manuscript financial contagion
TRANSCRIPT
The Effect of the US Sub-prime Crisis on Canadian Banks
Satiprasad Bandyopadhyay
Ranjini Jha
Duane Kennedy*#
School of Accounting and Finance
University of Waterloo
Waterloo, Ontario N2L 3G1
Canada
January 7, 2014
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*Corresponding author: Telephone: (519) 888-4752; Email: [email protected].# We gratefully acknowledge the financial support the Lois Claxton Humanities and Social Sciences Award from the University of Waterloo. We thank participants at the Canadian Academic Accounting Association Annual Conference 2011, and the Annual Conference of the Canadian Operational Research Society, 2011, for useful feedback
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The Effect of the US Sub-prime Crisis on Canadian Banks
Abstract
We examine whether the Canadian banking sector was afflicted
by financial contagion from the 2008 sub-prime crisis in the
United States financial sector. We examine this issue by using a
sample of the five largest Canadian banks and the 19 largest US
banks based on the value of total assets at the end of 2005. We
follow two approaches, namely, (i) a correlation framework and
(ii) incidences of simultaneous occurrences of extreme negative
stock returns. We find while both the US banks and Canadian banks
were affected by contagion, the Canadian banks were affected
later, relative to the US banks. We also find evidence consistent
with superior quality of monitoring of Canadian banks versus US
banks during and after the crisis period.
1
The Effect of the US Sub-prime Crisis on Canadian Banks
1. Introduction
In this paper we examine the nature of impact of the recent
sub-prime crisis in the United States (US) financial sector on
Canadian banks.1 There appears to be a broad consensus in the
financial press and among members of the international financial
institutions that Canadian banks managed to withstand the ravages
of the US sub-prime crisis.2 However, while the ability of
Canadian banks to withstand the adverse effects of the recent
financial crisis has been commented upon in the financial press,
there has been little academic work on this issue. This work is
intended to fill this void.
Commentators have attributed the strength of Canadian banks
to two distinct sets of reasons. For example, Ratnovsky and Huang
(2009), in an IMF working paper, argue that Canadian banks’
reliance on depository funding rather than other sources of
1 The sub-prime financial crisis that had its origin in large-scale delinquencies in sub-prime mortgage loans made by US mortgage lenders, have been extensively discussed in the literature. For a detailed discussion of thecrisis and its implications, see Demyanyk and Hemert (2011). 2 There has been a lot of discussion in the financial press about the extent to which the Canadian financial sector has been isolated from the sub-prime crisis. See for example, Heinrich (2008), Durocher (2008), Kay (2008), Carswell (2009) and many others.
pg. 2
funding, low leverage and low levels of securitized loans, are
the most important determinants of their ability to withstand
financial difficulties that faced US and European banks. Richburg
(2008) suggests that the resilience of Canadian banks also arises
from their higher liquidity and larger capital bases. In
contrast, Krugman (2010) focuses on the strength of Canadian
banks arising from better monitoring by Canadian regulators.
Other commentators, in different contexts, have commented on the
resilience of the Canadian banking system vis-à-vis US banks. For
example, Saunders and Wilson (1999) provide a detailed background
of the evolution of both the Canadian and US banking systems
since the late 1800s. They point out that “… US historically
combined a weak safety net with restrictions on branching and
other activities that resulted in a system prone to disruptive
banking crises and bank failures. By contrast, both Canada and
the UK evolved consolidated and highly branched banking systems
that proved resilient to economic crises such as those during the
1930s.” In this paper, we not only examine the extent to which
Canadian banks were affected adversely during the crisis (or
pg. 3
isolated from it), but we also attempt to compare the effects of
regulatory monitoring of US versus Canadian banks.
We rely on the concept of “contagion” of financial crises
over geographical boundaries and across financial institutions to
examine the spread of the sub-prime crisis from US financial
institutions to Canadian banks. While early work (e.g., Glick and
Rose (1999), Mason (1999), Kaminiski and Reinhart (1998) and
others) on contagion related to the spread of currency crises
across national boundaries and continents, more recent work on
contagion has examined how markets for risky securities have
experienced “meltdowns” that spread across geographical
boundaries (e.g., Bae, Karolyi and Stulz 2003).
Our examination of the contagion of the sub-prime crisis
from US to Canada consists of two closely related methodologies.
First, we estimate the probability with which extreme negative
stock returns of Canadian banks are correlated with the extreme
negative movements of the index of US banks, during and
surrounding the financial crisis period. Also, following Bae,
Karolyi and Stutlz (2003), we examine the number of times that
sample banks (US versus Canadian banks) exhibit greater frequency
pg. 4
of simultaneous extreme negative returns over simultaneous
extreme positive returns (daily as well as monthly returns)
during the pre-crisis, crisis period and the post-crisis period.
We use a sample of five Canadian banks to estimate the
extent of the sub-prime crisis contagion from USA to Canada. We
present results based on a sample consisting of 19 US banks with
total assets greater than $100 billion in 2005. Our results find
support for financial contagion from the US banks to the Canadian
banks. We find that while the Canadian banks were affected by
contagion, the Canadian banks were affected later relative to the
US banks.
In order to examine the effects of regulation on bank
operations, we adopt an indirect approach by examining
differences in accounting transparency across Canadian and US
banks. The relation between transparency on the one hand and bank
monitoring and effective bank regulation on the other hand, has
been long recognized. For example, the Basel Committee on Bank
Regulation (2000, page 3) states that “The Committee views
increased disclosure, enhanced transparency and market discipline
as becoming an ever more important tool of supervision.” The US
pg. 5
banking regulator, the Federal Deposit Insurance Corporation
(FDIC 2002) reiterates the same view; “accounting standards and
regulatory framework can or should be modified to better ensure
financial transparency and protect and inform the investor.”
Transparent reporting about the quality of bank assets is viewed
as the foundation of effective bank regulation and monitoring
both in Bank Regulation and the academic literature and as well.
For example, the Federal Reserve Board (FRB 2006) urges Bank
Examiners to evaluate banks’ accounting systems that recognizes
and reports of Allowance for Loan and Lease Losses in order to
provide credible information about the quality of bank assets.
The relation between effective regulation and transparency
of asset quality, as measured by the Allowance for Loan and Lease
Losses (ALLL), has also been examined in the academic literature.
For example, Ng and Rusticus (2011) find that the larger the
deviation of ALLL from an economic model of loan loss provisions,
the greater the proportion of banks’ non-performing loans and the
more likely that the bank will fail.3 Following Ng and Rusticus
3 In a multi-country study, Bushman and Williams (2009) show that income smoothing through managerial discretion on the magnitude of ALLL adversely affects the information content of bank financial statements.
pg. 6
(2011), we measure bank transparency and quality of bank
regulatory monitoring as the standard deviation of the residual
of the regression of ALLL on the amount of loan and lease write
offs (and other variables) for the surrounding period. The larger
the standard deviation, the more unpredictable the deviation of
reported ALLL values from an economic model and less the
transparency of disclosures of quality of bank assets and less
the effectiveness of regulation of the banking activities (see
FRB 2006 above).
Our paper makes a number of contributions to the literature.
As stated earlier, while there have been extensive discussions in
the financial press about the extent to which Canadian banks were
isolated from the US subprime crisis, there has been a lack of
academic studies on this issue. Our study will provide some
preliminary findings on this issue. Second, in this study we
attempt to identify both the regulatory and firm specific reasons
that caused Canadian banks and US banks to behave differently
during the sub-prime crisis. The foregoing distinction between
regulatory versus firm-specific reasons is important for a number
of reasons. In the wake of the financial crisis that started in
pg. 7
US and then spread to other parts of the world, the Canadian
banking system has been held up as an example of good governance
that permitted Canada to avoid the economic downturn to which
many other industrialized countries were subject. Governments and
regulators around the world are currently studying different
financial regulatory models that might avoid a recurrence of the
crisis.4 Our results about the transparency of Canadian bank
disclosure (specifically ALLL) have implications for these
decisions.
Our results show that extreme negative Canadian bank returns
were concentrated more during the post-crisis period as compared
to the pre-crisis and crisis periods. This is the first
systematic evidence that the Canadian banks were indeed affected
by the financial crisis but it happened with a lag. Our results
also show that the Canadian banks’ returns are more correlated
with US banks returns during downswings as compared to upswings,
specifically during the post-crisis period, rather than the
crisis period, which is another indication of delayed financial
contagion in Canadian banks. We also provide indirect evidence
4 See, for example, Avgouleas (2009).
pg. 8
supporting the media speculation that Canadian banks withstood
the effects of the financial crisis better because they were
better capitalized and demonstrated better liquidity. Our results
show that Canadian banks that were more capitalized and liquid,
demonstrated fewer negative returns.
The co-exceedance analyses, motivated by Bae et al. (2011)
also support the notion that while Canadian banks were affected
by the financial crisis, it happened with a lag. The excess of
the frequency of simultaneous extreme negative returns (bottom 5
percentile of returns) over simultaneous extreme positive returns
(top 5 percentile of returns) occurred during the post-crisis
period rather the crisis period for Canadian banks. On the other
hand, the excess of negative over positive extreme returns co-
exceedances occurred both during the crisis period and the post-
crisis period as well for US banks
Our tests of transparency tests for Canadian versus US
banks’ disclosures show that the standard deviation of the ALLL
residuals (Ng and Rusticus 2011) of Canadian banks was smaller
than that of US banks, during the crisis and the post-crisis
period. The situation was reversed during the pre-crisis period
pg. 9
when the transparency of US bank reporting was better. This is
consistent with the notion that Canadian banks were subject to
better quality regulatory monitoring during the crisis and post-
crisis periods.
