Suppose after you just joined a risk management team, the boss says: Welcome, I have been doing this for twenty years. Here are the volatilities and VaRs I calculated every day. Take a look and see if you can find any problem and make some improvement of the model.

Q: if you found a predictor (or called indicator) that predicts the next day's volatility, You say: that is not supposed to happen, there is something wrong with the volatility model. Boss will say

Question

Suppose after you just joined a risk management team, the boss says: Welcome, I have been doing this for twenty years. Here are the volatilities and VaRs I calculated every day. Take a look and see if you can find any problem and make some improvement of the model.

Q: if you found a predictor (or called indicator) that predicts the next day's volatility, You say: that is not supposed to happen, there is something wrong with the volatility model. Boss will say

BlackTom AI · Accepted Answer

The question presents a scenario in which a new team member examines an existing volatility model and discovers a predictor that forecasts the next day's volatility. The prompt then asks what the boss would say in response to this finding.
Option 1: 'Make sense'
- This choice is arguing that the discovery is sensible or reasonable in the given context. If a predictor can forecast next-day volatility, it flags that there may be information not properly captured by the current volatility model. It could indicate model misspecification, data leakage, overfitting, or an opportunity to improve the model by incorporating the predictive signal. In other words, the presence of a predictive indicator challenges the adequacy of the existing model and warrants investigation rather than dismissal. Therefore, the reaction that such a finding 'makes sense' aligns with a cautious, inquiry-driven response typical in risk management.
Option 2: 'Make no sense'
- This option asserts that the predictor’s ability to forecast volatility is illogical or impossible under the current framework. However, in practice, discovering a predictive signal for volatility would prompt questions about model assumptions, data handling, and potential leaking of future information. It would be unusual to conclude immediately that the finding is nonsensical without validating the signal, checking for spurious correlations, and testing robustness. Hence claiming 'Make no sense' would overlook standard diagnostic steps and the possibility of genuine improvement, making this option less reasonable.
In summary, Option 1 correctly paths the sensible reaction to a detected predictive signal by prompting scrutiny and model refinement, while Option 2 prematurely rejects a potentially informative finding and ignores standard due diligence steps in model validation.

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