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Modeling volatility changes in the 10-year Treasury

Author

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  • 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.

Suggested Citation

  • Covarrubias, Guillermo & Ewing, Bradley T. & Hein, Scott E. & Thompson, Mark A., 2006. "Modeling volatility changes in the 10-year Treasury," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 737-744.
    • Handle: RePEc:eee:phsmap:v:369:y:2006:i:2:p:737-744
    DOI: 10.1016/j.physa.2006.01.074
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    4. Bhuyan, Rafiqul & Robbani, Mohammad G. & Talukdar, Bakhtear & Jain, Ajeet, 2016. "Information transmission and dynamics of stock price movements: An empirical analysis of BRICS and US stock markets," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 180-195.
    5. Go Tamakoshi & Shigeyuki Hamori, 2014. "Greek sovereign bond index, volatility, and structural breaks," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 38(4), pages 687-697, October.
    6. Fernandez, Viviana, 2009. "The behavior of stock returns in the mining industry following the Iraq war," Research in International Business and Finance, Elsevier, vol. 23(3), pages 274-292, September.
    7. Malinda & Maya & Jo-Hui & Chen, 2022. "Testing for the Long Memory and Multiple Structural Breaks in Consumer ETFs," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(6), pages 1-6.
    8. Ewing, Bradley T. & Thompson, Mark A., 2008. "Industrial production, volatility, and the supply chain," International Journal of Production Economics, Elsevier, vol. 115(2), pages 553-558, October.
    9. Gordon J. Ross, 2012. "Modeling Financial Volatility in the Presence of Abrupt Changes," Papers 1212.6016, arXiv.org.
    10. 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.

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