Assessing financial model risk
Model risk has a huge impact on any financial or insurance risk measurement procedure and its quantification is therefore a crucial step. In this talk, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.
Dirichlet Process and Poisson-Dirichlet Distribution
Dirichlet process and Poisson-Dirichlet distribution are closely related random measures that arise in a wide range of subjects. The talk will focus on their constructions and asymptotic behaviour in different regimes including the law of large numbers, the fluctuation theorems, and large deviations.
Extracting Latent States from High Frequency Option Prices
We propose the realized option variance as a new observable variable to integrate high frequency option prices in the inference of option pricing models. Using simulation and empirical studies, this paper documents the incremental information offered by this realized measure. Our empirical results show that the information contained in the realized option variance improves the inference of model variables such as the instantaneous variance and variance jumps of the S&P 500 index. Parameter estimates indicate that the risk premium breakdown between jump and diffusive risks is affected by the omission of this information.