Model-based measurement of latent risk in time series with applications
AbstractRisk 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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Bibliographic InfoArticle 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 ()
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Other versions of this item:
- Frits Bijleveld & Jacques Commandeur & Phillip Gould & Siem Jan Koopman, 2005. "Model-based Measurement of Latent Risk in Time Series with Applications," Tinbergen Institute Discussion Papers 05-118/4, Tinbergen Institute.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
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