One of the main tasks of commercial banks is to offer loans, and their
main source of risk is credit risk, that is, the uncertainty associated with
borrowers‟ repayment of these loans. A non-performing loan (NPL) may
be defined as a loan that has been unpaid for ninety days or more.
For the
purpose of this study, we analyse the non-performing loan ratios of the
commercial banking sector calculated by dividing gross classified debt by
total loans.
The commercial banking sector of Barbados consists of six
commercial banks which are currently all foreign owned, and presently the
aggregate NPL ratio is approximately 3.138%.
The magnitude of non-performing loans is a key element in the
initiation and progression of financial and banking crises.
Ahmad (2002),
in analyzing the Malaysian financial system, reported a significant
relationship between credit risk and financial crises and concluded that
credit risk had already started to build up before the onset of the 1997
Asian financial crisis, and became more serious as NPLs increased. Li
(2003) and Fofack (2005) also found this relationship to be significant.
Further, the current global financial crisis, which began in the United
States, is attributed to the August 2007 collapse of the sub-prime
mortgage market. In fact, there is evidence that the level of NPLs in the
US started to increase substantially in early 2006 in all sectors.
NPLs are
therefore a measure of the stability of the banking system, and thereby the
financial stability of a country.
Given the above discussion, it is not difficult to see why the ability
to forecast non-performing loans is important.
Generally, previous
empirical studies have modeled NPLs through the use of various
multivariate analyses. For example, Chase et al. (2005) used OLS to
forecast non-performing loans using the treasury bill rate, the consumer
price index, real gross domestic product (GDP) and a lagged dependent
variable.
This study contributes to the existing literature by modeling the
NPL ratio of the commercial banking sector in Barbados, not only on an
aggregate level but also on an individual bank level.
This research paper therefore attempts to use a multivariate model to forecast non-performing
loans using quarterly bank specific data, as well as macroeconomic factors
The structure of the paper is as follows: section 2 provides an
overview of non-performing loans in Barbados; section 3 provides a
review of existing literature; section 4 then presents the model estimates
and results; section 5 offers a discussion of results and concludes with a
summary of the findings, including limitations and policy implications.
Overview of Non-Performing Loans in Barbados
This section reviews the evolution of NPLs in the banking system of
Barbados. As a precursor to the discussion, it should be noted that the
Barbadian financial sector is well developed and encompasses a wide
range of financial institutions.
There are currently six commercial banks,
13 non-bank financial institutions, 34 credit unions, 11 life insurance and
16 general insurance companies2
. At end-2008, assets of commercial
banks accounted for 142% of GDP and about 80% of the assets of all
deposit-taking institutions.
In addition, commercial banks accounted for
82% of all deposits and around 74% of loans and advances.
Our study utilises quarterly data spanning the period 1996 to 2008.
Prior to1995 there was no standard treatment or interpretation of nonperforming loans.
Information was received on past-due loans that did
not include all the features of what is now termed as classified debt. Each
bank employed its own rating system, and some still retain their own
internal classification system which runs parallel to that instituted by the
Central Bank of Barbados.
The Asset Classification and Provisioning
guidelines, which are based on the Basle Committee‟s Core Principles,
were written into law in 1996. Over time there has been general adherence
to these guidelines and standardisation has been largely achieved.
Therefore, figures on classified debt are available on a quarterly basis from
1996. However, since complete adherence to the new provisioning
guidelines was not immediately achieved, figures may have been misrepresented in the earlier stages.
In fact, during this period, it was not
unusual for examiners to adjust the level of classified debt reported by
banks on conclusion of an on-site examination. However, these
adjustments were usually minor.
Literature Review
Despite the importance of the examination and monitoring of nonperforming loans, forecasting these ratios has only received moderate
attention in the literature. There is a general consensus that the level of
NPLs experienced by banks is determined by internal and/or external
factors.
For instance, Keeton and Morris (1987) pointed out that local
economic conditions and the poor performance of certain industries
explain the variation in loan losses. However, commercial banks with
greater risk appetite and that are more willing to make loans with a higher
probability of default, tend to record higher losses.
Sinkey and Greenwalt
(1991) also shared this general view, and posited that NPLs reflect realized
credit risk for banks arising either from external factors such as depressed
economic conditions, or internal factors such as poor lending decisions or
both.
