macroeconomic determinants of banking financial...

MAS Staff Paper No. 38

December 2004

MacroeconomicDeterminants of

Banking FinancialPerformance and

Resilience inSingapore

MACROECONOMIC DETERMINANTS OF BANKING FINANCIAL PERFORMANCE AND

RESILIENCE IN SINGAPORE*

BY

ROBERT ST. CLAIR

MACROECONOMIC SURVEILLANCE DEPARTMENT MONETARY AUTHORITY OF SINGAPORE

DECEMBER 2004

* THE VIEWS IN THIS PAPER ARE SOLELY THOSE OF THE AUTHOR AND SHOULD NOT BE ATTRIBUTED TO THE MONETARY AUTHORITY OF SINGAPORE. THE AUTHOR IS GRATEFUL TO KHOR HOE EE, WONG FOT CHYI, AND EDWARD ROBINSON FOR USEFUL DISCUSSION AND COMMENTS. THE MONETARY AUTHORITY OF SINGAPORE JEL Classification Number: C5, C22, G21. KEYWORDS: BANKING SECTOR, FINANCIAL PERFORMANCE, FINANCIAL STABILITY, FORECASTING, EMPIRICAL, CAUSALITY, MACRO ENVIRONMENT, MICRO FACTORS.

MAS Staff Paper No. 38 December 2004

MONETARY AUTHORITY OF SINGAPORE i

ABSTRACT Using data available since 1990, this study explores possible macroeconomic determinants of changes in Singapore’s local banks’ financial performance and resilience. Econometric estimation focuses on changes in core banking indicators, such as income, expenditure, profitability, labour demand, capital holdings, and liquidity. The research aids banking sector surveillance, by highlighting a core set of macro indicators, which may, in turn, forewarn of periods of financial stress. The most important macroeconomic indicators are changes in interest rates, exchange rates, unemployment, and aggregate demand. On average, roughly two-thirds of the changes in the local banks’ aggregate financial performance can be explained by changes in the macro environment. The results provide a couple of insights for risk-focused banking supervisors. Firstly, it is important to monitor lending behaviour, credit quality, and expense controls, when the business cycle and loan growth strengthens. This is because there is the potential risk that growth in profits, and financial resilience more generally, will be eventually eroded by extra provisioning for NPLs. Secondly, supervisors need to be aware that sharp rises in interest rates may place significant downward pressures on capital and liquidity.


MONETARY AUTHORITY OF SINGAPORE ii

TABLE OF CONTENTS

ABSTRACT i

TABLE OF CONTENTS ii

1. INTRODUCTION 1

2. DETERMINANTS OF FINANCIAL PERFORMANCE AND RESILIENCE 3

2.1. MACROECONOMIC FACTORS AND FINANCIAL PERFORMANCE 3

2.2. MACROECONOMIC FACTORS AND FINANCIAL RESILIENCE 4

Chart 1: Singapore’s Growth in Real GDP and NPLs 5

3. METHODOLOGY AND KEY FINDINGS 7

3.1. METHODOLOGY 7

Table 1: Macroeconomic Data 7

Table 2: Nominal Bank Data 8

Figure 1: An “Ideal” Framework of the Determinants of Bank Performance and Resilience 9

Figure 2: A Feasible Framework of the Determinants of Bank Performance and Resilience 9

Figure 3: Modelled Determinants of Bank Performance and Resilience 12

3.2. KEY FINDINGS 13

Figure 4: Inter-linkages Between the Macro Environment and Banking 14

Table 3: Estimated Models (in first differences) 15

4. FORECASTING PERFORMANCE 19

Table 4: Models’ Out-of-sample Forecasting Performance for 2003 20

Table 5: Starting-Point Revisions Explain Some of the Error 20

Chart 2: Consolidated Interest Income 1-step ahead Forecasting 21

Chart 3: Consolidated Interest Paid 1-step ahead Forecasting 21

Chart 4: Consolidated Net Interest Income 1-step ahead Forecasting 22

Chart 5: Consolidated Total Capital 1-step ahead Forecasting 22

5. LIMITATIONS AND FUTURE RESEARCH 25

6. CONCLUSIONS 27

REFERENCES 29


MONETARY AUTHORITY OF SINGAPORE 1

1. INTRODUCTION 1.1 For many central banks, the monitoring of financial stability relies on qualitative analysis based around Financial Stability Indicators (FSIs). While FSIs are a useful diagnostic for the current health of financial institutions, they fail to quantify possible inter-relationships with the macroeconomy and the shocks that may stress financial institutions. Scenario-based stress-testing frameworks are often used as a vehicle for the latter, although a major limitation is the over-reliance on ad hoc judgement in framing the scenarios and on the transmission mechanism. 1.2 To overcome these limitations, the financial stability literature emphasises the importance of quantitative modelling of linkages between the financial sector and the macro environment. In turn, policymakers have realised that quantitative modelling could be a key element in their financial stability surveillance. Further, the IMF (2002), in promoting rigorous financial stability surveillance, emphasises the importance of developing quantitative tools which can explain the transmission of changes in the macro environment, into potential credit risk events, and impacts on banks1. 1.3 These quantitative models usually focus on the importance of macro or FSI indicators in determining the performance and stability of financial institutions, as well as providing a framework for forecasting, scenario analysis, and stress-testing. For example, the Bank of England (2003) has employed quarterly VAR models to estimate bank losses from changes in the macroeconomic environment2. The modelled shock transmission is from the macro environment, through to household and corporate sector default rates, and on to bank profitability and changes in capital. The Reserve Bank of Australia has devoted some resources to try and quantify relationships between banks’ profitability, resilience, and macroeconomic variables (see Gizycki 2001).

