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 17 - Time Series Analysis
Authors
Piet de Jong | Macquarie University
piet.dejong@mq.edu.au
Chapter Preview
This chapter deals with the analysis of measurements over time, called time series analysis. Examples of time series are in inflation and unemployment indices, stock prices, currency cross rates, monthly sales, the quarterly number of claims made to an insurance company, outstanding liabilities of a company over time, internet traffic, temperature and rainfall, the number of mortgage defaults, and so on. Time series analysis aims to explain and model the relationship between values of the time series at different points of time. Models include ARIMA, Structural, and Stochastic Volatility models and their extensions. The first two classes of models explain the level and expected future level of a time series. The last class seeks to model the change over time in variability or volatility of a time series. Time series analysis is critical to prediction and forecasting. This chapter explains and summarises modern time series modelling as used in insurance, actuarial studies and related areas such as finance. Modelling is illustrated with examples, analysed with the R statistical package.
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| Chapter 17 Code |