Credit Risk Measurement in UK Banks Post Brexit Vote

A STUDY ON CREDIT
RISK MEASUREMENT OF UK BANKS

INTRODUCTION

Credit risk measurement is one of the primary aspects in the banking industry. Major problems faced by the banks are related to slack credit standards for borrowers and counterparties, poor portfolio risk management, lack of attention towards economic changes or other situations that can lead to deterioration in the credit standing of the bank’s counterparties.

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Credit risk for UK Banks has been on the rise over the year since the Brexit vote, this is according to the latest figures from credit benchmark.

This research focuses on the credit
risk measurement of UK banks after the Brexit vote. Since Brexit, Using credit
data collected from global IRB banks, credit benchmark has found upcoming
changes in the credit risk of British banks, compared to their continental
Europe peers.

This paper will provide a clear image of the changes that
have taken place in credit risk management in UK banks. Some of the top banks
in the UK have been chosen for the research purpose and they are HSBC, BARCLAYS,
LLOYD BANK etc. a total of 5 banks altogether. Using KVM credit risk model we
would be able to measure banks credit risk accordingly. The changes caused by
Brexit vote in the UK Banks will be shed light on with the help of a model.

THEORETICAL FRAMEWORK

Credit risk is simply defined as the potential that a bank
borrower or the counterparty will fail to meet its obligations in accordance
with agreed terms. The main goal of the credit risk management is to maximise
the bank’s risk-adjusted rate of return by maintaining credit risk exposure
within acceptable parameters.

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The main aim for most of the banks is
to make money/profit on the interest that they charge for loans and mortgages
that are provided to businesses and individuals.they receive savings from the
depositors at a specific interest rate, and then tender the funds to debtors
for a higher interest rate, ending up making a profit off the difference in
interest rates. Therefore all banks need to lend money and tender loans if they
were to make profit/money in any competitive environment. however, the risks
involved in such practices as encountered in the face of the financial crisis
shows that their lending practices should possess appropriate risk management
systems which would ensure best practice and a healthy administration of good
corporate governance practices. basically, if banks do not manage their credit
risk management effectively, they would eventually end up having too many
defaulters which would reflect negatively on their profits.

For the long-term success of any banking organization, there should be an effective management of credit risk. Banks should also consider the relationship between credit risk and other risks like consumer risks, corporate risk and country risk. In this paper, we mainly focus on credit risk which bank consider as one of the primary issues. Banks face credit risk in various types of financial instrument, for example, forex transaction, financial futures, swaps, bonds, equities, interbank transaction etc.

Worldwide the leading source of
problems for banks is credit risk and because of that, the supervisors are well
informed by the risk considering their past experience. The main awareness that
the banks should possess is to identify, monitor, measure and control credit
risk as well as to determine that they hold sufficient capital against these
risk which would sufficiently be compensated for the risks incurred. the Basel
committee tries to encourage the banking supervisors by providing the documents
to promote ideal practices for managing credit risk. but these principles are
mainly applicable to the business of lending and should be applied to all major
activities where credit risk is present.

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Basel committee provides some sound
practices which are set in the document that the banks should follow in order
to manage credit risk and are as follows;

Firstly, the bank should operate
under a sound credit granting process; secondly, it should establish an
appropriate credit risk management environment; third, maintaining an
appropriate credit administration, measurement and monitoring process. And
finally, ensuring adequate controls over credit risk.

The bank’s performance, survival and
profitability hugely rely on precise measurement and effective management of
credit risk. the Basel committee on banking supervision has stated that “credit
risk is the potential risk of loss due to the failure in payment by the
obligatory in terms of the loan or other types of credit”.

In banks, risk management which
happens internally is very important because it involves the identification of
potential risk factors, predicting the consequences, monitoring activities
which are exposed to the identified risk factors and put in place control
measures to prevent or deduct the undesirable effects(muelbroek, 2002).

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Chen and pan (2012), they termed
credit risk as “the extent of value fluctuation in the debt derivatives and
instruments due to transform in the core credit quality of counterparties and
borrowers”.

