Bank Stability and Macro Stress Testing of
the Top 24 Universal and Commercial Banks in
the Philippines

Merino, Michaela Nicole1, Dacanay, Jovi2

1 Graduate Staffmember, School of Economics, University of Asia and the Pacific
2 Assistant Professor and Doctoral Program Director, School of Economics, University of Asia and the Pacific

新型コロナの感染爆発による経済の混乱で出稼ぎ・移民労働者からの送金が減ったことはフィリピンの銀行にどのような影響を与えたか。国内の主要24行についてストレス検査を施した。

Abstract

Stress testing on banks is an exercise that helps bank managers and regulators to understand the financial rigidityof banks. In the context of the Philippines, stress testing will serve as a guide for the financial institutions on survivability in times of recessions or financial crises. Banks are still not invincible to economic shocks. Hence, thispaper aims to discover which banks will be affected the most given the changes in macroeconomic, such as external shocks, and bank specific variables. The study seeks to answer the question: Does better credit quality result
from an improvement in the macroeconomic environment and bank performance? The study aims to answer thefollowing objectives: First, to determine the presence of long-run relationships between the macroeconomic andbank specific variables, Second, to examine the effect of the macroeconomic and bank specific variables on thecredit quality of the entire Top 20 UKBs, Third, to quantify the impact of macroeconomic and bank specific variableson the credit quality for each individual bank under the Top 20 Philippine UKBs, under a Covid19 Pandemic situation.
With the use of a vector-autoregressive (VAR) estimation procedure, the study will be able to determine how banks
interact during periods of financial volatility, as well as how they perform during financial crises. The effects of thedecrease in remittances, especially during the Covid19 pandemic, to bank performance and lending, shall be incorporated in the study. Banks have provided enough liquidity in spite of the decrease in bank performance, due totheir stability.
Keywords

Bank stability, Financial stability, performance, volatility, macro-stress testing, trust

Introduction

The world was plunged into global financial crisis in2008 when Lehman Brothers, then America’s fourthlargest bank, incurred huge subprime mortgages thatwere backed by securities.1 This caused a contagion that
affected, not only the USA, but also Europe and parts ofAsia. The experience of the world economy in the postglobal financial crisis shows the impact of the financial

1 Denning, S. (2011, December 05). Lest We Forget: Why We Had
A Financial Crisis. Retrieved May 11, 2019, from https://www.
forbes.com/sites/stevedenning/2011/11/22/5086/#75f21049f92f

sector on a country’s economy as well that of othernations.2 No matter how big or how much money a bankmakes, it can fail due to rising vulnerabilities, such as
economic slowdown in developing countries and emerging markets.3 A misstep by one major bank can trigger
2 Dua, P. & Kapur, H., 2018. Macro Stress Testing and Resilience
Assessment of Indian Banking. Journal of Policy Modeling.
Available at: https://ideas.repec.org/a/eee/jpolmo/v40y2018i2p452475.html.
3 Chow, J. (2015). Stress Testing Corporate Balance Sheets in
Emerging Economies. [Working Paper] International Monetary
Fund, IMF Working Papers.

157

Merino, Michaela Nicole, et al.

the collapse of the entire banking system, especially of
public and commercial banks that are vulnerable toshocks in interest rates, exchange rates, and earnings.
Stress testing can help managers and regulators
understand the financial rigidity of banks.4 It presents
scenarios that call for strategic survival measures in thecase of an economic downturn. Such scenarios present
the consequences of loss due to different macroeconomic variables.5 Major lessons can be drawn from the
Global Financial Crisis (GFC) of 2007-2008 that saw anaverage increase of 30% in borrowing costs, 20%
decline in earnings before income taxes (EBIT), andcurrency depreciation averaging 30% against the USD.
This was the case of the 1990 financial crisis, provingthat banks are not invincible to economic shocks.6
This paper aims to discover which banks will bemost affected by changes in macroeconomic andbank-specific variables. Does better credit quality result
from an improvement in the macroeconomic environment and bank performance? Specifically, this study
aims to (1) determine the relationship between macroeconomic and bank specific variables; (2) examine theireffect on the credit quality of the Top 24 universal andcommercial banks (UKBs); and (3) quantify their impact
on the credit quality of each bank under the Top 24
Philippine UKBs.
Banks are not just for saving money; more importantly, they are critical to stimulating the economy bylending to firms for the sustenance of business and tohouseholds for the purchase of big-ticket items, likehouses and automobiles.7 It is important that government
4 Baudino, P., Goetschmann, R., Henry, J., Taniguchi, K., & Zhu,
W. (2018). Stress-testing banks - a comparative analysis (Financial
Stability Institute)workers know the effects of policies and projects on
macroeconomic variables because these affect the credit
quality of banks. The failure of major banks affects
businesses as the former will not be able to disburse
funds to those who need it. If firms and people cannot
borrow money, then there will be less spending.8 Banks
today are vulnerable to economic shocks but if they areable to anticipate potential problems, then they will bein a better position to apply the appropriate stabilizing
measures. This study should serve as a guide forresearchers and students to understand the impact ofmacroeconomic variables on the banking industry.
Focus will be on the relationship of macroeconomic
and bank-specific variables of the Philippines’ top 25
UKBs. Their quarter balance sheets from 2005 to the
4th quarter of 2018 will be examined for credit quality.
This research is limited to UKBs because they hold thebulk (91.2% or about P15,421.2 billion) of the banking
system’s assets. As of 2018, their combined assets total
97.9% of all the UKBs.9 Data comes from the Banko
Sentral ng Pilipinas (BSP).

