Risk Management: Credit and VAR Models in Banking Sector Analysis

This report focuses on the issues related to credit risk models and VAR models within the banking sector. The report aims to eliminate problems associated with risk assessment models. It analyzes the credit risk model, which calculates the chances of borrower defaults and potential losses, and VAR models, which measure and quantify financial risks. The report identifies issues such as credit concentration, incomplete or incorrect information, and the failure to evaluate the cyclic performance of industries in the credit risk model. In the VAR model, it discusses the use of assumptions, lack of standard protocols, and difficulty in forecasting daily risk factors. The report recommends that banks distribute lending practices broadly, thoroughly check borrower information, calculate asset values, and monitor the performance of loan portfolios. It suggests using financial software for VAR models, implementing sensitivity analysis, and using coherent risk measures. The conclusion emphasizes the need for banks to focus on minimizing risks and improving data management and risk assessment tools.

Running Head: RISK MANAGEMENT
RISK MANAGEMENT
Name of the Student
Name of the University
Author Note

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Executive Summary
The main objective of this report is to eliminate the issues associated with risk assessment
models in banks. The study has been supported by analysing credit risk model and VAR
models as a risk management tools. It is recommended, the banks are more focused on
maximising the returns but not on minimising the risks from credit models and VAR models.

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Table of Contents
Introduction................................................................................................................................3
Discussion..................................................................................................................................3
Part A.....................................................................................................................................3
Credit Risk Model..............................................................................................................3
VAR Models......................................................................................................................5
Part B......................................................................................................................................6
Recommendations..............................................................................................................6
Conclusion..................................................................................................................................9
References................................................................................................................................11

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Introduction
This paper will focus on issues and problems that is related with credit risk models
and VAR models in banking sectors. The problems of both the models has been discussed in
this paper. The last part of this paper has provided certain recommendations for the banks to
avoid such problem related to the risk management models. The main objective of this report
is to eliminate the issues associated with risk assessment models in banks.
Discussion
Part A
Risks in Banking
Banking sectors are exposed to variety of risks that needs to be controlled by
following the government regulations. This is a very important matter because it can lead to
failure in banks and may impact the privacy of millions of people. It also affects the
investor’s decisions and will be not interested to invest in the banks and hence, can cause
lower profits for the banks (Alodayni, 2016).
Credit Risk Model
Credit Risk Model is very important in solving the various problems related to credit
risks. Credit risk is the risk of borrower not paying back the loan, credit card and some other
type of loan (Doss, 2017). Credit Risk model calculates the chances of defaults of borrowers
on loans. If he/she fails to repay the loans, it calculates how much loss the lender will bear
from the outstanding amount. Banks faces credit risks also from financial instruments like
interbank transactions, foreign exchange transactions, trade financing, options and many
more (Yamanaka & Kinoshita, 2018).
Problems in Credit Risks Model

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 Credit Concentration- Credit Risk Model is only concerned with only specific
borrower of any specific sectors. This can cause credit concentrations of the
connection of the borrowers (Yang & Lv, 2019. It doesnot identifies the risks that is
related to risk of large exposure. For example, in case of any credit risks in particular
sector or group of entities, the whole sector will be affected, which will automatically
create loss to the banks (Khokhlova, Kretova & Burov, 2019). This model doesnot
calculates the loss in this scenario. It doesnot distributes its lending practices among
all the business sectors.
 This model evaluates all the information that the bank needs. This information
includes; the borrower’s credit history, capacity of borrower to repay the loan, total
capital of the borrower, loan conditions and the collaterals of the borrower. But, there
are various companies and borrowers who donot furnish sufficient information. The
information may be incorrect and can cause higher credit risk for the company
(Garcia, Sanchez & Marques, 2019). In this case, if the banks lends money to the
company, then it will ultimately poses a higher credit risk for the banks. Credit Risk
Model doesnot evaluate this, whether the information is complete or correct. It only
calculates the credit risks on the basis of the credit history that is provided by the
borrower or any company.
 Credit risk model only checks the collaterals that the borrowers have to secure their
loans. They mitigates the risks exposure to their properties or assets. But, it doesnot
analyse the value of assets. The value of assets can deteriorate over a particular period
of time (Khashei & Torbat 2019). It doesnot monitor the performance of the value of
assets. This will ultimately create credit problems for the banks. There can be also
frauds related to these collaterals. Credit risk model doesnot verify these
circumstances. Hence, this is one of the most important problem of this model.