The rest of the paper is organized as follows. The next
section reviews the prior literature on contagion and the
Canadian response to the financial crisis. The third section
discusses the research design, the fourth section examines the
results and the fifth section summarizes the conclusions.
2. Literature Review
Financial integration has increased dramatically in recent
years among advanced economies due to capital account openness
and financial market reforms. While integration has resulted in
increased international risk sharing, it has also increased the
risk of transmitting financial shocks across borders. Claessens,
Dell’Arriccia, Igan, and Laeven (2010) report that during the
recent financial crisis, “the increasing interconnectedness of
financial institutions and markets and more highly correlated
financial risks intensified cross-border spillovers early on
through many channels—including liquidity pressures, global sell-
pg. 10
off in equities (particularly, financial stocks), and depletion
of bank capital.” Allen, Babus, and Carletti (2009) note that
Lehman’s demise in September 2008 compelled markets to re-assess
risk and precipitated contagion. The authors note that contagion
was the most important market failure during the recent financial
crisis and needs to be investigated.
There is a rich literature in economics and financial
economics that examines the spread (or contagion) of financial
crisis from one country to another (see e.g., Allen et al. (2009)
for a recent overview). The notion of contagion refers to the
increase in co-movement of returns of risky securities (or market
indices) during crisis periods relative to stable periods (Bae,
Karolyi and Stulz 2003) though the concept of contagion has been
hard to define for measurement purposes (Dornbusch, Park and
Claessens 2001; Allen et al. 2009). Most of the early work on
contagion was related to the currency crisis that occurred in
different parts of the world and how it was transmitted across
national boundaries. These early articles, for example, Glick and
Rose (1999), Mason (1999), Kaminiski and Reinhart (1998) and many
others, compared the pairwise correlation of changes in currency
pg. 11
exchange rates between different countries. The broad consensus
in these papers was that a currency crisis in one part of the
world is quickly transmitted to other parts of the world and the
correlations are stronger during crisis periods as compared to
tranquil periods. The problem with this approach is the
underlying volatility of currency exchange rate changes is
greater during crisis periods and this can overstate correlations
during crisis periods. (See De Bandt and Hartmann (2000) for a
survey). Later papers adopted a different methodological approach
to address the foregoing spurious correlation problem and also
considered the effect of contagion, if any, on stock prices. For
example, Bae, Karolyi and Stulz (2003) and Boyson, Stahl and
Stulz (2008) pioneered the estimation of probability of joint
occurrence of extreme events across countries on the same day or
with a lag, and in particular whether this probability is higher
during economic downturns as compared to upturns. The first paper
found that stock return contagion among Latin American emerging
markets was greater than that in Asian emerging markets over a
nine-year period from 1992 to 2000. The second paper, using the
same methodology for hedge funds, found evidence of contagion
pg. 12
across different hedge fund styles. In this paper, we use this
methodology to examine the nature of the spread of the financial
crisis from US financial institutions to Canadian banks.
Commentators have attributed a number of reasons why
Canadian banks remained relatively unaffected by the financial
turmoil. Krugman (2010) attributes the strength of Canadian banks
to their better monitoring by Canadian regulators. This is
consistent with the views of Saunders and Wilson (1999) and
others that Canadian banks are more resilient and in a better
position to withstand periodic crises. In addition to these
reasons, Richburg (2008) suggests that the resilience of Canadian
banks also arises from their higher liquidity and larger capital
bases. Ratnovsky and Huang (2009), in an IMF working paper,
provide preliminary evidence that Canadian banks’ reliance on
depository funding rather than other sources of funding, the low
levels of exposure to US subprime-based assets and low levels of
securitized loans, as the most important determinants of their
ability to withstand financial difficulties that faced US and
European banks. In our paper, we examine the nature of the impact
of the sub-prime crisis on the Canadian banks, whether the
pg. 13
Canadian banks escaped the severity experienced in the US and
elsewhere and attempt to distinguish between regulatory versus
firm-specific reasons that might have permitted them to escape
the problems that decimated much of the financial sector in many
jurisdictions.
3. Research Design
Empirical tests are based on a sample of five largest
Canadian banks and the 19 largest US banks with total assets
greater than $100 billion at the end of 2005 and are listed in
the Appendix. This date is used in selecting the banks so that
the asset amounts are not affected by the crisis period.5 Both
the US and Canadian banks in this sample are banks that are “too
big to fail.”
The sample period extends from 1999 to 2011 which covers the
subprime crisis. We classify the sub-periods based on the crisis
timeline provide by the St. Louis Federal Reserve
(http://timeline.stlouisfed.org/pdf/CrisisTimeline.pdf). We
5 Two additional samples were used to test the sensitivity of results. One sample consists of the 13 banks used by Khan (2009) in his test of contagion in the US banking sector and the second sample consists of the 19 banks used in the study by King (2009).
pg. 14
define three sub-periods, namely, the pre-crisis period from
January 1999 to March 2007, the crisis period from April 2007 to
September 2008, and the post-crisis or recovery period from
October 2008 to December 2011. Many economists agree that the
financial crisis started in 2007. In April 2007, New Century
Financial Corporation, a leading sub-prime mortgage lender, filed
for Chapter 11 bankruptcy protection. Bear Stearns announced in
June 2007 that its two hedge funds, worth an estimated $1.5
billion at the end of 2006, were almost worthless. American Home
Mortgage Investment Corporation filed for Chapter 11 bankruptcy
in August 2007. Bear Stearns itself was in distress and was
acquired by J. P. Morgan Chase in March 2008. In September 2008,
the Treasury Department took over Fannie Mae and Freddie Mac,
Lehman declared bankruptcy, Merrill sold itself to Bank of
America, the Federal Reserve bailed out AIG, and Washington
Mutual was acquired by J. P. Morgan Chase with FDIC’s assistance.
Accordingly, we define the crisis period as the period from April
2007 to September 2008.
On October 1, 2008 the U.S. Congress passed the Troubled
Asset Relief Program (TARP). The U.S. Federal Reserve, the
pg. 15
Treasury and FDIC implemented various measures to prevent a
credit crisis and to provide liquidity to the system. The
measures included making available capital to banks by buying
their preferred stock, reducing costs of borrowing for the banks,
creation of a Commercial Paper Funding Facility and the Term
Asset-Backed Securities Lending Facility. These measures
stabilized the markets and paved the road for recovery.
Accordingly, we define the post-crisis period as the period from
October 2008 to December 2011.
We utilize monthly income statement data and quarterly
balance sheets for the Canadian banks available from the Office
of the Superintendent of Financial Institution of Canada (OSFI)
website. Stock returns for Canadian banks and Canadian market
data are obtained from the Canadian Financial Markets Research
Centre (CFMRC) with comparable data for the US available through
the Center for Research in Security Prices (CRSP). Additional
financial data are collected from Compustat.
Empirical tests.
pg. 16
We identify the sub periods during which the probability
with which Canadian banks’ negative returns were associated with
the negative index returns of a sample of US banks, by estimating
the following model (Bae, Karolyi and Stulz 2003 and Khan 2009):
ExtremNegit = 1 + It+ control variables + error
(1)
where:
ExtremNegit = 1 if the return for Canadian bank i in period t
is in the bottom 10% of the returns for the time series of
monthly returns from January 1999 to December 2009; and 0
otherwise.
It = 1 if the US Bank index in period t is the bottom
quartile of the index return for the sample period from January
1999 to December 2009; and 0 otherwise.
A positive and significant estimate of is consistent with
the notion that the financial crisis in US spread to Canadian
banks by contagion.
Examination of whether the bank characteristics listed in
the previous section influenced the extent of contagion in
pg. 17
Canadian banks implies the coefficient in equation (1) is a
function of those bank characteristics. In other words,
= 1 + 2 Capitalization + 3 Liquidity + 4 Deposit Funding
+ error (2)
Replacing in equation (1) results in
ExtremNeg it = 1 + (1 + 2Capitalization + 3 Liquidity + 4
Deposit Funding) It+ control variables + error
(3)
Negative and significant coefficients for 2, 3 and 4 are
consistent with the contagion being reduced for high
capitalization, high liquidity and high deposit funding banks.
The coefficient 1 reflects factors that are common to all
Canadian banks.
Co-exceedance tests
In order to examine whether the financial crisis spread to
Canada and if so, whether Canadian banks were less affected than
comparable US banks, we follow the univariate methodology used by
Bae, Karolyi and Stulz (2003, Table 2). In the analysis, we
compare, first over the entire sample period, and then for each
sub period, the number of trading days that US and Canadian banks
pg. 18
exhibit extreme negative returns simultaneously versus the number of
days that banks exhibited simultaneous extreme positive returns.
According to Bae, Karolyi and Stulz (2003, Table 2), contagion
exists if the number of joint occurrences of extreme negative
returns exceed the number of joint occurrences of extreme
positive returns during a pre-specified time period. A comparison
of the results for the US versus the Canadian banks indicates
which of the two subsamples suffered more from the financial
crisis by contagion and during which sub period, namely, pre-
crisis, crisis and post-crisis period. The greater the excess of
the negative joint occurrences over positive joint occurrences,
the greater the contagion.
