Model Risk in Financial Modelling

Motivated by current post-crisis discussions and the corresponding shift in regulatory requirements, this thesis is dedicated to the study of model risk in financial modelling. It is well-known that the majority of finance quantities that are involved in asset pricing, trading, and risk management activities are dependent on the chosen financial models. This gives rise to model risk in all financial activities. Even when the chosen model form is appropriate, model outputs are still subject to parameter estimation uncertainty. Therefore, among different sources of model risk, we mainly focused on investigating the impact of parameter estimation risk and model selection risk in different financial models. Models investigated in this thesis are key models in option pricing, credit risk management, stochastic process of security returns and hedge fund return forecasting. We provoke a solution, which naturally stems from the Bayesian framework. Regarding parameter estimation risk, instead of focusing on point estimation value, it is possible to gauge the rich information about parameter uncertainty from the posterior distribution of parameters. Subsequent impact to model final outputs can be easily accessed by inserting the posterior distribution of parameters into the model. Depending on the related financial activities, model users may find it useful to adopt the estimated value at a certain percentile (e.g. 97.5%) of the posterior distribution as an overlay to the estimated mean value. While more than one candidate model is considered, posterior or predictive probability of a candidate model derived from the likelihood of the model output in fitting the data is applied for a model averaging exercise to account for model selection risk.