Theoretical Framework and Empirical
Methodology

The standard macroeconomic model does not accountfor credit frictions because the money supply curve,
which indicates monetary liquidity, assumes that all
debt instruments can be lumped together in a bondmarket. This approach makes bank liabilities central tothe monetary transmission mechanism, while giving norole to bank assets.10 Credit frictions have been introduced in the standard Keynesian macroeconomic model,
which indicated demand for credit. It relies on the bank

5 Pesaran, M., Schuermann, T., Treutler, B. and Weiner, S. (2006).
Macroeconomic Dynamics and Credit Risk: A Global Perspective.
Journal of Money, Credit, and Banking, 38(5), pp.1211-1261.

8 Pritchard, J. (2018). Closer Look at Bank Failure and What Happens
to Your Money. [online] The Balance. Available at: https://www.
thebalance.com/bank-failures-315791 [Accessed 14 May 2019].

6 Chow, J. (2015). Stress Testing Corporate Balance Sheets in
Emerging Economies. [Working Paper] International Monetary
Fund, IMF Working Papers.

9 Bangko Sentral ng Pilipinas. Bangko Sentral ng Pilipinas Publications & Research. Available at: http://www.bsp.gov.ph/publications/regular_annual.asp [Accessed July 15, 2019].

7 Alemayehu, F. and Teklemedhin, M. (2012). Economic Significanceof Banks. [online] abyssinialaw.com. Available at: https://www.
abyssinialaw.com/component/k2/item/394-economic-significance-of-banks [Accessed 12 May 2019].

10 Bernanke, B. and Blinder, A. (1988). Credit, Money, and Aggregate
Demand. NBER Working Paper Series. Cambridge: National
Bureau of Economic Research. Available at: https://www.nber.
org/papers/w2534.pdf.

158

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lending rate and interest rate.11 For the bank balance
sheet, another asset is added in the form of loans. Since
a new asset is added, the real side of the economy will
now depend upon the interest rate on deposits and thelending rate.12 Thus, the loan supply of banks increases
with the interest rate on loans and decreases with interest rate on bonds. On the other hand, banks demand forloans rises with the interest rate on bonds and falls with
the interest rate on loans.
Monetary expansion or an increase in bank reserves
would bring about an increase in the quantity of money
and in the volume of credit, which stimulates investment
demand. The central bank reserves are a shift parameterof the credit frictions curve as reserves influence theeffectiveness of monetary policy. An increase in thereserves available to banks will boost the supply of loans
and the demand for bonds. Monetary policy may also
affect the external finance premium by shifting the
supply of intermediate credit, particularly loans by commercial banks. If the supply of bank loans is disrupted,
then bank-dependent borrowers will incur the costs offinding a new lender and establishing a credit relationship. Therefore, a reduction in the supply of bank credit,
relative to other forms of credit, is likely to increase theexternal finance premium and reduce real activity.13
The framework of the study links credit quality, ordefault rate. It is a measure that reflects the credit risk
and quality of the aggregate bank portfolio and key
macroeconomic variables and bank-specific factors.14
Following is the equation for the model:
Probability of Defaulti = f (output growth, inflation
rate, interest rate, exchange rate, profit, size indicator)
where the Probability of Default (PD, or credit quality
indicator) of an individual bank group is specified as a
11 Chinn, M. (2009). The Financial and Economic Crisis Interpreted
in a CC-LM Model. Ssc.wisc.edu. Available at: https://www.ssc.
wisc.edu/~mchinn/e302_creditcrisis_f09.pdf.
12 Ibid
13 Bernanke, B. and Gertler, M. (1995). Inside the Black Box: The
Credit Channel of Monetary Policy Transmission. Journal of
Economic Perspectives, 9(4), pp.27-48.
14 Dua, P. and Kapur, H. (2017). Macro Stress Testing of Indian
Bank Groups. Ph.D. University of Delhi.

function of key macroeconomic variables that represent
macroeconomic conditions and a set of bank-specificfactors that refer to the behavior of a specific group.
The study uses NPL ratio as an empirical measure of
the probability of default. Furthermore, the model indicates that sounder economic conditions, which are
determined by specific metrics on macroeconomic
development, implies lower default rate and better credit
quality (PD). Table 1 lists the explanatory indicators ofcredit quality, what these indictors mean, data used, andthe expected signs.
Table 1. Credit Quality Determinants, What They Indicate,
Data Used, and Expected Signs

Source: Dua Pami and Hema Kapur (2017)15

The macroeconomic variables used in the model
would take into consideration the cycles of the variables
as well as their growth rates, the inflation, interest andexchange rates. The bank-specific factors would bemeasured by profitability and asset size. The economy’s
growth rate is expected to have a negative impact on
credit risk. This is because an economic recession, as
reflected by a declining GDP, is likely to discourage
investment which, in turn, causes a drop in a bank’s
ability to create credit. The impact of inflation on credit
risk may be negative due to the effects on borrowing andlending. Price directly reduces real interest rate and promotes investment and economic growth. On the other
hand, an increase in the volume of loans causes a decline
in the real value of the borrowed amounts of debtors.
This phenomenon usually comes about during periodsof inflation.
15 Dua, P. and Kapur, H. (2017). Macro Stress Testing of Indian Bank
Groups. Ph.D. University of Delhi.

159

Merino, Michaela Nicole, et al.

