Is the Canadian banking system really “stronger” than the ... · Is the Canadian banking system...

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Is the Canadian banking system really “stronger” than the U.S. one?

Christian Calmèsa,*, Raymond Théoret b

a Chaire d’information financière et organisationnelle (UQAM); Laboratory for Research in Statistics and Probability, and Université du Québec (Outaouais), 101 St. Jean Bosco, Gatineau, Québec, Canada, J8X 3X7. b Chaire d’information financière et organisationnelle (UQAM); École des sciences de la gestion, 315 Ste. Catherine est, R-3555, Montréal, Québec, Canada, H2X 3X2, and Université du Québec (Outaouais).

___________________________________________________________________________

* Corresponding author. Tel: +1 819 595 3900 (1893)

E-mail addresses: [email protected] (Christian Calmès),

[email protected] (Raymond Théoret)

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Is the Canadian banking system really “stronger” than the U.S. one?

Abstract

The Canadian banking system is considered one of the “best” in the world (Bordo et al., 2011). To examine this question we compare the risk-return trade-off of Canadian and U.S. banks in the context of market-based banking. We find that the sources of non-traditional income are actually more volatile in Canada, essentially because Canadian banks are more involved in trading and capital markets business lines than their U.S. peers. Even though U.S. banks are more exposed to securitization, which contributes to increase bank risk, our analysis does not allow us to conclude that the Canadian banking system is performing significantly better. On one hand, Canadian banks do better in downturns; on the other hand however, depending on the statistics, U.S. banks tend to benefit more from the transition to market-based banking. JEL classification: C32; G20; G21. Keywords: Bank performance; Market-oriented banking; Securitization; Non-interest income; Financial stability.

Le système bancaire canadien est-il vraiment plus robuste que son homologue américain?

Le système bancaire canadien est considéré comme l’un des plus robustes au monde (Bordo et al., 2011). Dans le but d’analyser cette question, nous comparons l’arbitrage rendement-risque des banques canadiennes et américaines dans le contexte du banking de marché. Nous établissons que les sources de revenu non-traditionnelles font montre de plus de volatilité au Canada qu’aux États-Unis, cela essentiellement parce que les banques canadiennes sont davantage impliquées dans le commerce de valeurs mobilières et dans les activités de banques d’affaires. Même si les banques américaines sont plus exposées à la titrisation, ce qui contribue éventuellement à accroître le risque bancaire, notre analyse ne nous amène pas à conclure que le système bancaire canadien performe mieux que son homologue américain. D’une part, les banques canadiennes tirent mieux leur épingle du jeu en périodes de basse conjoncture; de l’autre cependant, dépendamment des statistiques utilisées, les banques américaines tendent à davantage tirer parti de la transition vers le banking de marché.

Classification JEL : C32 ; G20 ; G21. Mots-clefs : Performance bancaire; Banking de marché; Titrisation; Revenu autre que d’intérêt; Stabilité financière.

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1. Introduction

The Canadian banking system has the reputation of being one of the most robust

systems in the world (Ratnovski, and Huang, 2009; Bordo et al., 2011)1. Many arguments are

proposed to justify this relative performance. One that is often invoked is the more efficient

regulation of banks. For example, the Office of the Superintendent of Financial Institutions

(OSFI) has imposed leverage constraints to Canadian banks well before the Basel I

Agreement, and the rules regarding bank capital keep banks’ probability of default low (Liu et

al., 2006). Second, in terms of risk-weighted assets, regulatory capital ratios are higher in

Canada than in many industrialized countries, which ought to deliver a more robust system2.

Furthermore, Canadian banks seem to have access to relatively more stable sources of funding

and hold more liquid assets so they can absorb liquidity shocks more easily. Fourth, the

Canadian banking system is very concentrated in comparison to the U.S one (Ratnovski and

Huang, 2009)3. This is another factor that might help explain why Canadian banks could

better resist to adverse shocks (Allen and Gale, 2004; Bordo, 2011)4. Finally, Canadian banks

might also manage their non-traditional activities with lower costs—i.e., more efficiently

(Smith, 1999; Liu et al., 2006; Jason and Liu, 2007; Clark and Siems, 2002).

All these arguments focus on the robustness of the banking system and do not really

consider its performance, as if these two dimensions could be examined separately. This study

is a first attempt to fill this gap. Our primary motivation is to compare the relative

performance of the Canadian and U.S. banking systems in terms of risk-return trade-off,

taking explicitly into account the transition of both systems toward market-based banking.

1 In his speech delivered at the University of Alberta on May 1st 2013, Mark Carney, Governor of the Bank of Canada, argued that the Canadian financial system is one of the strongest —if not the strongest—in the world. 2 Bank of Canada, Financial System Review, June 2009. Note that this argument does not hold for broad measures of leverage. As discussed later, the asset to equity ratio is much higher for Canadian than U.S. banks. See also Calmès and Théoret (2012). 3 Historically, the Canadian banking system is considered as a multi-branch system, which contributes to its concentration. However, the branching constraints which prevailed in the U.S. until recently have fostered the creation of local monopolies, which might partly explain why bank net interest margins are higher in the U.S. 4 This argument is debatable however. For example, Anginer et al. (2011) argue that “greater competition makes the banking system less fragile to shocks.”

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Since bank non-traditional, off-balance-sheet activities are considered riskier than the

traditional business lines (e.g., Stiroh and Rumble, 2006; Calmès and Théoret, 2010),

investigating the impact of the change in banks’ product mix on the risk-return trade-off

provides a natural experiment to gauge the relative merits of each banking system.

The intuition behind our main argument is that profits are essential to build a strong

equity basis, and obviously the most effective way to privately avoid banking fragility. In this

respect, depending on the statistics, U.S. banks actually appear to be more profitable than

Canadian ones in most periods. Furthermore, monitoring the historical comovements between

bank accounting returns and their key determinants reveals that banks’ financial results

display a greater average volatility in Canada than in the U.S. We attribute this seemingly

paradoxical result to the greater average volatility of the Canadian banks’ product-mix—

based more on securities markets—and to a larger financial leverage. Even if Canadian banks

display a slight advantage in terms of loan loss provisions, suggesting less risky loans, and a

clear advantage in terms of non-interest expenses (likely attributable to their product mix and

their lower net interest rate margin), our results suggest that the performance of the Canadian

banking system might not be as impressive as previously thought.

This paper is organized as follows. Section 2 discusses our Canadian and U.S. datasets

and compares the relative structure of the two banking systems. Section 3 examines the

transition of the U.S. and Canadian banking systems from traditional towards market-based

business lines. Section 4 provides our model estimations of the performance of the two

banking systems. Section 5 focuses on the volatility of Canadian and U.S. banks’ financial

results and Section 6 concludes.

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2. Data

2.1 The U.S. and Canadian samples

Our dataset is annual and runs from 1986 to 2009—i.e., until the end of the subprime

crisis. The Canadian database includes the eight domestic banks, which control more than

90% of total banks’ assets. Data come from the Canadian Bankers Association, the Bank of

Canada, Statistics Canada and the Office of the Superintendent of Financial Institutions. The

U.S. database is drawn from the Federal Deposit Insurance Corporation (FDIC), and from the

Federal Reserve Bulletin series of reports on “the profits and balance sheet developments at

U.S. commercial banks”. In addition to the aggregate of all banks, we also sample U.S. banks

by size. Big banks are the 10 largest U.S. banks in terms of assets, while medium banks are

the 11 to 100 largest banks. Small banks are the ones not ranked among the 1000 largest by

assets. Note that each of the 5 Canadian biggest banks holds larger assets than the fifth-U.S.

bank. At the end of 2011, the total assets of the 8 Canadian domestic banks amount to $3.03

trillion while those of the top 10 U.S. banks amount to $7.16 trillion—i.e. a ratio of 42.4%.

