Financial Risk and Regulation 2016-FRR Syllabus Exam Questions Answers

Estimating the statistical distribution of bank losses is challenging due to several factors:

I. The needed data is vast in quantity: Gathering comprehensive data covering all potential risk factors and historical loss events is extensive.

II. The data requires bringing together significantly different measures of risk: Banks face multiple types of risks (e.g., credit, market, operational) which need to be integrated into a single cohesive loss distribution model.

III. Some risks are difficult to quantify and hence the data might involve subjective elements: Certain risks, particularly operational and reputational risks, are inherently difficult to measure and may require judgment and subjective assessment.

All these factors make assembling the necessary data for accurate loss distribution estimation complex.References: How Finance Works, discussions on risk measurement and data challenges in banking​​.

Implementing a successful RCSA program requires thorough preparation. This includes interviewing participants, stakeholders, and support functions before launching the RCSA to ensure it is well designed and effectively addresses all relevant risks and controls.

Day 1:

Bank Milo has $4 million in cash.

$5 million in loans coming due with an expected default rate of 1%.

The proceeds from the loans will be $5 million * (1 - 0.01) = $4.95 million.

Total cash available at the end of Day 1 = $4 million + $4.95 million = $8.95 million.

No outflows on Day 1.

Cumulative liquidity = $8.95 million (positive).

Day 2:

No cash inflow.

$9 million is due for a securities purchase.

Cumulative liquidity = $8.95 million - $9 million = -$0.05 million (negative).

Day 3:

No cash inflow.

$8 million due for commercial paper pay off.

Cumulative liquidity = -$0.05 million - $8 million = -$8.05 million (negative).

Therefore, Bank Milo faces negative cumulative liquidity on Days 2 and 3.

References:Based on the financial risk and regulation analysis, these calculations align with standard liquidity management practices detailed in the provided materials.

Value at Risk (VaR) is a statistical measure used to assess the risk of loss on a specific portfolio of financial assets. In this case, Bank G has a 1-year VaR of USD 20 million at 99% confidence level, while Bank H has a 1-year VaR of USD 10 million at the same confidence level. The VaR figure indicates the maximum potential loss over a given period (1 year) at a certain confidence level (99%). Therefore, Bank G is taking twice the risk of Bank H as the VaR is double that of Bank H.