Interest rate is expected to have a direct positive association with credit risk by increasing the cost of borrowed funds. However, interest rates also have an indirect
effect on credit risk through increased profitability of thecorporate sector. The impact of the exchange rate on
credit risk can be mixed and indirect. Lowered nominal
exchange rates are expected to boost the competitivenessof exports while an increase in export earnings would
favorably affect the banks’ capacity to service their debt
and promote export-oriented sectors and economic
growth. On the contrary, the impact of lower exchange
rates on imports is expected to work in the opposite
direction. Regarding bank-specific factors, the assets ofa bank indicate its size and strength while its return on
assets measures profitability.
Non-Performing Loans (NPLs) are expected to proxy
the credit risk itself. According to Reinhart and Rogoff(2010), Nkusu (2011), and Louzis et al (2012), NPLs cansignal the beginning of a banking crisis.16 Taking into
consideration the aggregate level of NPLs, most studies
on the determinants of NPLs assume that macroeconomic or bank-specific factors should serve as explanatory determinants. However, in their attempt to explain
aggregate NPLs of Spanish Commercial and Savings
Banks in 1985–1997, Salas and Saurina (2002), Mabvure
et al (2012) and Louzis et al (2012) took into accountmacro and microeconomic variables, demonstratingthat bank-specific determinants are early warning indicators for future NPLs.17
The non-performing loans (NPL) ratio of the totalbanking industry is on a downward trend. Asset quality
has been improving considerably with commercial
banks’ NPL ratio settling at single digits at the start of2005.18 The BSP’s efforts to strengthen the foundationsof the financial system through the Special Purpose
16 Abid, L., Ouertani, M., & Zouari-Ghorbel, S. (2014). Macroeconomic
and Bank-specific Determinants of Household’s Non-performing
Loans in Tunisia: A Dynamic Panel Data. Procedia Economics
And Finance, 13, 58-68. doi: 10.1016/s2212-5671(14)00430-4
17 Ibid
18 Bangko Sentral ng Pilipinas. Bangko Sentral ng Pilipinas Publications & Research. Available at: http://www.bsp.gov.ph/publications/regular_annual.asp

160

Vehicle (SPV) Law in 2015 and the adoption of the BaselIII capital adequacy standards for UKBs reflects that thebanking system is in a position to undertake far-reaching reforms. The promotion of Philippine Financial
Reporting Standards (PFRS) 9 Financial Instruments in2018 in which banks will be recognizing allowance forprobable losses at an earlier stage, providing buffers topotential credit losses. In addition, the BSP Financial
Supervision Sector (FSS) continues to improve its financial surveillance tools. The FSS is also vigilantly monitoring actions taken by banks to effectively manage
exposures to specific sectors for which NPLs may rise,such as real estate lending and consumer loans, to mitigate signs of overheating that could lead to the deterioration of loan quality. Furthermore, the BSP imposed asingle 20 percent overall limit on the exposure of the U/
KBs to the real estate industry. The new limit, whichprimarily serves as a prudential safeguard against theoverconcentration of credits of U/KBs to commercial
lending. These measures together with the rise in thedeposit base, capital and total resources, along withlending activity led to a significant improvement in thebanking industry’s asset quality with NPA and NPL
ratios falling to single-digit levels. (See Figure 1).
Moreover, during the pre-crisis level in 2007, thebanking industry remained stable as it was anchored ongood asset quality and enhanced capital base. This
proved the resilience of the banking industry as growthindicators such as asset levels, loans and deposits continued to post robust increases despite the negative
developments emanating from the continuing global
financial crisis and episodes of rising inflation during
the first eight months in 2008. The banking sector’s
asset quality also exhibited a notable improvement asshown by the continued decline in NPL ratio while thecapital adequacy ratio (CAR) remained above global
standards and the BSP’s regulatory requirement. At thesame time, BSP implemented additional liquidity-enhancing measures to provide additional liquidity thatwill help ensure the orderly functioning of financial
markets should global financial conditions worse.

6th AFC Best Papers

Figure 1.	Historical Non-Performing Loans Ratio of the
Banking Industry (2005 to 2018)
Source: Bangko Sentral ng Pilipinas
Notes: (1) B SP amended the banks’ reporting standard for NPLs in 2013.
Banks have been required to report their “gross” NPLs andtheir “net” NPLs.
(2) Data used for 2013 onwards is the “gross” NPLs.

Moving on, this paper is based on the paper of Dua
and Kapur which examined how various bank groups
operating in India have fared macro stress events andconduct macro stress testing to trace the impact of certain macroeconomic stress scenarios on the credit quality using panel data from 1997 to 2014 of five Indianbank groups: the State Bank of India and its associates,
nationalized banks, private sector banks, new private
sector banks, and foreign banks.19 For the empirical estimation of the macro stress testing analysis, Panel Unit
Root and Panel Cointegration Test were used to determine a relationship between the NPL ratio and its potential determinants. Then, FMOLS was conducted inorder to examine the effect of the macroeconomic andbank specific variables on the credit quality as predictedby economic theory in respect of their signs, estimates,and significance of the relevant coefficients.
The study used the Panel Cointegration Technique by
Dua Pami and Hema Kapur in their paper, Macro Stress
Testing of Indian Bank Groups.20 The objective of panel
cointegration estimation is to examine the existence andnature of long-run relations among variables on a single
cross-sectional unit, as predicted by economic theory in
19 Dua, P. and Kapur, H. (2017). Macro Stress Testing of Indian Bank
Groups. Ph.D. University of Delhi.
20 Ibid.

