange rate: US$3.5 per KD for the years 2012 to 2014, and US$3.6 per KD for 2015… 2017. Expressed in million KD unless otherwise specified.
Percentage changes from previous year indicated in blue Percentage changes from previous year indicated in blue. Non-performing loans as a ratio of total bad loans to total loans are shown in red in last row of the following table.
Variable 2011 2012F 2013F 2014F 2015F 2016F 2017F
44409 38561 37869 37129 36546 35695 34775 Gross Domestic Product
-13.16 -1.79 -1.95 -1.57 -2.32 -2.57
28673 22057 20753 19450 18335 17016 15698
Oil GDP -23.07 -5.91 -6.27 -5.73 -7.19 -7.74
15735 16504 17116 17679 18211 18679 19077
Non-oil GDP 4.89 3.71 3.29 3.01 2.57 2.13
10125 10822 11547 12257 12993 13658 14205
Private Consumption 6.89 6.70 6.15 6.01 5.12 4.01
6060 6436 6823 7212 7589 7954 8276
Goods Imports 6.21 6.02 5.71 5.23 4.81 4.05
148 153.9 159.9 165.9 171.5 176.8 182
Consumer Price Index
(2000=100) 4.02 3.94 3.82 3.43 3.11 3.01
25611 27247 28939 30678 32491 34365 35784 Credit to Residents
6.39 6.21 6.01 5.91 5.77 4.13
6208 6767 7360 7975 8650 9320 9965
Narrow Money (M1)
9.01 8.76 8.63 8.19 7.75 6.93
27345 29890 32541 35333 38272 41138 44099 Broad Money (M2)
9.31 8.87 8.58 8.32 7.49 7.20
24248 26364 28604 31003 33517 36155 38866 Private Sector Deposits
in KD 8.73 8.50 8.39 8.11 7.87 7.50
Non-Performing Loans (NPL)
.0640 .0948 .1144 .1335 .1512 .1680 .1847 F = Forecast
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INTERPRETATION OF RESULTS OF STRESS SCENARIO 2 (Deeper Recession)
Stress Scenario 2, shown above, is designed to show the impacts of a deliberate choice of policy variables/instruments which would show much worse scenario than stress scenario 1. Crude oil prices are now assumed to be US$ 80,
$75, $70, $65, $60 and $55 for the years 2012 through to 2017 respectively, while crude oil production in million barrels/day stands at 2.5 for the years 2012 to 2014, and 2.6 for the years 2015 to 2017. The impact of oil price decline is directly reflected in oil GDP and GDP, as expected. These two variables show deteriorating negative percentage growth during simulation period, much worse than stress scenario 1.
Fiscal policy variable (represented by government expenditure, GE) is assumed to decrease by 10% per year from 2012 through to 2017. Monetary policy variable (represented by short-term interest rate, RSD) is assumed to stand at 1.9%, 2.2%, 2.4%, 2.6%, 2.8%, and 3% for the years 2012… 2017 respectively. Exchange rate is assumed to be US$3.5 per KD for the years 2012 to 2014, and US$3.6 per KD for the years 2015… 2017. The joint policy instruments assumed represent a recessionary scenario relative to stress scenario 1. If the contraction is observed in our simulated dependent variables by applying the chosen policy, then it can be safely concluded that our macro model is working very well as stipulated and designed. As such, simulated results are exactly reflecting that stipulation, that is, all the variables are showing more depressing growth in most variables relative to stress scenario 1. It should be noted that scenario in Table 3 is the worst of all the scenarios presented in this report. The situation could be much worse than this depending on the dose of shock we chose both in terms of domestic shocks (induced through tighter fiscal and monetary policy) and foreign shocks (induced through lower crude oil price). Non-performing loans (NPL) presented in the last row of table 3 for Scenario 2 is showing much worse result compared to that of scenario 1. This is expected because of our assumption of deeper recession by adopting much tighter domestic fiscal and monetary policy, as well as adverse external conditions. NPL as a ratio of total loan is increasing through to 2017 reaching about 0.185. The ratio is slightly above the Capital Adequacy Ratio, observed in Kuwait’s banking sector in 2011 (which is about 0.178). This indicates that Kuwait’s banking sector is not so resilient in the face of deeper recession but not severely vulnerable in the face of such a recession that we experimented through our macro model.
V. Conclusion
We have observed that the global financial crisis (GFC) that started in 2007-08 created vavoc in terms of severe downturn in financial and real sectors of many economies, especially USA and Europe. The world economy received a rude awakeing and once again a shocking lesson that instability and too much risk-taking in financial system could cause devastating consequences in the real economy and tremndous human sufferings with long term adverse impacts. Many non-OECD countries, that did not have a regular Financial Stability Report (FSR) in their Central Banks, initiated a new department of financoial stability with the responsibility of FSR, to get a priori
signals of overtly risk-taking in the financial/banking system in the country. This phenomenon is a direct off-shoot of the GFC. Central Bank of Kuwait also started to rethink tightening regulatory regime in the country and created Financial Stability Office in 2011 to produce FSR on a regular basis so that the authority may adopt pre- emptive actions to avoid unnecessary risk taking by the banking system. This paper provides a methodologcal and empirical foundation and frameworks to assess the vulnerability and resilience of the banking system in Kuwait (may be easily extended to other GCC economies). Specifically, we develop a technique of stress testing of the impacts of macroeconomic shocks (domestic and external shocks) on the asset/credit quality of the banking system in Kuwait.
