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Approximating risk-free curves in sparse data environments

(2018) FINANCE RESEARCH LETTERS . 26. p.112-118
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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.
Keywords
Sparse data, Fair value, Simulation, Risk-free curves

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Citation

Please use this url to cite or link to this publication:

MLA
van der Merwe, Carel Johannes, Dries Heyman, and T. de Wet. “Approximating Risk-free Curves in Sparse Data Environments.” FINANCE RESEARCH LETTERS 26 (2018): 112–118. Print.
APA
van der Merwe, C. J., Heyman, D., & de Wet, T. (2018). Approximating risk-free curves in sparse data environments. FINANCE RESEARCH LETTERS , 26, 112–118.
Chicago author-date
van der Merwe, Carel Johannes, Dries Heyman, and T. de Wet. 2018. “Approximating Risk-free Curves in Sparse Data Environments.” Finance Research Letters 26: 112–118.
Chicago author-date (all authors)
van der Merwe, Carel Johannes, Dries Heyman, and T. de Wet. 2018. “Approximating Risk-free Curves in Sparse Data Environments.” Finance Research Letters 26: 112–118.
Vancouver
1.
van der Merwe CJ, Heyman D, de Wet T. Approximating risk-free curves in sparse data environments. FINANCE RESEARCH LETTERS . Elsevier BV; 2018;26:112–8.
IEEE
[1]
C. J. van der Merwe, D. Heyman, and T. de Wet, “Approximating risk-free curves in sparse data environments,” FINANCE RESEARCH LETTERS , vol. 26, pp. 112–118, 2018.
@article{8572535,
  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.},
  author       = {van der Merwe, Carel Johannes and Heyman, Dries and de Wet, T.},
  issn         = {1544-6123},
  journal      = {FINANCE RESEARCH LETTERS },
  keywords     = {Sparse data,Fair value,Simulation,Risk-free curves},
  language     = {eng},
  pages        = {112--118},
  publisher    = {Elsevier BV},
  title        = {Approximating risk-free curves in sparse data environments},
  url          = {http://dx.doi.org/10.1016/j.frl.2017.12.016},
  volume       = {26},
  year         = {2018},
}

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