Title: Conditional information using copulas with an application to decision making
Abstract: We focus on the task of calculating conditional probabilities of the form Prob(U≤x|V≤y). We point out that in this case the relevant probabilities, Prob(U≤x) and Prob(V≤y), have the nature of a cumulative distribution. This enables us to use the Sklar theorem to directly calculate the required joint probability as a simple binary aggregation of these marginals using a copula. Here the choice of copula reflects the type of correlation between U and V. We study in considerable detail the effects of using different copulas. We also show that this enables us to simply and directly calculate the probability that U=x conditioned on the knowledge of the Prob(V≤y). We use this result to aid in decision-making where we compare alternative's expected payoffs based on the conditioned probabilities.
Publication Year: 2015
Publication Date: 2015-08-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 5
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot