Bayesian lessons for payout structuring

Derivatives businesses are under enormous pressure to deliver a clear rationale and unprecedented transparency for their products. Specialised quantitative support of structuring is required. Andrei Soklakov exposes a link between payout structures and the likelihood functions that are implied by investment research. The resulting framework leads to rationale-driven, simple and transparent product designs

In the past few decades, the world of financial derivatives has experienced an explosion in the development of quantitative methods. Remarkably, very little of that development went into supporting the process of product innovation and structuring – the cornerstone of every derivatives business. Compared with modelling, which continues to attract most quantitative effort, product innovation remains essentially a form of art.

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Bayes