1 Notwithstanding that the IMF states that recent efforts to explain, and in turn predict,

changes in the performance and resilience of banks have met with limited success. This may go some way to explain the lack of published research by central banks on their use of quantitative modelling for monitoring bank performance and stability.

2 Macro variables include GDP growth, house price inflation, exchange rate movements, changes in employment, and growth in equity prices.



1.4 This paper quantifies key macroeconomic determinants affecting the performance of Singapore’s three local banks over a short/medium-term horizon. That is, around a year or less, in terms of explaining the influence of the macro environment on changes in bank behaviour, with the horizon narrowing to one quarter ahead for forecasting application. Nine single equation regression models are estimated from MAS’ Banking Statistical Returns Database for system-wide macroeconomic determinants of financial performance and resilience. Changes in core banking indicators are modelled, such as income, expenditure, profitability, labour demand, capital holdings, and liquidity. 1.5 Hopefully this research will, in turn, aid financial sector surveillance, by having a core set of macro indicators that may signal periods of financial stress. More generally, having quantitative models of banks’ financial performance provides a convenient framework for alternative scenario analysis or macro system-wide stress-testing. The main advantages of such a framework are that they are tractable, and allow judgement to be imposed in a transparent and systematic way. Less complete tools often fail to enforce structure and discipline on the analysis, and as a result, recommendations may be incomplete because a key issue or relationship is overlooked. 1.6 This paper is set out as follows. Section 2 reviews empirical research covering the relationships expected between banks and the macro environment. Section 3 covers the methodology and presents the key macro relationships uncovered and the main empirical findings. Section 4 uses the models for forecasting financial performance and considers an alternative scenario of a weaker business cycle. Section 5 presents a discussion on the limitations of this research, followed by some conclusions.



2. DETERMINANTS OF FINANCIAL PERFORMANCE AND RESILIENCE

2.0 Theoretical determinants of banks’ performance and financial resilience (or stability) stem from two broad sources: micro bank-specific factors and the macroeconomic environment. Micro bank-specific factors include the individual risk exposure, operating strategies, and the degree of management expertise. Macro factors include GDP growth, unemployment, interest rates, exchange rates, and the level of competition. Evidence from the 1994 Mexican financial crisis suggested that bank-specific variables explained the likelihood of bank failure, while macroeconomic factors determined financial performance more generally and the timing of bank failures (see Gonzalez-Hermosillo, Pazarbasioglu and Billings 1997).

2.1. MACROECONOMIC FACTORS AND FINANCIAL PERFORMANCE

2.1.1 Many studies show that bank financial performance is influenced by the business cycle (Lowe and Rohling 1993; Calomiris et al 1997; Kaufman 1998). During boom times, firms and households commit larger proportions of their income flows to debt servicing with preferences for leverage following a pro-cyclical pattern. Assuming all else constant, both the demand for leverage and banks’ income will rise with the business cycle. More recent studies surveyed in Laker (1999) find that the variables most often found to be positively associated with strong bank income growth are GDP growth and changes in real interest rates. Banks’ total expenditure, including interest paid, may also follow a pro-cyclical pattern along the business cycle. Interest paid may rise as savings increase, while wages and operating expenses may face upward pressure as labour markets tighten during economic booms.



2.1.2 The relationship between changes in banks’ profitability and the business cycle is not straightforward, with mixed empirical findings. As both income and expenditure are likely to be pro-cyclical, the outcome for profit depends significantly on the banks’ expense policy and their credit risk profile. The relationship between risk and return depends on how banks price for risk, and the lags between taking on risk and the crystallisation of the risk into realised profits or losses. When GDP increases, banks may earn higher returns by taking on greater risk, which boosts profits. However, if a bank experiences losses subsequently beyond what it had provisioned for, such losses will reduce profitability. 2.1.3 Profit could also be counter-cyclical if the operational strategy is to try and cushion the balance sheet from the business cycle. For example, expenditure policies may be strongly pro-cyclical, loosening up during boom times and clamping down during recessions. Alternatively, bank profit may be counter-cyclical if national savings are more pro-cyclical than the demand for credit. In this case a rise in GDP may increase banks’ total expenditure to a much greater extent than its income. Empirical evidence of this feature could be found across countries where the national savings-investment gap (the current account balance) is pro-cyclical3.

2.2. MACROECONOMIC FACTORS AND FINANCIAL RESILIENCE

2.2.1 Widely used measures of banking resilience or stability include the ratio of classified non-performing assets to total assets (NPLs), changes in profitability, changes in capital, and changes in liquidity. For example, Gonzalez-Hermosillo (1999) finds that changes in bank capital, which is influenced by profitability and retained earnings growth, is a useful indicator of bank fragility. Key macro determinants of changes in banking resilience often cited are GDP growth, changes in interest rates, and gearing levels. 3 Vredin and Warne (1991) report that Sweden has a pro-cyclical current account.

Mendoza (1991) finds a similar result for Canada. Penati and Dooley (1984) find some evidence, from a sample of 14 industrial countries, that Canada, Norway, New Zealand, and Switzerland experienced a pro-cyclical current account over certain periods since the 1950s.