2.2 U.S. and Canadian banking systems Contrary to the U.S. banking system, which until recently was a unit-branch system,

the Canadian banking system is a multi-branch system, a tradition borrowed from the Scottish

financial system. Also contrary to its U.S. counterpart, the Canadian banking system is very

concentrated, the eight domestic banks controlling more than 90% of banks’ total assets. The

Canadian banking system was compartmentalized into four pillars until 1987: commercial

banks, insurance companies, investment banking (brokers) and trusts. However, the 1987

Amendment to the Bank Act allows banks to get involved in investment banking. The

following amendments—especially the 1992 one—grant insurance and fiduciary powers to

banks.

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Looking at the raw data, note that the loan to asset ratio, a measure of traditional

banking, is much higher for Canadian banks than for the 10 largest U.S. banks at the end of

the 1980s. However, the ratio declines sharply thereafter (Figure 1). The Canadian ratio

eventually becomes similar to the U.S. one, and both are on a downward trend over the

sample period (Boyd and Gertler, 1994; Calmès, 2004). Interestingly, the composition of

Canadian bank loans changes sharply over the sample period (Figure 2). The proportion of

commercial loans drops, with a corresponding increase in mortgage loans, and to a less extent

in personal loans. Corporate loans are replaced by corporate securities in bank balance sheet.

This increase in the relative share of corporate securities may be explained by the downward

trend in interest rates over the sample period5. It is also related to the fact that business loans

are penalized by the Basel I and Basel II risk-weighted ratios used to compute regulatory

capital. This eventually leads to a banking strategy aiming at transferring bank risk off-

balance-sheet to decrease credit risk (Brunnermeier, 2009). The rapid development of

financial markets since the 1980s—especially the junk bonds and commercial paper

markets—also contributes to slow the growth of commercial loans (Berger et al., 1995).

In the U.S, the banking system is traditionally considered as a unit-branch system, the

McFadden Act of 1927 prohibiting interstate branch banking. However, in 1994, the Riegle-

Neal Interstate Banking and Branching Efficiency Act repeals the McFadden Act. In parallel

to this regulatory change there is a trend toward a much greater concentration of bank assets

since the early 1990s (Figure 3). Indeed, the 10 largest U.S. banks control less than 25% of

total assets in 1990, but this proportion jumps to 53% in 2009. Similar to the Canadian

regulation, the Graham-Leach-Bliley Financial Services Modernization Act grants broad

investment banking and insurance powers to U.S. banks in 1999. It repeals the 1933 Glass-

Steagall Act that prohibits such activities. Importantly, note that the investment restrictions

included in the Glass-Steagall Act were already more flexible before 1999. Consequently, as 5 The net interest margins on commercial loans are low compared to other kinds of loan margins like the mortgage net interest margin.

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in Canada, the U.S. loan to asset ratio is on a downward trend for the big banks since 1990,

and for the medium banks since 2000 (Figure 1 and Table 1).

Table 1. Loan to asset ratio of Canadian and U.S. banks

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 70.78 67.50 57.16 60.11 55.39 52.47 62.27

US big 60.19 52.26 52.46 52.40 52.79 50.28 54.40

medium 61.09 60.70 62.58 62.05 61.89 59.87 61.49

small 53.74 55.39 62.35 60.36 63.70 67.25 59.29

all 59.43 57.64 58.36 58.15 58.18 56.35 58.30 Notes. Canadian banks comprise the eight domestic commercial banks. U.S. big banks are the top ten banks. Medium banks are the 11 to 100 largest banks. Small banks are the banks not ranked among the 1000 largest by assets. Sources. Statistics Canada (CANSIM); FDIC; Federal Reserve Bulletin.

Figure 1. Loan to asset ratio: Canadian banks and U.S. big banks

45

50

55

60

65

70

75

86 88 90 92 94 96 98 00 02 04 06 08 10

loan to asset ratio: 10 largest U.S. banksloan to asset ratio: 8 Canadian domestic banks

Sources. Statistics Canada (CANSIM); Federal Reserve Bulletin.

Figure 2. Relative shares of bank loan categories, Canadian banks

0

10

20

30

40

50

60

70

1970 1975 1980 1985 1990 1995 2000 2005 2010

commercial loans

mortgage loans

personal loans

Source. Statistics Canada (CANSIM).

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Figure 3. Share of the 10 and 100 largest banks in U.S. banks’ total assets

20

30

40

50

60

70

80

90

84 86 88 90 92 94 96 98 00 02 04 06 08 10

10 largest

100 largest

53%

81%

51%

25%

About 14000 banks in 1986

6900 banks at the end of 2009

Source: FDIC.

3. The evolution of the Canadian and U.S. banking systems6

3.1 Trends in U.S. banking

Based on an extended sample running from 1935 to 2011, a structural break can be

identified in the U.S. accounting return data in the early 1990s (Figure 4). There is a clear

surge in banks’ return on assets (ROA), and to a less extent in return on equity (ROE). The

mean of ROA is equal to 0.63% from 1934 to 1991, but rises to 1.23% from 1992 to 20067. A

Markov switching regime (MSR) procedure confirms the structural break in ROA, with a

probability of 0.8 (Figure 5). The likelihood function estimated with the MSR procedure

indicates that ROA increases from 0.54% to 0.83% from the low to the high volatility regimes,

with a corresponding increase in volatility from 0.05 to 0.12. Importantly, note that the ROA

mean computed over the low regime corresponds to the Canadian banks’ average ROA before

the 1987 Amendment to the Bank Act.

6 Data used in this section are drawn from the Federal Deposit Insurance Corporation (FDIC) historical database with a sample comprising the whole U.S. banks’ universe. 7 We exclude the subprime crisis to perform this computation.

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Figure 4. Canadian and U.S. banks’ accounting returns

-1.0

-0.5

0.0

0.5

1.0

1.5

-8

-4

0

4

8

12

16

35 40 45 50 55 60 65 70 75 80 85 90 95 00 05 10

Canadian ROA US ROA US ROE

Sources. Statistics Canada (CANSIM); Bank of Canada; Canadian Bankers Association; FDIC.

Figure 5. Markov switching regime applied to U.S. banks’ ROA

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

35 40 45 50 55 60 65 70 75 80 85 90 95 00 05

Annual probabilitySmoothed probability

Note. This Figure is built using a Markov switching regime algorithm.

The analysis of the residuals of the recursive regression run on ROA confirms this

trend. Consider our benchmark model:

1 2t t t tROA snonin llpα β β ε= + + + (1)

where snonin is the share of non-interest income in banks’ net operating income; llp is the

ratio of loan loss provisions to assets; and ε is the innovation. The confidence interval of the

recursive residuals is clearly wider after 1990, which corroborates the presence of a structural

break (Figure 6). A MSR procedure applied to ROE also tends to display a similar break

(Figure 7).

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Figure 6. Recursive residuals from the U.S. banks’ ROA model

-.6

-.4

-.2

.0

.2

.4

.6

40 45 50 55 60 65 70 75 80 85 90 95 00 05 10

Recursive Residuals ± 2 S.E.