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 Credit risk model doesnot completely evaluates the cyclic performance of an industry.
In some cases, certain industries can go through depression period but may cause
boom period in the future. It only looks on the current trends of the business, but
doesnot calculates the future slumps of the industry performances.
Therefore, Credit risk model doesnot measures the above problems and this causes
various credit problems for the banks (Chen & He, 2017). This only implement a sound risks
management system in order to maximise their returns from the loans, but doesnot focus on
minimising the above risks of the banks.
VAR Models
VAR models or Value at Risk Models are a type of statistical measures that helps in
measuring & quantifying the financial risks that is associated with a portfolio or a firm over a
specific period of time. This model is widely used by banks to determine the ratio of potential
loss in the portfolio of the financial firms (Elhorst, Gross & Tereanu, 2018). This model helps
to control these financial risks. It identifies the probability of loss that can occur in a specific
time frame. For example, it can identify the probability of loss associated with a trading
assets by assessing the cumulative risks of the assets positions from different trading
department. Banks estimates this measures by using historical simulation methods and Monte
Carlo methods.
Problems in VAR models
 Value at Risk model uses analytical and Monte Carlo method. These method is
calculated using assumptions that the assets are normally distributed. It only estimates
the risks. It doesnot calculates the exact risks associated with the assets. It is only
assumed (Billio, Casarin & Rossini, 2019).

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 This model donot have any standard protocol to evaluate the risks. The risks are
calculated using probabilities and the potential loss determines the lowest amount of
risk associated with the outcomes. This underestimated the other risk magnitudes that
may be associated with the portfolios.
 This model provide descriptive statistics on the trading revenues from the trading
accounts for the banks. It forecasts the estimations from the trading activities. For this,
it uses daily profit & loss from the trading activities for analysing the trading risks. It
is difficult to forecast the daily risks factors of the trading portfolios like interest rates
risks, exchange rate risks, risks in commodity prices and equity risks (Carlini &
Santucci de Magistris, 2019). It is difficult to measure all the market risk factors on a
daily basis. It can only identifies the regulatory constraints of the market. Banks may
make approximations to roughly estimate the figure. It did not properly the changes in
the profit and loss volatility. Hence, it reduce the forecast advantage and harm the
accuracy.
 The VAR models doesnot capture the variances in the market risks. It cannot evaluate
the risks related to hedging effects or diversification effects of the portfolios (Atwood,
2019). Therefore, banks can only have limited exposures to stress on such portfolios.
 Since, VAR models identifies the regulatory standards, there are certain regulatory
guidelines that may contribute VAR being conservatives. This can provide a limited
changes in the volatility and may use simple procedure to calculate these risks. Hence
this will affect the banks performance (Skripnikov & Michailidis, 2018). They will
also be conservative and actual risks related to the trading accounts will be affected.
 This model is unable to capture the credit and liquidity risk of the banks.

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Part B
Recommendations
Credit Risk Model
 Banks must distributes its lending practices at a broader perspectives to all the
borrowers and business sectors. They must describe the level of risks that is
associated in the bank’s portfolio with respect to concentration of the overall sector.
They should calculate the concentration risk by observing the concentration ratio,
which is the proportion of the portfolio the loan represents (Alam et al., 2019). They
can also use herfindahl index, which measures the size of firm with related to the
industries in order to calculate the degree of concentration of the particular sector.
 Banks should thoroughly check the borrower’s personal information in order to
ensure that the information is sufficient and complete. They should build a strong
relationship with the customers before extending any credit. This will help to
determine the attitude of credit & clearly understand what the borrower expects from
them. Banks can use credit agency reports as a tool to check the financial history of a
company, bank reports of the company to evaluate the details of the bank’s relation
with the company & borrowing capacity of the company, Review the financial
statements to evaluate the profitability and liquidity position of the company.
 Banks must focus on calculating the value of the assets by calculating the collateral
value of the assets. This can be done by evaluating the recent sale price of the asset.
They can take advice from a qualified expert before lending a loan to the borrower.
This will help the banks to secure their loans (Hargarter & Van Vuuren, 2019).
 Banks can also evaluate and monitor the performance of and cyclic trends of the loan
portfolio by closely visualising the information in the charts & pictures to clearly

