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Stochastic economic models for actuarial use: an example from China

Published online by Cambridge University Press:  15 May 2014

Fei Huang ,
Adam Butt  and
Kin-Yip Ho
Fei Huang*
Affiliation:
Research School of Finance, Actuarial Studies and Applied Statistics, Australian National University, Australia
Adam Butt
Affiliation:
Research School of Finance, Actuarial Studies and Applied Statistics, Australian National University, Australia
Kin-Yip Ho
Affiliation:
Research School of Finance, Actuarial Studies and Applied Statistics, Australian National University, Australia
*
*Correspondence to: Fei Huang, Research School of Finance, Actuarial Studies and Applied Statistics, College of Business and Economics, Australian National University, Canberra, ACT 0200, Australia. Tel: (+61) 2 612 57390. Fax: (+61) 2 612 50087. E-mail: [email protected]
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Abstract

In this paper, the first study of stochastic economic modelling with Chinese data is conducted for actuarial use. Univariate models, vector autoregression and two cascade systems (equity-driving cascade system and price-inflation-driving cascade system) are described and compared. We focus on six major economic assumptions for modelling purposes, which are price inflation rate, wage inflation rate, long-term interest rate, short-term interest rate, equity total return and bond total return. Granger causality tests are used to identify the driving force of a cascade system. Robust standard errors are estimated for each model. Diagnostic checking of residuals, goodness-of-fit measures and out-of-sample validations are applied for model selection. By comparing different models for each variable, we find that the equity-driving cascade system is the best structure for actuarial use in China. The forecasts of the variables could be applied as economic inputs to stochastic projection models of insurance portfolios or pension funds for short-term asset and liability cash flow forecasting.

Keywords

Chinese economic modelsStochastic modellingCascade systems
Type
Papers
Information
Annals of Actuarial Science , Volume 8 , Issue 2 , September 2014 , pp. 374 - 403
Copyright
© Institute and Faculty of Actuaries 2014 

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