Measuring Financial Stability: The Composition of an Aggregate Financial Stability Index for

Bangladesh

Md. Zulkar Nayn1 Mohammad Shahriar Siddiqui2

Abstract

Monitoring and ensuring financial stability, generally characterized by the absence of

excessive volatility, stress or crises in the financial system, has become an

overarching objective of the central banks around the world especially following the

recent global financial crisis. Although there has been no consensus on defining and

measuring financial stability, constructing a composite or aggregate measure of

financial system stability through some sort of index has started gaining recognition

as a part of early warning indicators for assessing the vulnerability of the financial

system as a whole. This paper represents a modest attempt to construct an aggregate

financial stability index (AFSI) for the financial system of Bangladesh. The study

finds that the AFSI for Bangladesh performed reasonably well in identifying stresses

in the financial system during FY 2008-9 and again at the end of 2010, when the

country's stock market crashed and the banking system faced a liquidity crunch.

However, the AFSI needs to be aided with other relevant data and qualitative

information for making a sound judgment.

Keywords: Financial Crisis, Banking Stability Index, Aggregate Financial Stability Index, Early Warning Indicators, Financial Vulnerability Index, Regional Economic Climate Index. JEL Classification: F47, G15, G18, G21

1 Deputy General Manager, Financial Integrity and Customer Services Department, Bangladesh Bank (e-mail: mdzulkar.nayn@bb.org.bd). 2 Deputy Director, Financial Stability Department, Bangladesh Bank (e-mail: shahriar.siddiqui@bb.org.bd).

Measuring Financial Stability: The Composition of an Aggregate Financial Stability Index for

Bangladesh

1. Introduction: The incidence of financial crises worldwide over the past two

decades has raised concerns about the stability of the financial system of an economy

and its interdependence with other sectors of the economy. Earlier the central banks

and banking supervisors put emphasis on the risk of solvency and liquidity of

individual banks rather than banking system as a whole. Over the years the focus has

shifted from micro-prudential to macro-prudential dimensions of financial stability -

generally characterized by the absence of excessive volatility, stress or crises in the

financial system. Especially following the global financial crises during 2008-09,

monitoring and ensuring financial system stability has become an overarching

objective of the central banks around the world. According to ECB (2007) financial

stability is a condition in which the financial system - comprising financial

intermediaries, markets and market infrastructure - is capable of withstanding shocks

and the unraveling of financial imbalances, thereby mitigating the likelihood of

disruptions in the financial intermediation process which are severe enough to

significantly impair the allocation of savings to profitable investment opportunities.

Although there has been no consensus on defining and measuring financial stability, a

number of central banks around the world have started assessing the risks to financial

stability through various indicators and indexes that are included in their periodic

financial stability reports (FSRs). Constructing a composite or aggregate measure of

financial system stability through some sort of index has started gaining recognition

as a part of early warning indicators for assessing the vulnerability of the financial

system. The International Monetary Fund (IMF) regularly publishes "Global Financial

Stability Report" reviewing strengths and weaknesses in the world financial system

based on a range of indicators and indexes.

Bangladesh Bank, like many other central banks around the world, has been

publishing a financial stability report incorporating some financial system indicators

and stress testing that could signal the strength and vulnerability of the financial

system. While individual variables and indicators are useful in analyzing the strengths

and weaknesses of a financial system, various studies have attempted to develop

composite indicators which could be convenient for the policy makers for triggering

any action based on the signals of vulnerability. Some central banks including South

Asian central banks such as the Reserve Bank of India and the Central Bank of Sri

Lanka also have been publishing periodic reports on financial stability that also

include an aggregate index, comprising some sub-indices, indicating a measure of

soundness of the banking and financial system.

While there is no widely accepted single measure or index for assessing financial

system soundness, central banks adopt different methodologies for constructing their

own financial stability indexes based on their financial and economic conditions,

availability of data and perceived risk of vulnerability. In this backdrop, it may be a

good idea to construct a composite/aggregate financial stability index for the financial

system of Bangladesh on experimental basis.

Thus the objectives of this study are as follows:

a. To construct an Aggregate Financial Stability Index (AFSI) for the financial system

of Bangladesh.

b. To assess the effectiveness of the index for signaling any vulnerability in the

system.

c. To identify other relevant information required for an overall assessment of the

financial sector stability.

2. Literature Review: The recognition of the need for FSI statistics among the

international community arose out of the financial crises of the 1990s. A review of

recent decades shows that many IMF member countries experienced financial crises

that often resulted in severe disruptions of economic activity. The significant costs of

these crises, both direct (such as the cost of recapitalizing the deposit takers) and

indirect (such as the loss of real economic activity), have highlighted the need to

develop a body of—preferably high frequency—statistics that could help

policymakers in macro prudential analysis, that is, in identifying the strengths and

vulnerabilities in their countries’ financial systems. Such analysis could form the basis

for taking action to prevent crises from occurring.