The study found a significantly positive relationship between the
level of loan defaults and high interest rates, excessive lending and volatile
funds.
The existing literature however suggests a variety of determinants
and approaches to be used in the forecasting of non-performing loans.
Graham and Humphrey (1978) presented one of the early attempts at
predicting non-performing loans. The authors suggested that, in general,
banks with larger amounts of classified loans (loans with more than
normal risk) will experience greater amounts of future losses, and hence classified loan data should be included as an indicator of these loan losses.
The authors therefore evaluated whether taking classified loan data into
account improves forecasts of future net loan losses. Subsequent models are of a more complex nature and include a
greater selection of variables for the forecasting of non-performing loans.
For example, Barr et al. (1994) argued that bank failure prediction studies
have continually concluded that the level of efficiency of a bank‟s
management is the leading cause of failure, yet few researchers have
attempted to quantify management quality or incorporate it into predictive
models.
Seballos and Thomson (1990) and Hsing et al. (1991) also
supported the view that a key determinant is management‟s ability to
operate efficiently and manage risks.
Barr et al. (1994) therefore attempted
to incorporate management quality as an explanatory variable through the use of a data-envelopment analysis (DEA), which combines multiple
inputs and outputs to compute a scalar measure of efficiency.
In addition,
the authors included variables representing Capital Adequacy, Asset
Quality, Earnings Ability and Liquidity Position, to complete the CAMEL
rating, as well as a proxy for local economic conditions.
The performance
of the DEA management variable is assessed using a Probit regression
model to develop one- and two-year ahead forecasts. Their results
supported the claim that management‟s efficiency is indeed important in
forecasting bank failure.
More recently, Chase et al. (2005) modelled non-performing loans
using the Treasury bill rate, the consumer price index, real GDP and a
lagged dependent variable. The authors use a similar technique to Graham
and Humphrey (1978), where Ordinary Least Squares (OLS) is employed
to forecast the NPL to total loans ratio for the banking system in
Barbados.
All of the explanatory variables were found to be significant.
Subsequent research conducted in the Caribbean includes that of Khemraj
and Pasha (2009), who examined the determinants of non-performing
loans in Guyana.
Using a panel dataset and a fixed effect model, the
authors regressed the NPL ratio on the GDP growth rate, inflation rate,
real effective exchange rate, and the bank specific variables, loans to total
assets ratio, size, real interest rate and annual growth in loans.
The
empirical results revealed that with the exception of the inflation rate and
bank size, all other factors have a significant relationship with the NPL
ratio.
However, note should be made of an earlier argument by Smith
and Lawrence (1995) that macroeconomic variables have limited
predictive powers in explaining loan defaults, and that explicitly including
them in the forecasting model is unlikely to improve its effectiveness for
forecasting purposes.
They specified a mortgage-loan-default forecasting
model based on a Markovian structure, as an extension of the work of
Lawrence et al. (1992), who examined the determinants of default risk for
mobile home loans.
Smith and Lawrence‟s findings suggested that
payment history, the geographical area in which the home is located, and
the number of months expired and remaining in the loan‟s term, are the main contributions to loan default.
The authors also noted that several
papers have concentrated on the identification of factors that help in the
prediction of default, but neglect issues in the development of long-term
forecasts of losses on loan portfolios.
Nonetheless, Betancourt (1999) remarked that although the
Markov Chain technique is a reasonable approach for estimating loan
losses, a common problem with these models is the requirement of very
strong assumptions regarding stationarity and homogeneity, which are not
usually satisfied.
The author estimated loan losses from a portfolio of
mortgages, where in any month, a mortgage could be classified into one
of the following categories:
- Active,
- Thirty days delinquent,
- Sixty
days delinquent,
- Ninety plus days delinquent,
- foreclosure,
- Real
estate owned (REO) and
- Paid off.
If B0 represents a start vector of
mortgages at time 0, then multiplying the vector B0 times the transition
matrix P yields a forecast B1 of how the mortgages in the start vector will
be distributed at time 1.
A forecast of loan losses (REO acquisitions) at
time t can be generated by simply observing the number of loans expected
to transition to REO at time t. The authors concluded that when using the
most recent information on transition probabilities, the Markov Chain
approach could provide a more accurate forecast of loan losses than a
random walk model.
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