Explanation:
Clustering of exceptions indicates that the model's breaches are not independent over time. This typically happens when the VaR model fails to account for time-varying volatility, such as volatility clustering (where periods of high volatility are followed by periods of high volatility) or regime shifts in the market. As a result, the model is slow to adjust its risk estimates during turbulent periods, leading to multiple exceptions in a short timeframe.
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Q.25 A bank is performing backtesting of its VaR model. They observe several exceptions clustered together within a short period. What does this clustering of exceptions suggest about the VaR model?
A
The model's confidence level is set too low, resulting in an expected higher number of exceptions
B
The model is not adequately capturing the dynamics of market risk, such as volatility clustering or regime shifts.
C
The model is calibrated to a different time period with lower market volatility, making it appear inadequate during periods of high volatility.
D
The model is using an inappropriate lookback period for the historical data, failing to capture recent market events.
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