Approximating risk-free curves in sparse data environments

Abstract

Accounting standards require one to minimize the use of unobservable inputs when calculating fair values of financial assets and liabilities. In emerging markets and less developed countries, zero curves are not as readily observable over the longer term, as data are often more sparse than in developed countries. A proxy for the extended zero curve, calculated from other observable inputs, is found through a simulation approach by incorporating two new techniques, namely permuted integer multiple linear regression and aggregate standardized model scoring. A Nelson Siegel fit, with a mixture of average forward rates as proxies for the long term zero point, and some discarding of initial data points, was found to perform relatively well in the training and testing data sets.