respect of their signs, estimates and significance of therelevant coefficients.
For empirical estimation, a panel cointegration methodology is used and three familiar steps are followed:
examining the stochastic properties of the variables
involved by means of panel unit root tests; testing forpanel cointegration in order to assess for the presence ofa long-run relationship; and applying the fully modified
OLS (FMOLS) estimates to the cointegration vector in
the empirical model.
Panel Unit Root Tests. These tests are generally preferred over various other available panel unit root tests
because they allow for heterogeneity of the autoregressive (AR) roots under the alternative hypothesis andcombine individual unit root tests to derive a panel-specific result. In order to conduct a Panel Cointegration
Test, one must conduct a Panel Unit Root Test to checkif the variables are non-stationary at level and stationary
at first difference assuming individual intercept.
Researchers will use two panel unit root tests: Im,
Pesaran, Shin (IPS) Test and the Augmented DickeyFuller - Fisher (ADF) Test. These tests are used to determine whether the time-series variables considered are
non-stationary. In the macro stress test model, non-stationarity is favorable for the variables because the variables we consider are macroeconomic and bank-specificvariables that exhibit seasonality over time. The optimallag length is chosen based on the Schwarz Information
Criterion (SIC) for the variables such as: growth rate,interest rate, exchange rate, and assets. If other variables
will not perform non-stationary properties at lag lengthof 2, then the researchers will change the lag length
according to the minimum lag needed for it to become
non-stationary.
Panel Cointegration Tests. The next step is to carryout tests for panel cointegration to find the existence ofa statistically acceptable cointegration relationshipbetween the NPL ratio and its potential determinants as
specified in our empirical model which is:
Nit = β0i + β1yt + β2it + β3πt + β4et + β5Rit + β6ait + εit
Where:
Nit = NPL ratio of the ith bank group at time t161

Merino, Michaela Nicole, et al.

yt = economy’s growth rate in time t
it = nominal interest rate in time tπt = inflation rate in time tet = nominal exchange rate in time t
Rit = return on assets (ROA) of the ith bank group attime tait = assets of the ith bank group at time t
β0i = an intercept term
The end objective is to estimate the long-run relationships
between the said variables and employ them to conduct
MST (Macro Stress Testing) for the Top 24 Philippine
UKBs.
Similar to panel unit root tests, panel cointegration
tests provide more reliable results in testing of cointegration presence relative to those obtained by individual
tests. To carry out tests for panel cointegration between
the NPL ratio and its potential determinants specified inour empirical model, the optimal lag length is chosen
based on the Schwarz Information Criterion (SIC). The
panel cointegration test used was proposed by Pedroni(2004) on seven statistics for testing the null hypothesisof no cointegration against two alternative hypotheses—with a common AR root (within-dimension or panel
statistics) and taking into account the heterogeneity of
the AR roots (between- dimension or group statistics).
Pedroni Panel Estimation Technique (FMOLS).
This technique uses a test of the OLS estimator with abias- corrected t-statistic in finite sample size, but that is
not an improvement over the OLS estimator in general.
Furthermore, the FMOLS estimator is used in this study
for estimation of panel cointegration. In addition, thisestimator employs a semi-parametric correction toeliminate the problems caused by the long run correlation between the cointegrating equation and stochastic
regressors innovations.

Conceptual Framework

The researchers constructed a reduced macroeconomic model based on Dua and Kapur’s paper on MacroStress Testing of Indian Bank Groups in order to estimate the impact of macroeconomic variables and bankspecific factors to the credit quality of the Top 24 UKBs

162

as seen in Figure 2. Macroeconomic variables include
macroeconomic (GDP) growth rate, interest rate, inflation rate, and exchange rate while bank specific factors
include total assets to explain the bank’s size and returnon assets to account for its profitability. Furthermore,these determinants explain the credit quality of thebanks which is captured by the probability of default
using the NPL ratio.

Figure 2. Reduced Macroeconomic Modelling Framework
Sources: Author’s Theoretical Framework

Depicted in the model, cointegration between macroeconomic variables and the bank-specific variables isrepresented by the intersection of the orange and redboxes. Cointegration between the bank-specific variables and the macroeconomic variables is crucial since
it determines the presence of long-run relationships
which is the first objective of the study. The integration
will be tested through the panel unit root test and thepanel cointegration test as explained in the methodology. Given the existence of the long-run relationships ofthe variables, the effect of the credit quality can be estimated per individual bank in the top 24 UKBs using
FMOLS estimation.
Multiple studies cite that there is a strong association
between the macroeconomic variables and banks but theissue of the direction of causality is yet to be resolved.21
In addition, there is a positive relationship between
financial development and economic growth has been
21 Degryse, H. (2011). Microeconometrics of banking: methods,
applications, and results. [Erscheinungsort nicht ermittelbar]:
Cram101, pp.135-163.

6th AFC Best Papers

proven. The causality relationship between bank credit
and economic growth suggests that economic development is a key economic engine to boost credit quality.
The Time Series data done by Rousseau and Wachtel
back in 1998 shows that an increase in credit quality
does not affect the growth in the economy.22
The risk considered is credit risk, which is represented by credit quality and is affected by the cointegration between the bank-specific factors andmacroeconomic factors. This is represented in the modelthrough the orange and red arrows pointing towards theyellow credit quality box. The shocks stem from problems at specific banks which are idiosyncratic or bankspecific shocks, or from macroeconomic imbalancesthat affect the financial system, which are called systemic shocks. Much more can be explained in the next
chapter in the results of Objective 1.23
The impact from credit risk is observed in the individual banks as vulnerabilities in the respective factorsthat have sourced the credit risk. This is depicted by thecredit quality’s yellow arrow pointing towards the
UKBs of the banking industry. The results of Objective
3 in the next can help further your understanding about
the effect of said variables to the credit quality. An
increase in the credit risk is a decrease in the credit
quality of the banks since a bank’s credit quality is represented by their NPL ratio.