Figure 7. Markov switching regime applied to U.S. banks’ ROE

.0

.2

.4

.6

.8.0

.2

.4

.6

.8

35 40 45 50 55 60 65 70 75 80 85 90 95 00 05

Annual probabilitySmoothed probability

Note. This Figure is built using a Markov switching regime algorithm

Since the most important change in banking over the last decades is its transition from

traditional to market-based banking, it is interesting to examine how this transition relates to

the structural break observed in banks’ performance. To monitor the evolution of the banking

system, we rely on the Herfindahl index, computed with snonin:

( )221 1H snonin snonin⎡ ⎤= − + −⎣ ⎦ (2)

The H index is defined over the interval [0, 0.5]. Complete diversification is associated with

the supremum of this interval. The plot of the H index shows that U.S. banks are quite

diversified before the enforcement of the Glass-Steagall Act, the index being around 0.48

(Figure 8). Then the Act constrains them to abandon investment banking and to only focus on

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more traditional banking activities. This leads to a marked increase in the loan to asset ratio

and a corresponding decrease in the H index until the middle of the 1950s. However, the

index resumes its upward trend in the 1980s with the development of securitization.

Figure 8. U.S. banks’ Herfindahl index

.24

.28

.32

.36

.40

.44

.48

.52

35 40 45 50 55 60 65 70 75 80 85 90 95 00 05 10

1999: 0.49

Traditional banking Market-oriented banking

Source. FDIC.

The structural rise of ROA concomitantly relates to its growing sensitivity to the ratio

of non-interest income to assets. Figure 9 plots the conditional covariances8 between ROA and

three of its key determinants: net interest margin (ni), the ratio of non-interest income to

assets9 (nii), and the ratio of loan loss provisions to assets (llp). Not surprisingly, the

covariance between ROA and nii features a structural break similar to the ROA profile: the

covariance takes off in the early 1990s and moves on a steady upward trend until the middle

of the 2000s. In parallel, the covariance between ROA and ni moves downward over the same

period. This set of results is a prima facie evidence of a change in U.S. banks’ performance

starting in the early 1990s, which coincides with the greater banks’ involvement in non-

traditional business lines.

8 The conditional covariances are computed with a multivariate GARCH process using a BEKK procedure (Bollerslev et al., 1988; Engle and Kroner, 1995) 9 nii must not be confused with snonin, which is the share of non-interest income in net operating income.

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Figure 9. Conditional covariance between U.S. banks’ ROA and some key components

-1.2

-0.8

-0.4

0.0

0.4

0.8

1.2

1.6

35 40 45 50 55 60 65 70 75 80 85 90 95 00 05 10

covariance between ROA and the ratio of loan loss provisionscovariance between ROA and net interest margincovariance between ROA and non-interest income as % assets

Note. The conditional covariances are computed using a multivariate GARCH procedure based on a BEKK process.

To further investigate the relative contribution of non-interest income to ROA, we run

a simple regression à la Stiroh (2006) over the period 1935-2011. We first decompose ROA in

its two main components, ni and nii: ni niiROA w ni w nii= + , the weights being the relative

shares of net interest and non-interest income in banks’ net operating income10. We then

estimate the following regression: 0 1 2t nit niit tROA w wθ θ θ ξ= + + + . Since the weights add to

one, we just retain niitw in the regression, such that: 0 1t niit tROA wγ γ ξ= + + . Hence, the

estimated coefficient 1γ provides the relative contribution of non-interest income to ROA. The

estimated ROA equation then reads as follows: 0 67 0 42t niit tROA . . w ξ= + + —i.e., non-interest

income may have added up to 42 basis points to ROA11.

To complete the analysis of the contribution of non-interest income to banks’

performance we regress directly ROA on ni, nii and llp. Given the obvious endogeneity issue

stemming from the interaction between the equation variables, we rely on the Generalized

10 Note that we implicitly remove non-interest expenses from ni and nii. 11 Full description of these results is available upon request.

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Method of Moments (GMM) procedure to run the regression. The estimated ROA equation

obtains12:

10 22 0 14 0 35 0 79 0 03t tROA . . ni . nii . llp . ROA −= + + − + (3)

All estimated coefficients are significant at the 1% level and the results confirm that nii might

contribute significantly more than ni to bank performance.

Finally, estimating our benchmark ROA model (equation (1)) provides additional

support to these findings. We rely on Almon lags to estimate the structure of the lagged

coefficients for snonin and llp. For all U.S. banks the sum of the lags of the snonin

coefficients is equal to 0.96, significant at the 5% level (Table 2). However, this result should

be adjusted for risk. Indeed, banks’ non-traditional activities tend to be more volatile than

traditional business lines (Stiroh and Rumble, 2006; Calmès and Liu, 2009; Calmès and

Théoret, 2010). Furthermore, the conditional variance13 of U.S. banks’ ratio of non-interest

income to assets (nii) is increasing since the end of the 1980s, and this increase corresponds to

a structural rise in the ROA conditional variance (while the conditional variance of the net

interest margin moves downward, Figure 10).

12 Full description of these results is available upon request. 13 The conditional variances are estimated using a multivariate GARCH process (Bollerslev et al., 1988; Engle and Kroner, 1995).

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Table 2. Estimation of the ROA model on all U.S. banks, 1934-2011

ROA RA_ROA

c 0.64 2.26

3.88 2.35

snonint 1.49 3.67

7.87 0.63

snonint-1 -0.51 3.25

-2.93 3.00

snonint-2 -0.01 1.40

-0.06 0.51

snonint-3 - -1.87

- -0.81

Sum of lags 0.96 6.46

2.19 2.04

llpt -0.22 -1.21

-3.84 -6.40

llpt-1 -0.15 -0.90

-3.84 -6.40

llpt-2 -0.07 -0.60

-3.84 -6.40

llpt-3 - -0.30

- -6.40

Sum of lags -0.44 -3.03

-3.84 -6.40

yt-1 0.94 0.75

26.49 15.74

R2 0.70 0.47

DW 2.16 1.72

Notes. Risk-adjusted ROA (RA_ROA) is a five-year moving average of ROA scaled up by a rolling ROA standard deviation of five years. The explanatory variables are: snonin, share of non-interest income in net operating income; llp, ratio of loan loss provisions over total assets, and yt-1, the dependent variable lagged one period. The t statistics are reported in italics. The coefficients of snonin and llp are estimated using an Almon polynomial.

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Figure 10. Conditional variances of U.S. banks’ ROA and some of key components

0

1

2

3

4

5

6

35 40 45 50 55 60 65 70 75 80 85 90 95 00 05 10

conditional variance: llpconditional variance: niconditional variance: niiconditional variance: ROA

Notes. The conditional variances are computed using a multivariate GARCH procedure based on a BEKK process. The reported variables are: llp: loan loss provisions as % of assets; ni: net interest margin as % of assets; nii: non-interest income as % of assets.

Given the greater risk embodied in non-traditional activities, it is important to estimate

our equation (1) model on a risk-adjusted basis to rigorously assess the impact of the change

towards market-based banking (Stiroh and Rumble, 2006; LePetit et al., 2008; Calmès and

Théoret, 2010). We thus define risk-adjusted ROA as:

( )ROARA _ ROA

sd ROA= (4)

where ROA is the five-year moving average on ROA, and ( )sd ROA is the standard

deviation of ROA computed over five years. Using this risk-adjusted performance measure we

find that the sum of the Almon lags for the coefficients of snonin is equal to 6.46, significant

at the 5% level (Table 2). To summarize, our results seem to suggest that the U.S. banks’ risk-

return trade-off might have improved pari passu with the rise of market-based banking.