Tests of Asset Quality Transparency
Following Ng and Rusticus (2011, equation 2) we estimate the
following equations separately for Canadian and US banks and
compare standard deviation of their residuals:6
6 “ LLR (t-1) is the loan loss reserves at time t-1, i.e., the loan loss reserves at the end of the prior period, reflect an estimate of the loans thatwere expected to be charged-off. During the period, actual net charge-offs reduce the available reserve. The loan loss provisions are then used to increase the loan loss reserves, such that the ending balance reflects the loan losses that are expected to occur in the future (Wall and Koch, 2000). This set-up suggests the following two drivers of the loan loss provisions made at time t. First, banks are expected to make more loss loan provisions ifthe beginning loan loss reserves were insufficient to cover current charge-
pg. 19
ALLLt = β0 + β1 LLRt-1 + β2 NCOt + β3 NCOt+1 + β4 CH_NPLt +β5 CH_NPLt+1
+ εt, (4)
Where ALLL = Allowance for loan and lease losses
LLR = beginning of period loan loss reserves
NCO = Net Charge off (at time t or t+1) as the case may be
CH_NPL = Change in non performing loans (at t or t+1) as the case
may be, that is, loans more than 90 days past due)
The residuals (Actual value of ALLL minus fitted values of ALLL)
are calculated separately for individual US and Canadian banks
and country specific magnitudes of their standard deviations
compared. Following Ng and Rusticus (2011), the standard
deviation of the residuals is multiplied by minus one to obtain
the loan loss provision quality (LLPQ). Higher values of LLPQ are
consistent with less volatility of ALLL and higher transparency.
offs. Hence, a positive association between loan loss provisions and both lagged loan loss reserves and contemporaneous net charge-offs is expected. Second, banks are expected to make more loan loss provisions if they expect future loan losses to be higher. To capture these expectations, we rely on twopredictors, realized net charge-offs at t+1 and the change in non-performing loans at time t. Net charge-offs at t+1 proxy for banks‟ ex-ante expectations of these losses at time t; we expect a positive association between loan loss provisions and net charge-offs at t+1. This is similar in spirit to Dechow andDichev (2002), who regress accruals on lagged, current and future cash flows to assess the quality of the accrual estimates.” (See Ng and Rusticus, 2011)
pg. 20
4. Results
Table 1 reports descriptive statistics for the 19 largest US
banks and the 5 largest Canadian banks for the time periods
before, during and after the financial crisis.7 Tier 1 ratio, a
measure of soundness of bank operations, appeared fairly stable
over the whole period for both US and Canadian banks except for
the post-crisis period, which exhibits a big jump in this ratio
for both countries. Figure 1 completes this picture. The Tier 1
ratios have been higher for Canadian banks relative to US banks
throughout the sample period. In comparison with US banks,
Canadian banks have had lower Tier 2 capitalization ratios
throughout the sample period.
Table 1 and Figure 2 show that both US and Canadian banks
exhibit steady increase in the reported value of bank (total)
assets over the entire sample period. However, Figure 3 appears
to indicate that reported book values of US banks’ common equity
track their market values less closely as compared to Canadian
banks. For example, while the average market value of US banks
7 The pre-crisis period is divided into two sub-periods for purposes of the descriptive statistics due to the substantial growth of both the Canadian and US banks during the pre-crisis.
pg. 21
fell sharply during the crisis and some more during the post-
crisis periods (after peaking during April 2005 to March 2007),
the book value of common equity continued to rise during this
period . In contrast, as Figure 3 shows, Canadian banks’ book
values are better able to track their market values. Both market
values and book value of common equity for Canadian banks rose
during the crisis and post-crisis periods.8
These contrasting patterns of reported values of net assets
against their market values for US versus Canadian banks likely
have implications for the relative quality of regulatory
monitoring of financial institutions in these two countries. This
issue will be discussed later in this section. Another related
financial reporting issue is the pattern of intertemporal changes
in bank ROEs over time in the two countries, as shown in Figure
4. While both US and Canadian ROEs exhibit similar pattern during
the pre-crisis period, US banks’ ROEs exhibit an extremely
volatile pattern during the crisis and post-crisis periods. We
will return to this issue later in this section.
8 The market value of Canadian banks fell in the first 2 quarters of the post-crisis period.
pg. 22
Table 2 examines extreme negative returns of Canadian banks
to test for the existence of contagion and the impact of bank
characteristics on the extent of contagion. Note that in the
extant literature, financial contagion is defined as a setting
where negative stock returns are more highly correlated across
stocks as compared to positive returns. In Panel A, for the
sample period, 26.67% of Canadian returns observations are in the
bottom 10% of observed returns when the Index of US bank returns
is in the bottom quartile (Index = 1). In contrast, only 4.8% of
Canadian return observations are in the bottom 10% when the
values of the US bank return index are NOT in their bottom
quartile (Index = 0). This is an overall evidence of contagion,
namely, that Canadian banks are more likely to demonstrate
extreme negative returns when US banks returns are also very
negative. Extreme Canadian negative returns are less likely when
US index returns are less negative.
The results reported in Panel B of Table 2 are consistent
with the notion that the contagion in Canadian bank occurs with a
lag. This panel shows that the extreme negative Canadian bank
returns are concentrated more during the post-crisis period as
pg. 23
compared to the pre-crisis and the crisis period. Extreme
negative returns occur most frequently (15.38%) during the post-
crisis period, as compared to the pre-crisis (8.11%) and the
crisis period (11.11%) for Canadian banks. Instead, for US banks,
the extreme negative return observations are concentrated during
the crisis period (22.52%). The figure declines to 20.26% during
the post-crisis period.
Prior literature argued that Canadian banks escaped the
severity of the financial crisis because they had greater
capitalization, more liquidity, and greater reliance on
depository funding. Panels C and D provide descriptive statistics
for these bank characteristics for the Canadian banks.
Panel E of Table 2 uses variations of the regression models
(1) and (3) to test for evidence of contagion. Consistent with
Bae et al. (2003) and Khan (2009), the dependent variable
(ExtremNegit) takes a value of one if the return for the Canadian
bank is in the bottom 10% of the returns for the entire time
series of monthly return from January 1999 to December 2011,
otherwise the value of ExtremNegit is zero. The independent
variable (Indext) is also a dummy variable that takes a value of
pg. 24
one if the US Bank index is the bottom quartile of the index
return for the entire sample period from January 1999 to
September 2011. The other variables, namely, Capitalization,
Liquidity, Deposit Fund, TBill and MktRet, are control variables
that could affect the frequency of negative bank returns.
The Model 6 specification has the Crisis * Index and the
Post-Crisis * Index variables on the right hand side. This model
allows us to test for the existence of delayed contagion. The
Crisis * Index is not significant while Post-Crisis * Index is
strongly significant (p<.01) with a coefficient estimate of
1.7305. These results show that during the post-crisis period,
extreme negative Canadian bank returns move very closely with low
US bank index returns. However, during the actual crisis period,
the association between extreme Canadian negative returns and low
US bank returns is not any different from the relationship that
existed prior to the crisis period, consistent with the
insignificance of the Crisis*Index variable. This shows that the
Canadian banks’ returns are more correlated with US banks returns
during downswings as compared to upswings, specifically during
the post-crisis period, another indication of delayed financial
pg. 25
contagion in Canadian banks. In Models 3 and 5, the crisis period
and the post-crisis-period variables both enter the models as
main effects and not in interaction with the Index variable.
However we observe the same pattern as above, namely that the
post-crisis period is significant and the crisis period is not
significant.
The control variables have the expected signs in Model 6.
The Capitalization variable is negative (0.7586) and significant
(p<.05), which is consistent with the notion that banks with high
capitalization will demonstrate fewer negative returns.
Similarly, a significant (p<.10) and negative coefficient (-
0.1110) for the Liquidity variable implies that banks with strong
liquidity will experience fewer extreme negative returns. The
other control variables, namely, Deposit Fund, TBill and MktRet
are insignificant. It is possible that these insignificant
results arise from the strong correlations (panel D of Table 2)
between the Capitalization and Liquidity variables on the one
hand and the Deposit Fund, TBill and MktRet variables on the
other.
pg. 26
Analyses of Co-exceedances
Table 3 examines financial contagion using the results of
the co-exceedances analyses based on daily returns. Consistent
with Bae et al. (2003), this analysis consists of comparing,
first over the entire sample period, and then for each sub
period, the number of trading days that a specified number of US
banks and Canadian banks exhibit simultaneous extreme negative
returns versus the number of banks that exhibit simultaneous
extreme positive returns. Extreme positive and negative returns
are measured as the top and bottom 5% of the return observations
during each sample period. Bae et al. (2003) argue that contagion
exists if the number of joint occurrences of extreme negative
returns exceeds the number of joint occurrences of extreme
positive returns.
Table 3 compares the number of negative co-exceedances
versus the number of positive co-exceedances for each of the sub-
period for Canadian banks and US banks separately using daily
returns. Table 4 does the same comparison using monthly returns.
Tables 5 and 6 compare the number of actual number of negative
pg. 27
(positive) co-exceedances to the expected number of negative
(positive) co-exceedances using Monte Carlo simulation.
Panels A and B of Table 3 report the number of co-
exceedances for the entire period. Panels C, D and E of Table 3
report the number of positive and negative co-exceedances of US
and Canadian banks for the three sub-periods.
