Abstract
Risk measurement and pricing of financial positions are based on modeling assumptions, which are common assumptions on the probability distribution of the position's outcomes. We associate a model with a probability measure and investigate model risk by considering a model space. First, we incorporate model risk into market risk measures by introducing model weighted and superposed market risk measures. Second, we quantify model risk itself and propose axioms for model risk measures. We introduce superposed model risk measures that quantify model risk relative to a reference model, which is the financial institution's model of choice. Several risk measures that we propose require a probability distribution on the model space, which can be obtained from data by applying Bayesian analysis. Examples and a case study illustrate our approaches.
Highlights
Mathematical methods for financial risk measurement and pricing are based on assumptions that form models
One example will be quantile-based market risk measures, like value at risk or expected shortfall that account for model risk
Other examples, introduced below, are value at risk, expected shortfall and spectral risk measures that account for model risk
Summary
Mathematical methods for financial risk measurement and pricing are based on assumptions that form models. Models capture only some particular characteristics of real-world phenomena. One should not expect that they always give us a decent answer to the problem at hand. Improper modeling assumptions can lead to wrong business decisions and significant financial losses. China Aviation Oil (Singapore) Ltd. faced losses of USD 550 million in November 2004 from using inadequate modeling approaches for derivatives.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