Gadanecz and Jayaram (2011) makes a review of the measures of financial stability

developed by researchers, central banks around the world and the International

Monetary Fund (IMF) and summarizes the measures commonly used in the literature;

their frequency, what they measure, as well as their signaling properties. They

identify some key indicators under six sectors of the economy for studying financial

system stability, where the sectors are the real sector, the corporate sector, the

household sector, the external sector, the financial sector and the financial markets.

Cheang and Choy (2011) shows some components of macro prudential analysis for

assessing financial system stability that include macroeconomic data (prices,

exchange rate etc), market-based data (stock prices, credit rating etc), financial system

data, structural information (i.e., relative size and ownership of corporation) and

qualitative information (i.e., compliance with standards).

Earlier studies on financial system distress are based on early warning indicator

methods for currency and balance of payment crises to banking crises. Demirguc-

Kunt and Detragiache (1997) use a multivariate Logit model approach to identify

determinants of banking crises such as slow GDP growth and high inflation, sudden

capital outflow, low liquidity in the banking sector, a high share of credit to private

sector, past credit growth etc. Kaminsky and Reinhardt (1999) identify early warning

indicators of twin (banking and balance of payments) crises, such as credit growth and

equity prices. Goodheart et al (2006) state financial crisis monitoring can be

effectively done with an indicator of banking sector profitability as well as probability

of default.

Cardarelli, Elekdag and Lall (2008) construct the Financial Stress Index in the

aftermath of the global financial crises based on equal-variance weighted average of

seven variables associated with stock market returns, the volatility of stock returns ,

foreign exchange, liquidity, sovereign debt spreads, international reserves, and the

risk and profitability of the banking system. Each variable is standardized, i.e.

demeaned (using the arithmetic mean), and divided by its standard deviation.

Individual components are summed up using weighted average to yield the aggregate

financial stress index.

The Swiss National Bank constructs a composite stress index for the Swiss banking

system and regularly publishes the index in its annual Financial Stability Report. The

index measures the level of stress experienced by the banking sector at a given date

by combining several variables that represent possible symptoms of stress in the

banking sector, including banks’ profitability and capital base.

Following Illing and Liu (2003) and Van den End (2006) respectively the Bank of

Canada and the Netherlandsche Bank construct a measure of financial stability,

although not published in their FSRs. It is argued that a single aggregate measure is

better able to flag crises than partial measures commonly used in the literature. The

Financial Conditions Index of the Netherlandsche Bank makes significant

contribution towards understanding financial vulnerability.

The Bank of England presents its outlook for the financial system stability in

qualitative terms, supported by quantitative modeling of the key vulnerabilities and a

composite market liquidity index of the financial system. The Federal Reserve System

has an index of financial fragility.

Geršl and Hermánek (2006) propose in the Czech National Bank’s Financial Stability

Report an aggregate financial stability indicator based on the values of the IMF’s core

financial soundness indicators. Similarly, the Central Bank of the Republic of Turkey

(2006) constructs a financial strength index using six sub-indices covering asset

quality, liquidity, foreign exchange risk, interest rate risk, profitability and capital

adequacy.

It is observed that the IMF's financial stress index for emerging economies (2009) is

quite robust in identifying the major financial stress episodes, including:

(i) 1997Q4: Asian Financial Crisis;

(ii) 1998Q2: Default on Russian external obligations and collapse of LTCM;

(iii) 2000: Dot-com crash;

(iv) 2008Q3: Global financial tsunami.

Reserve Bank of India regularly publishes Financial Stability Report, which includes

an aggregate index based on six sub-indices of financial soundness indicators. The

sub-indices include (i) Capital to Risk Weighted Assets Ratio, (ii) Leverage Ratio,

(iii) Overall Asset Quality, (iv) Profitability, (v) Liquidity and (vi) Efficiency.

Central Bank of Sri Lanka regularly publishes Financial Stability Report, which

includes an aggregate banking soundness index based on six sub-indices. The sub-

indices include (i) Capital to Risk Weighted Assets Ratio, (ii) Non-performing Loan

Ratio, (iii) Profitability, (iv) Liquidity, (v) Efficiency and (vi) interest rate and foreign

exchange risk.

Cheang and Choy (2011) construct an aggregate indicator for the Macao banking

sector to indicate the state of its stability under the context of an early warning system

(EWS), which is constructed as a weighted average of a set of selected indicators

covering different aspects of financial stability.