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3.2. The structural break in Canadian banking financial results

As in the U.S., a structural break can be pinned down in Canadian banks’ returns

(Calmès and Théoret, 2010). This change comes later, in 1997, but is also characterized by a

jump in ROA, albeit not as impressive (Figure 4 and Table 3). The average ROA of Canadian

banks is equal to 0.60% compared to 0.74% for the U.S. big banks, and 1% for the U.S.

medium ones. Importantly, this discrepancy is also higher during the 2000-2006 period. Note

that, ceteris paribus, this result already suggests that U.S. banks might actually perform better

than Canadian banks. The next section aims at studying this question more comprehensively.

Table 3. ROA of Canadian and U.S. banks

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 0.45 0.61 0.69 0.67 0.70 0.64 0.60

US big 0.26 0.91 1.08 1.05 1.14 0.45 0.74

medium 0.48 1.28 1.50 1.44 1.51 0.18 1.00

small 0.77 1.20 1.18 1.19 1.16 0.44 0.97

all 0.54 1.19 1.28 1.22 1.30 0.35 0.93

Notes. Canadian banks comprise the eight domestic commercial banks. U.S. big banks are the top ten banks. Medium banks are the 11 to 100 largest banks. Small banks are the banks not ranked among the 1000 largest by assets. Sources. Canadian Bankers Association; Bank of Canada; FDIC; Federal Reserve Bulletin.

4. The relative performance of U.S. and Canadian banking systems Turning to the evolution of the risk-return trade-off during the transition period, we

restrict our analysis to the 1986-2009 period, a period for which Canadian data are available.

The statistics we use to make this comparison divide the sample period into four important

subperiods: the 1986-1992 subperiod, which includes the sovereign debt turmoil, the real

estate collapse episodes and the 1990 recession; the 1997-2006 subperiod, which includes the

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structural break in the Canadian banks’ financial data; the 1993-2006 subperiod, which

includes the structural break in the U.S. banks’ financial data; and finally the subprime crisis

(2007-2009).

4.1 The relative levels of average performance

As expected, using a risk-adjusted ROA based on a moving average of the

unconditional volatility, Canadian banks seem to outperform their U.S. peers over the whole

sample period (Table 4). However, we argue that this result is only driven by the periods of

financial crisis and economic slowdowns, Canadian banks displaying a better resiliency than

the U.S. big banks in times of crises (1986-1992)—especially the subprime crisis (2007-

2009). Indeed, in the other periods, both the 1993-2006 and 1997-2006 subperiods, Canadian

banks actually underperform their U.S. peers in terms of risk-adjusted ROA.

Table 4. Risk-adjusted ROA of Canadian and U.S. banks

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 1.32 5.54 4.93 5.15 4.38 2.37 2.61

US big 0.43 8.27 7.20 7.00 10.36 1.29 1.48

medium 1.37 25.60 13.64 10.29 12.58 0.24 1.61

small 4.05 30.00 19.67 19.83 29.00 0.85 2.94

all 2.08 59.50 16.00 15.25 14.44 0.66 2.11 Notes. Canadian banks comprise the eight domestic commercial banks. U.S. big banks are the top ten banks. Medium banks are the 11 to 100 largest banks. Small banks are the banks not ranked among the 1000 largest by assets. ROA is adjusted for risk with unconditional volatility. Risk-adjusted ROA (RA_ROA) is a five-year moving average of ROA scaled up by a rolling ROA standard deviation of five years. Sources. Canadian Bankers Association; Bank of Canada; FDIC; Federal Reserve Bulletin.

Using ROE (ROA scaled up by financial leverage, i.e., the assets to equity ratio)

Canadian banks still seem to perform better than U.S. banks—especially the ten largest U.S.

banks. Over the whole sample period, ROE is equal to 12.70% for Canadian banks and

10.07% for the 10 largest U.S. ones (Table 5). However, this result hides a crucial caveat,

namely the fact that the Canadian ROE is in fact magnified by leverage. For example, over the

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whole sample period, Canadian banks’ leverage is equal to 21.17 compared to only 13.61 for

the top ten U.S. banks (Table 6). Furthermore, U.S. banks’ leverage decreases substantially

over the 1986-2009 period, whereas Canadian banks’ leverage remains near 21 (Figure 11).

Table 5. ROE of Canadian and U.S. banks

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 9.16 12.16 15.07 14.23 15.03 13.82 12.70

US big 5.16 14.40 13.31 13.62 13.68 4.97 10.07

medium 8.01 16.69 16.20 16.34 15.54 1.65 12.07

small 8.98 12.26 11.40 11.65 11.17 4.00 9.92

all 8.25 14.79 14.20 14.38 13.98 3.47 11.23


Table 6. Leverage (A/E) of Canadian and U.S. banks

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 20.36 19.93 21.84 21.24 21.47 21.59 21.17

US big 19.85 15.82 12.32 12.97 12.00 11.04 13.61

medium 16.69 13.04 10.80 11.35 10.29 9.17 12.07

small 11.66 10.22 9.66 9.79 9.63 9.09 10.23

all 15.28 12.43 11.09 11.79 10.75 9.91 12.08


Figure 11. Canadian and top ten U.S. banks’ leverage (A/E)

10.0

12.5

15.0

17.5

20.0

22.5

25.0

27.5

30.0

84 86 88 90 92 94 96 98 00 02 04 06 08 10

8 Canadian banks

10 largest US banks

Source. Canadian Bankers Association; Bank of Canada; Federal Reserve Bulletin.

- 19 -

Not surprisingly, if we scale up ROE for risk with an unconditional volatility moving

average, the return differential narrows substantially, and Canadian banks do no longer

outperform U.S. banks by much over the whole sample period (Table 7). More importantly,

this result is again driven by the crisis episodes. Indeed, both in the 1993-2006 and 1997-2006

subperiods, the risk-adjusted ROE is systematically lower for Canadian banks. For instance,

during the 1997-2006 subperiod, risk-adjusted ROE is equal to 5.25 for Canadian banks

versus 9.57 for the big U.S. ones.

Table 7. Risk-adjusted ROE of Canadian and U.S. banks

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 1.26 4.53 5.25 4.70 4.43 2.68 2.46

US big 0.40 9.00 9.57 9.20 11.50 1.31 1.24

medium 1.44 59.60 10.25 12.19 11.18 0.25 1.84

small 4.63 42.27 18.69 17.65 25.39 0.84 3.17

all 2.21 41.08 17.97 19.70 18.64 0.67 2.35

Notes. Canadian banks comprise the eight domestic commercial banks. U.S. big banks are the top ten banks. Medium banks are the 11 to 100 largest banks. Small banks are the banks not ranked among the 1000 largest by assets. ROE is scaled up for risk with unconditional volatility. Risk-adjusted ROE is a five-year moving average of ROE scaled up by a rolling ROE standard deviation of five years. Sources. Canadian Bankers Association; Bank of Canada; FDIC; Federal Reserve Bulletin.

4.2 ROA and conditional volatility

Since the unconditional return volatility is not model-based, it tends to be a rough

measure of bank risk. Besides, this indicator is generally unstable as it is computed on a

rolling window. By contrast, the conditional volatility computed with a GARCH procedure

better captures the non-linearities embedded in returns, like the asymmetries and fat tails

typical of return distributions. The conditional volatility is also smoother as it is based on an

- 20 -

autoregressive process, which translates into a smoother time series for the risk-adjusted

measure of bank return.

