ACCG 399 Accounting in context

ACCG 399 Accounting in context

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The Bank has to face with many risks in the business, like Interest rate risk, Foreign exchange risk, Credit risk, and Operation risk…Lending money is one of the main operations of each Bank. By mobilizing money as saving account, the Bank uses this money for individual customer, or business to borrow and make profit. But recently, with the inflation, the credit risk increases a lot which causes the bad debt in many countries, as well as the regression of world economy. “Credit risk is the distribution of financial losses due to unexpected changes in the credit quality of counterparty in a financial agreement” (Morgan, 1997). According to the Karen A. Horcher, Credit risk will arise from default or failure loans which are expected to be paid, between the Bank and other individual or oganisations. There are many types of Credit risk, such as: Default risk

Counterparty risk
Legal risk
Sovereign or country risk
Concentration risk

Besides the Default risk is defined as the traditional risk, the Counterparty risk is also one of the most commons risk in the Banking industry. It is usually a loan which will be paid in the future but the counterparty defaults before this time, and arises from 3 typical derivative instruments are Interest rate and currency swaps, Forwards, and Options. The Counterparty risk is considered into 2 parts: Pre-settlement risk and Settlement risk. Pre-settlement risk comes from the case of default of the counterparty or the financial intermediary responsible for the settlement, before the end of the contract with the Bank, so the loan cannot be paid. In this case, the Bank have to consider about the cost of new contract which is similar to the default but with other parties and may be lower profit. For example, if A makes contract about borrowing money from Bank but before the end of the contract, A goes bankrupt and contract settlement is impossible. So Bank will make loss with the loan of A is bad debt, and faces a cost when substituing the defaulted contract. This is the reason why it is also called Replacement risk and the debt and asset trading operation was born. The Bank can sell the loan for Debt and Asset trading Company, with the lower price than the value, and A becomes the debtor of this Company. The Pre-settlement risk is also considered in the current exposure, and the potential exposure. Settlement risk comes from exchange of payment, which is already paid but does not receive yet. The losses are usually large, and relate to the foreign exchange trading, interest…For example, the trading between the 2 companies, in different countries, currencies, and times. The value of money can be changed day by day, so the amount of loan also changed, especially if relating to the third party, the risk is expanded. There will have the delay between transactions and make the value of trading be changed. This risk is called Herstatt risk comes from a small bank in German went to bankrupt in 1974 with many unfinished payments, and led the loose for counterparties in a lot of businesses in the world at this time (Karen A. Horcher). There are many theories, as well as models, to calculate the credit risk or management to reduce the risk at the lowest effect. 1. CreditMetrics:

CreditMetrics was published by J.P. Morgan, with the main aim is to estimate the portfolio risk due to credit events, which means the Bank will consider customers under the frameworks given, to predict the risk. The determination is divided into levels: BBB is started point, upper are A, AA, and AAA; lower are BB, B, CCC; and defaults. According to the model, firstly, the Bank needs the matrix of Calculating volatility of value due to credit quality changes, and the information should be come from the statistic of credit rating, like Standard and Poor, or Moody’s…After that, the beta will be used to describe the recovery rate uncertainty. Each customer will have the probability of recovery rate uncertainty, and it is estimated by the correlation of changing equity of customers. Because the changing of equity cannot be seen exactly, so the model suggests using bond as equity to evaluate. Therefore, the correlation of credit risk is equal to the correlation of recovery rate uncertainty of customers at the same time. For example, this following table shows the data from Standard & Poor, about the transition matrix, with today rating on the left and the horizon is rating at risk. Firstly, the Bank will calculate the transition matrix that comes from historical rating data, and BBB can up or down bases on the rate of A or BB. So in this case, BBB can upgrade to A (5.95%), and it becomes less risk.

Source: CreditMetrics™ (Greg M. Gupton, 1997)
Secondly, the Bank will consider this change, bases on credit quality migration. It can be down/up grade or defaults. In case of default, there will be a recovery rate for the debt depends on the historical debt and put into the seniority class of the debt, with the banks or other organizations. In addition, the recovery rate is different between countries, or cases. This information about the recovery rate is collected as consultation to renew or update. And with the down/up grade, the Bank can collect date about market yield and credit spread data by currency, rating category, industry and product as available, then revaluating the bond valuation of the business.

