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Could you give me some examples and/or references on "risk-averse" loss functions (i.e. penalizing the underestimates more then the overestimates)?

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Bayesian Methods for Hackers has a pretty informative chapter on loss functions and touches on this. They provide python function below as an example of a loss function that penalizes overestimates more heavily:

def stock_loss(true_return, yhat, alpha = 100.):
    if true_return * yhat < 0:
        #opposite signs, not good
        return alpha*yhat**2 - np.sign(true_return)*yhat \
                        + abs(true_return) 
    else:
        return abs(true_return - yhat)

Note: you don't need to go full bayesian for this chapter to be relevant

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