Bank Indonesia has been using a Financial Stability Index or FSI since 2007 to

evaluate financial sector resilience. The maximum indicative limit of FSI is 2.00.

When the global crisis befell Indonesia FSI peaked at 2.43 in November 2008 and at

the height of the 1997/1998 crisis FSI soared to 3.23, indication financial

vulnerability.

In sum, it is argued that composite indicators of financial stability are better suited for

the definition of threshold or benchmark values to indicate the state of financial

system stability than individual variables (Gadanecz and Jayaram, 2011). And also,

whether a single aggregate measure of financial stability is constructed or not, FSRs

would need to analyze key variables in the real, banking and financial sectors as well

as variables in the external sector.

3. Methodology:

The Aggregate Financial Stability Index (AFSI) is constructed for the financial

system of Bangladesh following Cheang and Choy (2011). They constructed the

Aggregate Financial Index for Macao based on three sub-indices namely, i) Financial

Stability Index (FSI), ii) Financial Vulnerability Index (FVI), and iii) Regional

Economic Climate Index (RECI).However, some modifications have been made in

constructing the AFSI for Bangladesh based on availability of data and some other

practical considerations.

The AFSI also adopts the broad framework of the core set of FSIs recommended by

the IMF (2006) for monitoring and assessing the soundness and stability of the

financial sector in its member countries. The core set is a small set of indicators that

are widely agreed to be important and operationally useful for periodic monitoring the

soundness and vulnerabilities of the banking sector, while there are some additional

set of FSIs encouraged by the IMF.

Table 1: List of Individual Indicators for Bangladesh Aggregate Financial Stability Index

Category Indicator Data Source

Banking Soundness Index

Capital adequacy Capital Adequacy Ratio (CAR) BS1 BBQ

Asset quality Ratio of NPL to Total Loans BS2 BBQ

Liquidity Credit to Deposit Ratio BS3 ET

Weighted Interest Rate Spread BS4 ET

Profitability Return on Assets (ROA) BS5 DOS

Return on Equity (ROE) BS6 DOS

Net Interest Margin (NIM) BS7 DOS

Financial Vulnerability Index

External sector Current account balance-to-GDP ratio

FV1 ET

Ratio of M2 to foreign exchange reserves

FV2 ET

Real Effective Exchange Rate (REER) (Base 2000-2001=100)

FV3 MPD

Financial sector M2 Multiplier FV4 BBQ

Ratio of domestic credit to GDP FV5 ET

General Stock Price Index movement (Dhaka Stock Exchange)

FV6 BBQ

Real sector Fiscal balance-to-GDP ratio FV7 BBQ

CPI inflation FV8 BBQ

Global Petroleum Price Index FV9 BBQ

Regional Economic Climate Index

CPI Inflation of India RE1 BBQ

GDP growth of India RE2 TE

Data Sources: BBQ= Bangladesh Bank Quarterly, ET =Economic Trends, Bangladesh Bank, DOS =Department of Off-Site Supervision, MPD=Monetary Policy Department, TE=Trading Economics (http://www.tradingeconomics.com/gdp-growth-rates-list-by-country).

3.1 Banking Soundness Index

The first category is renamed as banking soundness index (BSI) instead of financial

soundness index (FSI), as the indicators are based on banking sector data. This

category focuses on four major aspects of the banking sector, namely capital

adequacy, asset quality, liquidity and profitability. Each aspect is represented by at

least one indicator: the ratio of regulatory capital to risk-weighted assets i.e., CAR is

selected as the indicators of capital adequacy. The ratio of NPL to total loans is used

to measure asset quality, while the ratio of liquid assets to total assets and the loan-to-

deposit ratio measure liquidity.

(i) Capital adequacy ratio (BS1):

It indicates the cushion which a bank has at its disposal against potential risks. It

measures banks' strength to absorb unexpected loss and provides an indication of the

banks’ financial leverage — the extents to which banks’ assets are financed by

sources other than the banks’ own capital.

(ii) Ratio of NPL to total loans (BS2):

The ratio is intended to identify problems with asset quality in the loan portfolio and

the degree of credit risk. An increasing ratio may signal deterioration in the quality of

the credit portfolio though this is typically a backward-looking indicator in that NPLs

are identified when problems emerge. Aggregate Financial Stability Index for an

Early Warning System

(iii) Credit-to-deposit ratio (BS3):

It indicates the percentage of deposit funding that is tied up in loan portfolio and

assesses vulnerability to loss of access to customer deposits. For a ratio over one, it

implies that banks depend on borrowing to finance lending.

(iv) Weighted interest rate spread (BS4)

It represents the difference between weighted lending and deposit interest rates. The

higher the interest rate spread, the higher would be the market liquidity.

