2005
DOI: 10.2139/ssrn.873466
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Model-Based Measurement of Latent Risk in Time Series with Applications

Abstract: Summary.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 pro… Show more

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Cited by 2 publications
(3 citation statements)
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“…A recent example of stochastic and multivariate approach is the latent risk time series (LRT) modelling (Bijleveld, 2008;Bijleveld, Commandeur, Gould, & Koopman, 2008) which applies to risk in general. In the case of road risk, both the accident risk and the fatality risk may be modelled.…”
Section: 22mentioning
confidence: 99%
“…A recent example of stochastic and multivariate approach is the latent risk time series (LRT) modelling (Bijleveld, 2008;Bijleveld, Commandeur, Gould, & Koopman, 2008) which applies to risk in general. In the case of road risk, both the accident risk and the fatality risk may be modelled.…”
Section: 22mentioning
confidence: 99%
“…So far, it had only been applied to individual countries (e.g., Bijleveld et al, 2008;Van den Bossche et al, 2007;Antoniou and Yannis, 2013). The present study is the first large scale field trial to model road traffic fatalities in terms of fatality risk and exposure to that risk using structural time series methods.…”
Section: Resultsmentioning
confidence: 99%
“…This specification is identical to the specification of the local linear trend model (see Harvey, 1989;Commandeur and Koopman, 2007;Durbin and Koopman, 2012). 1 We can now provide the complete formulation of the LRT model as it was presented in Bijleveld et al (2008): log(Traffic Volume t ) = level(log(Exposure t )) + ε e t level(log(Exposure t+1 ) = level(log(Exposure t )…”
Section: Methodsmentioning
confidence: 99%