Optimal Bayesian Hedging Strategies in the Context of Model Uncertainty
نویسندگان
چکیده
We investigate calibrating financial models using a rigorous Bayesian framework. Non-parametric approaches in particular are studied and the local volatility model is used throughout as an example. By incorporating potential calibration error into our method we design optimal hedges that minimise expected loss statistics based on different Bayesian loss functions decided by an investor’s preferences. Comparisons made with the standard hedge strategies show the Bayesian hedges to outperform traditional methods.
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