In panel C, for the pre-crisis period, the number of times
that 3 or more Canadian banks exhibit an extreme negative return
on the same day (i.e., a co-exceedance greater than or equal to
3) is 43 (10 + 11 + 22) versus 53 positive co-exceedances (9 + 15
+ 29). Since the number of negative co-exceedances is smaller
than the number of positive co-exceedances, per Bae et al.
(2003), there are no indications of a contagion in the Canadian
banks during the pre-crisis periods. The corresponding negative
versus positive co-exceedances for US banks follows a similar
pattern. For example, during this period, the number of times
that the US sample banks exhibit negative co-exceedance of 3 or
more is 116 (27 + 9 + 11 + 15 + 20 + 34) versus 135 positive co-
exceedances (38 + 14 + 13 + 21 + 22 + 27). These results show
pg. 28
lack of contagion amongst both US and Canadian banks during the
pre-crisis period of January 1999 to March 31, 2007.
Panel D of Table 3 provides negative and positive values of
co-exceedances during the crisis period. For Canadian banks, the
frequency of negative and positive co-exceedances in 3 or more
banks are 21and 22 respectively, again indicating that there is
probably no contagion in Canadian banks during the crisis period.
Note however that the crisis period is defined in terms of the
financial crisis that exhibited itself in the US. Not
surprisingly, the number of negative co-exceedances in 3 or more
US banks is 84 versus 69 positive co-exceedances. This indicates
the existence of contagion in the financial crisis across major
US banks.
Panel E of Table 3 reports results of co-exceedances for the
post-crisis period. The number of negative co-exceedances of 3 or
more Canadian banks is now 51 (26+13+12), which is greater than
the number of 44 positive co-exceedances (23+7+14) during the
period, implying contagion. The corresponding figures for the US
sample banks are 132 negative co-exceedances, which are greater
than 119 positive co-exceedances for 3 or more banks. This shows
pg. 29
that the contagion continued to affect US banks in the post-
crisis period.
These results show that while both US and Canadian banks
were affected by the financial crisis, the Canadian banks were
affected later relative to the US banks. For the April 2007 to
September 2008 (crisis) period, the US banks but not Canadian
banks exhibit more negative co-exceedances than positive ones.
This shows that US banks were affected during this period by the
financial crisis but not Canadian banks. In contrast, for the
October 2008 to December 2011 period, Canadian banks as well as
US banks, exhibit more negative co-exceedances than positive
ones. These findings are consistent with the financial crisis
affecting Canadian banks with a lag.
Table 4 reports results of the co-exceedances analyses based
on monthly returns. Panel A reports the number of co-exceedances
for the entire period and Panels B, C, and D report the positive
and negative co-exceedances of US and Canadian banks for the
three sub-periods. For the pre-crisis period, in Panel B, the
number of times that 3 or more Canadian banks exhibit extreme
negative returns in the same month is 0 whereas the figure for
pg. 30
positive co-exceedances is 1, suggesting no indications of a
contagion in the Canadian banks during the pre-crisis periods.
Panel C provides negative and positive values of co-exceedances
during the crisis period. For Canadian banks, the number of
negative and positive co-exceedances in 3 or more Canadian banks
is 1and 0 respectively, indicating that there is probably little
or no contagion in Canadian banks during the crisis period.
Interestingly, the number of negative co-exceedances in 3 or more
US banks is 8 versus 2 positive co-exceedances, indicating the
existence of contagion in the financial crisis across major US
banks.
Panel D reports results of co-exceedances for the post-
crisis period. The number of negative co-exceedances in 3 or more
Canadian banks is now 4, which is greater than 2 positive co-
exceedances during the period, supporting contagion. The
corresponding US figures are 6 negative as well as 6 positive co-
exceedances in 3 or more US banks. As in the case with daily
returns, these results show that while both US and Canadian banks
were affected by contagion, the Canadian banks were affected
later relative to the US banks.
pg. 31
Tables 5 and 6 shows the results of Monte Carlo simulations
for the co-exceedances for Canadian and US banks using
multivariate normal and Student t distributions, respectively. A
Monte Carlo simulation is used to evaluate the number of co-
exceedances for each set of banks. The sample mean and variance-
covariance matrix is calculated and then 5,000 random
realizations are generated using either a multivariate normal
distribution or a multivariate t-distribution with 5 degrees of
freedom. For each realization, the number of co-exceedances are
calculated using a 5% threshold, similar to Table 4. The
simulated p-value is calculated as the number of replications
with co-exceedances in a specific category which exceed the
actual number of co-exceedances.
In Table 5, we assume that the returns are jointly
distribution as multivariate normal. Panel B, reports the results
for Canadian banks for the crisis period. There are 7 instances
of 3 Canadian banks exhibiting extreme negative returns during
this period, which are not significantly different (p=.27) from
6.17 expected number of such instances assuming multivariate
normal distribution of bank returns. There are 8 occurrences of
pg. 32
extreme positive co-exceedances in 3 Canadian banks, which again
are not significantly different (p=.14) from the expected 6.17
occasions. The frequency of 4 banks exhibiting extreme negative
co-exceedances is 4 but that is also not significantly different
from the expected 3.73. On the other hand, the 7 actual instances
of 4 positive co-exceedances are significantly more (p=.01) than
the expected 3.71 instances. The frequencies of positive 5 co-
exceedances (7) and negative 5 co-exceedances (10) are
significantly greater than their respective expected values. That
is, there are 22 (8+7+7) occurrences of positive co-exceedances
in 3 or more Canadian banks that are greater than expected
values. The corresponding figure for negative co-exceedances in 3
or more Canadian banks is 21. This evidence is not consistent
with the contagion which implies more frequent negative rather
than positive co-exceedances.
Results reported in Panel C of Table 5 for the post-crisis
period are dramatically different. The frequency of negative co-
exceedances in 4 or more Canadian banks that are significantly
greater than their expected values is 39 (26+13). The
corresponding figure for 4 or more positive co-exceedances is 30.
pg. 33
Thus, the results in Panel C are consistent with contagion
amongst Canadian banks for the post-crisis period. This implies
that the economic crisis did affect Canadian banks, but with a
lag.
Panel E of Table 5 shows the co-exceedances for US banks for
the crisis period. The frequency of 5 or more negative co-
exceedances that are greater than expected values is 59 (35 + 7 +
6 +11) as compared to only 45 cases of 5 or more positive co-
exceedances greater than their expected values. This implies that
the banking crisis affected had a greater negative effect on US
banks as compared to the Canadian banks during the actual crisis
period. Panel F of Table 5 shows the simulation results for the
post-crisis period for US banks. It is interesting to note that
the observed frequencies of 7 or less negative and 7 or less
positive co-exceedances are not different from their expected
values. The frequency of more than 7 negative co-exceedances is
57 which is significantly (p=.00) greater than its expected
value. Similarly, the frequency of more than 7 positive co-
exceedances is 48 which is significantly (p=.00) greater than its
expected value.
pg. 34
The results reported in Panels E and F are consistent with
the notion that US banks were affected more negatively than
Canadian banks during the actual crisis period. However, the
situation seems to have reversed itself after the crisis period.
While US banks still are affected negatively, Canadian banks were
worst affected in the post-crisis period.
Panels B, C, E, and F indicate that the assumption of a
multivariate normal distribution of bank returns does not
generate simulated co-exceedances as large as in the actual
sample observations. To test sensitivity of the results, we use
the Student t distributional assumption to accommodate fat tails
in the joint distribution of returns. These results are reported
in Table 6.
The findings in Table 6 are similar to those in Table 5 for
Canadian and U.S. banks. The Student's t distributional
assumption cannot deliver simulated co-exceedances as indicated
for the actual sample. For example, in Panel C, the frequency of
negative co-exceedances in 5 Canadian banks at 26 is
significantly greater than their expected values of 7.55 with the
corresponding figure for 5 or more positive co-exceedances being
pg. 35
23 that is significantly greater than the expected value of 7.47.
While the expected values are greater under the t-distribution
(7.55 and 7.47 for 5 negative and positive co-exceedances) in
comparison with the normal distribution (5.17 and 5.13 for 5
negative and positive co-exceedances), the actual occurrences are
still significantly greater than their expected values.
The results in Tables 5 and 6, while providing support for a
delay in contagion from US to Canadian markets, also indicate
that the distributional assumptions of multivariate normal and
Student t do not explain the actual pattern of co-exceedances
exhibited during and after the crisis in both US and Canadian
markets.
Quality of Loan Loss Provision
As discussed earlier, one possible explanation for the
difference between the Canadian versus the US banks’ reaction to
the financial crisis is the difference in the relative quality of
bank monitoring, if any, between these two jurisdictions. The
financial press and several commentators have proposed that
pg. 36
Canadian banks were able to avoid the disastrous effects of the
financial crisis because of better quality monitoring by the
Canadian bank regulators. In this section, we provide results of
tests that attempt to capture the relative quality of bank
monitoring in the two jurisdictions. The underlying notion is
that the efficacy of bank monitoring is reflected in the quality
of bank assets. As a practical matter, bank regulators are
concerned about the quality of allowance of loan losses
recognized by banks. For example, the Federal Reserve Board (FRB
2006) urges Bank Examiners to evaluate banks’ accounting systems
that recognize and report Allowance for Loan and Lease Losses
(ALLL) in order to provide credible information about the quality
of bank assets.
