Mack Model practice Stochastic Reserving SP7 ST7

Mack Method in Stochastic Reserving

Thomas Mack, In his original paper outlines a method to estimate the standard error of chain ladder estimates. The Method is now is generalised with the name of Mack Method. SP7 (formerly ST7) contains an introduction on stochastic reserving where Mack model estimates the standard error.

The introduction of the paper states “The chain ladder method is probably the most popular method for estimating IBNR claims reserves. The main reason for this is its simplicity and the fact that it is distribution-flee, i.e. that it seems to work with almost no assumptions. On the other hand, it is well-known that chain ladder reserve estimates for the most recent accident years are very sensitive to variations in the data observed. Moreover, in recent years many other claims reserving procedures have been proposed and the results of all these procedures vary widely and also differ more or less from the chain ladder result. Therefore it would be very helpful to know the standard error of the chain ladder reserve estimates as a measure of the uncertainty contained in the data and in order to see whether the difference between the results of the chain ladder method and any othermethod is significant or not.” This explains the purpose of stochastic reserving for which you may have to read SP7.

you can download it here: Mack paper. SP7 (formerly ST7) students will also find it easier to remember the three assumptions underlying this method after seeing them stated in equations.

To see an excel demonstration of this method, download this spreadsheet here

Other resources to learn about Mack method can be found on this page: CAS presentations

About the Author

Mayank Goyal

Facebook Twitter

Redmond Lover(Microsoft), London Dreamer(Actuary), California Thinker(Entrepreneur). Actuarial Science, Blogger, Web Developing, Winphan India, App development, Social Media Managing, Event Managing & bla bla bla.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.