(v) Return on assets (ROA) (BS5):

The ratio measures banks’ profitability or efficiency in using their assets and reflects

the cushion which a bank has at its disposal against potential risks.

(vi) Return on assets (ROE) (BS6):

The ratio measures banks’ profitability or efficiency in using their equity and reflects

the cushion which a bank has at its disposal against potential risks.

(vii) Net interest rate margin (NIM) (BS7):

The ratio measures the difference between interest expenses and interest income per

unit of total bank assets.

3.2 Financial Vulnerability Index

The financial vulnerability index (FVI) mainly focuses on three key areas reflecting

macroeconomic conditions including the current account, the capital and financial

account, and the real sector. Each aspect is represented by at least one indicator.

(i) Current account balance-to-GDP ratio (FV1):

It gives an indication of vulnerability on the external sector of the economy if the

deficit widens.

(ii) Ratio of M2 to foreign exchange reserves (FV2):

The growth of money supply over international reserves provides an indication of

reserve adequacy. The ratio measures the ability to withhold external shocks and

ensure the convertibility of the local currency.

(iii) Real Effective Exchange Rate (REER) (FV3):

Although not included in the original work of Cheang and Choy (2011), it is added as

an indicator for constructing AFSI. The REER index is an indication of export

competitiveness of an economy. If it appreciates, the competitiveness of the export

sector increases. Six-monthly point-to-point REER of Bangladesh Taka (BDT) is

taken.

(iv) M2 multiplier (FV4):

The M2 multiplier is defined as the ratio of M2 to monetary base. Monetary base is

defined as notes and coins issued and banks’ deposits with the Bangladesh Bank. It

measures how much an increase of base money leads to the expansion of money

supply through the banking system.

(vi) Ratio of domestic credit to GDP (FV5):

Rapid loan growth is often accompanied by declining loan standards and precedes

banking crisis.

(vi) General stock price index movement (FV6):

Although not included in the original work of Cheang and Choy (2011), it is added as

an indicator for constructing AFSI. A stock market index is an indication of investors'

confidence in an economy as well as potential vulnerability of the economy, when it

goes out of line from the fundamentals. Six-monthly point-to-point General stock

price index (Dhaka Stock Exchange) movement calculated from changes in the value

of the index.

(vii) Fiscal balance to GDP ratio (FV7):

Ratio of deficit financing to GDP ratio is taken as an indication of financial system

stress.

(viii) CPI Inflation (FV8):

CPI gives an indication of overheating of the economy from a mismatch between

aggregate demand and supply situation of an economy. Six-monthly point-to-point

CPI inflation of Bangladesh is taken as a proxy.

(ix) Global petroleum price index (FV9):

Although not included in the original work of Cheang and Choy (2011), it is added as

an indicator for constructing AFSI. As we know when the price of petroleum goes up,

Bangladesh economy experience pressure in the foreign exchange market to meet

additional demand for foreign exchange.

3.3 Regional Economic Climate Index

The regional economic climate index (RECI) is primarily composed of inflation and

economic growth of Bangladesh's neighboring economy of India.

(i) CPI inflation of India (RE1):

Although not included in the original work of Cheang and Choy (2011), it is added as

an indicator for constructing AFSI. Because, Bangladesh economy is dependent to

some extent on import of essentials from India. So higher inflation in India means

higher demand for foreign exchange and imported inflation in the domestic economy.

Six-monthly point-to-point CPI inflation of India is taken.

(ii) GDP growth rate of India (RE2):

Semi-annual GDP growth rate of India calculated by simple arithmetic average of

quarterly GDP growth statistics of India. .

4. Construction of AFSI for Bangladesh:

4.1 Normalization/transformation of variables:

All selected individual indicators are available in six-monthly frequency. In order to

combine the individual indicators described above into one single synthetic index,

they need to be put on a common basis or scale. All individual indicators were

normalized before aggregation so that they have the same variance. In other words,

we apply the equal-variance weighting method to compute the aggregate index.

Statistical normalization converts indicators to a common scale with an average of

zero and standard deviation of one. The zero average avoids introducing aggregation

distortions stemming from differences in indicators’ means. The scaling factor is the

standard deviation of the indicator. Thus, an indicator with extreme value will

intrinsically have a greater effect on the composite indicator. This might be desirable

if the intention is to reward exceptional behavior, i.e. if an extremely good result on a

few indicators is thought to be better than a lot of average scores. By this approach,

the range between the minimum and the maximum should be varied among the

normalized indicators.

The formula of statistical normalization is:

s

u) - (X = Z t

t (1)

Where, Xt represents the value of indicator X during period t; u and s is the mean

and standard deviation respectively recorded by indicator X in the analyzed period;

Zt is the indicator’s normalized value.

