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Modeling volatility changes in the 10-year Treasury

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Author Info

  • Covarrubias, Guillermo
  • Ewing, Bradley T.
  • Hein, Scott E.
  • Thompson, Mark A.

Abstract

This paper examines the daily volatility of changes in the 10-year Treasury note utilizing the iterated cumulative sums of squares algorithm [C. Inclan, G. Tiao, Use of cumulative sums of squares for retrospective detection of changes of variance, J. Am. Stat. Assoc. 89 (1994) 913–923]. The ICSS algorithm can detect regime shifts in the volatility of the interest rate changes. A general model allows for endogenously determined changes in variance while the more restrictive model forces the variance to follow the same process throughout the sample period. A comparison of the out-of-sample volatility forecasting performance of two competing models is made using asymmetric error measures. The asymmetric error statistics penalize models for under- or over-predicting volatility. The results shed light on the importance of ignoring volatility regime shifts when performing out-of-sample forecasts. The findings are important to financial market participants who require accurate forecasts of future volatility in order to implement and evaluate asset performance.

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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 369 (2006)
Issue (Month): 2 ()
Pages: 737-744

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Handle: RePEc:eee:phsmap:v:369:y:2006:i:2:p:737-744

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Forecasting; Volatility; Asymmetric forecast evaluation; Interest rate; Regime shifts;

References

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Citations

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Cited by:
  1. Kang, Sang Hoon & Cho, Hwan-Gue & Yoon, Seong-Min, 2009. "Modeling sudden volatility changes: Evidence from Japanese and Korean stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3543-3550.
  2. Ross, Gordon J., 2013. "Modelling financial volatility in the presence of abrupt changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(2), pages 350-360.
  3. Ewing, Bradley T. & Thompson, Mark A., 2008. "Industrial production, volatility, and the supply chain," International Journal of Production Economics, Elsevier, Elsevier, vol. 115(2), pages 553-558, October.
  4. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2011. "Structural changes and volatility transmission in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4317-4324.
  5. Gordon J. Ross, 2012. "Modeling Financial Volatility in the Presence of Abrupt Changes," Papers 1212.6016, arXiv.org.
  6. Fernandez, Viviana, 2009. "The behavior of stock returns in the mining industry following the Iraq war," Research in International Business and Finance, Elsevier, Elsevier, vol. 23(3), pages 274-292, September.

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