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understand the industry trend, industry concentration and evaluate to a large
exposures.
Therefore, banks should focus on minimising the impact of credit risks by properly
evaluating the factors and controlling the actions. They should focus on minimising the
problems of the above three factors that includes; credit concentration, credit issuing
problems and cyclic performances.
Value at Risk Model
 Banks should use this model to forecast and compare the different financial
instruments. This model will help to compare between the trading accounts and
identifies the instruments which is more risky than the other one. They should not use
this model to forecast the risks associated with the trading accounts.
 Banks can use financial software’s of VAR models to properly estimate the
probabilities instead of simply writing down the assumptions according to the
historical data. Financial software will be more concise on the results as compared to
manual prediction of the figures.
 Banks can successfully implement the VAR model by analysing the portfolio return
distribution. They must appropriately identify the value of each portfolio. They must
properly handle the disadvantages of VAR models before examining the value of
portfolio.
 Banks should use this method only if all the approaches of Value at Risk Model gives
similar result. If the results is different for the same portfolio with different methods.
This will protect from market risks of the portfolio. They can do for sensitive analysis
techniques to determine how the target variables are affected with the changes in
input variables. This method will help to predict the results under certain market

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conditions. It will consider the market risks and determines the input variables
(Hugonnier & Morellec, 2017). Banks can study the change in interest rates on bonds
and accordingly predict the outcomes. Therefore, sensitivity analysis can be selected
by back as an alternative of Value at Risk Model.
 Banks can use the coherent risk measure that is more sensitive to wrong estimation of
the distribution. It can combine this risk measure with Value at Risk Model to get a
more realistic and reliable measure of risks. The coherent risk measures will capture
the current market events of the portfolio and measures the risks with the financial
instrument.
Conclusion
Therefore, it can be concluded from the above report that, there are various problems
associated with the risk measurement model in banking sectors. The most important problem
in credit risk model is inefficient data management, lack of proper risk assessment tools and
less risk visualization & reporting in the borrower’s information’s. The banks are only
focusing only on maximising the revenues and not on minimising the risks. It is
recommended that banks should focus on minimising the three problems that includes; credit
concentration, credit issuing problems and cyclic performances. They can take advice from a
qualified expert before lending a loan to the borrower. This will help the banks to secure their
loans. They should visualise the information in the charts & pictures to clearly understand the
industry trend, industry concentration and evaluate to a large exposures. It is also reviewed
that Value at Risk Model (VAR) model estimates the normal distribution probabilities, which
is not accurate with respect to market risk of the portfolios. It is recommended that, banks can
combine coherent risk measure with Value at Risk Model to get a more realistic and reliable
measure of risks. The coherent risk measures will capture the current market events of the
portfolio and measures the risks with the financial instrument.

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References
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Optimization Subject to VaR, cVaR and Related Risk Constraints (No. 2217-2019-
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Journal of Econometrics, 212(1), 97-115.
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cointegrated VAR models with the F (d) condition. Journal of Business & Economic
Statistics, 37(1), 134-146.
Chen, W., & He, X. (2017). Pricing credit default swaps under a multi-scale stochastic
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Doss, S. (2017). Evaluation of Deposit Insurance Fund Adequacy Using Credit Risk Model—
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Elhorst, J. P., Gross, M., & Tereanu, E. (2018). Spillovers in space and time: Where spatial
econometrics and global VAR models meet.
García, V., Sánchez, J. S., & Marqués, A. I. (2019). Synergetic application of multi-criteria
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