Results of the Study

The results are obtained based on the following
hypotheses:
Hypothesis 1: Both macroeconomic factors and bankspecific features affect credit quality.
Hypothesis 2: Improved macroeconomic environment andspecific bank performance result in better credit quality.

Results of Objective 1
Objective 1: To determine the presence of long-run
relationships between the macroeconomic and bankspecific variables. To determine the presence of longrun relationships between the variables, the stochastic
properties of the variables must be tested using the Panel
Unit Root test. Table 2 shows that the variables havenon-stationary properties at level and stationary at firstdifference, thus the conclusion I (1). Since the variables
present non-stationary properties, it is possible to apply
the panel co-integration method to verify if there exists
a stable long-run relation among the variables.
The results of the Pedroni Co-integration Test indicate that the majority of the tests reject the hypothesis ofno co-integration at significant levels (see Table 3).24
This provides evidence of a long-run relationshipbetween NPL ratios of the top 25 UKBs and the explanatory variables. Hence, the impact of these macroeconomic and bank specific factors can be estimated in theevent of an adverse short-term shock.
Table 2.	P anel Unit Root Tests (Assuming Individual
Intercept)
Variable

IPS

ADF- FISHER

Conclusion

Levels

1st Difference

Levels

1st Difference

Non-Performing
Loans-0.90286(0.1833)[14]-31.9063***(0.0000)[SIC]

48.4256(0.2296)[14]

658.182***(0.0000)
[SIC]

I (1)

GDP Growth Rate

-1.02220(0.1533)[4]-183.856***(0.0000)[SIC]

52.0440(0.3943)[SIC]

460.517***(0.0000)
[SIC]

I (1)

Nominal Interest
Rate-0.75510(0.2251)[20]-19.5715***(0.0000)[SIC]

14.0905(1.0000)[SIC]

399.232***(0.0000)
[SIC]

I (1)

Headline Inflation
Rate-0.60102(0.2739)[8]-15.616***(0.0000)[SIC]

66.9330(0.0550)[7]343.045***(0.0000)
[SIC]

I (1)

Nominal Exchange
Rate-0.41019(0.3408)[SIC]-26.3147***(0.0000)[SIC]

36.8328(0.9171)[SIC]

637.102***(0.0000)
[SIC]

I (1)

Return on Assets

1.52470(0.0637)[11]-43.4197***(0.0000)[SIC]

63.9776(0.0884)[5]

969.374***(0.0000)
[SIC]

I (1)

Total Assets

12.7871(1.0000)[SIC]-31.3802***(0.0000)[SIC]

17.2902(1.0000)[SIC]

777.947***(0.0000)
[SIC]

I (1)

22 Ibid.,

Source: Authors’ calculations.
Notes: (1) p-values are indicated in parentheses(2) lag length used is indicated in brackets(3)	Asterisks: the null hypothesis of the unit root is rejected at
1%.

23 Abid, L., Ouertani, M., & Zouari-Ghorbel, S. (2014). Macroeconomic
and Bank-specific Determinants of Household’s Non-performing
Loans in Tunisia: A Dynamic Panel Data. Procedia Economics
and Finance, 13, 58-68. doi: 10.1016/s2212-5671(14)00430-4

24 Five out of 10 tests prove the co-integration of said variables.

163

Merino, Michaela Nicole, et al.

Table 3. Pedroni Co-integration Test Results
Model: N = f (y, i, r, e, R)
Dependent variable: NPL ratio (N)Pedroni Co-integration Tests

Statistic

p-value

Alternative Hypothesis: Common AR Coefficients (within dimension)
Panel v-Statistic-74.37305

1.0000Panel rho-Statistic-9.942781***0.0000Panel PP-Statistic-25.33938***0.0000Panel ADF-Statistic-24.01009***

0.0000

Alternative Hypothesis: Individual AR Coefficients (between dimension)
Group rho-Statistic

0.312251

0.6626

Group PP-Statistic-10.02973***0.0000Group ADF-Statistic-7.153871***0.0000Source: Authors’ calculations
Notes: (1)	Asterisks indicate that the hypothesis of no co-integrationis rejected at 1 % level of confidence(2) Total number of observations, NT= 1400
(3)	Deterministic trend specification includes individualintercept(4)	The lag length selection is based on automatic SIC with amaximum lag of 2(5)	For semiparametric corrections, Newey–West automatic
bandwidth selection and Barlett kernel are used(6)	Bank 23, The Bank of Tokyo-Mitsubishi UFJ LTD, wasdropped from the test.

Degryse pointed out the strong cooperative link
between financial intermediation and growth, provingthat these variables have a long run relationship.25 Banks
are vital to economic growth because they have control
over the circulation and spending of money in an economy. Take the Financial Crisis of 2008: the failure of one
back led to the collapse of the entire American economy—proof to the strong connection between growthand intermediation.
Results of Objective 2
Objective 2: To examine the effect of the macroeconomic and bank-specific variables on the credit qualityof all the Top 24 UKBs. Table 4 presents the results of
the Pooled Weighted FMOLS which indicate the signsof all variables and their statistical significance.
Macroeconomic growth, exchange rate, and return on
25 Degryse, H. (2011). Microeconometrics of banking: methods,
applications, and results. [Erscheinungsort nicht ermittelbar]:
Cram101, pp.135-163.