2.2.2 Bank resilience will be affected by the ability of firms and households to service their debt. This will be determined by their income growth, changes in gearing, and interest rates (Lowe and Rohling 1993; Calomiris et al 1997; Kaufman 1998). All else equal, growth in aggregate income and output will strengthen firms’ and households’ ability to meet their debt obligations, and in turn improve bank stability. Over longer horizons these relationships may be less clear or even work in the opposite direction. An acceleration in output growth may lead to excessive lending and therefore a rise in bank fragility4. This is the so-called financial accelerator, and one motivation for fiscal and monetary policies to be somewhat counter-cyclical, or “lean-against the wind”, to curb excessive lending and the potential risk of bank fragility. 2.2.3 Consistent with this theory, relationships between aggregate output and bank stability in Singapore does not appear clear. The relationship between the business cycle and bank fragility exhibits both positive and negative co-movements over time. For example, during the economic slowdown after the Asian Crisis, NPLs increased sharply, squeezing profitability growth and capital. However, when economic growth rebounded to around 10% in 1999 NPLs remained high. Towards the end of 2000, NPL ratios improved significantly despite the economic recession in 2001 [see chart 1].

Chart 1: Singapore’s Growth in Real GDP and NPLs

3Q98 3Q99 3Q00 3Q01 3Q02 3Q03-15

-10

-5

0

5

10

15

20

25

0

YoY%

Gro

wth

5

6

7

8

9

10

11

12

13

Perc

ent

Real GDP (SA) SQPY%Local Banks' Global NPLs to Non-Bank Loans (RHS)

2Q04 4 For example, in the short term, increased GDP growth leading to rapid credit growth

will add to banks’ total assets without immediately increasing classified assets, thereby reducing NPL ratios. Over the longer term, however, credit growth may lead to higher NPL ratios as more marginal borrowers default.



2.2.4 Previous research at MAS has found that increases in Singapore’s NPLs were largely driven by increases in the unemployment rate rather than weak output growth. A 100 basis point increase in the annual unemployment rate was estimated to increase NPLs by around 350 basis points. Perhaps reflecting changes in the lag structure since 2000, one limitation is that it is difficult to explain the significant decline in NPL ratios with unemployment rising sharply5. Largely reflecting uncertainty about the appropriate lag structure, and quarterly data limitations, this paper does not explore the possible relationship between the business cycle and bank fragility as measured by NPL data. Instead, the focus is on changes in profits, liquidity, and capital, and what they may imply about bank fragility6. 2.2.5 Although rising interest rates may increase bank income they may also lead to bank fragility. Real interest rates influence companies’ choice between risky and safe projects and thereby affect banks’ financial performance and fragility (Diamond 1991). Rising real interest rates are predicted to increase the adoption of risky investment projects, increase the likelihood of counterparty default, which in turn, undermines bank stability.

5 From around 2.5% at the start of 2001 to 5.5% percent by mid 2003.

6 Perhaps reflecting concerns about the appropriate lag structure, or data limitations, the Swedish Riksbank also emphasises the importance of monitoring bank profitability, rather than NPLs, as a key indicator of bank fragility (Financial Stability Report June 2003).



3. METHODOLOGY AND KEY FINDINGS 3.1. METHODOLOGY 3.1.1 A wide-range of macroeconomic variables were selected as potential determinants, while the nominal bank data was sourced from MAS' Statistical Returns Database for the dependent variables [tables 1 and 2]. With a sample period of 1990Q1 to 2003Q2, all data were seasonally adjusted where appropriate using Census X12 and first difference stationary I(O). Variables were expressed as natural logarithms where appropriate i.e equivalent to quarterly percentage change (or basis point).

Table 1: Macroeconomic Data

Bankruptcy: Individual Orders Made (SA) Inflation: Nominal GDP Goods Deflator (SA)

Bankruptcy: No Companies Wound-Up (SA) Inflation: Nominal GDP Services Deflator (SA)

Bankruptcy: No Petitions Filed for Firms (SA) Inflation: Residential Property Prices (SA)

Exchange Rate: Nominal Effective (IMF) Interest Rate: 3-mth Domestic Deposit

Exchange Rate: Real Effective (IMF) Interest Rate: 3-mth Domestic Interbank

Exchange Rate: SGD per unit of USD Interest Rate: Domestic Prime Lending

GDP: Nominal (SA) Interest Rate: 3-mth USD SIBOR

GDP: Nominal Financial Services (SA) Interest Rate: Domestic Prime Mark-up on USD SIBOR

GDP: Nominal Goods Industries (SA) Interest Rate: Prime Mark-up on Deposit

GDP: Nominal Regional (TH, MY, HK, KR)7 (SA) Interest Rate: Prime Mark-up on Interbank

GDP: Nominal Services Industries (SA) Share Market: SGX Turnover Value (SA)

Inflation: CPI (SA) Share Market: SGX Turnover Volume (SA)

Inflation: CPI Regional (TH, MY, HK, KR) (SA) Share Market: STI Price Index

Inflation: MAS Underlying Unemployment Rate (SA)

Inflation: Nominal GDP Deflator (SA)

7 The regional GDP and CPI indices comprise: Hong Kong (45%), Malaysia (35%),

Thailand (10%), and Korea (10%). The weights (in brackets) are derived from the aggregate local banks’ asset exposure to these countries. Around 70% of the local banks’ asset exposure is Singapore based.



Table 2: Nominal Bank Data

Annualised Interest Paid to Interest Liabilities Interest Received (SA)

Annualised Interest Received to Interest Assets Investment Income

Annualised NIM Liquid Assets (SA)

Annualised ROA Net Interest Income (SA)

Annualised ROE Other Expenditure (SA)8

Capital (SA) Profit (SA)

Commission Fee Income (SA) Salaries Paid (SA)

Individual Bank Share Prices Total Assets (SA)

Interest Bearing Liabilities (SA) Total Expenditure (SA)

Interest Earning Assets (SA) Total Income (SA)

Interest Paid (SA) Total Number Employed (SA)

3.1.2 A major problem in developing quarterly growth models is the volatility of the nominal bank data. Growth rates for most of the series are three times more volatile than nominal GDP growth. One solution explored was to establish relationships using lower frequency annual data. While this strategy improved the explanatory power, the drawback was that it generated significant autocorrelation in the estimated residuals. This implies that annual models may be unreliable and overstate the significance of macroeconomic factors in determining banks’ financial performance and stability. 3.1.3 Figure 1 presents a stylised representation of the determinants of bank resilience and performance. This “ideal” framework diagram depicts transmission mechanisms, acceptable to most as being important influences, on bank financial performance and fragility. Key inter-linkages between the income statement, the balance sheet, the macro environment, and bank-specific factors are captured. However, because of data limitations and measurement issues, Figure 2 presents an adapted version of the ideal framework. The transmission of changes in bank-specific factors, leading to specific adverse risk events (i.e the black-shaded boxes in figure 1), are outside the scope of this paper9. 8 Comprises all expenditure excluding salaries and interest paid e.g. provisions.

9 For example, it is difficult to observe (and model) the affect of bank management strategies, or individual operational risk systems, on financial performance and stability. Modelling the probability of an adverse credit risk event would require a transition matrix of corporate default rates.



Figure 1: An “Ideal” Framework of the Determinants of Bank Performance and Resilience

Figure 2: A Feasible Framework of the Determinants of Bank Performance and Resilience

Changes in Macro Economic Indicators

Changes in Income Statement

Performance

Balance Sheet

Activity



Performance

Balance Sheet

Activity

Changes in Micro Bank Specific

Factors

Adverse Credit/Market Risk

Events



3.1.4 Given the representation depicted in figure 2, the optimal methodology is to build a multi-equation framework that would capture the inter-linkages. For example, the income statement-balance sheet nexus can be represented by the following structural simultaneous equation system [equation 1]. Changes in the balance sheet, tH∆ , is affected by its own past realisations (k lags) and the current and past realisations of profit growth. Similarly, changes in profit, tP∆ , are determined by their own past values and the current and past realisations of changes in the balance sheet. Both equations contain feedback effects since tH∆ and tP∆ are assumed to affect each other. For example, changes in interest earning assets will impact on changes in interest income and profitability, while changes in profitability will affect changes in retained earnings.

tktkktktt

tktkktktt

PHHP

PHPH

222202

111101

εφβδα

εφβδα

+∆+∆+∆+=∆

+∆+∆+∆+=∆

−−

−−

(1)

To solve this endogenous system the reduced form is derived (or standard form) where all variables are jointly endogenous. Each variable is allowed to depend on changes in its past realisations as well as the past realisations of all other variables in the system [equation 2]. The number of lags, k, is an empirical question which can be determined by information criteria testing e.g Akaike and Bayesian.

tktkktkt

tktkktkt

HPP

HPH

2222

1111

εφβα

εφβα

+∆+∆+=∆

+∆+∆+=∆

−−

−−

(2)



3.1.5 A reduced form vector auto regression (VAR) model, with the addition of equations for macro environmental variables, was employed to try and capture the core relationships in Figure 2. To identify the model, in the absence of an overriding theory, Choleski decomposition was employed. Although the most successful ordering had changes in the balance sheet as the most endogenous, a multi-equation model with sensible dynamics could not be developed. High volatility in the nominal bank data, as outlined earlier, probably contributed to the difficulty in generating plausible dynamics10. 3.1.6 Section 5 of this paper suggests that future research should try and develop a multi-equation framework from Singapore’s financial sector real value added data, which is significantly less volatile. In turn, this real value added data may facilitate the development of an empirical framework that is able to utilise information more effectively from financial sector interactions. The downside is that the National Accounts data set is more limited in its coverage of bank financial performance variables than the MAS' Statistical Returns Database. For example, the Statistical Returns Database allows the analysis of possible determinants for changes in expenditure, changes in labour demand, capital, and liquidity. 3.1.7 With this limitation of the financial sector value added data in mind, a single equation framework was followed, largely as a “second-best approach” to try and explain bank financial performance and resilience [figure 3]. Despite its obvious disadvantage in not being able to explain some key interactions, the single equation framework adopted has the advantage of being simple to employ, in a spreadsheet environment, with the right-hand side comprising solely of readily available macro variables. Bi-variate Granger Causality testing was employed to gauge information content, establish precedence, and thereby confirm that the single equations were correctly specified with the right-hand side being strictly exogenous.

10 For example, some of the Impulse Response Functions appeared unstable, with decay

rates in excess of eight years after a one-off one quarter shock. The imposition of contemporaneous identifying restrictions, instead of utilising Choleski, failed to improve the results significantly.



Figure 3: Modelled Determinants of Bank Performance and Resilience

3.1.8 Nine single equation regression models were estimated, covering banks’ income performance, expenditure patterns, profitability, labour demand, capital holdings, and core liquidity. These models explain changes in banks’ financial performance arising from changes in the macro environment over the short/medium-term. This is the more relevant horizon for financial monitoring and surveillance11. In turn, each model was subjected to several diagnostic tests to ensure that the relationships were robust. 3.1.9 Panel regression analysis was considered, as it would explore the effect of the macroeconomic cycle on banks while testing the influence of interbank differences. This approach was not followed largely because interbank differences don’t appear to be significant. For example, the aggregated model (vis-à-vis fixed-effects paradigm) is given empirical support by the absence of significant heteroskedasticity in the residuals from the estimated models12. Furthermore, individual bank models do not show significant differences in the sensitivities of the three local banks to changes in the macro environment13.

11 The models do not explain the long-run determinants of bank financial performance

and resilience. This is left as a possible direction for future research, where the real financial sector value added data could be used instead. Cointegration could be employed to model long-run determinants, and an error correction framework could be implemented to explain short/medium-term dynamic adjustment to the long-run.