We build a macro model of Kuwait for the purpose, and then estimate a Credit Risk Satellite Model to carry out the stress testing. Simulation of macro model variables produces a range of future economic and financial variables such as GDP, PC, PI, CPI, M2, and CRD etc in the first stage. We estimate the Satellite Model that links, in the second stage of stress testing, NPL (non-performing loans as measure of credit risk) to the predicted macro model variables, thus mapping external shocks onto banks’ asset quality shocks. Finally, we make various assumptions about macro shocks in terms of domestic policy shocks as well as external shocks to generate baseline and recession scenarios to assess if deliberate introduction of adverse economic conditions produce any vulnerability or instability in the banking system in Kuwait. We conclude that scenario of mild recession will not produce any banking crisis in Kuwait, but more adverse conditions would produce just a little vulnerability, but not a financial crisis, as witnessed in Kuwait (Manakh crisis) in 1982.
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APPENDIX I: 3SLS ESTIMATES OF MACRO MODEL (Structural Model) (t-values in parentheses)
1. Non-Oil Output (GDPNOP)
GDPNOP = -4498.05 + 0.26GE + 15.11LNKP – 63.45RSD; R2 = 0.99 SER=363.02 DW = 2.26 (-13.51) (19.80) (39.81) (-3.02)
2. Private Consumption (PC)
PC = 1783.07 + 0.53GDPNOP + 0.43PC–1; R2 = 0.97 SER = 381.69 DW = 1.69 (6.38) (12.89) (4.70)
3. Private Investment (PI)
PI = -1382.78 + 0.28GE + 2.17LNKP; R2 = 0.97 SER = 271.76 DW = 1.61 (-4.72) (15.60) (5.61)
4. Non-Kuwaiti Labor in the Private Sector (LNKP)
LNKP=95.63 +0.05GDPNOP –4.25LKP +0.46LNKP–1; R2 = 0.99 SER = 20.06 DW = 1.77 (5.03) (43.31) (-18.18) (21.22)
5. Imports of Goods (IMG)
IMG=1101.41 +0.36GDPNOP -10.20NEERM-1 +0.05SPI; R2=0.99 SER=177.76 DW = 2.37 (2.74) (35.99) (-2.84) (8.58)
6. Consumer Price Index (CPI)
CPI = 30.01 + 0.0011M2 + 0.61CPI–1; R2 = 0.99 SER = 1.85 DW = 2.19 (5.40) (6.76) (8.40)
7. Domestic Credit (CRD)
CRD = -2627.56 + 0.90GDPNOP + 0.58CRD–1; R2 = 0.99 SER = 567.92 DW = 1.56 (-6.37) (16.32) (24.41)
8. Narrow Money (M1)
M1 = 503.32 + 0.16GDPNOP – 141.60RSD + 0.56M1–1
(2.82) (7.30) (-7.84) (12.70)
R2 = 0.98 SER = 249.24 DW = 1.77
9. Broad Money (M2)
M2 = -673.90 + 0.42GDPNOP + 0.84M2-1 (-1.92) (7.25) (25.31)
R2 = 0.99 SER = 524.24 DW = 2.21
10. Private Sector Deposits in KD (PSDK)
PSDK = -1378.31 + 0.34GDPNOP + 165.60RSD + 0.89PSDK–1
(-4.78) (6.86) (5.08) (26.43)
R2 = 0.99 SER = 410.43 DW = 1.98
SATELLITE/AUXILIARY MODEL (linking banking sector to other sectors) Non-Performing Loan NPL (as a ratio to total loans)
NPL = -0.18 – 0.0000082GDPNOP + .003RSD + .003CPI – 0.0000041GE (2.07) (-3.87) (1.84) (2.96) (-1.89)
R2 = 0.86 SER = .011 DW = 2.12
VARIABLES IN MACRO MODEL List of Endogenous Variables (Variables
modeled are indicated in bold)
List of Exogenous Variables
1. BD – Budget Deficit
2. CPI – Consumer Price Index (2000=100)
3. CRD – Domestic Credit
4. CRDNCL – Non-consumer Loans 5. GDP – Gross Domestic Product 6. GDPNOP – Non-oil GDP
7. GNDI – Gross National Disposable Income
8. GNP – Gross National Product
9. GOE – Government ‘Other’ Expenditure 10. GR – Government Revenues
11. I – Investment
12. IMG – Imports of Goods
13. IMX – Imports of Non-specified Goods 14. LK –Kuwaiti Labor
15. LNK – Non-Kuwaiti Labor
16. LNKP – Non-Kuwaiti Labor in the Private Sector
17. M1 – Narrow Money 18. M2 – Broad Money 19. NFI – Net Factor Income 20. NOR – Non-Oil Revenues
21. NPL – Non-performing loans (ratio of total bad loans to total loans)
22. PC – Private Consumption 23. PI – Private Investment 24. PSD – Private Sector Deposits
25. PSDK – Private Sector Deposits in KD 26. QM – Quasi-Money
1. ADRD – Average Deposit Rate 2. CAB – Current Account Balance 3. CRDCL – Consumer Loans
4. GC – Government Current Expenditure 5. GDPOS – Oil-GDP excluding Oil Refining
6. GDPREF – Oil Refining component of GDP
7. GE – Government Expenditure 8. GI – Government Investment
9. GII – Government Investment Income 10. IMC – Imports of Consumption Goods 11. IMIG – Imports of Intermediate Goods 12. IMPDUT – Import Duties
13. IUV – Import Unit Value (Import Prices) 14. L –Labor Force
15. LKG – Kuwaiti Labor in the Government Sector
16. LKP – Kuwaiti Labor in the Private Sector
17. LNKG – Non-Kuwaiti Labor in the Government Sector
18. N – Population
19. NK – Kuwaiti Population 20. OREV – Oil Revenues
21. PII – Private Investment Income 22. PSDF – Private Sector Deposits in Foreign Currency
23. RLF – Interest Rate on 12-month USD deposits
24.RSD – Interest Rate on 6-month KD deposits
25. SPI – Stock price index (29/12/1993=1000)
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