Real-Time Bayesian Nonparametric Prediction of Solvency Risk

16 Pages Posted: 13 Jun 2017 Last revised: 10 Dec 2017

See all articles by Liang Hong

Liang Hong

The University of Texas at Dallas

Ryan Martin

North Carolina State University - Department of Statistics

Date Written: December 8, 2017

Abstract

Insurance regulation often dictates that insurers monitor their solvency risk in real time and take appropriate actions whenever the risk exceeds their tolerance level. Bayesian methods are appealing for prediction problems thanks to their ability to naturally incorporate both sample variability and parameter uncertainty into a predictive distribution. However, handling data arriving in real time requires a flexible nonparametric model, and the Monte Carlo methods necessary to evaluate the predictive distribution in such cases are not recursive and can be too expensive to rerun each time new data arrives. In this paper, we apply a recently-developed alternative perspective on Bayesian prediction based on copulas. This approach facilitates recursive Bayesian prediction without computing a posterior, allowing insurers to perform real time updating of risk measures to assess solvency risk, and providing them with a tool for carrying out dynamic risk management strategies in today's "big data"' era.

Keywords: density estimation; mixture model; nonparametric Bayes; risk management; value-at-risk; conditonal tail expectation.

Suggested Citation

Hong, Liang and Martin, Ryan, Real-Time Bayesian Nonparametric Prediction of Solvency Risk (December 8, 2017). Available at SSRN: https://ssrn.com/abstract=2984878 or http://dx.doi.org/10.2139/ssrn.2984878

Liang Hong (Contact Author)

The University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Ryan Martin

North Carolina State University - Department of Statistics ( email )

Raleigh, NC 27695-8203
United States

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