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Model-based measurement of latent risk in time series with applications

Listed author(s):
  • Frits Bijleveld
  • Jacques Commandeur
  • Phillip Gould
  • Siem Jan Koopman

Risk is at the centre of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning countermeasures, the risk and severity of counterparty default concerns bank risk managers daily and the risk of infection has actuarial and epidemiological consequences. However, risk cannot be observed directly and it usually varies over time. We introduce a general multivariate time series model for the analysis of risk based on latent processes for the exposure to an event, the risk of that event occurring and the severity of the event. Linear state space methods can be used for the statistical treatment of the model. The new framework is illustrated for time series of insurance claims, credit card purchases and road safety. It is shown that the general methodology can be effectively used in the assessment of risk. Copyright 2008 Royal Statistical Society.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-985X.2007.00496.x
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Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

Volume (Year): 171 (2008)
Issue (Month): 1 ()
Pages: 265-277

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Handle: RePEc:bla:jorssa:v:171:y:2008:i:1:p:265-277
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  1. De Jong, Piet & Boyle, Phelim P., 1983. "Monitoring mortality : A state-space approach," Journal of Econometrics, Elsevier, vol. 23(1), pages 131-146, September.
  2. Lel Li & Karl Kim, 2000. "Estimating driver crash risks based on the extended Bradley-Terry model: an induced exposure method," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(2), pages 227-240.
  3. B. F. Finkenstädt & B. T. Grenfell, 2000. "Time series modelling of childhood diseases: a dynamical systems approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 187-205.
  4. Gaudry, M., 1984. "Drag, un Modele de la Demande Routiere, des Accidents et de Leur Gravite, Applique au Quebec de 1956 a 1982," Cahiers de recherche 8432, Universite de Montreal, Departement de sciences economiques.
  5. Harvey, Andrew, 2001. "Testing in Unobserved Components Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 1-19, January.
  6. Francesca Dominici & Aidan M.C. Dermott & Trevor J. Hastie, 2004. "Improved Semiparametric Time Series Models of Air Pollution and Mortality," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 938-948, December.
  7. Linda Allen & Anthony Saunders, 2003. "A survey of cyclical effects in credit risk measurement model," BIS Working Papers 126, Bank for International Settlements.
  8. Alexander Morton & Bärbel F. Finkenstädt, 2005. "Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 575-594.
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