12 Since the explanatory variables vary through time, but are constant across the three local banks, the estimated regression models may be prone to heteroskedasticity because of interbank differences.

13 There is a significant loss in explanatory power because individual bank behaviour is only influenced in part by the macro economy. In other words, the local banks can decouple themselves from the macro environment to a certain degree, perhaps reflecting the impact of micro strategies.



Performance

Balance Sheet

Activity



3.2. KEY FINDINGS 3.2.1 The most important macroeconomic indicators appear to be changes in interest and exchange rates, aggregate demand, and unemployment. From the list of 29 macroeconomic indicators [see table 1], considered by most as being key determinants, only 14 variables contain the most useful information [see table 3]. From a financial surveillance perspective this is useful, as duplication in information content across indicators is distilled, creating a succinct list of core indicators. Figure 4 is a stylised diagram summarising the potential inter-linkages between the macro environment and the banking sector. The diagram summarises the variables that performed best in terms of explaining changes in banking financial performance and resilience [see table 3], and draws on the causality testing results. As expected by theory, the macro environment is generally exogenous. However, there is some limited evidence that financial performance may feed-back into domestic interest rate setting (deposit rates), and inflationary pressures in the goods and housing market. The latter may suggest that Singapore’s banks, in carrying out their role as a passive supplier of credit, may stimulate excess demand pressure in the property market.



Figure 4: Inter-linkages Between the Macro Environment and Banking

Interest Income

Non-Interest Income

Interest Paid

Non-Interest Expenses

PROFIT

Number Employed

Capital

Liquid Assets

Total Assets

Total Liabilities

Income Statement Performance Balance Sheet Structure

Domestic Interest Rates

USD SIBOR

Excess Demand Pressures in

Housing Market

Excess Demand Pressures in

Goods Market

MAS Policy

Activity across Financial Industry e.g Share Market,

Other FIs

AGGREGATE DEMAND

Unemployment

Rate

Exchange Rate



Table 3: Estimated Models (in first differences)

Exogenous Determinants: Interest Income

Fee Income

Total Income

Interest Paid

Salaries Paid

Total Expenses Employment Capital

Liquid Assets

Exchange Rate: Nominal (SGD per USD) 0.65 (t-3)* 0.83 (t) 0.92 (t-3)* 1.09 (t-4)* Inflation: Property Price 0.72 (t)* Inflation: Underlying -4.80 (t-2) Interest Rate: Deposit (bp) 6.16 (t-1)* Interest Rate: Interbank (bp) -1.24 (t-1)* -1.90 (t)* Interest Rate: Prime Lending (bp) 3.41 (t-1)* Interest Rate: SIBOR USD (bp) 4.14 (t)* 6.50 (t)* 7.04 (t)* 3.82 (t-4)* 7.17 (t)* 1.61 (t-2)* Mark-up: Lending minus Deposit Rate (bp) 12.52 (t-4)* 10.87 (t)14 9.32 (t)* 9.58 (t) * Mark-up: Lending minus SIBOR USD Rate (bp) 1.64 (t) Nominal GDP 0.68 (t-2) 1.71 (t-2)* 1.07 (t-4) 0.20 (t) 0.70 (t-4)* Nominal GDP: Financial Services 0.41 (t)* 0.48 (t-2)* Nominal GDP: Goods 0.62 (t-2)* 0.38 (t-4) Nominal GDP: Services 0.75 (t-1)* 0.76 (t)* Unemployment Rate (bp) -6.94 (t-2)* -7.60 (t-2)* -7.41 (t-2)* -14.82 (t-2)* -3.53 (t-2)* -4.35 (t-2)* -4.89 (t)* Lagged Dependent 0.47 (t-1)* 0.35 (t-3)* 0.23 (t-3) -0.37 (t-1)* 0.30 (t-3)* 0.19 (t-3) -0.32 (t-4)* Adjusted R-squared 0.76 0.38 0.54 0.68 0.43 0.52 0.66 0.7515 0.38 Standard Error of Regression 3.4% 6.6% 5.5% 6.0% 5.3% 7.7% 1.8% 2.2% 4.1% Standard Deviation of Dependant Variable 6.9% 8.5% 8.1% 11.0% 7.0% 11.2% 3.1% 4.4% 5.2% Estimation Period 90:3-03:2 90:2-03:2 91:1-03:2 91:1-03:2 90:3-03:2 91:1-03:2 91:1-03:2 91:2-03:2 90:2-03:2

Notes: (a) The nine dependent variables are listed at the top of the table. The constants are not reported. (b) All data are seasonally adjusted, expressed as natural logarithms where appropriate, and first difference stationary i.e equivalent to quarterly percentage change (or basis point). For example, model coefficients in the table above can be interpreted as follows: a 1 percentage point increase in quarterly nominal GDP results in 0.7 percentage point increase in quarterly total income after 6 months. A 100 basis point increase in the quarterly nominal SIBOR interest rate results in a 6.5 percentage point increase in total income. (c) All reported coefficients are significant with at least 95% confidence, and * denotes significance with 99% confidence. (d) Diagnostic testing finds well behaved residuals. There is no evidence of significant autocorrelation (up to four lags), heteroskedasticity, specification error (RESET test), or non-normality (Jarque-Bera test).

14 Due to two-way causality concerns, an alternative version is estimated without the interest rate mark-up. Encouragingly the overall relationships are

not affected. 15 The model also includes a dummy variable for extreme changes in capital over the acquisition period (2001:3=1, else=0). For example, UOB acquired

OUB (over September/October 2001), DBS acquired Dao Heng (June/July 2001) and Vickers Ballas (September 2001).



3.2.2 Based on the average R-squared, the regression models suggest that around two-thirds of the changes in the banks’ financial performance and resilience is driven by changes in the macro environment [refer to table 3]. The remaining one-third or so can be explained by factors outside of the analysis such as banks’ risk exposure, feed-back effects from the balance sheet, operating strategies, operational efficiency, and the level of competition in the market. As noted previously, there is no evidence to suggest that individual banks are affected differently by changes in the macro environment, although they can decouple themselves to a certain degree. This may reflect the impact of bank-specific or micro strategies on performance. 3.2.3 Consistent with the theoretical underpinnings outlined, the local banks’ aggregated income and expenditure increases with the business cycle, with expenditure being somewhat more pro-cyclical over the medium term [see table 3]. Specifically, a one-period 1 percentage point increase in quarterly GDP, followed by stronger credit demand, will lead to (assuming all else constant):

• a 0.7 percentage point rise in quarterly income after six months;

• a 0.1 percentage point rise in quarterly total expenditure after

six months, and by around 1 percentage point after twelve months;

• a 1.6 percentage point rise in profit after six months, but by

only 0.1 percentage points after twelve months, because of increased expenditure pressures16.

16 This calculation is based on the ratio of total expenditure to total income being below

0.64 which is the case for Singapore’s local banks (this ratio has averaged 0.60 since 2002). The level of profit is therefore procyclical with GDP. The percentage increase in profit is eroded in the medium-term by strongly procyclical expenditure. In the medium-term the percentage change in expenditure is greater than income.



3.2.4 Strongly pro-cyclical expenditure may suggest that banks take on additional risk during the upturn, which impairs profitability growth17. For example, stronger credit demand during the upturn may lead to higher NPL provisioning over the medium term as marginal borrowers default. Alternatively, strongly pro-cyclical expenditure may imply that banks utilise profits as a counter-cyclical buffer, being somewhat less concerned with expense control when the business cycle is booming. The implication for financial surveillance is to pay close attention to the banks’ lending behaviour, credit quality, and expense controls when the business cycle starts to strengthen. 3.2.5 Higher domestic interest rates, or a weaker SGD-USD nominal exchange rate, are significant determinants of financial performance18. Banks’ income and expenditure are equally responsive and positively related to changes in interest rates. This may suggest that lending behaviour remains prudent during periods of rising interest rates. In contrast to periods of rapid credit and GDP growth, where expenditure growth is strongly pro-cyclical, bank profitability growth does not appear to be eroded by the possibility that rising interest rates leads to more marginal borrowers defaulting and higher provisioning.

17 The estimated relationships also show that changes in expenditure are more sensitive

than income growth to changes in the unemployment rate. This reinforces potential counter-cyclical pressures on profit [see table 3].

18 Both US interest rates and the nominal exchange rate are important determinants of financial performance, with an estimated VAR model for Singapore confirming that uncovered interest rate parity (UIP) applies. With static exchange rate expectations, a 100 basis point increase in the 3-mth SIBOR USD interest rate will increase (on average): the 3-mth domestic interbank interest rate by 70 basis points; the 3-mth domestic fixed deposit interest rate by 25 basis points; and the domestic prime lending interest rate by 20 basis points. Furthermore, the UIP condition for Singapore has been empirically validated in the MAS Occasional Paper No. 20 (2000).



3.2.6 Changes in bank stability, as indicated by changes in capital, is determined by changes in aggregate demand and interest rates. When the economy is weak and unemployment is rising, capital is eroded by around 4 percentage points after six months for every 100 basis point rise in the unemployment rate. This may reflect lower retained earnings when economic conditions are fragile. Liquidity tends to come under downward pressure during periods when the business cycle is weak, when interest rates are rising, or when mark-ups on prime lending are falling (vis-à-vis potentially lower profit). 3.2.7 Rising interest rates also put downward pressure on capital, perhaps reflecting banks’ desire to lend more when the volume of deposits increases. When interest rates are rising, the opportunity cost of holding capital in the balance sheet increases19. On the other hand, when mark-ups on prime lending are rising (vis-à-vis potentially higher profit), capital will tend to strengthen. For every 100 basis point increase in the interest rate mark-up on prime lending rates, banks’ capital will tend to strengthen by around 1.5 percentage points.

19 Higher interest rates may lead to increased provisioning as NPLs unfold, resulting in

lower growth in profit, retained earnings, and capital, than otherwise. However there was no clear evidence to support this from the income statement models. Total expenditure and total income were almost equally pro-cyclical with respect to increases in interest rates [see table 3].



4. FORECASTING PERFORMANCE 4.1 To gauge the potential of the model framework to provide some warning signals when financial performance and resilience may come under downward pressures, this section evaluates forecasting ability over a short-term horizon. More generally, if the models forecast efficiently, then to a certain degree, it strengthens the evidence supporting the importance of the core macro indicators. That said, it is not the primary objective of this research to try and develop robust early warning systems to periods where bank performance may come under downward pressures. Especially since the modelling framework, a second-best choice, is single-equation based and potentially misses important inter-linkages. 4.2 The models utilise readily available forecasts of macro explanatory variables i.e nominal GDP growth, changes in the unemployment rate, changes in interest rates and the exchange rate20. The 1-step ahead forecasting performance of the models in explaining key components of the local banks’ aggregate consolidated accounts appears reasonable, even for profits, which is a derived item21. For the main components of the local banks’ aggregate income statement, charts 2 to 5 show the 1-step ahead forecast performance, while the annualised root mean squared error (RMSE) gives an idea of the degree of confidence attached to the models’ projections. 4.3 Table 4 shows the models’ performance in estimating key components of the local banks’ accounts over 2003. The out-of-sample forecasting performance was better than the average error from historical 1-step ahead projections22. Starting-point revisions to the banks’ accounts explain some of the error, in particular for net interest income and non-interest income [see table 5]. 20 These can be obtained from Consensus Forecasts and the MAS Survey of

Professional Forecasters.