Scaling ROA with the conditional volatility strengthens the results previously obtained

with the unconditional volatility moving average (Figure 12). When the conditional

volatility14 of ROA is low—i.e., in times of economic expansion—ROA tends to be relatively

higher (left panel), which then boosts the risk-adjusted ROA measure (right panel). And vice-

versa in times of slowdowns or financial crises. These comovements tend to amplify the

cycles of the risk-adjusted ROA measure. More importantly, they have important implications

in the comparative study of the two banking systems. Indeed, the results show that the U.S.

conditional volatility tends to be relatively higher in downturns, whereas the Canadian

conditional volatility seems to remain higher in normal times. This explains why Canadian

banks appear to be outperforming. In fact, our results suggest that this tendency is mainly

driven by the higher conditional volatility of U.S. banks’ returns during financial crises.

Figure 12. ROA volatility and risk-adjusted ROA

ROA GARCH conditional volatility Risk-adjusted ROA

0.0

0.4

0.8

1.2

1.6

2.0

84 86 88 90 92 94 96 98 00 02 04 06 08 10

Canadian banks U.S. big banksU.S. medium banks U.S. all banks

-5

0

5

10

15

20

25

30

86 88 90 92 94 96 98 00 02 04 06 08 10

Big U.S. banks Medium U.S. banksAll U.S. banks Canadian banks

All banks

Medium U.S. banks

Big U.S. banks

Canadian banks

Notes. Risk-adjusted ROA is the ratio of ROA to its conditional volatility computed with a standard GARCH process.

14 Note that this pattern holds also for the unconditional volatility and it is even more pronounced.

- 21 -

This alternative risk-adjustment method thus confirms that Canadian banks essentially

perform better than U.S. banks in periods of turmoil—i.e., the subperiods 1986-1992 and

2007-2009. The conditional volatility of Canadian banks’ ROA is rather low during these

crises periods, while the U.S. banks’ volatility jumps for all size categories (due mainly to the

increase in loan loss provisions). Furthermore, Figure 12 also reveals that the cycles of risk-

adjusted ROA display a greater amplitude in the U.S. than in Canada, risk-adjusted ROA

increasing more in the U.S. than in Canada in expansion, but decreasing more in

contraction15.

Not surprisingly, since the risk-adjustment method based on conditional volatility

explicitly accounts for banks’ returns non-linearities, the performance of Canadian banks

appears more similar to U.S. big banks’ pattern over the whole sample period. In other words,

this estimation method suggests that the performance of Canadian banks might be less

impressive than previously thought (right panel of Figure 12). This observation relates to the

fact that bank risk, as captured by the conditional volatility of ROA, is actually higher in

Canada than in the U.S. during the expansion periods (left panel of Figure 12).

4.3. ROA estimation results

An estimation of our ROA model (equation (1)) based on absolute and risk-adjusted

measures sheds additional light on the relative merits of the Canadian and U.S. banking

systems. Table 8 provides the estimation of our benchmark ROA model (equation (1)) for

Canadian banks and U.S. banks classified by size. This estimation is performed using a

GARCH (1,1) process in order to tackle the conditional heteroskedasticity embedded in the

equation innovation (Bollerslev, 1986)16. Table 9 concerns the corresponding estimation of

the risk-adjusted ROA model based on the conditional volatility defined as:

15 This discrepancy might be partly explained by the greater operational leverage of U.S. banks. As discussed later, the ratio of non-interest expenses to assets is greater in the U.S. 16 During financial crises or slowdowns the conditional volatility of ROA tends to jump, which explains the use of the GARCH procedure.

- 22 -

Risk-adjusted conditional volatility

tt

t

ROAROA = . To compute the conditional volatility we also

rely on a GARCH(1,1,) process. We estimate these models over the whole sample period

(1986 to 2009), but also over the 1997-2009 subperiod since a structural break is identified

around 1997 in the Canadian database17.

Table 8 Estimation of the ROA model

1986-2009 1997-2009 1986-2009 1997-2009 1986-2009 1997-2009 1986-2009 1997-2009 1986-2009 1997-2009c 0.73 -0.11 -0.05 -0.38 -0.41 -1.40 1.13 1.17 0.12 -0.66

10.93 -0.67 -0.27 -1.65 -2.24 -1.61 10.48 5.14 1.23 -2.72snonin 0.23 1.93 2.80 3.55 5.33 6.74 1.57 1.28 3.41 5.22

4.17 6.10 6.43 8.03 13.67 4.00 4.00 1.55 13.63 9.52llp -0.44 -0.63 -0.59 -0.44 -0.94 -0.57 -1.30 -1.24 -0.74 -0.68

-6.14 -7.89 -27.23 -37.30 -19.73 -5.35 -14.54 -5.93 -23.43 -122.16ROA t-1 -0.06 0.22 0.55 0.45 0.06 0.41 0.15 0.46 0.31 0.36

-0.96 2.09 13.01 11.12 0.37 3.18 1.33 3.45 4.41 3.81Adjusted R 2 0.48 0.61 0.77 0.83 0.90 0.88 0.85 0.81 0.94 0.94D.W. 2.10 2.10 2.25 1.55 1.61 1.55 1.70 2.00 1.50 1.64

Canadian banksBig Medium Small

U.S. banksALL U.S. banks

Notes. The explanatory variables are: snonin, share of non-interest income in net operating income; llp, ratio of loan loss provisions over total assets, and ROAt-1, the dependent variable lagged one period. The t statistics are reported in italics.

Table 9 Risk-adjusted ROA estimated with conditional volatility, 1986-2009

1986-2009 1997-2009 1986-2009 1997-2009 1986-2009 1997-2009 1986-2009 1997-2009 1986-2009 1997-2009c 5.57 -0.31 -2.92 -12.42 -20.59 9.45 -6.50 -21.14 -7.12 -8.95

2.39 -0.10 -3.30 -2.47 -1.42 0.29 -0.77 -1.08 -1.73 -0.75snonin 2.47 17.00 15.20 33.97 77.73 15.28 62.83 101.11 64.39 52.73

0.51 2.94 8.29 3.19 2.18 0.22 1.82 1.21 6.00 1.83llp -4.63 -5.96 -1.56 0.50 -4.03 -6.36 -5.28 0.76 -12.90 -6.76

-5.90 -3.05 -10.94 0.87 -1.50 -1.75 -1.25 0.23 -1.98 -3.59RA_ROA t-1 0.53 0.82 0.59 -0.01 0.53 0.35 0.81 0.90 0.14 0.24

2.07 3.84 9.97 -0.03 2.45 1.15 5.47 5.19 0.69 1.12Adjusted R 2 0.52 0.49 0.56 0.55 0.56 0.41 0.73 0.69 0.45 0.65D.W. 1.86 1.50 1.50 1.72 1.50 1.60 0.93 0.79 1.50 2.20

ALL U.S. banksU.S. banksCanadian banks

Big Medium Small

Notes. The explanatory variables are: snonin, share of non-interest income in net operating income; llp, ratio of loan loss provisions over total assets, and RA_ROAt-1, the dependent variable lagged one period. The t statistics are reported in italics. Risk-adjusted ROA is the ratio of ROA to its conditional volatility, where conditional volatility is estimated using a standard GARCH process.

17 We also estimated the model over the period 1993-2009 to account for the structural break in the U.S. data but the results are very similar to those obtained for the 1997-2009 period. These results are available upon request.

- 23 -

First note that the performance of our ROA model is quite good—especially for the

U.S. sample (the adjusted R2 exceeds 0.80). Moreover, the DW statistics do not signal any

particular autocorrelation problem. Surprisingly, we find that the sensitivity of Canadian

banks’ ROA to snonin is rather low, being equal to 0.23 and significant at the 1% level over

the whole sample period (Table 8). In contrast, the impact of snonin on the U.S. banks’ ROA

is substantial. For instance, the snonin coefficient is equal to 2.80 for the big banks and even

higher, at 5.33 for medium banks, and these coefficients are also more significant than in the

Canadian case (Table 8).

