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Approximating risk-free curves in sparse data environments

Author

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  • van der Merwe, C.J.
  • Heyman, D.
  • de Wet, T.

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 one year 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.

Suggested Citation

  • van der Merwe, C.J. & Heyman, D. & de Wet, T., 2018. "Approximating risk-free curves in sparse data environments," Finance Research Letters, Elsevier, vol. 26(C), pages 112-118.
  • Handle: RePEc:eee:finlet:v:26:y:2018:i:c:p:112-118
    DOI: 10.1016/j.frl.2017.12.016
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    References listed on IDEAS

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    More about this item

    Keywords

    Sparse data; Fair value; Simulation; Risk-free curves;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing

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