A number of academic studies have examined the quality of
loan loss provisions recognized by banks. For example, Ng and
Rusticus (2011) find that the larger the deviation of ALLL from
an economic model of loan loss provisions, the greater the
proportion of banks’ non-performing loans and more likely the
bank to fail. Table 7 provides results for estimating equation
(4). Panels A and B show the descriptive statistics for the loan
pg. 37
loss provision (ALLL) related variables for US banks and Canadian
banks respectively. The ALLL variable exhibits a 3 fold increase
from the 1999-2006 pre-crisis period (median = 0.53) to the 2009-
2011 crisis and post-crisis period (median = 2.18). This increase
is not surprising because of the sharp increase of the Net Charge
Off (NCO) variable during the crisis/post-crisis period
reflecting the poor quality of bank assets that had to be written
off. The NCO variable rose from 0.56 to 1.72 over from the pre-
crisis to the crisis/post-crisis period. ALLL is significantly
related to NCO as shown the highly significant coefficient on the
NCO variable (coefficient estimate of 1.42, ‘t’ = 7.48) for the
2007-2011 period regression in Panel C.
Note that the NCO variable actually exhibits a decline from
0.51 to 0.47 for Canadian banks in Panel B of Table 7, from the
pre-crisis period to the crisis/post-crisis period. The increase
in the ALLL variable from 0.40 to 0.50 for the Canadian banks is
relatively more modest as compared to US banks.
Consistent with Ng and Rusticus (2011) the quality of ALLL
recognized by banks is measured by the volatility of the
residuals from the economic model for ALLL. Panels C and D report
pg. 38
the regression results for the economic models for ALLL for US
and Canadian banks. The more volatile the residuals from the
models reported in Panels C and D, the more unpredictable the
ALLL and the lower its quality. Panel E of Table 7 provides
comparative measures of Loan Loss Provision Quality for the two
jurisdictions for the pre-crisis versus crisis/post-crisis
period. For the pre-crisis 1999-2006 period, LLPQ for US banks is
-0.192 versus -0.263 for Canadian banks implying better
monitoring quality for US banks. The figures reverse dramatically
in the crisis period when LLPQ for US banks is -0.517 versus -
0.170 for Canadian banks implying better monitoring quality for
Canadian banks. In the post-crisis period, the related figures
are -0.318 for US banks and -.07 for Canadian banks. This
indicates that while the loan loss provision quality for both US
and Canadian banks improved in the post-crisis period, the
difference between the two sets of banks widened in the post-
crisis period. The ALLL quality data in Table 7, Panel E is
consistent with the pattern of market values and book values of
equity in Figure 3 discussed earlier. For US banks market values
and book values moved in opposite directions, during and after
pg. 39
the crisis period, indicating a disconnect between reported
values of assets and market values of assets. This disconnect in
Figure 3, is probably at least partly captured in the higher
standard deviation of ALLL residuals. This implies that allowance
for loan losses were recognized at values that could not be
explained by the level of charge-offs, nonperforming loans and
the magnitude of loan loss reserves. This caused net asset values
to rise while their market values fell during and after the
crisis period. Also note the higher volatility of ALLL residuals
is consistent with the higher ROE volatility of US banks in
crisis and post-crisis period shown in Figure 4. On the other
hand, the quality of loan loss provisions for Canadian banks has
steadily improved in the crisis and post-crisis period compared
to the pre-crisis period. This implies that in the crisis/post-
crisis period the Canadian banks’ monitoring is superior to that
experienced by US banks.
5. Conclusion
The Canadian banking system has been held up an example of
good governance that permitted Canada to avoid the economic
pg. 40
downturn to which many other industrialized countries were
subject in the aftermath of the 2008 sub-prime crisis that
started in the US. In this paper, we investigate whether the
Canadian banking sector was afflicted by the financial contagion
from crisis in the United States financial sector using two
approaches. We find while both the US banks and Canadian banks
were affected by contagion, the Canadian banks were affected with
a lag, relative to the US banks. Our results indicate that the
Canadian banks’ returns are more correlated with US banks returns
during downswings as compared to upswings during the post-crisis
period, rather than the crisis period, supporting the notion of
delayed financial contagion in Canadian banks. We also provide
indirect evidence supporting the media speculation that Canadian
banks withstood the effects of the financial crisis better
because they were better capitalized and demonstrated better
liquidity. Our analysis of loan loss provision quality supports
superior quality of monitoring of Canadian banks versus US banks
during and after the crisis period. However, though the quality
of Canadian regulatory monitoring appears to have improved in the
crisis period and the post-crisis period, it could not totally
pg. 41
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AppendixSample Firms
Five largest Canadian banks based on total assets in 2005Royal Bank of CanadaToronto Dominion BankCanadian Imperial Bank of CommerceBank of MontrealBank of Nova Scotia
US banks with total assets greater than $100 billion in 2005Bank of AmericaBank of New York Mellon CorpBB&T CorpBear Stearns Companies IncCitigroup IncCountrywide Financial CorpFannie MaeFifth Third BancorpGoldman Sachs Group IncJP Morgan Chase & CoLehman Brothers Holdings IncMerrill Lynch & Co IncMorgan StanleyNational City CorpSuntrust Banks IncUS BancorpWachovia CorpWashington Mutual IncWells Fargo & Co
pg. 48
Table 1Descriptive Statistics
Before, During, and After the Crisis PeriodSample Period: January 1999 to December 2011
Crisis Period: April 2007 to September 2008
Jan 1999 toMar2005
Pre-crisisperiod
Apr 2005 toMar2007
Pre-crisisperiod
Apr 2007 toSep2008
Crisis period
Oct 2008 toDec2011
Post-crisisperiod
USBanks
CdnBanks
USBanks
CdnBanks
USBanks
CdnBanks
USBanks
CdnBanks
Market Value 50,440(34,797
)
16,949(15,344
)
70,903(47,372
)
35,175(33,751
)
60,477(43,356
)
44,185(46,388
)
58,318(38,546
)
45,334(40,108
)Assets 345,185
(249,154)
196,413(185,98
3)
573,877(437,38
9)
314,164(293,34
3)
732,906(605,86
1)
429,762(384,48
1)
1,009,093
(794,939)
493,274(480,67
0)
Liabilities 323,863(236,77
0)
186,628(177,37
6)
534,783(404,68
1)
299,196(279,32
5)
687,208(568,25
7)
409,600(366,26
1)
934,863(724,84
5)
465,533(450,99
8)Shareholders’ Equity
21,242(15,397
)
9,267(8,698)
38,618(25,975
)
14,116(13,432
)
46,006(28,957
)
19,556(19,691
)
73,181(43,479
)
26,824(25,720
)Common Equity 20,841
(14,204)
8,121(7,558)
37,853(22,596
)
13,218(13,004
)
42,474(26,324
)
17,857(18,064
)
58,296(37,086
)
23,547(23,108
)
pg. 49
Preferred Stock 409(9)
1,147(1,113)
795(139)
898(533)
3,912(1,082)
1,699(1,792)
14,885(4,917)
3,277(3,146)
ROE 4.81(4.71)
3.64(3.85)
4.43(4.18)
4.66(4.92)
-0.85(2.18)
3.57(4.67)
5.63(1.87)
3.35(3.55)
Tier 1 capital ratio
8.42(8.17)
9.09(8.92)
8.11(8.30)
10.14(10.06)
8.10(8.24)
9.75(9.66)
11.63(11.5)
12.09(12.18)
Tier 2 capital ratio
3.66(3.76)
3.18(3.21)
3.68(3.50)
2.50(2.07)
3.99(3.89)
2.54(2.21)
3.69(3.70)
2.77(2.50)