The mean is subtracted from each variable before it is divided by its standard

deviation. Z has normal distribution with zero mean and unit variance, and is also

called standard normal distribution, N (0,1). However, the range of the standard

normal distribution is not between zero and one. Indeed, it is about the range from

minus three to plus three. All individual indicators are converted so that a positive

value implies that an indicator is above its historical average, which is calculated

since 2004, and a negative value is below its historical average indicating unfavorable

development for stability.

4.2 The Aggregate Index

The normalized indicators are then combined into a single index. We assign the same

weight to all individual indicators in order to calculate the composite indices of BSI

and FVI. The indices thus give equal importance to each individual indicator. It is the

most popular weighting method used in relevant research.

The normalized variables are aggregated into an aggregate index using the arithmetic

mean, according to the following formulas:

7

7

1

BS

BSI (2)

9

9

1

FV

FVI (3)

2

2

1

RE

RECI (4)

There are some studies that assign different weights to individual indicators, for

example, according to past experience of crises. However, it is arguable that indicator

that is important in one crisis may not be important in another.

In this study the Aggregate Financial Stability Index (AFSI) is obtained by the

weighted average of the three sub-indices following Cheang and Choy (2011).

AFSI=

2

9

4.07

6.0

2

1

9

1

7

1

RECFVBS

(5)

= 0.6×BSI +0.4× (FVI+RECI) (6)

5. Result and Interpretation: The index is compiled using statistical normalization

which expresses indicators in terms of standard deviation from mean. Therefore, a

level above zero implies that the stability is higher than average and vice versa for a

level lower than zero. Our objective is trying to understand the causes of ups and

downs in the indices in relation to performance of the financial system during the

period.

Although, Bangladesh economy had not experienced the kind of financial crises that

the developed countries experienced recently and the South Asian countries in late

1990s, it went through some sort of financial stress and vulnerability. Following table

shows some major internal and external events and their impact on the financial

system of Bangladesh for evaluating the performance of the AFSI during the period

under consideration.

Table 2: Major Internal and External Events and performance in the Financial System of Bangladesh during Dec 2004 to Dec 2011

Time Period Internal and External Events Impact on Financial System

January-June,2005 Marked deterioration of external

current account deficit as well as

BOP condition (Current Account

deficit of USD 326 million during

3rd quarter of 2005).

-Pressure on the exchange rate of

Taka

-Central Bank intervention to the

tune of USD 311 million.

January-June 2007 -Persistence of high oil prices and

increase in other international

commodity prices;

-High inflationary expectation.

-Uncertainty over political

transition in the country.

-Devastating cyclone and tidal

surge.

- Slower growth in credit to the

government as well as private

sector.

July 2008-June 2009 -Sluggish world economic growth

in response to global financial

crisis that began in late 2007.

-The pace of export growth

slowed significantly, although

remittances from workers abroad

showed a strong and steady

growth (BB Annual Report).

-Downward trend of DSE general

price index largely persisted, with

some volatility, since July 2008.

-Bangladesh Economy only

mildly impacted by the ongoing

global slowdown (BB Annual

report 2008-9).

-BB pursued supportive monetary

policy seeking to maintain

adequate credit flow.

July-December 2009 The rising trend of DSE General

Index continued since July 2009

with some volatility.

Bubble like condition in the

Large liquidity overhangs in the

banking sector.

Time Period Internal and External Events Impact on Financial System

country's real estate market

continued.

July-December 2010 -Overheating situation of the

stock market gives signal of risks

in the financial system and finally

the bubble burst in December,

2010.

Monetary measures focused

mainly on reducing credit growth

in the unproductive sector.

Continued increase in inflation

poses a big challenge for the

government. BB raised CRR and

SLR, both by 50 basis points

which stood at 6.0 and 19.0

percent respectively.

-Banking sector indicators

performed reasonably well,

although liquidity situation in

banking sector came under strain

due to record high private sector

credit growth.

January-June, 2011 -Share price index went through

deep correction which continued

upto June 2011. Banking sector.

-Heavy debt burdens and weak

growth prospect in a number of

advanced economies pose a risk

to the economy of Bangladesh.

- The interbank money market

faced liquidity stress throughout

the 2011 that started from share

price debacle at the end of 2010,

while borrowing from the central

bank increased significantly.

July-December 2011 -Demand pressure in the

interbank foreign exchange

continued and BB intervened in

the market. BB took a cautiously

accommodative monetary policy.

-Domestic credit growth slowed

and helped stabilize the exchange

rate of Taka at a new equilibrium

(Taka 81.85 per USD at end

December 2011 against Taka

70.75 per USD at end December,

2010.

