2016-FRR Exam Dumps - GARP Financial Risk and Regulation Questions and Answers

Mathematical valuation and risk assessment models can be misleading or inaccurate for several reasons:

Missing factors in models: Important variables or conditions might be overlooked.

Bad or wrong data: Incorrect or poor-quality data input can lead to faulty outcomes.

Misinterpretation of results: Users might not correctly understand or use the results.

Errors in the derivation of the model: Mistakes in the mathematical formulation or assumptions can lead to errors.

All these factors contribute to the potential inaccuracy or misleading nature of such models.

Floating rate bonds have coupon payments that are tied to a floating interest rate or index, such as LIBOR. This means their coupon payments adjust periodically with changes in the underlying interest rates. Due to this mechanism, floating rate bonds typically have less price risk compared to fixed-rate bonds because their coupon payments reset in line with current market rates. Hence, floating rate bonds are generally not very sensitive to changes in interest rates since the adjustments in coupon payments help maintain their value. Therefore, the statement that floating rate bonds are very sensitive to changes in interest rates is incorrect.

The Sarbanes-Oxley Act includes risk and control requirements for financial statements in the United States. It mandates strict reforms to improve financial disclosures from corporations and prevent accounting fraud. This Act directly impacts operational risk management by setting standards for all U.S. public company boards, management, and public accounting firms.

Short selling in physical commodities markets involves borrowing the commodity and selling it with the hope of buying it back at a lower price. The right combination of characteristics that generally allows short selling without making the transaction prohibitively expensive includes factors like liquidity, storage costs, and ease of borrowing.

Oil: While the oil market is highly liquid, storage costs and logistical challenges can make short selling more expensive.

Natural Gas: Similar to oil, natural gas involves significant storage and transportation costs, making short selling less attractive.

Grain: Grain markets can have high volatility and storage costs that could complicate short selling.

Gold: Gold has the ideal combination of characteristics for short selling. It is highly liquid, has relatively low storage costs, and is easy to borrow. These factors make short selling gold less prohibitively expensive compared to other commodities.

The delta-normal method, which uses the delta to approximate the change in the price of an option, has limitations when dealing with options. This method can significantly misrepresent the risks associated with options, particularly because it only considers the linear component of the price change (delta) and ignores the convexity (gamma). Specifically, it tends to understate the risks of long positions in calls because it does not fully account for the increasing delta as the underlying asset price rises, leading to potential underestimation of the risk. Conversely, it overstates the risks of short positions in puts, as the delta of puts decreases less dramatically when the price of the underlying asset falls.

The portfolio VaR when the returns of two assets are uncorrelated can be calculated using the formula:

Portfolio VaR=(VaR of ThetaGroup)2+(VaR of VolgaCorp)2Portfolio VaR=(VaR of ThetaGroup)2+(VaR of VolgaCorp)2​

Plugging in the values:

Portfolio VaR=(0.3)2+(0.4)2=0.09+0.16=0.25=0.5Portfolio VaR=(0.3)2+(0.4)2​=0.09+0.16​=0.25​=0.5

So, the portfolio VaR is $0.5 million.

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.

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.

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.

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​​.