Fat Tails and Extreme Events [26]

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Bullet points include: Output results are notoriously sensitive to input assumptions. Possible responses include: Treat quant models with scepticism (the fundamental manager’s approach?). Focus on reverse optimisation. Book covers all the main quantitative refinements, including: Robust approaches and Bayesian priors/anchors, e.g. Black-Litterman. Shrinkage. Resampled optimisation. And ties them back to earlier chapters. E.g. how resampled optimisation doesn’t avoid ‘fine structure’ problem, instead it just inherits it from the dataset being used for bootstrapping purposes

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