Importantly, remark that the Canadian ROA sensitivity to snonin substantially

improves after the 1997 structural break. More precisely, the sensitivity of Canadian banks’

ROA to snonin is higher over the 1997-2009 period than before, the snonin coefficient being

estimated at 1.93, significant at the 1% level (Table 8). However, the sensitivity of U.S.

banks’ ROA to snonin increases even more after 1997. Over the whole sample period (1986-

2009), the sensitivity of big banks’ ROA to snonin is equal to 2.80, but to 3.55 for the period

1997-2009. The corresponding figures for medium banks are even higher, 5.33 and 6.74

respectively. Hence, even if their performance in non-traditional activities improves,

Canadian banks do not catch up in relative terms. Indeed, these results reveal that U.S. banks

clearly get better gains from market-based banking.

Since the 1997-2009 period is characterized by a new volatility regime, adjusting for

risk with the conditional volatility better reflects the change in the banking return patterns.

However, the estimations only come to reinforce our previous findings. Indeed, although the

fit of the model tends to deteriorate in the second subperiod, the results are broadly consistent

with those obtained with the ROA benchmark model (Table 9). While the sensitivity of

Canadian banks’ ROA to snonin, albeit low, is significant from 1986 to 2009, this is not the

case for risk-adjusted ROA. Furthermore, although the coefficient of snonin become

- 24 -

significant and jumps to 17.00 after the structural break, the corresponding change in the U.S.

is obviously more pronounced, the respective coefficients being equal to 15.20 and 33.97—

both significant at the 1% level.

Given its lower operational leverage—i.e., a lower share of fixed costs in total costs—

the Canadian banking system might be more resilient during periods of economic downturns

and financial crises. Nevertheless, insofar as the behaviour of the Canadian banking system

shares similarities with both big and medium U.S. banks, our results clearly suggest that we

cannot be too conclusive about its overall performance, which actually appears to be weaker

along important dimensions. In particular, regardless of the method used, U.S. banks seem to

derive greater benefits from market sensitive business lines.

4.4. What difference the product-mix can make? To understand why U.S. banks seem to benefit more from market-based banking, it is

necessary to examine the sources of bank income. The average snonin is equal to 41.6% for

Canadian banks and to 44.8% for the ten largest U.S. banks over the whole sample period

(Table 10). Since the beginning of the transition towards market-based banking, snonin is

greater for Canadian banks than for the top 10 U.S. banks, and the snonin of the two groups

are quite close since the subprime crisis (Figure 13). Despite their apparently greater

involvement in non-traditional activities, Canadian banks actually display a lower ratio of

non-interest income to assets (nii) than U.S. banks. For example, over the whole sample

period, the nii of Canadian banks and the ten largest U.S. banks are respectively equal to

1.54% and 2.24% (Table 11).

- 25 -

Table 10. snonin of Canadian and U.S. banks

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 29.03 34.81 51.27 46.57 52.51 47.59 41.58

US big 43.41 46.58 46.02 46.18 45.61 41.97 44.30

medium 36.68 38.72 46.98 44.62 47.66 43.80 41.51

small 20.08 23.60 25.57 25.00 25.35 24.54 23.16

all 31.92 35.82 42.67 40.71 43.16 40.14 37.64 Notes. Canadian banks comprise the eight domestic commercial banks. U.S. big banks are the top ten banks. Medium banks are the 11 to 100 largest banks. Small banks are the banks not ranked among the 1000 largest by assets. Sources. Canadian Bankers Association; Bank of Canada; FDIC; Federal Reserve Bulletin.

Table 11. Non-interest income as percentage of assets

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 1.16 1.37 1.91 1.75 1.97 1.42 1.54

US big 2.21 2.51 2.37 2.41 2.33 1.92 2.24

medium 1.88 2.41 3.13 2.92 3.13 2.48 2.52

small 1.03 1.35 1.40 1.39 1.35 1.17 1.23


Figure 13. snonin of Canadian and U.S. banks

10

20

30

40

50

60

84 86 88 90 92 94 96 98 00 02 04 06 08 10

Canadian banks

Top 10 U.S. banks

All U.S. banks

Small U.S. banks


- 26 -

Beyond size effects, we can safely assume that U.S. banks get a greater return from

their market-based banking because of their particular product-mix. For example, U.S. banks

are obviously more involved in securitization than Canadian banks. Ginnie Mae engineers the

first mortgage-backed security in 1970, while a similar program is launched only in 1987 by

the Canadian Mortgage and Housing Corporation (CMHC). In 1999, the share of

securitization in Canadian bank funding is close to 0% and rises to only 14% at the end of

2009. At the end of 2007 still, only 22% of the outstanding home mortgages are securitized in

Canada, versus almost 60% in the U.S (Figure 14)18.

Figure 14. Shares of Canadian securitized loans

0

5

10

15

20

25

30

35

90 92 94 96 98 00 02 04 06 08 10 12

Mortgage loans

Personal loans

Commercial loans

Source. Statistics Canada (CANSIM).

Canadian banks rely on securitization mainly to overcome the decline of the share of

personal deposits in their sources of funds (Martin-Olivier and Saurina, 2007; Agostino and

Maccuza, 2008)19. Yet, if Canadian banks rely less on securitization to fund their operations,

ceteris paribus they must resort to more debt, which contributes to increase their on-balance-

sheet leverage (Pennacchi, 1988; Calomiris and Mason, 2004; Ambrose et al., 2005; Uzun

and Web, 2007). Reciprocally, leverage and the share of securitization in banks’ net operating

18 At the end of 2010, 32% of outstanding mortgages are securitized in Canada while the proportion of U.S. mortgages securitized by private ABS issuers has collapsed since the subprime crisis. For instance, in 2006, private ABS issuers originated 20% of home mortgages. In the end of 2010, this proportion has dropped to 12%. 19 In contrast, securitization in the U.S. relates to risk shifting, the search for performance and capital arbitrage (Cardone-Riportella et al., 2010).

- 27 -

income are negatively correlated (Gorton and Metrick, 2012). In other words, the fact that

Canadian banks rely less on securitization might be due to their relatively higher leverage, and

this, in turn could help explain why U.S. banks can benefit more from market-based banking

in most periods (Bannier and Hansel, 2008; Cardone-Riportella et al, 2010).

As a matter of fact, the fees generated by securitization seem to have a major positive

impact on banks’ risk-return trade-off (Calmès and Théoret, 2013). First, in normal times,

securitization is a quite stable source of income since the risk of default on the SIV’s

portfolios is low and a great proportion of the securities included in these portfolios is

guaranteed by government agencies. Second, the correlation between securitization fees and

the other kinds of bank income streams is rather low, suggesting that securitization provides

significant diversification benefits (Calmès and Théoret 2013). Third, securitization tends to

be a less expensive source of funds compared to borrowed funds. Finally, although this fact is

not documented in the literature, some experiments we make suggest that the ratio of

expenses related to securitization could be relatively low in terms of assets. In conclusion, the

relative performance of the two banking systems could well be related to product-mix

differences. We investigate this question in more detail in the next section.

5. How Canadian and U.S. banks compare in terms of financial results? 5.1 The volatility of Canadian banks’ financial results In this section we study how the two systems compare in terms of financial results. As

discussed earlier, the conditional volatility of Canadian banks’ returns tends to be higher in

most periods. Since Canadian banks are also more leveraged than U.S. banks, we should

expect a greater volatility with respect to their operating income. This is actually the case.