Table 3: Major Events and performance of Banking Industry in Bangladesh

(During Dec 2004 to Dec 2011)

Year(s)/Period Events Impact on Banking System

July-December, 2007

Three State-owned commercial banks (SCBs) transfer their combined cumulative loss of Taka 87.9 billion by creating goodwill (valuation adjustments account) at the time of corporatization. (BB Annual Report 2007-08)

The aggregate capital adequacy ratio (CAR) of SCBs (7.9 percent) as well as system (9.6 percent) improved after transferring the cumulative loss. ROA stood at 0.9 percent while ROE at 13.8 percent.

January-June, 2008 Minimum CAR set at 10.0 percent of their RWA or Taka 2.0 billion, whichever is higher. However, it allowed dateline of 30 June 2009.

CAR stood at 9.49 percent. ROA went up to 1.24 percent while ROE increased to 21.26 percent. (Dept. of Offsite Supervision, BB)

July-December, 2008

Banks have written-off an amount of Taka 22.5 billion during 01/07/2008 to 30/06/2009. (BB Annual Report 2008-09)

CAR stood at 10.1 percent and gross NPL decreased to 10.8 percent. ROA remained almost same and ROE falls to 15.8 percent.

January-June, 2009 BB prescribed interest rate ceiling of 13 percent per annum on bank lending for most purposes other than consumer credit and loans to small enterprises. (BB Quarterly Jan-Mar and Apr-Jun, 2009)

The interest rate spread (IRS) reduced. CAR stood marginally above the regulatory requirement. Gross NPL declined and ROA and ROE increased.

July-December, 2009

BB relaxed the conditions for loan rescheduling to help sectors affected by export decline (waver of down payment requirements) up to September 30, 2009.

Gross NPL ratio decreased to 9.21 percent in end December 2009. ROA noticeably increased to 1.37 percent while ROE increased to 21.7 percent. (BB Quarterly Jul-Sep and Oct-Dec, 2009)

January-June, 2010 Introduced Basel II capital adequacy framework as regulatory compliance. Higher falling rate of deposit than that of lending rate. (BB Quarterly Jan-Mar and Apr-Jun, 2010)

CAR decreased to 7.91 percent and gross NPL decreased to 8.67 percent. ROA went up to 1.58 percent while ROE increased to 22.94 percent. ISR increased to 5.28 percent.

July-December, 2010

Basel II capital adequacy framework continued with regulatory higher benchmark than that of previous period. Rupali Bank Limited (one of the SCBs) transfers its cumulative loss of Taka 12.08 billion by creating goodwill in August 2010 with the decision of Ministry of Finance.

CAR improved to 9.31 percent and in gross NPL decreased to 7.27 percent. ROA went down to 1.11 percent while ROE also declined to 20.97 percent. IRS marginally decreased to 5.12 percent. (BB Quarterly Jul-Sep and Oct-Dec, 2010)

January–June, 2011

SEC revamped with new leadership. BB directed banks to retain

The overheated capital markets undergoing sharp price correction

Year(s)/Period Events Impact on Banking System

undistributed their 2010 profits from capital market activities. Interest rate caps on bank lending withdrawn. Liquidity and asset-liability management in banks came into BB’s intensive supervisory attention. BB’s financial inclusion drive emphasized to channelize credit to productive pursuits.

in December-January was in process of recovery and stabilization in Q3 FY11. Gross NPL ratio slightly declined and both ROA and ROE turned down. IRS stood at 5.14 percent. (BB Quarterly Jan-Mar and Apr-Jun, 2011)

July-December, 2011

BB raised the minimum requirements of capital of a bank to the level of BDT 4.0 billion from August 2011 instead of BDT 2.0 billion. (BB circular [BRPD circular letter no. 11/2008])

The CAR stood at 11.35 percent and gross NPL decreased to 6.12 percent. ROA stood high at 1.54 percent while ROE recorded higher at 17.02 percent against 15.45 percent in June 2011. (BB Quarterly Jul-Sep and Oct-Dec, 2011)

2007 to 2011 Internal restructuring of SCBs to strengthen their loan recovery mechanism and initiation of recovery drive and write-off measures.

The gross NPL ratio shows an encouraging trend since its decline from 13.2 percent in 2007 to 7.3 percent in 2010. (BB Annual Report 2010-11)

2010-2011 Banks are advised to limit spread within lower single digit. (BB circular [BRPD circular letter no. 01/2012]). However, the same instruction was first given in 2010 in a Bankers’ meeting at BB.

IRS reported 5.12 percent at the end June 2011 and reached to 5.43 percent at the end December 2011. (BB Annual Report 2011-12)

The aggregate financial stability index (AFSI) as well as the sub-indices for the

financial system of Bangladesh were prepared for the period from December 2004 to

December 2011, which is shown in a graph (Chart-1).