164

assets are inversely and significantly related to credit
quality while interest rate is positively and significantly
related to credit quality. In addition, the remaining variables, inflation rate, and assets, were insignificantly
related to credit quality where the former is negatively
related while the latter is positively related which is the
only variable that is inconsistent with economic theory.
The results also indicate the statistical significance ofthe coefficients through the specified p-values. The variables with the most statistically significant coefficient at
the 1% level of confidence are the exchange rate and thereturn on assets variables. The coefficients of interest
rate and GDP growth rate variables are also statisticallysignificant but to a lower degree.
This implies that adverse changes in exchange rate
and the return on assets of banks will significantly affectthe overall non-performing loans of the top 24 UKBs. A
shock in the form of a drop in profitability or thestrengthening of the peso relative to the US dollar will
increase the non-performing loans of the UKBs.
Adversely, increased profitability and a weak peso willcause a decrease in the non-performing loans of UKBs.
The statistically insignificant variables are inflation
rate and bank assets. The effects of inflation on NPL areminimal because both lender and borrower benefit fromthe price increase. On the other hand, a bank’s expansion does not automatically translate to revenue andcould contribute to a decrease in NPL.
Table 4. FMOLS Pooled Weighted Estimation Results(Assuming Constant Level)
Independent
Variable

Expected
Sign
(+/-)

ActualSign(+/-)

Constant

P-value

GDP Growth
Rate

-

-

-0.059152*

0.095Interest Rate++

0.018143**

0.042

Inflation Rate--

-0.007318

0.6839Exchange Rate+/--

-0.029901***

0.0099Return on Assets---0.621238***0.0000Assets

-+0.002964

0.4648Source: Author’s calculations
Notes: 1) Asterisks indicate statistical significance of the coefficientsat 10, 5, and 1 percent levels of confidence, respectively.

6th AFC Best Papers

Results of Objective 3
Objective 3: To quantify the impact of macroeconomic and bank-specific variables on the credit quality
for each bank under the Top 24 UKBs. The results of theestimated equations for each bank are presented in the
Appendix. Comparing the coefficients and significanceof the factors that determine the credit quality of the top
24 UKBs yields six important findings. First, the impact
of macroeconomic growth significantly affects BDO
Private Bank, Inc., which is the most benchmarked
among the country’s financial institutions. On the other
hand, the credit quality of Mizuho Bank LTD - Manila
Branch, Citibank, N.A., and Maybank Philippines
Incorporated are the least likely to be affected by thegrowth in GDP. Consistent with economic theory, themajority, or 13 out of the 24 banks studied, showed negative correlation between credit quality and macroeconomic growth. This implies that it is most likely that
economic recession, as reflected by a declining GDP,
will not depress investment optimism and the subsequent creation of bank credit.26
Second, the direct impact of monetary policy may begauged by the coefficient of the nominal interest rate. It
is important to examine this variable because it has
implications in the rise of policy rate in a tightened monetary policy and its transmission to the bank default rate
via pass-through effects to other rates in the economy,
including lending rates. The interest rate is expected tohave positive association with the bank’s credit quality
due to the direct impact of the increased cost of borrowed funds and the indirect effect of the rise in thecorporate sector’s profitability. Thirteen banks wereconsistent with economic theory, while only four, Bankof Commerce, BDO Private Bank, Inc., China Bank
Corp., and Philippine Bank of Communications, are significantly affected by increased interest rates.
Third, it is highly probable that inflation would affecthalf of the banks’ respective NPL ratios. Increased
inflation particularly affects Philippine Bank ofCommunications, BDO Private Bank, Inc., and26 Dua, P. and Kapur, H. (2017). Macro Stress Testing of Indian
Bank Groups. Ph.D. University of Delhi.

Philippine Veterans Bank, while Mizuho Bank LTD Manila Branch, Bank of Commerce, Metropolitan Bank& TCO, and Deutsche Bank AG are the least affected.
Furthermore, 13 out of 24 banks scored negative, whichis consistent with economic theory. The impact of inflation on credit quality may be negative due to the direct
and indirect impact on borrowing and lending.27 The
direct, or price effect, reduces the real interest rate
through the Fishers’ effect, promotes investment andeconomic growth. On the other hand, the indirect, orvolume effect during periods of inflation, favors debtors
because the real value of loans declines.
Fourth, the variable exchange rate has the greatest
impact particularly on the NPL ratio of Asia United
Bank because it is a joint venture with Taiwanese investment banks, such as China United Trust & Investment
Corporation and China Development Industrial Bank.
The AUB portfolio consists of foreign investors, whichsuggests that foreign currencies make up a substantial
portion of their assets. Thus, when the nominal exchange
rate depreciates, that is, a stronger Philippine peso
against the US dollar, exports become more competitive
and the consequent increase in export earnings boosts
the banks’ capacity to service debts, stimulate
export-oriented sectors, and promote economic growth.28
On the other hand, a depreciated peso would mean
higher import and fuel prices and reduced debt-servicing capacity of those who borrow in foreign currency.
Moreover, the capacity of importers and borrowers tosettle their debts is expected to decline and, consequently, hike up NPL ratios. This goes to show that the
net impact of the exchange rate on NPL ratios can bepositive or negative.
Fifth, assets, which are specific to banks, significantly affect Bank of Commerce and Land Bank of thePhilippines. Meanwhile, based on the coefficients, theidiosyncratic variable of return on assets (ROA) notably
affects BDO Private Bank and Philippine Bank ofCommunications. The impact of ROA on the majority of