Source: CreditMetrics™ (Greg M. Gupton, 1997)
Because each customer has particular probability of recovery rate uncertainty, the Bank can combine the A customer with BB customer, or AAA with BB…to find out the correlation of credit risk. 2. Portfolio Manager of KMV:

This model also considers credit quality changes due to rating migrations like CreditMetrics, but finds the recovery rate uncertainty directly which called Expected Default Frequency (EDF), by the theory of Merton (1974). It means that this model focuses on the structure of equity of the customer, the insecure of the equity valuation, and the market value of equity. The Bank decides the loan for a company by characterizing the company's equity as a call option on its assets. The borrowing of the company stands for the put option to the equity owned by this company that the exercise price is equal to the value of the loan. At the time to pay back to the bank, if the market value of the equity is lower than the loan value, the company has to do the put option. The price of the put option can be calculated by the formular of Black-Scholes (1973). Basing on the Merton theory, the Bank can evaluate the value of EDF: 1. Identifying the market value of the equity (V), as well as the insecure of the equity valuation (σ). 2. Calculating the distance between EDF to the impossible pay back of the company (DD – Distance to default). 3. Exchange DD to EDF bases on historical debt and bond.

And about the market value of equity and the insecure of the equity, they are determined by the Merton model. Assuming that each company has a private capital, so it is defined as the Call option to the equity that market price is equal to the loan at the payback time. Therefore, the value of Call option (S) and the insecure of private capital value (σs) (Greg M. Gupton, 1997): S = f × (V, σ, LR, c, r)

(σs) = g (V, σ, LR, c, r)
LR: the current market value of capital structure of the company. c: the average value of interest paid recurrently of long term debt of the company. r: interest of unrisk.
S: the value of private capital of the company (the value of share) From this information, the Bank can calculate the V, as well as σ. Finally, the Distance to Default is presented by the formula (Greg M. Gupton, 1997): DD =

The loss of the Bank is estimated like the CreditMetrics. However, while the CreditMetrics describes the distribution of value of the credit list, the Portfolio Manager simulates directly the distribution of the loss. 3. Credit Risk+:

This model considers about the distribution of number of defaults over a period by identifying default rates and their volatilities but does not deal with the time of the defaults. To compare with 2 models above, the Credit Risk+ does not focus and depend on the Credit quality change. It collects information from the book value only of the company to work the model and divide customers: payback or not. The main idea of this model is to find the total loss distribution which according to the Poison distribution:

With “µ” is the average customer without paying back in the period of time, and “n” is the number of total of customers without paying back in the period of time. The loss for the Bank is calculated by the recovery rate for each type of customers. To find out the credit portfolio loss distribution, the customers are divided into groups and there are some expected loss numbers for each, relies on the weighted average of the unpaid loan. The unpaid correlation between customers is calculated when CreditRisk+ assumes that the average of unpaid rate in each group change randomly, following the Gamma distributed default rates. Finally, the credit portfolio loss distribution is found by the correlation of each group.

Model

CreditMetrics
Portfolio Manager
Credit Risk
The Credit Risk elements are considered
Including the portfolio of credit quality change and the portfolio of unpaid loan. The portfolio of unpaid loan, but be flexible to consider the change of credit quality The portfolio of unpaid loan.

The portfolio of credit quality changes
It is evaluated base of the credit rating at the beginning.
Based on the change of equity value and EDF each month.
Not mention
The Correlation between unpaid loans
Estimated bases on the correlation, between the share price and another factor in model. Estimated bases on the correlation, between the share price and another factor in model. Included into the instability of recovery rate uncertainty of each customer group. The expected loss

Calculated randomly by Beta distribution.
Calculated randomly by Beta distribution.
Set up already before.
The approach
Monte Carlo
Monte Carlo
Monte Carlo

4. Modelling Credit Exposure: Michael Pykhtin and Steven Zhu This model focuses especially the potential future exposure which is necessary for banks to compare exposure against limits, to price and hedge counterparty credit risk and to calculate economic and regulatory capital. There will be many differences of the exposure distribution, and allow calculate the entire exposure distribution at any future date. The aims of this model are to set of realization of counterparty-level exposure at each simulation date. 3 main components are used in this model: Scenario Generation: it means that the Bank will generate the market in the future with the fix set of the stimulation date. Each market scenario is a realization of a set of price factors (foreign exchange rate, interest rate, equity price…) that affect the values of the trades in the portfolio Instrument Valuation: is to evaluate the value of instrument in the Scenario Generation above. The model of valuation used to calculate exposure could be different from the front-office pricing models. Portfolio Aggregation: each simulation date and for each realization of the underlying market risk factors, counterparty-level exposure is obtained. Up to now, there are many models for the Bank when evaluating and making decision a loan for customers, but it is better if the Bank could combine them and use at the same time because it could consider the customer in many sides and more flexible. However, each model requires particular information and implementation, so the Bank should consider some problems: Is the Model suitable with the customers, as well as the structure of the Bank? Or Does it reflect most of the characters of the Credit risk? Is it easy to use and apply to the Bank?

Is it easy to collect the required information to set up the Model? How to evaluate the reliability of the result from the Model? The economic crisis is happening now, with the companies, including Banks, go bankrupt increases. Bad debt goes up, from countries to countries, and the cash flow in the world stuck. The Government and financial organizations also suggest the Bank improve the Credit risk Management system. Depending on the real situation, the Bank will choose the suitable models for itself.