Name
Abstract | ||
|---|---|---|
A risk measure is a mapping from the random variables representing the risks to a number. It is estimated using historical data and utilized in making decisions such as allocating capital to each business line or deposit insurance pricing. Once a risk measure is obtained, an efficient monitoring system is required to quickly detect any drifts in the risk measure. This paper investigates the problem of detecting a shift in value at risk as the most widely used risk measure in insurance companies. The probabilistic C control chart and the parametric bootstrap method are employed to establish a risk monitoring scheme in insurance companies. Since the number of claims in a period is a random variable, the proposed method is a variable sample size scheme. Monte Carlo simulations for Weibull, Burr XII, Birnbaum-Saunders and Pareto distributions are carried out to investigate the behavior and performance of the proposed scheme. In addition, a real example from an insurance company is presented to demonstrate the applicability of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2013.05.028 | Expert Syst. Appl. |
Keywords | Field | DocType |
insurance company,variable sample size scheme,risk monitoring scheme,random variable,bootstrap control chart,parametric bootstrap method,proposed scheme,risk measure,deposit insurance pricing,efficient monitoring system,control chart,quantile,bootstrap | Econometrics,Random variable,Deposit insurance,Computer science,Control chart,Dynamic risk measure,Statistics,Deviation risk measure,Risk measure,Value at risk,Pareto principle | Journal |
Volume | Issue | ISSN |
40 | 15 | 0957-4174 |
Citations | PageRank | References |
9 | 0.69 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Abbasi | 1 | 145 | 19.89 |
| Montserrat Guillen | 2 | 49 | 7.83 |