27 Ibid
28 Ibid165

Merino, Michaela Nicole, et al.

banks means that it is most likely to affect the entire
industry.
Finally, the constant term is estimated by the omission of predictors from a regression analysis. It captures
the positive impact of various unobservable characteristics that are specific to banks and can affect the majorityof the banks in this study.
Table 5. Summary Table of Objectives and Findings

importance and effectiveness of macro stress testing of
the top banks using different frameworks and methodologies. From this, two hypotheses were drawn. First,
both macroeconomic factors and bank-specific features
affect credit quality. Second, improved macroeconomic
environment and specific bank performance result inbetter credit quality. Moving on, the framework of thestudy focused on the credit quality of default rate. It is ameasure that reflects the credit risk and quality of theaggregate bank portfolio and key macroeconomic variables and bank-specific factors. The study used NPL
ratio as an empirical measure of the probability ofdefault. The macroeconomic factors used are GDP
growth rate, inflation rate, interest rate, and exchange
rate while the bank-specific features are assets andreturn on assets.
In order to prove the two hypotheses, three steps
were followed. The first step was to examine the stochastic properties of the variables involved through thePanel Unit Root tests. Next step was to conduct a Panel
Cointegration test in order to determine the presence ofa long-run relationship. Lastly, the Fully Modified OLS(FMOLS) was employed to estimate the impact of thevariables to credit quality.

Conclusion

Summary

The Global Financial Crisis (GFC) of 2008 gave alesson that no matter how big or how much money abank makes, it can fail due to rising vulnerabilities. It
prompted policy makers to implement stricter measures
such as stress testing banks and the financial system inorder to ensure its stability and soundness in the event ofa shock. Macro stress testing is a tool that helps financial institutions find out if the banking industry will survive an economic downturn. This study ultimately seeks
to find out if better credit quality results from an
improvement in the macroeconomic environment andbank performance of the top 24 UKBs.
There are multiple literatures that discussed the166

To conclude, this paper conducted a macro stress test
to discover the impact and relationship of macroeconomic and bank-specific factors to the credit quality ofthe top 24 Philippine UKBs. The first objective was todetermine the presence of long-run relationships
between the macroeconomic and bank specific variables
using Panel Unit Root and Panel Cointegration tests.
Results of the Panel Unit Root test showed that the variables present non-stationary properties at level and stationary at first difference, thus concluded as I (1). Due toits stochastic properties, it is possible to apply the Panel
Cointegration method to test the existence of the stablelong-run relationship among variables. The results indicate that majority of the tests reject the null hypothesisof no cointegration at significant levels. This provides
evidence for a long-run relationship between NPL ratiosof the top 24 UKBs and the explanatory variables.

6th AFC Best Papers

Hence, the impact of these macroeconomic and bankspecific factors can be estimated in the event of an
adverse shock in the short run.
For the second objective, Fully Modified OLS(FMOLS) method was used in order to examine theeffect of the macroeconomic and bank specific variableson the credit quality of the entire Top 24 UKBs. Results
showed that macroeconomic growth, exchange rate, andreturn on assets are inversely and significantly related tocredit quality while interest rate is positively and significantly related. The remaining variables, inflation rate
and assets, were insignificantly related to credit quality
where the former is negatively related while the latter ispositively related. The assets variable is the only determinant that is inconsistent with economic theory.
Furthermore, exchange rate and return on assets are thetwo variables that has the highest probability to affectthe overall non-performing loans of the top 24 UKBs
sector at 1% level of confidence.
For the last objective, the researchers employed the
FMOLS to quantify the impact of macroeconomic andbank-specific variables on the credit quality for eachindividual bank under the Top 24 UKBs. There are six
important findings that the model presents by comparing the coefficients and significance of the explanatory
variables. First, macroeconomic growth significantly
affects BDO Private Bank, Inc. the most. Second, anincrease in nominal interest rate will significantly affectthe credit quality of Bank of Commerce, BDO Private
Bank, Inc., China Bank Corp., and Philippine Bank ofCommunications. Third, inflation has a significant
impact on the banks’ respective NPL ratios namely
Philippine Bank of Communications, BDO Private
Bank, Inc., and Philippine Veterans Bank the most.
Fourth, the bank-specific factor, return on assets, affects
the most number of banks compared to any of the
explanatory variables. It has the biggest impact on BDO
Private Bank and Bank of Communications as seen in
their coefficients. Fifth, exchange rate significantly
affects Asia United Bank’s non-performing loans ratio
due to its bank portfolio. Lastly, the constant term isestimated by the omission of predictors from a regression analysis. It captures the positive impact of diverse

