Explanation:
This scenario is a classic example of failure to use appropriate risk metrics (as classified in risk management failure literature, like René Stulz's framework). Value at Risk (VaR) only measures the threshold loss amount at a given confidence level. A 99% VaR means the bank expects to exceed the $25M loss on 1% of trading days. Over 200 days, exceeding the VaR twice is exactly 1% of the time. This means the VaR metric was structurally accurate in estimating the frequency of the losses.
However, VaR completely ignores the severity of the losses in the tail (the magnitude of losses beyond the VaR threshold). Because the bank lost catastrophic amounts ($500 million and $200 million) during those two days, relying solely on VaR left them blind to tail severity. The failure here was relying on an inadequate metric for tail risks; the bank should have used an additional, more appropriate metric such as Expected Shortfall (ES) to measure the magnitude of expected tail losses.
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Q.42 A bank only uses VaR to measure the risk in its trading book. The bank has a one-day VaR of USD25 million at a confidence level of 99%. During the last 200 trading days, this VaR was exceeded only twice, with total losses of USD500 million and USD200 million being incurred. According to the VaR measure on its own, the bank has been successful in managing its risk but, in reality, it has lost USD700 million. In this scenario, the failure of risk management can most likely be attributed to:
A
Failure to use appropriate risk metrics
B
Mismeasurement of known risk
C
Failure to take known risks into account
D
Failure in communicating risk to top management
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