Banking Risk Management (PD, EAD, LGD)

Key aspects of the IRB approach include:

1. Risk Differentiation: It allows for more risk sensitivity as banks can differentiate borrowers and exposures more granularly based on their own data.
2. Regulatory Approval and Oversight: Banks must obtain approval from their regulatory authority to use the IRB approach. This includes demonstrating the integrity and reliability of their internal risk assessment systems.
3. Capital Requirement Calculation: The IRB approach impacts the calculation of risk-weighted assets (RWAs), which in turn affects the amount of regulatory capital a bank needs to hold. Lower PDs, LGDs, and EADs typically lead to lower capital requirements.
4. Data and Model Management: Banks need to maintain comprehensive data on their credit exposures and develop sophisticated statistical models to estimate risk parameters.
5. Ongoing Monitoring and Validation: Banks are required to continuously monitor and validate their risk estimation models to ensure accuracy and compliance with regulatory standards.

The IRB approach is part of a broader move towards more risk-sensitive and sophisticated banking regulation, allowing banks to tailor their risk assessments and capital requirements to their specific risk profiles. However, it also demands high standards of risk management and internal controls.

The Probability of Default (PD) is a fundamental concept in finance, particularly in credit risk management. It represents the likelihood that a borrower will default on a loan or credit obligation within a given time frame, typically one year. Calculating PD is a key component in evaluating credit risk and is used by financial institutions for loan approvals, setting interest rates, and determining regulatory capital requirements.

How PD is Calculated:

1. Statistical Models: PD is often calculated using statistical models that analyze historical data on defaults. These models identify patterns and correlations between defaults and various borrower-specific and macroeconomic variables. Commonly used statistical techniques include logistic regression, probit models, and survival analysis.
2. Credit Scoring Models: For individual borrowers, PD is frequently estimated through credit scoring models like FICO scores in the United States. These models use data such as credit history, current debt levels, payment history, and other factors to calculate a score, which is then mapped to a probability of default.
3. Historical Default Rates: For corporate borrowers, PD can be estimated based on the historical default rates of similar companies or industries. Credit rating agencies often publish average default rates for different rating categories, which can be used as a benchmark.
4. Market-Based Measures: In some cases, PD can be inferred from market data. For instance, the prices of credit default swaps (CDS) can be used to derive the market’s view of the default probability of a company.
5. Internal Ratings-Based (IRB) Approach: In the banking sector, under the Basel Accords, banks using the IRB approach estimate PD internally based on their historical data and risk assessment methodologies. These estimates must meet certain regulatory standards and are subject to supervisory review.

Factors Influencing PD:

1. Borrower’s Credit History: Past credit behavior, payment history, and previous defaults are strong indicators.
2. Financial Health: For companies, factors like cash flow, debt levels, profitability, and leverage are crucial.
3. Economic Conditions: The broader economic environment, including GDP growth, unemployment rates, and industry health, can impact PD.
4. Loan Characteristics: The size, term, and nature of the loan (secured or unsecured) can influence the likelihood of default.

PD is a dynamic measure and can change over time with the borrower’s circumstances and economic conditions. It’s an essential tool in the management of credit portfolios and risk-based pricing of loans. Calculating PD accurately is crucial for lenders, as it impacts not only the risk and profitability of their loan portfolios but also compliance with regulatory requirements.

Exposure at Default (EAD) is a risk measurement concept used in banking and finance, particularly under the Basel Accords for credit risk management. It represents the estimated amount of loss a bank might face when a borrower defaults on a loan. Essentially, it’s the expected outstanding balance at the time of default.

How EAD is Calculated:

Calculating EAD is not as straightforward as calculating the current outstanding balance, as it often involves estimating future draws on lines of credit or changes in balances up to the point of default. Here’s a simple way to understand it with an example:

1. For Term Loans: If a loan has a fixed repayment schedule (like a term loan), the EAD can be relatively straightforward to calculate. It’s generally the outstanding balance at the time of default. For example, if a borrower has a Rs. 100,000 loan and has repaid Rs. 40,000 when they default, the EAD is Rs. 60,000.
2. For Revolving Credits and Lines of Credit: It’s more complex to estimate EAD because the borrower may not have drawn down the full credit line. Banks use models to predict how much of the line the borrower might use before default. For instance, if a borrower has a credit line of Rs. 50,000 and has currently drawn Rs. 20,000, but historically draws up to 80% of the line before trouble, the bank might estimate an EAD of Rs. 40,000 (80% of Rs. 50,000).

Factors Influencing EAD Calculation:

1. Loan Type: Term loans, revolving credit, lines of credit, etc., each have different considerations.
2. Credit Utilization Patterns: How the borrower typically uses and repays credit.
3. Credit Limit Changes: Any anticipated increases or decreases in the credit limit before default.
4. Macroeconomic Conditions: Economic factors that might influence the borrower’s credit usage.

Importance of EAD:

EAD is crucial in credit risk management and regulatory capital calculation. Along with the Probability of Default (PD) and Loss Given Default (LGD), it forms a key component in determining the capital requirements under the Basel Accords. Accurate estimation of EAD helps banks in managing their credit risk effectively and in maintaining financial stability.

Loss Given Default (LGD) is a key concept in credit risk management, representing the percentage of an exposure that a lender expects to lose if a borrower defaults on a loan. It essentially measures the severity of loss in the event of default.

How LGD is Calculated:

To calculate LGD, you take the total amount lost in the event of default and divide it by the total exposure at the time of default (EAD). The formula for LGD is:

LGD = Loss in the event of default / Exposure at Default (EAD)

Here’s a simple example to illustrate LGD:

1. Imagine a bank has lent Rs. 100,000 to a business.
2. If the business defaults, the bank estimates it can recover Rs. 40,000 by selling the collateral (like property or equipment) that secured the loan.
3. The loss in the event of default would be Rs. 100,000 (total loan amount) minus Rs. 40,000 (recovery), which equals Rs. 60,000.
4. The Exposure at Default (EAD) is Rs. 100,000 (since this is the total outstanding amount when the borrower defaults).
5. Therefore, the LGD would be Rs. 60,000 / Rs. 100,000 = 60%

This means the bank expects to lose 60% of the loan amount in the event the borrower defaults.

Factors Influencing LGD:

1. Collateral: The quality and value of collateral can significantly impact LGD. Higher value and easily liquidated collateral typically reduce LGD.
2. Seniority of Debt: Secured and senior debts usually have lower LGDs compared to unsecured and subordinated debts.
3. Borrower’s Financial Health: The worse the financial condition of the borrower, the higher the LGD might be.
4. Economic Conditions: In a poor economic environment, the value of collateral might decrease, and the recovery costs could increase, leading to a higher LGD.

LGD is a critical component in the risk management and capital requirement calculations for banks, especially under the Basel Accords. Along with Probability of Default (PD) and Exposure at Default (EAD), it helps in determining the economic capital that banks need to set aside to cover potential losses from credit risks. Accurate estimation of LGD is vital for effective risk management in lending activities.