GARP Financial Risk and Regulation (FRR) Series 2016-FRR Exam Dumps: Updated Questions & Answers (October 2025)

Early models of statistical credit analysis typically operated under the assumption that the default of any given obligor was independent of the default of any other. This simplification made the models more tractable but less realistic, as it did not account for potential correlations between defaults (e.g., economic downturns affecting multiple obligors simultaneously).

The risk manager is analyzing a long position worth USD 10 million. To hedge this portfolio, the risk manager uses options that decrease in value by JPY 0.50 for every JPY 1 increase in the long position. This effectively means the options are shorting the currency. Therefore, if the long position is fully hedged by these options, the overall exposure of the portfolio will be equivalent to the full value of the long position but in the opposite direction. Thus, the portfolio has the same exposure to USD as a portfolio that is short USD 10 million.

Market-linked credit risk drivers often face specific disadvantages:

Need to supply a large number of input parameters to the model:Complex models require numerous data inputs, which can be challenging to obtain and maintain.

Slow computation speed due to higher simulation complexity:The complexity of these models can lead to slower computation times, making them less efficient for real-time analysis.

Non-linear nature of models and the need to estimate unknown variables are also relevant but are not as primary disadvantages as the first two mentioned.

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A non-exotic (vanilla) call option gives the buyer the right, but not the obligation, to purchase the underlying asset at a predetermined price (strike price) on or before a specified expiration date. This characteristic is fundamental to call options, providing the buyer with the flexibility to exercise the option if it is advantageous.

Option prices are affected by several factors, including the volatility of the underlying asset. Volatility measures the degree of variation of the underlying asset's price over time. Higher volatility increases the likelihood of the option ending in-the-money, thus increasing its value. This relationship is directly proportional: as volatility increases, the potential for profitable movements in the underlying asset also increases, leading to higher option premiums.

The formula to estimate interest charges on a loan includes the risk-free rate, which is the base rate for borrowing with no risk of default. To this, the expected loss due to default (calculated as Probability of Default x Loss Given Default) and a spread to cover additional costs and profit margins are added. This comprehensive formula accounts for the risk and return expectations in lending.

The probability of default (PD) refers to the likelihood that a borrower will fail to meet its debt obligations over a specific time horizon. This is a core component in quantifying credit risk exposures.

Duration of default is not a commonly used term in credit risk analysis.

Exposure at default (EAD) measures the total value at risk in the event of default but does not directly refer to the likelihood of default.

Loss given default (LGD) measures the portion of the exposure that is lost when a borrower defaults but does not indicate the likelihood of default.

A country's creditworthiness is typically influenced by various performance statistics that indicate the overall health of its economy. Here’s a detailed analysis of each option:

A. Low unemployment:Low unemployment indicates a healthy labor market, suggesting economic stability and growth, which positively impacts creditworthiness.

B. Low inflation:Low inflation reflects price stability, which helps maintain the purchasing power of the currency and economic stability, thus benefiting creditworthiness.

C. High degrees of investment:High levels of investment indicate confidence in the economy and potential for future growth, positively affecting creditworthiness.

D. Low degrees of savings:Low savings rates can indicate potential issues with financial stability, as it suggests that households and the economy might lack a financial buffer against economic shocks. This can negatively impact creditworthiness, making it the exception in this list.

If the default risks of the two mortgage companies were independent, the probability of both defaulting at the same time would be the product of their individual probabilities of default.

Probability of a single default is 10%, so for both to default: 0.1 * 0.1 = 0.01 or 1%.

Initial Data:

Principal: $1,000,000

Initial Probability of Default (PD): 2%

Initial Loss Given Default (LGD): 50%

Risk-Free Rate: 6%

Interest Rate Spread: 3%

New Data:

New PD: 10%

New LGD: 75%

Expected Loss Calculation:

Initial Expected Loss:2%×50%=1%2%×50%=1%

New Expected Loss:10%×75%=7.5%10%×75%=7.5%

Interest Rate Adjustment:

The initial interest rate was:6%+3%=9%6%+3%=9%

To compensate for the increased expected loss, the new interest rate needs to reflect the higher risk.

New Interest Rate = Risk-Free Rate + Spread + Compensation for Additional Risk

New Spread:7.5%−1%=6.5%7.5%−1%=6.5%

New Interest Rate =6%+6.5%=12.5%6%+6.5%=12.5%

New Total Rate:12.5%+3%(????????????????????????????????????????????????????)=15.5%12.5%+3%(initialspread)=15.5%

Thus, considering the new conditions, the bank must adjust the interest rate to 20.5% to cover the increased risk.