Over the period 1986-2009, the standard deviation of Canadian banks’ operating income

growth is 7.01% versus 6.21% for the 10 largest U.S. banks (Table 12). This discrepancy

- 28 -

emerges at the beginning of the second millennium and is particularly important during the

subprime crisis. More importantly, the difference between the two banking systems is greater

for the standard deviation of noninterest income growth (Table 13). This standard deviation

jumps during the subprime crisis for both Canadian banks and the 10 largest U.S. banks, but it

is paradoxically larger for the former: 38.81% versus 18.96%. As a matter of fact, the

conditional variance of nii is also greater for Canadian banks than for the 10 largest U.S.

banks since the early 2000s (Figure 15). Finally, the Canadian banks’ nii variance skyrockets

during the subprime crisis, while the U.S. banks’ nii variance seems more stable.

Table 12. Standard deviation of the operating income growth

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 4.07 3.94 5.39 4.87 4.43 18.14 7.01

US big 5.28 9.35 4.30 6.43 2.96 4.91 5.89

medium 3.49 3.22 5.79 5.53 4.31 4.33 4.79

small 3.32 1.07 3.16 2.81 3.21 3.02 3.21


Table 13. Standard deviation of the non-interest income growth

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 5.34 6.70 12.59 10.97 9.43 38.81 15.37

US big 5.94 16.17 5.08 9.31 6.20 18.96 9.80

medium 7.31 6.77 9.57 8.90 7.10 1.66 7.83

small 8.08 4.94 6.90 6.22 7.07 9.40 6.45

all 3.36 7.39 4.43 5.13 2.44 10.96 5.31


- 29 -

Figure 15. Conditional variances of non-interest interest income as percentage of assets

.0

.1

.2

.3

.4

.5

.6

.7

.8

84 86 88 90 92 94 96 98 00 02 04 06 08 10

Canadian banks

10 largest U.S. banks

Note. Conditional variances are computed using a GARCH process (Bollerslev, 1986).

Financial results are therefore more volatile for Canadian banks than for the top 10

U.S. banks, an important fact largely overlooked in the literature. We assume that this result

might relate to a Canadian product mix weighting more volatile activities, i.e., market-

oriented sources of income such as trading income and fees stemming from capital markets

(underwriting fees, etc.). Indeed, Table 14 shows that the components most related to market-

based banking—trading income, investment banking fees and mutual fund fees—weight

heavily in the Canadian banks’ financial income statements. For instance, at the end of 2006,

the share of trading income in non-interest income is equal to 18% for the Canadian banks

versus 8% in the U.S., trading income being the major source of the non-interest income

growth conditional volatility (Stiroh and Rumble, 2006; Calmès and Liu, 2009; Calmès and

Théoret, 2013). The contrast between the product-mix of the two banking systems is even

more striking if we look at the share of investment banking fees in non-interest income. At the

end of 2006, these shares are respectively 19% for Canadian banks versus only 5% for U.S.

banks. In contrast to the U.S. banking system, where commercial banks control only a limited

portion of investment banking, Canadian banks own the majority of domestic investment

- 30 -

banks since the 1987 Amendment to the Bank Act (Bordo et al., 2011). This greater

involvement in investment banking partly explains why Canadian banks are actually more

exposed to financial market volatility than their U.S. peers. Since deposit fees represent a

larger share of banks’ non-interest income in the U.S. than in Canada (respectively 15%

versus 12% in 2006, Table 14), and given that these fees are a relatively stable source of

income (Calmès and Théoret, 2012), they certainly contribute to explain the relative stability

of U.S. banks’ non-interest income.

- 31 -

Table 14. Canadian and U.S. banks’ product-mix

Components of non-interest income ($ millions)

Canadian banks U.S. banks 2001 2006 2009 2001 2006 2009

$ millions

Assets 1481506 1961024 2705350 7730464 11587972 13279658

net interest income 27596 30012 44090 251132 330124 397677

non-interest income 31383 39147 37847 170574 240444 260502

operating income 58979 69159 81937 421706 570568 658179

share noninterest income 0.53 0.57 0.46 0.40 0.42 0.40

non-interest income components

fiduciary activities 4010 6376 6933 20829 25322 24454

deposit fees 3900 4634 5534 27208 36300 41675

Trading income 6658 7190 2961 12532 19036 24926

Investment banking fees 10932 12751 9377 9132 12001 12001

Net servicing fees - - - 10593 14483 29667

Securitization income 1325 1435 3845 16246 22170 4761

Insurance commissions 2715 5569 8574 2779 4437 3882

Other 1843 1192 623 71255 106695 119136

Shares of non-interest income components in total non-interest income

fiduciary activities 0.13 0.16 0.18 0.12 0.11 0.09

deposit fees 0.12 0.12 0.15 0.16 0.15 0.16

Trading income 0.21 0.18 0.08 0.07 0.08 0.10

Investment banking fees 0.35 0.33 0.25 0.05 0.05 0.05

Net servicing fees - - - 0.06 0.06 0.11

Securitization income 0.04 0.04 0.10 0.10 0.09 0.02

Insurance commissions 0.09 0.14 0.23 0.02 0.02 0.01

Other 0.06 0.03 0.02 0.42 0.44 0.46

Shares of non-interest income components in total non-interest income (excluding other income)*


deposit fees 0.13 0.12 0.15 0.27 0.27 0.29

Trading income 0.23 0.19 0.08 0.13 0.14 0.18




Insurance commissions 0.09 0.15 0.23 0.03 0.03 0.03 * We exclude the other non-interest income from the total because the share of other non-interest income in the total is much higher in the U.S. than in Canada.

- 32 -

Non-interest income as percentage of total assets

Canadian banks U.S. banks 2001 2006 2009 2001 2006 2009

net interest income 1.86 1.53 1.63 3.25 2.85 2.99

non-interest income 2.12 2.00 1.40 2.21 2.07 1.96


deposit fees 0.26 0.24 0.20 0.35 0.31 0.31

Trading income 0.45 0.37 0.11 0.16 0.16 0.19




Insurance commissions 0.18 0.28 0.32 0.04 0.04 0.03

Other 0.12 0.06 0.02 0.92 0.92 0.90

Sources: Bank of Canada, OSFI, FDIC (Bank Call Report).

Ceteris paribus, these findings help explain why the Canadian risk-adjusted ROA

tends to be lower than the U.S. measure in most periods. However, securitization fees

somewhat narrow the non-interest income volatility gap—and actually reverses it in

downturns. As previously mentioned, Canadian and U.S. banks have evolved differently with

respect to securitization (Berger et al., 1995; Bordo et al., 2011). In the U.S., regulation

encourages banks to hold mortgage-backed securities and to securitize mortgages. This is not

the case in Canada, where regulation is more restrictive with respect to off-balance-sheet

activities. In 2006, the share of securitization fees in non-interest income is equal to 4% for

Canadian banks versus 9% for U.S. banks, and in terms of assets the ratio of securitization is

0.07% in Canada versus 0.19% in the U.S. Insurance commission fees and income are another

component which contributes to reduce the non-interest income volatility gap between the

two countries (Calmès and Théoret, 2012). Indeed, Canadian banks are more involved in

insurance than their U.S. counterparts, insurance commission fees and income amounting to

0.13% of assets in Canada versus 0.04% in the U.S.

