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Answer: Market risk events are easier to map to risk factors than operational risk events.
## Explanation Option A is the correct answer because: - **Market risk events** can be more easily mapped to observable risk factors (such as interest rates, equity prices, exchange rates) that have historical data and can be modeled quantitatively - **Operational risk events** are more diverse and harder to quantify - they include internal fraud, external fraud, employment practices, clients/products/business practices, damage to physical assets, business disruption, and execution/delivery/process management - Operational risk events often lack clear risk factors and historical data, making them more difficult to model using the same VaR framework developed for market and credit risk Option B is incorrect because quantitative methods for operational risk VaR do exist (such as loss distribution approach). Option C is incorrect because market and credit VaR also consider both frequency and severity aspects in some advanced approaches. Option D is incorrect because Monte Carlo techniques can be used for operational risk VaR, though the distributions may not be normal.
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Your supervisor is an expert in market and credit risk. He recruits you to manage the operational risk department. He would like to use VaR to measure the firm's operational risk and proposes that you use the same VaR framework previously developed for market and credit risk. Which of the following arguments is a valid argument for why it is difficult to estimate an operational VaR using the same framework as market and credit VaR?
A
Market risk events are easier to map to risk factors than operational risk events.
B
Quantitative methods for estimating operational risk VaR do not exist.
C
Market and credit VaRs are estimated using only frequency distribution, but operational VaR is estimated using both a frequency distribution and a severity distribution.
D
Monte Carlo techniques cannot be used for an operational risk VaR because the underlying risk factors are not normally distributed.