Tag: prediction
Estimating from historical data requires many assumptions about similarity. Reducing the number of estimated parameters can control model risk.
If detecting jumps is not hard enough, we have to deal with them afterwards. Models must handle jumps in a way that does not introduce excess whipsaw.
Detecting jumps in asset prices is important for robust parameter estimation, especially when estimating volatility.
Building a model around predictions, may be asking for trouble. As the data that was used to predict changes, the model may fall victim to errors.
Black swans, by definition, are unknowns unknowns. When we design models, we incorporate rules to mitigate the risk of model failure regardless of cause.