Nevertheless, Canadian financial results appear more volatile than their U.S.

counterparts, essentially due to the greater involvement of Canadian banks in investment

banking and related activities. The lower share of their non-interest income in securitization

- 33 -

fees and the higher share of insurance commission fees and income can only mitigate partly

the non-interest income volatility gap. As a matter of fact, we are inclined to think that, ceteris

paribus, the acute problems of the U.S. banks might be more related to the magnitude of the

shocks they had to absorb during the collapse of the mortgage-backed security market—i.e.,

an event quite exogenous to the banking system and essentially caused by housing policy

mistakes encouraging subprime mortgages (Calomiris and Mason, 2004; Rajan, 2005;

Lemieux, 2009).

Table 15. Net interest margin

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 2.83 2.58 1.81 2.03 1.78 1.53 2.20

US big 2.87 2.86 2.78 2.80 2.79 2.65 2.80

medium 3.35 3.81 3.53 3.61 3.43 3.19 3.48

small 4.11 4.37 4.09 4.17 3.99 3.59 4.07


5.2 Net interest margin

Canadian banks’ traditional activities do not look better either. In fact, Canadian banks

display a net interest margin lower than U.S. banks, and the recent decrease in margin is more

important in Canada than in the U.S. (Table 15). The average of the Canadian banks’ interest

margin is equal to 2.20% compared to 2.80% for the top ten U.S. banks, and 3.48% for the

U.S. medium banks. The picture is even more striking for the 2000-2006 period, with net

interest margins equal to 1.78%, 2.79% and 3.43% respectively. Actually, it is difficult to

believe that such differences might be only attributable to a greater risk-premium in the U.S.20

Given the structure of the Canadian and U.S. banking systems, the presence of U.S. local

monopolies might also help explain the interest margin gap.

20 Angbazo (1997) provides some evidence of a link between net interest margin and risk. He also finds that off-balance-sheet activities tend to rise net interest margins. See also Ennis (2004).

- 34 -

Table 16. Ratio of loan loss provisions

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 0.70 0.45 0.23 0.29 0.24 0.25 0.40

US big 1.12 0.28 0.34 0.32 0.37 1.27 0.67

medium 1.09 0.43 0.62 0.57 0.64 1.65 0.85

small 0.58 0.23 0.28 0.27 0.27 0.66 0.40


Figure 16. Ratio of loan loss provisions

0.0

0.4

0.8

1.2

1.6

2.0

2.4

84 86 88 90 92 94 96 98 00 02 04 06 08 10


Canadian banks


5.3. Loan loss provisions and non-interest expenses

So why do we believe that the Canadian banking system might be stronger? There are

still two remaining components of ROA to consider: loan loss provisions and non-interest

expenses. As a percentage of assets, Canadian banks show lower loan loss provisions (llp)

than U.S. banks (Table 16). Banks’ loans thus seem riskier in the U.S. than in Canada.

Canadian banks display a comparative advantage of 27 basis points over the top 10 U.S.

banks with respect to the llp ratio. Note however that a major portion of this gap actually

develops during the subprime crisis (Figure 16). Excluding this crisis, the llp basis points

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difference between Canadian and U.S. banks is lower—i.e., 17 basis points—the average llp

being equal to 0.42% for Canadian banks versus 0.59% for the ten largest U.S. banks over the

1986-2006 period. Furthermore, the fact that U.S. banks maintain greater loan loss provisions

may be related to the higher proportion of securitized loans in the U.S. Indeed, banks have a

tendency to retain their riskiest loans on-balance-sheet and to securitize less risky loans in

order to increase the ranking of securitized loans—especially the collateralized mortgage

obligations (CMO)—and this tends to increase llp (Cardone-Riportella et al., 2010). Besides,

a lower llp ratio is not necessarily an indicator of better performance.

Table 17. Ratio of non-interest expenses to assets

1986-1992 1993-1996 1997-2006 1993-2006 2000-2006 2007-2009 1986-2009

CAN 2.39 2.51 2.49 2.50 2.55 1.94 2.39

US big 3.45 3.64 3.17 3.31 3.08 2.74 3.28

medium 3.45 3.86 3.76 3.79 3.65 3.43 3.64

small 3.51 3.75 3.59 3.64 3.53 3.49 3.58

all 3.50 3.76 3.48 3.56 3.38 3.06 3.48


Figure 17. Ratio of non-interest expenses

1.6

2.0

2.4

2.8

3.2

3.6

4.0

4.4

84 86 88 90 92 94 96 98 00 02 04 06 08 10

Canadian banks



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Some authors generally argue that the relative resiliency of the Canadian banking

system in downturns can be explained by the efficiency with which Canadian banks manage

their assets (Smith, 1999; Liu et al. 2006). Indeed, during the 1986-2009 period, Canadian

banks display an advantage of almost 100 basis points over the top ten U.S. banks with

respect to the ratio of non-interest expenses to assets (Table 17). Canadian banks also reacted

more forcefully to the subprime crisis by decreasing their ratio of non-interest expenses by a

greater amount, perhaps suggesting a better ability to adjust their cost structure to business

conditions (Figure 17). This fact might be explained by a lower operational leverage for

Canadian banks—i.e., a lower share of fixed costs in total costs. But even from these

observations, can we really infer that Canadian banks manage their assets more efficiently

than U.S. banks? Not necessarily. The difference in banks’ cost structures between the two

countries might be in part imputed to the difference in banks’ product-mix, and to a lower net

interest margin, which can actually act as a serious drag on Canadian banks’ non-interest

expenses, and possibly on their relative growth.

6. Conclusion The Canadian banking system has the reputation of being one of the best in the world.

However, based on the empirical evidence we gather in this study this conjecture seems

questionable. Market-based banking is now well entrenched in Canada and the U.S. This new

regime is characterized by greater risk but also larger compensating risk premia on banks’

business lines. A structural break in banks’ accounting returns is indeed observed in Canada,

but even more so in the U.S. After the break, we find that U.S. banks often display greater

returns than Canadian banks, both on their traditional and non-traditional activities.

Importantly, we also find that banks’ financial results are more volatile in Canada than in the

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U.S. Overall, when properly scaling up performance for risk, it is hard to believe that the

Canadian banking system is really “stronger”21.

In this paper we argue that a major difference between the Canadian and U.S. banking

models pertains to their relative product-mix. U.S banks rely more on securitization, while

Canadian banks generate more income from the activities related to market-oriented banking,

like trading income and capital market and mutual fund fees. Securitization should increase

banks’ on-balance-sheet risk, which could reduce the relative stability of the U.S. banking

system. However, market-oriented business lines lead to even more volatility in Canadian

banks’ financial results, which is compounded by a higher financial leverage. Although it

would be difficult to disentangle the risk premia associated with each source of risk, it

remains that the Canadian banking system does not seem to take full advantage of the market-

sensitive income sources.

Figure 18. Commercial paper issued by Canadian financial societies

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

60 65 70 75 80 85 90 95 00 05 10

mill

ion

$

Source. Statistics Canada (CANSIM)

Lower loan riskiness, higher regulatory capital ratios, and lower interest margins can

certainly yield lower ratios of loan loss provisions, but even this fact appears insufficient to

argue that the Canadian banking system is preferable. The subprime crisis was caused by

events that were for a great part exogenous to the U.S. banking system. Securitization per se is

not to blame—au contraire—but rather bad housing policy. If the Canadian banking system

21 This terminology refers to Mike Carney’s address at the University of Alberta–Monetary Policy after the Fall, Eric J. Hanson Commemorative Conference– delivered on May 1st 2013.

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had been confronted to a comparable shock, its resiliency would have likely been limited.

Indicative of this is the impact the collapse of the asset-backed commercial paper (ABCP)

market has had on Canadian banks during the subprime crisis (Figure 18). This would have

been much worse had Canadian banks been more exposed to the ABCP market. Fortunately

they were not.

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