21 The regression models are used to forecast the growth in interest income, total income, interest paid, and total expenditure. The forecast quarterly percentage changes, after adding back seasonal factors, can then be applied to the local banks’ aggregate consolidated income statement. In turn, the level of profits is derived by total income less total expenditure. The level of non-interest income and non-interest expenditure can be found by residual, or supplemented with information from the estimated models for the growth in fee income and salary expense. For total capital, the split into tier-1 and tier-2 capital was based on historical average shares.

22 The usual quarterly one-step ahead forecast error for interest income is +/-0.8%, +/-1.3% for interest expense, and +/-2.4% for total capital.



Table 4: Models’ Out-of-sample Forecasting Performance for 2003

Billions ($SGD) Actual Model Estimate Error (%) Error ($m) Year Ended Revenue Statement Performance: Dec-03 Dec-03 Dec-03 Dec-03 Interest Income 9.3 9.3 -0.1 -8 Interest Expense 3.4 3.5 0.6 21

Net Interest Income 5.9 5.9 -0.5 -28 Fee and Commission Income 1.8 1.8 -1.0 -18 Total Non-Interest Income 3.7 3.8 2.7 100

Total Income 9.6 9.6 0.8 72 Staff Costs 1.9 1.9 0.8 14 Total Operating Expenses (ex Interest Paid) 4.2 4.2 -0.4 -17 Operating Profit Before Provisions 5.33 5.42 1.7 89 Capital Structure (as at): Tier-1 Capital 27.3 26.9 -1.3 -355 Tier-2 Capital 14.4 13.8 -4.2 -596 Total 41.7 40.7 -2.3 -950

Table 5: Starting-Point Revisions Explain Some of the Error

Year Ended Revenue Statement Performance: Revisions for the year

ended September 2003 (%) Model Error for year ended

December 2003 (%) Interest Income 0.7 -0.1 Interest Expense 0.2 0.6

Net Interest Income 1.0 -0.5 Fee and Commission Income 0.1 -1.0 Total Non-Interest Income -1.7 2.7

Total Income 0.0 0.8 Staff Costs 0.1 0.8 Total Operating Expenses (ex Interest Paid) 0.0 -0.4 Operating Profit Before Provisions 0.0 1.7 Capital Structure (as at): Tier-1 Capital 0.0 -1.3 Tier-2 Capital 0.0 -4.2 Total 0.0 -2.3



Chart 2: Consolidated Interest Income 1-step ahead Forecasting

(Quarterly: March 1991-September 2003)23

91 92 93 94 95 96 97 98 99 00 01 02 031000

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Chart 3: Consolidated Interest Paid 1-step ahead Forecasting (Quarterly, March 1991-September 2003)

91 92 93 94 95 96 97 98 99 00 01 02 03500

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23 The 1-step ahead forecasts presented are based on model parameters estimated over

the entire sample period i.e up to 2003Q2. Only the 1-step ahead forecasts for September and December 2003 [see table 4] are strictly out-of-sample.



Chart 4: Consolidated Net Interest Income 1-step ahead Forecasting (Quarterly, March 1991-September 2003)

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Chart 5: Consolidated Total Capital 1-step ahead Forecasting (Quarterly, December 1991-September 2003)

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4.4 Having quantitative models of banks’ financial performance provides a convenient framework for alternative scenario analysis or macro system-wide stress-testing. Alternative scenario analysis provides insights into the potential sensitivities of the financial sector to changes in the macro environment. The focus is not on forecast accuracy, but rather on the insights gained from projected deviations from a base-line (or trend) scenario. By examining the properties of the projected “gaps” (i.e deviations from base-line) we can gauge the magnitude of stress the financial sector may experience when the macro environment is weaker than anticipated. In turn, by running numerous alternative scenarios we can compile a distribution of “gap outcomes”, which provides insights into the probability that banking sector stress reaches a certain threshold, given shocks to the macro environment. 4.5 As an illustrative example, suppose nominal GDP growth was about 4.5 percentage points weaker, on average over 2004-2005, than expected in MAS Survey of Professional Forecasters (March 2004). That is, assume nominal GDP growth of around 2% on average over 2004 and 2005, instead of around 7% this year and 6% next year. Furthermore, also assume that the unemployment rate remains at 4.5%. Relative to “base-line” projections of bank performance where the macro environment unfolds as expected in the MAS Survey, the medium-term impact of this substantially weaker aggregate demand scenario would be:

• the level of aggregate net interest income would be 16% lower, by the end of 2005, than its “base-line” projection level;

• total income would be 12% lower than its “base-line” projection;

• the level of expenditure (excluding interest paid) would be 21%

lower than its “base-line” projection;

• the level of profits would be 6% lower than its “base-line”;

• and the level of capital would be 4% lower than its “base-line”.



4.6 The relatively larger pro-cyclical response from banks’ expenditure helps minimise the possible loss of profitability when the business cycle is weaker than expected. From a financial stability point of view this is somewhat encouraging, suggesting that the banks’ try and utilise their profits as a counter-cyclical shock buffer. With alternative scenarios, like the example outlined, we can gauge the models’ sensitivity and uncover a general “rule-of-thumb”. By running several experiments under alternative scenarios for economic conditions, and measuring the deviation in the alternative scenario projected path and the baseline projected path (where nominal GDP grows at its trend rate of around 6% per annum) we find the following property. For every 1 percentage point reduction in nominal GDP growth away from its trend, the level of profit will be around 1.5% lower than otherwise, and the level of capital around 1% lower than otherwise, after a medium-term horizon of 4 to 8 quarters.



