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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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    3. 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.
    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. Kim, Kyungwon, 2013. "Modeling financial crisis period: A volatility perspective of Credit Default Swap market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4977-4988.
    6. 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.
    7. Gordon J. Ross, 2012. "Modeling Financial Volatility in the Presence of Abrupt Changes," Papers 1212.6016, arXiv.org.
    8. 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.
    9. 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.
    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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