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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*
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: fei.huang@anu.edu.au

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.

Type
Papers
Copyright
© Institute and Faculty of Actuaries 2014 

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References

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307327.CrossRefGoogle Scholar
Bollerslev, T. & Wooldridge, J.M. (1992). Quasi-maximum likelihood estimation and inference in dynamic models with time varying covariances. Econometric Reviews, 11, 143172.CrossRefGoogle Scholar
Box, G. & Jenkins, G. (1976). Time Series Analysis: Forecasting and Control. Holden Day, San Francisco.Google Scholar
Butt, A. (2010). Actuarial Management of Closed Defined Benefit Retirement Schemes Using Stochastic Simulation. PhD thesis. Australian National University.Google Scholar
Chan, W.-S., Wong, A.C.S. & Tong, H. (2004). Some nonlinear threshold autoregressive time series models for actuarial use. North American Actuarial Journal, 8(4), 3761.CrossRefGoogle Scholar
Dickson, D.C.M. (2001). Modern landmarks in actuarial science, technical report, The University of Melbourne, Melbourne.Google Scholar
Enders, W. (2010). Applied Econometric Time Series. John Wiley & Sons, New York.Google Scholar
Fisher, I. (1930). The Theory of Interest. The Macmillan Company, New York, NY.Google Scholar
Frees, E.W., Kung, Y.-C., Rosenberg, M.A., Young, V.R. & Lai, S.-W. (1997). Forecasting social security actuarial assumptions. North American Actuarial Journal, 1(4), 4970.CrossRefGoogle Scholar
Geohegan, T., Clarkson, R., Feldman, K., Green, S., Kitts, A., Lavecky, J., Ross, F., Smith, W. & Toutounchni, A. (1992). Report on the Wilkie stochastic investment model. Journal of the Institute of Actuaries, 119(II), 173228.CrossRefGoogle Scholar
Hamilton, J. (1994). Time Series Analysis. Princeton University Press, Princeton.CrossRefGoogle Scholar
Hardy, M.R. (2001). A regime-switching model of long-term stock returns. North American Actuarial Journal, 5(2), 4153.CrossRefGoogle Scholar
Harris, G. (1999). Markov chain Monte Carlo estimation of regime switching vector autoregressions. ASTIN Bulletin, 29(1), 4780.CrossRefGoogle Scholar
Huber, P. (1997). A review of Wilkie’s stochastic asset model. British Actuarial Journal, 3, 181210.CrossRefGoogle Scholar
Hyndman, R.J., Razbash, S., Schmidt, D., Zhou, Z., Khan, Y. & Bergmeir, C. (2013). Forecast: forecasting functions for time series and linear models. R package version 4.8. Available online at the address http://CRAN.R-project.org/package=forecast [accessed 22-Feb-2014].Google Scholar
Impavido, G., Hu, Y.-W. & Li, X. (2009). Governance and fund management in the Chinese pension system, technical report, International Monetary Fund, Washington, D.C.Google Scholar
Kitts, A. (1990). Comments on a model of retail price inflation. Journal of the Institute of Actuaries, 117, 407413.CrossRefGoogle Scholar
Li, R. (2007). Increasing interest rates to cool down the equity market (Chinese), Beijing Morning Post, Available online at the address http://finance.people.com.cn/GB/1045/5752753.html [accessed 22-Feb 2014].Google Scholar
Lütkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Springer-Verlag, New York, NY.CrossRefGoogle Scholar
Oksanen, H. (2010). The Chinese pension reform first results on assessing the reform options, technical report, European Economy, Brussels.Google Scholar
Pan, H. & Cao, H. (2012). A study on interbank overnight offered rate recent fluctuations. China Money, 10, 47.Google Scholar
Rosenberg, M.A. & Young, V.R. (1999). A Bayesian approach to understanding time series data. North American Actuarial Journal, 3(2), 130143.CrossRefGoogle Scholar
Sahin, S., Cairns, A., Kleinow, T. & Wilkie, A. (2008). Revisiting theWilkie investment model, technical report, The 18th International AFIR Colloquium, Rome.Google Scholar
Sin, Y. (2005). Pension liabilities and reform options for old age insurance, technical report, World Bank, Washington, D.C.Google Scholar
Smith, A. (1995). Workshop on stochastic asset models, technical report, The Institute of Actuaries General Insurance Convention, Bournemouth.Google Scholar
Thomson, R.J. (1996). Stochastic investment modelling: the case of South Africa. British Actuarial Journal, 2, 765801.CrossRefGoogle Scholar
Thomson, R.J. & Gott, D.V. (2009). Stochastic models for actuarial use: the equilibrium modelling of local markets. Astin Bulletin, 39(1), 339370.CrossRefGoogle Scholar
Wang, X., Hu, J., Geng, S. & Liu, K. (2007). Yang Lao Bao Xian Jing Suan Li Lun Yu Shi Wu (Chinese). China Labor and Social Security Press, Beijing.Google Scholar
Wang, Z. (2012). The emipirical research on selecting the benchmark rate for China monetary market. Review of Investment Studies (Chinese), 1, 2540.Google Scholar
Whitten, S.P. & Thomas, R.G. (1999). A non-linear stochastic asset model for actuarial use. British Actuarial Journal, 5(5), 919953.CrossRefGoogle Scholar
Wilkie, A. (1986). A stochastic investment model for actuarial use. Transactions of Faculty of Actuaries, 39, 341403.CrossRefGoogle Scholar
Wilkie, A. (1995). More on a stochastic asset model for actuarial use. British Actuarial Journal, 1, 777964.CrossRefGoogle Scholar
Wilkie, A.D., Sahin, S., Cairns, A.J.G. & Kleinow, T. (2011). Yet more on a stochastic economic model: part 1: updating and refitting, 1995 to 2009. Annals of Actuarial Science, 5(1), 5399.CrossRefGoogle Scholar