unobservable bank-specific characteristics. It significantly affects the following banks: Bank of Commerce,Bank of the Philippine Islands, Banco de Oro Private
Bank, Citibank, Development Bank of the Philippines,
East West, Maybank, Mizuho, Philippine Veterans,
RCBC, Security Bank, and Unionbank. Furthermore,
the impact of the bank specific factors, specifically the
return on assets, is found to be significant and quite pronounced in comparison to the impact of the macroeconomic factors on their respective credit quality. The
findings suggest that policy prescription should be based
more on the individual bank management strategies.
Banks ought to focus on bank-specific factors over which
the management has more control, thus they can present
practical solutions given that they have guidelines andregulatory frameworks to improve their credit quality.
Future studies may look into modifying the macroeconomic and bank-specific variables by including otherfactors such as the stock market index, unemployment
rate, and market power. In addition, the dependent variable can be changed to another parameter such as loss
given default, exposure at default, and expected default
frequency in order to have another insight regarding
credit quality. These suggestions may produce different
and better results in terms of consistency with economic
theory. Another recommendation would be categorizing
and grouping the banks similar to the study of Dua andKapur as to gain an additional perspective on the credit
quality of the entire UKBs. Lastly, future researchers
should conduct a scenario analysis which includes
examining the quantitative impact of a baseline scenario
against a hypothetical modest and severe shocks. This
will give the management and policy makers a better
understanding of the interaction between variables andposition themselves better in the event of a shock.
Bank Performance and Bank Relief Ordinances with
Covid 19
To see the effect of Covid 19 to the Top 10 universal
and commercial banks (UKBs) of the Philippines, amacro stress-test was applied to the non-performing
loans ratio to total loans. The non-performing loans
ratio of the Top 10 UKBs is consistently less than 5% for167

Merino, Michaela Nicole, et al.

the past two decades. It registered 1.7% for 2019 or lessthan 2% for the past 5 years. Predicting a 10% decrease
in GDP and a 5% decrease in remittances for all quarters of 2020 is likely to result to a doubling of the
non-performing loans ratio over a span of 10 periods,that is, from 2020 to the second quarter of 2022 (SeeFigure 3). This means that with the economic downturn
experienced in 2020, the non-performing loans ratio
will reach 3.4% by the end of 2020, and about 5.0% by
the end of 2021. The top 10 UKBs will still comply with
the Basel III ordinance of setting the non-performing
loans ratio to less than 5.0%.
The presence of well capitalized and solvent big
banks in the Philippines has resulted to a less fragile
universal and commercial banking industry due to their
low non-performing loans ratio. This has enabled themto implement several regulatory relief efforts. The
Bayanihan to Heal as One Act (Bayanihan I) madeeffective on March 25, 2020 provided a minimum
30-day grace period on loan payments, without interest,
penalties, or other fees charged on these payments. Six
months later, the Bayanihan to Recover as One Act(Bayanihan II), which took effect on Sept. 15, 2020,
doubled the grace period and increased its coverage.
Separately, the Bangko Sentral ng Pilipinas (BSP)
issued Memorandum No. M-2020-074 on Sept. 28, 2020
which required all BSP-supervised financial institutions
to provide a one-time 60-day grace period for all existing, current and outstanding loans with principal and/orinterest, with amortization falling due between Sept. 15
and Dec. 31, 2020 without incurring additional interest,
penalties, fees, or other charges. The principal andaccrued interest for the 60-day grace period may be paidin installments until Dec. 31 or as may be agreed uponby the involved parties.

168

Figure 3.	Accumulated Response of NPL to a 10% Decrease
in GDP per capita and 5% Decrease in
Remittances for 2020
Source of Basic Data: BSP

Other government support included the lowering ofeffective lending interest rates and reserve requirements,
a three-year repayment term, and no collateral for loans
below P3 million; conditional loan interest rate subsidies for affected learning institutions; and an increase inmaximum loan amounts per borrower, reduced interest
rates, and extended loan terms for micro, small andmedium enterprises (MSMEs), cooperatives, hospitals,
tourism companies and overseas workers affected by the
pandemic. The government also initiated a low interest
and/or “flexible term” loan program for operating
expenses for businesses affected by the pandemic andprovided guarantees for non-essential businesses.
Moreover, it liberalized the grant of incentives for the
manufacture or import of critical or needed equipment
or supplies or essential goods and provided a loan interest rate subsidy to critically impacted businesses.
The general loan loss provision, which is part of abank’s Tier 2 capital, is limited to 1% of a banks’ credit
risk-weighted assets such that any excess amount will beconsidered in computing risk-based capital ratios. Nonbank financial institutions (NBFI) are also required to
set up an allowance for credit losses and to report
non-performing loans.
Bayanihan II and BSP Memorandum No. M-2020074 provides certain reliefs to banks and NBFIs

6th AFC Best Papers

regarding the mandatory grace period to borrowers.
These include the staggered booking of allowances for
credit losses, with banks and NBFIs given the option to
insulate net earnings and capital from the effects ofhigher credit risk, cushioning the banks and NBFI’s net
earnings and capital; exemption of borrowers availingof the mandatory grace period from the limits on realestate loans when applicable, and from related party
transaction restrictions, which gives the banks and
NBFIs more opportunity to extend financial assistanceto those affected by the pandemic; and non-inclusion in
the bank’s or NBFI’s reporting on non-performing
loans, reducing the regulatory burden on lenders as theyextend more loans and restructure facilities of financially-burdened borrowers.
These concessions are necessary to avoid puttingpressure on any one sector of society, especially on
sources of finance. They also ensure that policies toachieve recovery are sustainable in the long run. In summary, due to the presence of a financially stable banking
industry, they were willing to take the credit risk for the
purpose of solidarity with micro, small and medium
enterprises who are negatively affected by the pandemic.

Table 6. Estimated MST Specifications for Top 24 UKBs

Source: Authors’ calculations.
Source of Basic Data: BSP
Notes: (1)	Used Pooled Weighted FMOLS to account for the heterogeneity of the data.
(2) p-values are indicated in parentheses.
(3)	Based on the Panel Co-integration Test by Pedroni, N23,
The Bank of Tokyo-Mitsubishi UFJ LTD, was dropped
from the test.
(4) Asterisks indicate the statistical significance of the coefficients at 10, 5, and 1 percent levels of confidence,
respectively.

169