Credit risk and returns are
interconnected because the higher the credit risk, lower is the return and vice
versa. The tradeoff between the two state that high-risk securities i.e high
yielding loans will reward a risk premium/higher average return. This is
because of the increase in the insecurity of payment. So, eventually, the
returns and average revenue can be increased only by increasing risk. van Greuning
and Brajovic Bratanovic (2003) state that it is critical to understand that credit
risk has always been the major hazard to any banks performance and the main
cause of bank insolvency. 

Loan constitutes a big portion of the
assets within the bank, it is the biggest revenue generating asset and also the
most illiquid and risky. (Koch and MacDonald 2000).

Credit Risk is the measured by ‘character’, ‘capital’, ‘capacity’, ‘conditions’. Usually, banks develop a scorecard
which assesses each obligor strength in those four areas.

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Horcher (2005) explains credit risk in different forms

• Settlement risk
• Concentration risk
• Default risk
• Counterparty pre-settlement risk
• Counterparty sovereign risk

In this research paper, we will be
focusing on default risk as that would support the study appropriately.

Altman and Saunders (1998) they
classified credit risk measurement models into 5 types;

Expert systems and subjective analysis,
accounting-based credit scoring system, the other (newer) models of credit
measurement, measures of the credit risk of off-balance sheet instrument and
measures of credit concentration risk.

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KMV model is a newer model of credit risk measurement where it provides crucial inputs into the estimation of the probability of defaults.

A class of bankruptcy models with a
strong theoretical underpinning are ‘risk of ruin’ models. It is explained as
that when a firm goes bankrupt the market value of its assets falls below its
debt obligations to outside creditors. In Merton model volatility of the market
value of a firms asset is also included.

(EDF) the expected default frequency is
a predictive measure of the actual probability of default. EDF is frim
specific. credit risk is driven by the firm value process and this model is
based on a structural approach for calculating EDF.

It is best when applied to publicly
traded companies, where the value of the equity is determined by the stock
market. The implied risk of default is translated from the market information
which contains firms stock price and balance sheet.

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Here we have to find the actual
probabilities of default and the steps involved are as follows;

• Firstly,
  the estimation of the market value and the volatility of firms asset.
• Calculation
  of distance to default, an index measure of default risk.
• Scaling
  of the distance to default to actual probabilities to default using a default
  database.

Key features in KMV Model

As previously mentioned works best in
highly efficient liquidated market condition.

It compares firms net worth with its
asset volatility.

As the net worth is based on values
from the equity market, so it appropriate and provides a precise estimation of
the firm value.

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 It also has the ability to adjust to the
credit cycle and capable of quickly reflecting any deterioration in the credit
quality.

Strengths of KMVModel

Changes in EDF is anticipated much
quicker when compared to other rating agencies like Moodys and S & P’s.

EDF provides a cardinal (1,2,3..)
rather than ordinal (1^st, 2^nd, 3^rd …) ranking
of credit quality.

Uninterrupted monitoring is made easy
through accurate and timely information from the equity market. else, the
credit monitoring process would have been difficult and expensive to replicate
using traditional credit analysis.

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Traditional credit process cannot
maintain the same level of attentiveness, that EDFs calculation of monthly or a
daily basis can provide.

Weaknesses of KMV Model

KMV Model requires subjective
estimation of the input parameters.

In this model, the private firms EDF
can be calculated only by using some comparability analysis which is based on
accounting data. without the assumptions of normality of asset returns, it is
difficult to build theoretical EDF.finally, distinguishing among the long-term
bonds according to their collateral, covenants or convertibility is not
possible.

RESEARCH QUESTION

1. Does the
   Brexit vote have any impact on UK banks credit risk management?
2. Are
   there any changes in the credit quality before and after the Brexit vote?
3. Which UK
   bank has the good credit rating?
4. Which UK
   bank has the highest EDF(Expected default frequency)?
5. And to
   compare the UK banks ratings according to the KMV model.

