Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86521
Authors: 
Bijleveld, Frits
Commandeur, Jacques
Gould, Phillip
Koopman, Siem Jan
Year of Publication: 
2005
Series/Report no.: 
Tinbergen Institute Discussion Paper 05-118/4
Abstract: 
Risk is at the center of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning counter- measures, the risk and severity of counterparty default concerns bank risk managers on a daily basis and the risk of infection has actuarial and epidemiological consequences. However, risk can not be observed directly and it usually varies over time. Measuring risk is therefore an important exercise. In this paper we introduce a general multivariate framework for the time series analysis of risk that is modelled as a latent process. The latent risk time series model extends existing approaches by the simultaneous modelling of (i) the exposure to an event, (ii) the risk of that event occurring and (iii) the severity of the event. First, we discuss existing time series approaches for the analysis of risk which have been applied to road safety, actuarial and epidemiological problems. Seco! nd, we present a general model for the analysis of risk and discuss its statistical treatment based on linear state space methods. Third, we apply the methodology to 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.
Subjects: 
Actuarial statistics
Dynamic factor analysis
Kalman filter
Maximum likelihood
Road casualties
State space model
Unobserved components
JEL: 
C32
G33
Document Type: 
Working Paper

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