Predictive Modeling Applications in Actuarial Science
- Volume 1
- Introduction
- Predictive Modeling Foundations
- Predictive Modeling Methods
- Bayesian and Mixed Modeling
- Longitudinal Modeling
- Volume 2
- Generalized Linear Model
- Extensions of the Generalized Linear Model
- Unsupervised Predictive Modeling Methods
-
Applications on Current Problems in Actuarial Science
- Chapter 8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- Chapter 9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 10 - Fat-Tailed Regression Models
Authors
Peng Shi | University of Wisconsin-Madison
pshi@bus.wisc.edu
Chapter Preview
In the actuarial context, fat-tailed phenomena are often observed where the probability of extreme events is higher than that implied by the normal distribution. The traditional regression, emphasizing the center of the distribution, might not be appropriate when dealing with data with fat-tailed properties. Overlooking the extreme values in the tail could lead to biased inference for ratemaking and valuation. In response, this chapter discusses four fat-tailed regression techniques that fully utilize the information from the entire distribution: transformation, models based on the exponential family and on generalized distributions, and median regression.
| Data | R Code |
| Claim | |
| Officeout | |
| Section 3 | |
| Section 4.3(SAS Code) | |
| Section 5 |