Chart 1: The Aggregate Financial Stability Index and Its Sub-indices, 2004-2011

(Statistical Normalization)

Source: Note: 

Authors Calculation   Regime of calculation of CAR changed from 2010 [Basel I to Basel II]. 

Minimum capital (in amount) for banks increased (BDT 2 billion in 2007 and BDT 4 billion in 2011)  

Base Year of REER changed ( 1994‐95=100 to 2000‐2001=100)  

It is observed from the above chart that initially during January-June, 2005 the

aggregate index (AFSI) was far below the average level (zero line) indicating some

stress in the financial system. We can see from the Table-2 above that there was a

marked deterioration in our current account and the value Bangladesh Taka was under

pressure during this period. It is observed that from December, 2005 to June, 2008 the

aggregate index as well as all three sub-indices was around the average level,

indicating relative stability of the financial system of Bangladesh during this period.

Thereafter, from the middle of 2008, the AFSI along with all three sub-indices fell

gradually up to June 2009, which may be attributable to slow down in the global

economic growth following global financial meltdown. From the middle of 2009 the

AFSI and sub-indices began to rise sharply up to December, 2010, when the country's

stock market index recorded a historic peak before being crashed. From the end of

2010, the indices began to fall up to June 2011, when the banking system experienced

severe liquidity pressure in domestic money market and foreign exchange market. It is

observed that the AFSI as well as sub-indices went well with the performance of our

financial system.

5. Limitations of the Study:

The study was undertaken on experimental basis with a view to understanding and

capturing the performance of Bangladesh economy and financial system on an

aggregate basis from a single index, which was based on earlier empirical researches

in other countries. The variables and indicators were chosen partly on the basis of

theoretical assumptions and partly on the basis of practical considerations. Some of

the limitations of the study can be summarized as follows:

a) The variables and indicators included in constructing the aggregate index (AFSI)

and three other sub-indices are not comprehensive. Moreover, some indicators are

chosen based on practical considerations without any empirical study. On the other

hand, some core indicators suggested by the IMF like net open position in foreign

exchange to capital are missing for non-availability of adequate data. In this

backdrop, some indicators may be added and some may be dropped with a view to

getting a more reliable index.

b) The range of data is taken for a relatively short span of time - from 2004 to 2011,

because some of the banking data were not available before 2004.This small sample

size may not give a good statistical measure. However, the situation will continuously

improve when the data series accumulate over time and likely to include quarterly

data.

c) Some of the data were not available on quarterly basis. So the study was

undertaken based on half-yearly data, whereas other similar studies were undertaken

based on quarterly data. Although, GDP growth data for Bangladesh are not available

on half-yearly basis, previous year's available data were used for calculating some

ratios in relation to GDP.

d) The study is based on backward looking data. The study would be more valuable if

some forward looking projection could be made.

e) The index does not include indicators of non-bank financial institutions,

considering their importance in financial stability.

f) The constructed aggregate index is a decision making tool only and does not

include all the relevant information that a policy maker needs. Other related data and

qualitative information are needed for sound judgment. For example, the quality of

risk management and corporate governance in banks, quality of supervision of banks

and financial institutions, risk of fraud and forgeries, payment system risk, credit

concentration risk, regulatory weaknesses and political instability, risk emanating

from off-balance sheet items and contagion risks are also important for making a

sound and informed judgment.

6. Conclusion:

Monitoring and ensuring financial stability, generally characterized by the absence of

excessive volatility, stress or crises in the financial system, has become an

overarching objective of the central banks around the world especially following the

recent global financial crisis. Although there has been no consensus on defining and

measuring financial stability, constructing a composite or aggregate measure of

financial system stability through some sort of index has started gaining recognition

as a part of early warning indicators for assessing the vulnerability of the financial

system as a whole. This paper represents a modest attempt to construct an aggregate

financial stability index (AFSI) for the financial system of Bangladesh. The study

finds that the AFSI for Bangladesh performed reasonably well in identifying stresses

in the financial system during FY 2008-9 and again at the end of 2010, when the

country's stock market crashed and the banking system faced a liquidity crunch.

However, the AFSI needs to be aided with other relevant data and qualitative

information for making a sound judgment.

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Appendics: Chart 1: Capital Adequacy Ratio (CAR), Non-performing Loan (NPL) ratio and

Interest rate spread

Chart 2: Credit Deposit Ratio (CDR)

Chart 3: Return on Asset (ROA), Return on Equity (ROE), Net Interest Rate Margin (NIM)

Chart 4: Real Effective Exchange Rate (REER) Index and Global Petroleum Price Index

Chart 5: Current account balance, fiscal balance and domestic credit with GDP ratio

Chart 6: M2 to Foreign Exchange Reserve and M2 Multiplier

Chart 7: General Price Index (DSE) movements and CPI Inflation

Chart 8: CPI Inflation and GDP growth of India

Table I: Variables under Banking Stability Index (in percentage)

Year Month CAR NPL Ratio

Interest Rate

Spread CDR ROA ROE NIM

2004 Dec 8.78 17.63 5.27 100 0.6753 12.5665 1.9100

2005 Jun 7.11 15.79 5.31 98 0.5375 12.3070 1.1200 Dec 7.34 13.55 5.00 101 0.6974 15.0055 2.2000

2006 Jun 8.02 16.59 5.38 96 0.5779 16.4508 1.2100 Dec 5.29 13.15 5.61 97 0.7864 14.1307 1.9200

2007 Jun 6.48 13.96 5.92 96 0.7587 11.9192 1.3200 Dec 7.37 13.23 5.98 100 0.8942 13.7808 1.9700

2008 Jun 9.49 13.02 5.34 102 1.2433 21.2613 1.0600 Dec 10.05 10.79 5.00 103 1.1575 15.5960 2.1400

2009 Jun 11.68 10.5 4.86 99 1.3883 18.2062 1.0400 Dec 11.67 9.21 5.14 101 1.3743 21.7172 2.0500

2010 Jun 7.91 8.67 5.27 99 1.5794 23.4563 1.2400 Dec 9.31 7.27 5.23 103 1.7832 20.9682 2.3900

2011 Jun 9.75 7.14 5.15 103 1.3134 15.4511 1.1900 Dec 11.35 6.12 5.46 104 1.5398 17.0200 2.4800

Table II a: Variables under Financial Vulnerability Index

Year Month CAB-to-GDP

Fiscal balance to GDP

Domestic credit to GDP ratio

M2 to FX reserves M2 Multiplier

2004 Dec 0.0002 -1.10 0.4545 7.4069 1.3

2005

Jun -0.0100 -1.50 0.5527 8.1152 5.13 Dec 0.0061 -1.00 0.6046 8.8073 4.92

2006

Jun 0.0111 -2.06 0.6244 7.4602 4.79 Dec 0.0132 -1.82 0.6845 7.5268 4.36

2007

Jun 0.0087 -1.81 0.6789 6.0549 4.76 Dec 0.0094 -2.88 0.7479 6.1010 4.5

2008

Jun 0.0118 -2.27 0.7729 5.9052 4.7 Dec 0.0062 -1.92 0.8405 6.8168 4.54

2009

Jun 0.0164 -1.65 0.8482 5.7468 4.27 Dec 0.0297 -0.38 0.9038 4.5802 4.7

2010

Jun 0.0323 -2.24 0.9428 4.8633 4.5 Dec 0.0163 -0.67 1.0596 5.0504 4.7

2011

Jun 0.0059 -3.15 1.1259 5.4447 4.5 Dec -0.0012 -3.42 1.2506 6.0293 4.8

Table II b: Variables under Financial Vulnerability Index

Year Month REER Petroleum (US$/Barrel)

Change in General Price Index (DSE) CPI inflation

2004 Dec 93.64 42.70 49.44 6.5 2005 Jun 91.15 50.80 -13.09 7.4

Dec 91.74 53.60 -2.08 7.1 2006 Jun 87.15 68.30 -20.14 7.5

Dec 83.86 59.00 20.16 6.1 2007 Jun 83.43 66.10 33.54 9.2

Dec 86.55 89.40 40.38 11.6 2008 Jun 84.48 131.50 -0.55 10

Dec 86.02 41.00 -6.84 6 2009 Jun 92.77 69.20 7.69 2.3

Dec 91.30 75.50 50.67 8.5 2010 Jun 93.27 74.00 35.68 8.7

Dec 97.74 89.18 34.72 8.28 2011 Jun 94.70 107.52 -26.21 10.17

Dec 89.42 106.21 -14.05 10.63

Table III: Variables (in percentage) under Regional Economic Environment Index

Year Month CPI inflation

India GDP Growth

(India) 2004 Dec 3.7 6.3

2005

Jun 4.1 9.2 Dec 5.6 9.3

2006

Jun 6.3 9.9 Dec 6.9 9.8

2007

Jun 5.7 9.5 Dec 5.5 9.6

2008

Jun 7.8 8.2 Dec 9.7 6.8

2009

Jun 9.3 6.1 Dec 15 8.0

2010

Jun 13.7 9.4 Dec 9.47 8.6

2011

Jun 8.62 7.8 Dec 6.49 6.5