RESEARCH DESIGN/METHODS:

In this research paper, we try to
focus on measuring the credit risk of the  5 major commercial banks in the UK. They were
chosen because they are UK based companies and would be appropriate for this
particular study involving the Brexit vote. So, it would be interesting to find
out how these banks deal with credit risk management. these banks were chosen
because they are also the biggest banks in the UK and have sustained few major
shocks previously.

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For collecting the data it would be a
bit of a challenge, but it can be managed by the use of online financial database
and datastream software as well. The data needed for the analysis would be
collected by utilizing the primary sources, annual reports of the banks would
also be collected before and after the Brexit vote, notes on financial
statements within the annual report will be collected.

Data analysis in this research paper
would be a qualitative analysis.it is predictive in nature. but the KMV Model
is a quantitative model of credit risk. The data we use is worked on a credit
risk measurement model named the KMV model which provides a rating in cardinal
explaining how that particular bank has performed by predicting future
default.kmv model is a default prediction model developed by the United States
of KMV in 1995, its established theoretical basis is Merton (1974) option
pricing model. KMV approach of measuring credit risk follows structural
approach to a point of analysis.it is explained as the firm’s asset falls
behind a certain value then the default is occurred called as default point but
the probability of default comes as an endpoint also called as EDF (expected
default frequency). Over a period of time, the firm will start to earn some
return through assets and will trend with a given mean and volatility.

The credit risk assessment process
can be done in 3 following steps;

1. Determine the value of assets (V) and their volatility
2. Calculate the distance to default (DD)
3. Determination of EDF

The findings in this research paper
would provide certain ratings about the credit ratings of the UK banks. But
here as we have chosen only the major banks in the UK  the rest of the banks is untouched so it
really cant help the investors to know the complete information about the UK
financial markets and this study is only based on one credit risk model
measurement which cant be completely depended on when we are actually measuring
the credit in real time. As we are conducting this research considering the
data for a couple of years before Brexit and after, the size of the data would
not be enough for a better precise outcome.

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CONCLUSION

The research proposal shall certainly
explain the concept of credit risk and its measurement in UK banks. As
mentioned before the effects or the impact on UK banks after the Brexit vote
could be determined by measuring the credit, it will also clarify whether there
were any changes during this period. The probability of defaulters in the major
UK bank will be determined and a comparison will be done before and after the Brexit
vote time zone.

TIME FRAME

For this research paper, the time
frame is chosen by considering the KMV model in mind. since there are lots of
internal and external data that needs to be collected and to work on the
analysis it would at least require thirty days. The problem I would face is in
the data collection but with the help of datastream software, I would be able
to collect it on time. For the analysis part have to go through a few KMV model
research articles and as well as books and with the help of my supervisor will
be able to solve the problem on time.

REFERENCES

• Tudela, M. and Young,
  • G. A Merton-Model Approach to Assessing the Default Risk of UK Public Companies (Tudela and Young, 2003)
  • Tudela, M. and Young, G. (2003).
  • A Merton-Model Approach to Assessing the Default Risk of UK Public Companies. SSRN Electronic Journal.
• Altman, E. I. and Saunders, A. Credit risk measurement
  • (Altman and Saunders, 1996)
  • Altman, E. and Saunders, A. (1996). Credit risk measurement. New York, N.Y.: New York University Salomon Center.
• Altman, E. I. and Saunders, A. Credit risk measurement 
  • (Altman and Saunders, 1996)
  • Altman, E. and Saunders, A. (1996). Credit risk measurement. New York, N.Y.: New York University Salomon Center.
• (Anon, 2018)
  • Anon, (2018). [ebook] Available at: KMV Model
  • https://www.math.ust.hk/~maykwok/Web_ppt/KMV/KMV.pdf [Accessed 17 Jul. 2018].
• CHUN-PING WANG, LIN-JING QU AND JIAN-WEI LI (2015)  Commercial bank credit risk measurement based on KMV model studies