5. LIMITATIONS AND FUTURE RESEARCH 5.1 Economic models are, at best, simplified representations of key features of how parts of the economy may work. No model is going to capture all the forces at work in the economy. A model is by definition an abstraction, and can only incorporate the most important linkages. The models focus on changes in the macro environment, which may only explain around two-thirds of the changes in banks’ financial performance. Micro bank-specific factors such as the risk profile, operational efficiency, and management strategies also play a role. Furthermore, because Singapore’s three local banks account for only 20% of total financial sector assets, and 56% of domestic banking assets, insights into overall financial resilience may be limited. 5.2 The models are not general equilibrium in nature. In other words, there is no long-run rate of profitability that banks aim to maximise and that income and expenditure flows will converge toward. By construction these models imply that individual components of the income statement may converge to paths that are completely implausible. However, the models do capture short/medium-term dynamic adjustments in bank performance and resilience, which is more relevant for financial surveillance. Given this, a case can be made that a possible lack of long-run consistency is of second-order importance. 5.3 Probably the most notable flaw in the estimated single equation regressions is that they fail to capture the joint-determination of changes in the income statement and the balance sheet, and how these are influenced by changes in the macro environment. The modelled transmission mechanism is simplified significantly from the framework depicted in figures 1 and 2. Other potential problems, reflecting the extreme volatility in the nominal bank data, are that some of the estimated lag structures may not be as stable as implied by the equations. As outlined in section 3, future research should try to take further steps toward an “ideal” transmission framework, perhaps by utilising an alternative data set, such as the real value added data for the financial sector. Future research could also take a wider view, by exploring inter-relationships between the macro-environment, domestic banking, offshore banking, insurance, exchange rate and financial asset trading. This may facilitate the development of an empirical framework that is better able to utilise information from financial sector interactions.



5.4 This research has achieved its primary objective, by understanding better some key relationships between Singapore’s domestic banks and the macroeconomy. In turn, this helps identify potentially important indicators for monitoring financial performance and banking stability more generally. Notwithstanding the limitations, these single equation models provide a more disciplined framework for financial stability analysis. In line with IMF recommendations of best practice for financial surveillance, these models are a step forward in quantifying the transmission of changes in the macro environment to banks. Having quantitative models of banks’ financial performance provides a convenient framework for scenario analysis or stress-testing. The main advantages are that the models are tractable, which allows judgement, and alternative scenarios, to be imposed in a transparent and systematic way. As a result, assumptions can be tested and risk assessed.



6. CONCLUSIONS 6.1 This study employs simple regression equations to model possible macroeconomic determinants of changes in Singapore’s local banks’ financial performance and resilience. Adverse developments in the macro environment, potentially leading to periods of banking financial stress, usually coincide with significant downward pressures on profit, capital, and liquidity. More generally, the OLS regression models illustrate the relative importance of various macro indicators, as well as providing a framework for scenario analysis and stress-testing. 6.2 A key insight from the analysis is that out of a list of 30 or so indicators, considered to be important determinants of changes in financial performance and stability, only around a dozen variables contain the most useful information. From a financial surveillance perspective this is useful, as duplication in information content across indicators is distilled, creating a succinct list of core indicators. The most important macroeconomic indicators are changes in interest rates, exchange rates, unemployment, and aggregate demand. 6.3 On average, roughly two-thirds of the changes in the local banks’ aggregate financial performance can be explained by changes in the macro environment. The remaining third may be determined by bank-specific variables which are outside the scope of this paper e.g risk exposure, operating strategies, management quality, and the level of competition. There is no evidence to suggest that individual banks are affected differently by changes in the macro environment, although they can decouple themselves to a certain degree. This may imply that bank-specific variables are more important in determining individual performance than aggregate performance.



6.4 There is some evidence that banks’ expenditure is strongly pro-cyclical with GDP. Loan-driven profits may rise over the short term, but are then eroded somewhat over the longer term. This erosion in profit growth may reflect rapid credit growth during the upturn, and eventually, higher NPL provisioning as marginal borrowers default. Alternatively, it may be because the local banks’ use profits as a counter-cyclical shock buffer. For example, during recessions expenditure is reduced significantly to try and preserve profits. Rising interest rates reduce capital, which may reflect the tendency for banks to lend more. In addition, banks’ liquidity comes under downward pressures when the business cycle is weak and interest rates are rising. 6.5 When generating out-of-sample one-quarter ahead forecasts, the performance of the models appears to be reasonable. A short-term horizon, such as one-to-two quarters ahead, is a key period for financial sector surveillance. Although the RMSEs in predicting changes in the local banks’ financial outcomes are somewhat high for quarterly growth models, forecasting performance for 2003 was significantly better in comparison to within-sample historical errors. This suggests that the framework may provide some warning signals when bank financial performance and resilience will come under downward pressures.



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Lowe P and T Rohling (1993) “Agency Costs, Balance Sheets and the Business Cycle”, Reserve Bank of Australia Discussion Paper No 9331. Mendoza E (1991) “Real Business Cycles in a Small Open Economy”, The American Economic Review, No 81, pp. 797 - 818. Penati A and M Dooley (1984) “Current Account Imbalances and Capital Formation in Industrial Countries, 1949-1981”, IMF Staff Papers, No 31, pp 1-24 Swedish Riksbank (2003) Financial Stability Report (June). Vredin A and A Warne (1991) “Current Account and Macroeconomic Fluctuations”, Scandinavian Journal of Economics, No 93, pp. 511 - 530.

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