Presents a method for improving interpolation accuracy in non-linear actuarial data analysis, suggesting enhanced approaches.
Actuaries quite often have to interpolate data to obtain quantities such as loss development factors (LDFs) for maturities in between the maturities included in a loss development triangle, or increased limits factors for limits between the data points used in the increased limits analysis. This paper presents an approach that includes the advantages of using fitted curves for non-linear data, and that avoids the errors arising from mismatches between patterns in the data and patterns inherent to the curve family used for interpolation.
No takes yet. Share an insight, caveat, or question.
Joseph Boor (2014) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: