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Predicting Federal Funds Rate Using Extreme Value Theory

Author

Listed:
  • Dey Ashim Kumar

    (Department of Mathematics, Lamar University, Beaumont, TX 77710-0009, USA)

  • Das Kumer Pial

    (University of Louisiana, Lafayette, LA 70503-2014, USA)

Abstract

The extreme value theory (EVT) is used to assess the risk of extreme events caused by natural calamities or untoward circumstances in the social and economic sectors. The theory can be used to study the frequency of rare events and to build up a predictive model so that one can attempt to forecast the frequency of such future extreme events such as a financial collapse and the amount of damage from such a collapse. Even though many statistical techniques have been used to analyze the manner in which the Federal Reserve determines the level of the Federal Fund Rates, no known study has used EVT to analyze and predict the extreme fund rates. In this study, the US Federal Funds Rate, one of the most publicized and important economic indicators in the financial world, from 1954–2019 has been analyzed. The contributions of this study are: (1) to provide an appropriate model for the normalized Federal Funds Rate data; (2) to compare several estimation techniques in estimating parameters for two possible models; (3) to predict the maximum economic return rate from a Federal Funds Rate in the future by using the concept of the return period; and (4) to investigate the bias of estimated parameters applying a simulation study. Simulated data and real financial data are used for the study, and the outcome satisfies the efficiency of its application.

Suggested Citation

  • Dey Ashim Kumar & Das Kumer Pial, 2020. "Predicting Federal Funds Rate Using Extreme Value Theory," Stochastics and Quality Control, De Gruyter, vol. 35(1), pages 1-15, June.
    • Handle: RePEc:bpj:ecqcon:v:35:y:2020:i:1:p:1-15:n:3
    DOI: 10.1515/eqc-2020-0003
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    References listed on IDEAS

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    1. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    2. Das, Kumer Pial & Dey, Asim Kumer, 2016. "Quantifying the risk of extreme aviation accidents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 345-355.
    3. Younes Bensalah, 2000. "Steps in Applying Extreme Value Theory to Finance: A Review," Staff Working Papers 00-20, Bank of Canada.
    4. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
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