Notes: Mean (median) values shown. Sample consists of 19 US banks with total assets greater than $100 billion in 2005 and 5 largest
Canadian banks. Market Value, Assets, and Liabilities measured in millions of US dollars. ROE = Net Income divided by Beginning Shareholders’ Equity (note that net income for Canadian banks
converted to US dollars at end of quarter exchange rate rather than average for the quarter) expressedas a percentage
Information is obtained from Compustat except for the Tier 1 and Tier 2 capital ratios for the Canadian banks which are obtained from OSFI
pg. 50
Table 2Tests for the Existence of Contagion Surrounding the Financial Crisis
Sample Period: January 1999 to December 2011Crisis Period: April 2007 to September 2008
Panel A: Extreme Low Canadian Bank Returns Number of Observations
ExtremNeg Values (%)
Index = 0 583 4.80Index = 1 195 26.67Total no. of observations
778
Panel B: Extreme Low Bank Returns by Time PeriodCanadian Banks US Banks
Number ofObs
ExtremNegValues(%)
Numberof Obs
ExtremNegValues(%)
Before the crisis 493 8.11 1876 5.65During the crisis (April 2007 to September 2008)
90 11.11 333 22.52
After the crisis 195 15.38 459 20.26Total no. of observations
778 2668
Panel C: Descriptive StatisticsVariable Number
of ObsMean Std Dev Median Minimum Maximum
Capitalization
779 4.7477 0.6216 4.6638 3.4917 7.1248
Liquidity 779 21.7078 3.0993 21.4156 15.6260 34.0206Deposit Fund
779 66.2806 3.3225 66.7438 55.0256 72.9968
TBill 780 2.8337 1.5753 2.7205 0.1700 5.7510MktRet 780 0.0132 0.0592 0.0200 -0.2569 0.2154
Panel D: Correlations (p-values in parentheses)pg. 51
Capitalization
Liquidity DepositFund
TBill MktRet
Capitalization
1.0000 -0.0266(0.458)
0.1453(0.000)
-0.3553(0.000)
0.0561(0.118)
Liquidity 0.0091(0.799)
1.0000 0.0813(0.023)
-0.1630(0.000)
0.0295(0.411)
Deposit Fund
0.1213(0.000)
0.0272(0.448)
1.0000 0.2122(0.000)
-0.0255(0.478)
TBill -0.2644(0.000)
-0.0532(0.138)
0.1366(0.000)
1.0000 -0.0558(0.119)
MktRet 0.0302(0.400)
0.0136(0.705)
-0.0420(0.242)
-0.0941(0.009)
1.0000
pg. 52
Panel E: Logit Regression to Explain Incidences of Extreme Negative Canadian Bank returns (ExtremeNeg)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7Intercept -
3.6289***
(-4.03)
5.2571(1.47)
4.2936(1.32)
-4.5102**
*(-4.91)
-4.0985***(-6.38)
-3.6527**
*(-7.24)
-3.8823**
*(-6.06)
Index 1.6884***
(5.81)
1.6866***
(5.68)
1.8053***
(6.16)
11.3532***
(2.86)
10.5583***
(2.83)
8.5805**(2.27)
9.0606**(2.39)
Capitalization
-0.2582(-0.84)
-0.5244*(-1.86)
-0.3507(-1.02)
-0.6212*(-1.88)
-0.7586**(-2.26)
-0.7775**(-2.29)
Liquidity 0.0334(0.62)
0.0170(0.37)
-0.0419(-0.66)
-0.0946*(-1.66)
-0.1110*(-1.93)
-0.1132*(-1.96)
Deposit Fund -0.1434*
**(-2.72)
-0.0883*(-1.73)
-0.1064*(-1.86)
-0.0558(-1.03)
-0.0178(-0.31)
-0.0212(-0.37)
TBill 0.1761(0.85)
0.2900(1.33)
0.3002**
(2.10)
0.2956(1.38)
0.2580*(1.84)
0.1969*(1.77)
0.2499*(1.80)
MktRet -1.2686(-0.65)
-1.4308(-0.73)
-1.1105(-0.58)
-1.2085(-0.62)
-1.1316(-0.59)
-0.1731(-0.09)
-0.4280(-0.22)
Crisis -0.3128(-0.75)
-0.4371(-1.02)
-0.3283(-0.77)
Post-crisis 1.3252**
(2.31)
1.2119**(2.25)
0.5435(0.81)
Crisis * Index
-0.0085(-0.02)
Post-crisis * Index
1.7305***
(2.99)
1.2300*(1.80)
Interaction of Index with Firm Variables
No No No Yes Yes Yes Yes
Year fixed effects
Yes Yes No Yes No No No
Firm fixed Yes Yes Yes Yes Yes Yes Yes
pg. 53
Notes: Model 1 is based on equation 1 in Khan (2009):
o ExtremNeg it = 1 + 1 Indext + 5 TBillt + 6 MktRett + Year Fixed effects +Firm Fixed effects + error
Model 2 extends the basic model by introducing firm specific variables:o ExtremNeg it = 1 + 1 Indext + 2 Capitalizationit + 3 Liquidityit + 4
Deposit Fundit + 5 TBillt + 6 MktRett + Year fixed effects + Firm fixed effects + error
Model 3 is a variation of Model 2 with dummy variables for the Crisis and Post-crisis periods, instead of year fixed effects:o ExtremNeg it = 1 + 1 Indext + 2 Capitalizationit + 3 Liquidityit + 4
Deposit Fundit + 5 TBillt + 6 MktRett + 7 Crisist + 8 Post-crisist + Firm fixed effects + error
Model 4 extends the basic model by introducing firm specific variables interacted with the Index variable; it differs from Model 2 since the firm variables interact with the Index variable:o ExtremNeg it = 1 + ( 1 + 2 Capitalizationit + 3 Liquidityit + 4 Deposit
Fundit) * Indext + 5 TBillt + 6 MktRett + Year fixed effects + Firm fixed effects + error
Model 5 is a variation of Model 4 with dummy variables for the Crisis and Post-crisis periods rather than year fixed effects:o ExtremNeg it = 1 + ( 1 + 2 Capitalizationit + 3 Liquidityit + 4 Deposit
Fundit) * Indext + 5 TBillt + 6 MktRett + 7 Crisist + 8 Post-crisist + Firm fixed effects + error
Model 6 is a variation of Model 5 with the Crisis and Post-crisis dummy variables interacting with the Index variable:o ExtremNeg it = 1 + ( 1 + 2 Capitalizationit + 3 Liquidityit + 4 Deposit
Fundit + 9 Crisist + 10 Post-crisist) * Indext + 5 TBillt + 6 MktRett + Firmfixed effects + error
Model 7 is a variation of Model 4 which attempts to directly include the Crisis and Post-crisis dummy variables as well as interact these variables with the Index variable. The interaction of the Crisis dummy variable and Index variable cannot be included due to correlation:o ExtremNeg it = 1 + ( 1 + 2 Capitalizationit + 3 Liquidityit + 4 Deposit
Fundit + 10 Post-crisist) * Indext + 5 TBillt + 6 MktRett + 7 Crisist + 8
Post-crisist + Firm fixed effects + error
Variable definitions:o ExtremNeg it = 1 if Canadian bank i’s return is in the bottom 10% of the
returns for the entire time series of monthly return from January 1999 toDecember 2011. Bottom 10% is calculated as less than or equal to (<=) the 10th
percentileo Capitalizationit = Shareholders’ equity / Total assets (expressed as a
percentage) o Liquidityit = Liquid assets / Total liabilities (expressed as a
percentage)
pg. 55
o Deposit Fundit = Depository funding / Total assets (expressed as a percentage)
o Indext = 1 if the index, constructed from the set of 19 US Banks in the sample, is in the bottom quartile of the index return for the sample period from January 1999 to December 2011.
o MktRett is Canadian equally weighted market return. o TBillt is Canadian Treasury Bill rate.o Crisist = 1 for the crisis period from April 2007 to September 2008o Post-crisist = 1 for the period from October 2008 to December 2011
pg. 56
Table 3Summary of Extreme Co-exceedances Based on Daily Returns, 1999 to
2011
Panel A: Negative co-exceedances, Entire sample period (January 1, 1999, toDecember 31, 2011)Country
>13 13 12 11 10 9 8 7 6 5 4 3 2 1 0
Canada
46 28 41 72 213 2869
US 25 9 13 21 11 17 23 23 28 39 51 72 126 288 2525
Panel B: Positive co-exceedances, Entire sample period (January 1, 1999, toDecember 31, 2011)Country
0 1 2 3 4 5 6 7 8 9 10 11 12 13 >13
Canada
2875
194 81 51 29 39
US 2510
333 105 70 47 41 26 25 15 14 14 14 17 12 28
Panel C: Negative and positive co-exceedances, Pre-Crisis period, January 1999 to March 31, 2007Country
Negative co-exceedances Positive co-exceedances
>7
7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7
Canada
10
11
22
42
131
1861
1842
132
50
29
15
9
US 27 9 11
15
20
34
58
181
1717
1703
176
58
27
22
21
13
14
38
Panel D: Negative and positive co-exceedances, Crisis period (April 2007 to September 2008)Country
Negative co-exceedances Positive co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Canad 10 4 7 13 33 31 32 16 10 8 7 7pg. 57
a 0 9US 35 7 6 11 9 16 33 42 22
0242
53 15 14 10 6 6 5 28
Panel E: Negative and positive co-exceedances, Post-Crisis period (October2008 to December 2011)Country
Negative co-exceedances Positive co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Canada
26 13 12 17 49 698
704
46 21 14 7 23
US 57 7 11 13 22 22 35 65 588
565
104
32 29 15 14 7 6 48
Notes: A positive (negative) exceedance corresponds to the subset of daily returns
that comprise the highest (lowest) five percent of all returns, calculated separately for each bank. Co-exceedances represent joint occurrences across banks by day. For example, a positive co-exceedance of 4 means that 4 banks have a positive exceedance on the same day. (Bae, Karolyi, and Stulz, 2003)
pg. 58
Table 4Summary of Extreme Co-exceedances Based on Monthly Returns, 1999 to
2011
Panel A: Negative and positive co-exceedances, (January 1, 1999, to December 31, 2011)Country
Negative Co-exceedances Positive Co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Canada 1 2 2 4 13 13
4130
16 7 2 1
US 4 2 3 3 3 4 5 22 110
110
17 12 4 3 4 2 4
Panel B: Negative and positive co-exceedances, Pre-crisis period (January 1, 1999, to March 2007)Country
Negative Co-exceedances Positive Co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Canada 2 8 89 84 9 5 1US 2 1 1 1 2 10 82 74 11 5 3 1 1 1 3
Panel C: Negative and positive co-exceedances, Crisis period ( April 2007 to September 2008)Country
Negative Co-exceedances Positive Co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Canada 1 1 2 14 16 2US 2 1 1 1 3 5 5 13 1 2 1 1
Panel D: Negative and positive co-exceedances, Post-crisis period ( October2008 to December 2011)Country
Negative Co-exceedances Positive Co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Canada 1 1 2 1 3 31 30 5 2 1 1US 2 1 1 2 3 7 23 23 5 5 1 1 2 1 1
Notes:
pg. 59
A positive (negative) exceedance corresponds to the subset of monthly returnsthat comprise the highest (lowest) five percent of all returns, calculated separately for each bank. Co-exceedances represent joint occurrences across banks by month. For example, a positive co-exceedance of 4 means that 4 bankshave a positive exceedance on the same month. (Bae, Karolyi, and Stulz, 2003)
pg. 60
Table 5Monte Carlo Simulation of Daily Extreme Co-exceedances
Bank returns assumed to follow Multivariate Normal DistributionPre-crisis, Crisis and Post-crisis periods
Panel A: Canada, Period Before the Crisis, January 1999 to March 2007Negative co-exceedances Positive co-exceedances
5 4 3 2 1 0 0 1 2 3 4 5Actual 10 11 22 42 131 1861 1842 132 50 29 15 9
Mean 13.11
20.53
33.75
61.13
148.82
1799.66
1799.66
148.67
61.37
33.67
20.58
13.05
Standard deviation
3.14 4.03 5.23 7.08 11.88
9.24 9.09 11.90
7.03 5.13 3.98 3.12
5th percentile
8 14 25 50 129 1784 1784 129 50 26 14 8
95th percentile
18 27 43 73 168 1815 1815 168 73 42 27 18
p-value 0.79 1.00 0.99 1.00 0.92 0.00 0.00 0.92 0.94 0.79 0.91 0.88
Panel B: Canada, Period During the Crisis, April 2007 to September 2008
Negative co-exceedances Positive co-exceedances5 4 3 2 1 0 0 1 2 3 4 5
Actual 10 4 7 13 33 310 329 16 10 8 7 7
Mean 2.35
3.73
6.17
11.23
27.35
326.17
326.14
27.38
11.25
6.16
3.71
2.37
Standard deviation
1.36
1.72
2.18
3.08 5.05 3.90 3.91 5.06 2.98 2.22
1.67
1.38
5th percentile
0 1 3 6 19 320 320 19 7 3 1 0
95th percentile
5 7 10 16 36 332 333 36 16 10 7 5
p-value 0.00
0.32
0.27
0.22 0.12 1.00 0.19 0.99 0.59 0.14
0.01
0.00
pg. 61
Panel C: Canada, Period After the Crisis, October 2008 to December 2011
Negative co-exceedances Positive co-exceedances5 4 3 2 1 0 0 1 2 3 4 5
Actual 26 13 12 17 49 698 704 46 21 14 7 23
Mean 5.17
8.10
13.28
24.10
58.69
705.66
705.62
58.71
24.16
13.23
8.16
5.13
Standard deviation
2.02
2.50
3.23 4.50 7.47 5.77 5.64 7.26 4.36 3.18 2.46
1.97
5th percentile
2 4 8 17 47 696 696 47 17 8 4 2
95th percentile
9 12 19 32 71 715 715 71 32 19 12 9
p-value 0.00
0.02
0.59 0.94 0.89 0.89 0.58 0.96 0.72 0.34 0.59
0.00
pg. 62
Panel D: US Banks, Period Before the Crisis, January 1999 to March 2007Negative co-exceedances Positive co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Actual 27 9 11 15 20 34 58 181 1717 1703 176 58 27 22 21 13 14 38
Mean 60.64
18.69
24.38
32.98
46.05
68.38
116.21
263.81
1440.88
1441.04
263.41
116.21
68.55
46.04
32.94
24.50
18.67
60.64
Std dev 5.11
4.11 4.83 5.57 6.59 7.95 10.49
15.56
17.00 16.95 15.34
10.60
8.11 6.60 5.50 4.79 4.16 5.02
5th perc 52 12 17 24 35 56 238 1413 1414 238 99 55 35 24 17 12 5295th perc
69 26 33 42 57 82 134 289 1469 1469 289 134 82 57 42 33 26 2169
p-value 1.00
1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 1.00 1.00 1.00 1.00 0.99 0.99 0.84 1.00
Panel E: US Banks, Period During the Crisis April 2007 to September 2008Negative co-exceedances Positive co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7Actual 35 7 6 11 9 16 33 42 220 242 53 15 14 10 6 6 5 28
Mean 10.98
3.35
4.48
6.00
8.55
12.61
21.46
48.36
263.21
263.15
48.50
21.36
12.61
8.50
6.01
4.48
3.40
10.99
Std dev
2.17
1.79
2.07
2.35
2.83
3.46
4.51
6.62
7.24 7.23 6.56
4.55
3.42
2.87
2.42
2.04
1.77
2.19
5th perc
7 1 1 2 4 7 14 38 251 251 38 14 7 4 2 1 1 7
95th perc
15 6 8 10 13 19 29 60 275 275 59 29 18 13 10 8 7 15
p-value
0.00
0.02
0.15
0.02
0.35
0.13
0.00
0.81
1.00 1.00 0.23
0.91
0.28
0.23
0.40
0.16
0.12
0.00
Panel F: US Banks, Period After the Crisis October 2008 to December 2011Negative co-exceedances Positive co-exceedances
>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7
pg. 63
Actual 57 7 11 13 22 22 35 65 588 565 104 32 29 15 14 7 6 48
Mean 23.75 7.35
9.68
13.00
18.27
26.98
46.07
104.42
570.49
570.55
104.25
46.08
27.15
18.19
12.98
9.70
7.31
23.80
Std dev
3.27 2.61
3.07
3.52 4.09 5.04 6.63 9.71 10.65
10.63
9.53 6.63 5.14 4.15 3.47 3.05
2.61
3.19
5th perc
18 3 5 8 12 19 35 89 553 553 89 35 19 12 8 5 3 19
95th perc
29 12 15 19 25 35 57 121 588 588 120 57 36 25 19 15 12 29
p-value
0.00 0.46
0.27
0.42 0.15 0.81 0.95 1.00 0.04 0.68 0.48 0.98 0.31 0.73 0.33 0.76
0.60
0.00
Notes: The calculation of co-exceedances is based on daily returns. A Monte Carlo simulation is used to evaluate the number of co-exceedances for each set of banks. The
sample mean and variance-covariance matrix is calculated over the entire time period and then 5,000 random realizations are generated for each subperiod using a multivariate normal distribution. For each realization, the number of co-exceedances are calculated using a 5% threshold, similar to Table 4. The simulated p-value is calculated as the number of replications with co-exceedances in a specificcategory which exceed the actual number of co-exceedances.
pg. 64
Table 6Monte Carlo Simulation of Daily Co-exceedances
Bank returns assumed to follow Multivariate ‘t’ DistributionPre-crisis, Crisis and Post-crisis periods
Panel A: Canada, Period Before the Crisis, January 1999 to March 2007Negative co-exceedances Positive co-exceedances
5 4 3 2 1 0 0 1 2 3 4 5Actual 10 11 22 42 131 1861 1842 132 50 29 15 9
Mean 19.23
23.39
33.32
54.71
120.91
1825.44
1825.54
120.70
54.79
33.34
23.41
19.21
Std dev 3.68
4.21
5.21
6.85
11.19
9.15 9.15 11.17
6.88
5.09
4.19
3.74
5th perc 13 17 25 43 103 1810 1811 102 44 25 17 1395th perce 25 30 42 66 139 1840 1840 139 66 42 30 26p-value 0.9
91.00
0.99
0.96
0.17 0.00 0.03 0.15 0.73
0.78
0.98
1.00
Panel B: Canada, Period During the Crisis, April 2007 to September 2008
Negative co-exceedances Positive co-exceedances5 4 3 2 1 0 0 1 2 3 4 5
Actual 10 4 7 13 33 310 329 16 10 8 7 7
Mean 3.45 4.23 6.06 10.05
22.55
330.66
330.75
22.42
10.05
6.11 4.24
3.45
Std dev 1.55 1.80 2.25 2.98 4.83 3.93 3.92 4.81 3.02 2.17 1.79
1.59
5th perc
1 1 3 5 15 324 324 15 5 3 1 1
95th perc
6 7 10 15 30 337 337 31 15 10 7 6
p-value 0.00 0.42 0.25 0.12 0.01 1.00 0.50 0.89 0.43 0.14 0.04
0.01
Panel C: Canada, Period After the Crisis, October 2008 to December 2011
Negative co-exceedances Positive co-exceedances5 4 3 2 1 0 0 1 2 3 4 5
Actual 26 13 12 17 49 698 704 46 21 14 7 23
Mean 7.55 9.18 13.21
21.57
47.77
715.72
715.61
47.87
21.55
13.31
9.20 7.47
pg. 65
Std dev 2.34 2.60 3.28 4.40 7.05 5.77 5.62 6.98 4.37 3.27 2.65 2.32
5th perc 4 5 8 14 37 706 706 37 15 8 5 495th perc
11 14 19 29 60 725 725 60 29 19 14 11
p-value 0.00 0.05 0.57 0.82 0.39 1.00 0.97 0.57 0.50 0.35 0.73 0.00
pg. 66
Panel D: US Banks, Period Before the Crisis, January 1999 to March 2007Negative co-exceedances Positive co-exceedances>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7
Actual 27 9 11 15 20 34 58 181 1717 1703 176 58 27 22 21 13 14 38
Mean 76.79
18.11
22.56
29.14
38.56
55.06
89.88
196.61
1545.19
1545.48
196.44
89.60
55.13
38.78
29.07
22.67
18.04
76.80
Std dev 5.17 4.05 4.70 5.29 6.13 7.41 9.88 14.14
17.61 17.75 14.23
9.77 7.51 6.17 5.26 4.65 4.10 5.22
5th perc 68 12 15 21 29 43 74 174 1516 1516 174 74 43 29 20.5 15 12 6895th perc
85 25 31 38 49 67.5 106 221 1574 1575 220 106 68 49 38 31 25 85
p-value 1.00 0.99 1.00 1.00 1.00 1.00 1.00 0.86 0.00 0.00 0.92 1.00 1.00 1.00 0.93 0.98 1.00 1.00
Panel E: US Banks, Period During the Crisis April 2007 to September 2008Negative co-exceedances Positive co-exceedances>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7
Actual 35 7 6 11 9 16 33 42 220 242 53 15 14 10 6 6 5 28
Mean 13.90
3.29
4.11
5.33
7.18
10.19
16.61
36.67
281.72
281.78
36.61
16.56
10.14
7.20
5.37
4.14
3.35
13.86
Std dev
2.19 1.76
1.95
2.23
2.64
3.17 4.08 6.11 7.44 7.70 6.19 4.02 3.18 2.69
2.26
1.99
1.75
2.23
5th perc
10 1 1 2 3 5 10 27 269 269 27 10 5 3 2 1 1 10
95th perc
18 6 8 9 12 16 23 47 294 294 47 23 16 12 9 8 7 17
p-value
0.00 0.02
0.11
0.01
0.18
0.03 0.00 0.17 1.00 1.00 0.01 0.59 0.09 0.11
0.30
0.12
0.11
0.00
Panel F: US Banks, Period After the Crisis October 2008 to December 2011Negative co-exceedances Positive co-exceedances>7 7 6 5 4 3 2 1 0 0 1 2 3 4 5 6 7 >7
Actual 57 7 11 13 22 22 35 65 588 565 104 32 29 15 14 7 6 48
pg. 67
Mean 30.15 7.11
8.94
11.48
15.38
21.91
35.50
78.02
612.52
612.52
78.08
35.57
21.82
15.35
11.50
8.93
7.12
30.11
Std dev
3.25 2.56
2.95
3.30 3.90 4.66 6.08 9.01 11.09
11.26
9.01 6.04 4.66 3.91 3.29 2.97
2.62
3.25
5th perc
25 3 4 6 9 14 26 63 594 594 64 26 14 9 6 4 3 25
95th perc
36 12 14 17 22 30 46 93 631 631 93 46 30 22 17 14 12 36
p-value
0.00 0.42
0.19
0.07 0.04 0.44 0.49 0.92 0.98 1.00 0.00 0.69 0.06 0.46 0.18 0.67
0.57
0.00
Notes: The calculation of co-exceedances is based on daily returns. A Monte Carlo simulation is used to evaluate the number of co-exceedances for each set of banks. The
sample mean and variance-covariance matrix is calculated and then 5,000 random realizations are generatedusing a multivariate t-distribution with 5 degrees of freedom. For each realization, the number of co-exceedances are calculated using a 5% threshold, similar to Table 4. The simulated p-value is calculated as the number of replications with co-exceedances in a specific category which exceed the actual number of co-exceedances.
pg. 68
Table 7Test of Quality of Loan Loss Provisions
Based on Annual DataPanel A: Univariate Statistics – US Banks
1999 - 2006 2007 - 2011 1999 - 2011Mean Median Mean Median Mean Median
ALLLt 0.662032 0.527236 2.316235 2.179011 1.158293 0.704349LLR t-1 1.407074 1.342263 2.073158 1.390962 1.60690 1.34226NCOt 0.636152 0.555945 1.899284 1.721754 1.01509 0.65698NCO t+1 0.706104 0.626486 2.180193 2.065973 1.14833 0.84583CH_NPLt 0.042205 0.029619 0.572670 0.583406 0.201344 0.092962CH_NPL t+1 0.092856 0.036868 0.302766 0.203498 0.155829 0.045174Observations
84 36 120
Panel B: Univariate Statistics – Canadian Banks1999 - 2006 2007 - 2011 1999 - 2011
Mean Median Mean Median Mean MedianALLLt 0.504903 0.395077 0.522356 0.498312 0.511186 0.448334LLR t-1 1.421548 1.349667 0.869707 0.861841 1.22289 1.11254NCOt 0.523901 0.511606 0.470742 0.467696 0.50476 0.49282NCO t+1 0.507116 0.464062 0.488733 0.498801 0.50050 0.48794CH_NPLt -0.07971 -0.15728 0.365930 0.154894 0.080720 0.029847CH_NPL t+1 -0.06343 -0.15450 0.179181 0.126062 0.02391 -0.07093Observations
32 18 50
Panel C: Regression Analysis – US BanksDependent Variable (ALLL)
1999 -2006
2007 -2011
1999 -2011
1999 -2011
1999 -2011
Intercept 0.03577(0.49)
0.07112(0.32)
0.22899***
(2.64)
0.22159**(2.56)
0.2156**(2.51)
LLR t-1 -0.01657(-0.23)
-0.51304*
**(-3.25)
-0.33609*
**(-4.15)
-0.34068**
*(-4.16)
-0.3374***(-4.18)
NCOt 1.21138***(12.24)
1.42140***
(7.48)
1.24652***
(13.14)
1.30816***
(13.09)
1.3357***(13.10)
NCO t+1 -0.20495** 0.20097 0.11660 0.10855 0.0853
69
(-2.48) (1.43) (1.54) (1.38) (1.07)CH_NPLt 0.41742***
(5.80)0.33213*
**(2.83)
0.41156***
(6.30)
0.42286***
(6.42)
0.3668***(4.98)
CH_NPL t+1 0.06518(1.04)
-0.06362(-0.51)
-0.08173(-1.30)
-0.07403 (-1.00)
-0.0451(-0.70)
D 2007-08 -0.08180(-0.67)
D 2009-11 -0.19829*(-1.72)
D 2007-09 -0.0557(-0.54)
D 2010-11 -0.3094**(-2.12)
Observations
84 36 120 120 120
Adj R-square
0.8977 0.9385 0.9492 0.9499 0.9505
70
Panel D: Regression Analysis – Canadian BanksDependentVariable (ALLL)
1999 -2006
2007 -2011
1999 -2011
1999 -2011
1999 -2011
Intercept 0.04117(0.24)
0.28331(1.68)
0.02232(0.21)
0.08018(0.59)
0.0773(0.57)
LLR t-1 -0.36374*
*(-2.28)
-0.49089*
*(-2.99)
-0.30937*
**(-3.01)
-0.35323*
**(-2.94)
-0.3483***(-2.91)
NCOt 0.86529***
(3.03)
1.22009***
(5.57)
0.83280***
(4.33)
0.90170***
(4.15)
0.8990***(4.22)
NCO t+1 1.01358***
(3.06)
0.24697(1.61)
0.90591***
(5.13)
0.87330***
(4.72)
0.8678***(4.69)
CH_NPLt -0.03738(-0.22)
-0.03726(-1.00)
-0.03447(-0.49)
-0.02454(-0.34)
-0.0238(-0.33)
CH_NPL t+1 -0.16563(-1.52)
-0.08618(-1.12)
-0.15847*
*(-2.19)
-0.15184*(-1.90)
-0.1526*(-2.01)
D 2007-08 -0.05356(-0.47)
D 2009-11 -0.07786(-0.70)
D 2007-09 -0.0451(-0.45)
D 2010-11 -0.1108(-0.87)
Observations
32 18 50 50 50
Adj R-square
0.6600 0.8516 0.6751 0.6640 0.6658
Panel E: Loan Loss Provision Quality (LLPQ)1999-2006
2007-2008 2009-2011
US -0.19199 -0.51654 -0.31828Canada -0.26256 -0.16996 -0.07830
71
Notes: The regression model is a replication of Equation (2) in Ng and Rusticus (2011)
ALLLt = β0 + β1 LLRt-1 + β2 NCOt + β3 NCOt+1 + β4 CH_NPLt +β5 CH_NPLt+1 + εt,Where ALLL = Allowance for loan and lease losses; LLR = beginning of period loanloss reserves; NCO = Net Charge off, and CH_NPL = Change in non performing loansthat are 90 days past due
The dates are applied to time t. Since the model has a lag term (t-1) and lead term (t+1), time t effectively runs from 2000 to 2010.
The regressions in Panels C and D show the t-value in parentheses. D 2007-08 and D 2009-11 are dummy variables. D 2007-08 = 1 for the years 2007 & 2008 for
time t and 0 for all other years. D 2009-11 = 1 for the years 2009, 2010, & 2011 and 0 for all other years.
D 2007-09 and D 2010-11 are dummy variables. D 2007-09 = 1 for the years 2007, 2008 & 2009 for time t and 0 for all other years. D 2010-11 = 1 for the years 2010 & 2011 and 0 for all other years.
Loan loss provision quality during the crisis period (Panel E) is measured by taking the standard deviation of the residuals for all banks for observations inthe respective sub-periods then multiplying the resulting number by -1. It is calculated using the 1999-2011 regressions (without dummy variables) in Panels Cand D
***, **, * denote significance at the 1%, 5%, and 10% levels, respectively
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