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Measuring the Volatility in U.S. Treasury Benchmarks and Debt Instruments

  • Suhejla Hoti
  • Esfandiar Maasoumi
  • Michael McAleer
  • Daniel Slottje

As U.S. Treasury securities carry the full faith and credit of the U.S. government, they are free of default risk. Thus, their yields are risk-free rates of return, which allows the most recently issued U.S. Treasury securities to be used as a benchmark to price other fixed-income instruments. This article analyzes the time series properties of interest rates on U.S. Treasury benchmarks and related debt instruments by modelling the conditional mean and conditional volatility for weekly yields on 12 Treasury Bills and other debt instruments for the period January 8, 1982 to August 20, 2004. The conditional correlations between all pairs of debt instruments are also calculated. These estimates are of interest as they enable an assessment of the implications of modelling conditional volatility on forecasting performance. The estimated conditional correlation coefficients indicate whether there is specialization, diversification, or independence in the debt instrument shocks. Constant conditional correlation estimates of the standardized shocks indicate that the shocks to the first differences in the debt instrument yields are generally high and always positively correlated. In general, the primary purpose in holding a portfolio of Treasury Bills and other debt instruments should be to specialize on instruments that provide the largest returns. Tests for Stochastic Dominance are generally consistent with these findings, but find somewhat surprising rankings between debt instruments, with implications for portfolio composition. Thirty year treasuries, Aaa bonds, and mortgages tend to dominate the other instruments, at least to the second order.

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Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 28 (2009)
Issue (Month): 6 ()
Pages: 522-554

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Handle: RePEc:taf:emetrv:v:28:y:2009:i:6:p:522-554
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  1. Oliver Linton & Esfandiar Maasoumi & Whang, Yoon-Jae, 2002. "Consistent Testing for Stochastic Dominance: A Subsampling Approach," Cowles Foundation Discussion Papers 1356, Cowles Foundation for Research in Economics, Yale University, revised Mar 2002.
  2. Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," NBER Working Papers 6844, National Bureau of Economic Research, Inc.
  3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  4. Steven A. Sharpe, 2002. "Reexamining Stock Valuation and Inflation: The Implications Of Analysts' Earnings Forecasts," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 632-648, November.
  5. Michael J. Fleming, 2000. "The benchmark U.S. Treasury market: recent performance and possible alternatives," Economic Policy Review, Federal Reserve Bank of New York, issue Apr, pages 129-145.
  6. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
  7. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
  8. John M. Maheu & Thomas H. McCurdy, 2001. "Nonlinear Features of Realized FX Volatility," CIRANO Working Papers 2001s-42, CIRANO.
  9. Pierluigi Balduzzi & Sanjiv Ranjan Das & Silverio Foresi, 1998. "The Central Tendency: A Second Factor In Bond Yields," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 62-72, February.
  10. Ling, Shiqing & McAleer, Michael, 2002. "NECESSARY AND SUFFICIENT MOMENT CONDITIONS FOR THE GARCH(r,s) AND ASYMMETRIC POWER GARCH(r,s) MODELS," Econometric Theory, Cambridge University Press, vol. 18(03), pages 722-729, June.
  11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  12. Wayne E. Ferson & Sergei Sarkissian & Timothy Simin, 2002. "Spurious Regressions in Financial Economics?," NBER Working Papers 9143, National Bureau of Economic Research, Inc.
  13. Ling, Shiqing & McAleer, Michael, 2002. "Stationarity and the existence of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 109-117, January.
  14. Michael J. Fleming, 2001. "Measuring treasury market liquidity," Staff Reports 133, Federal Reserve Bank of New York.
  15. Jeantheau, Thierry, 1998. "Strong Consistency Of Estimators For Multivariate Arch Models," Econometric Theory, Cambridge University Press, vol. 14(01), pages 70-86, February.
  16. Harrison Hong & Jeffrey D. Kubik, 2003. "Analyzing the Analysts: Career Concerns and Biased Earnings Forecasts," Journal of Finance, American Finance Association, vol. 58(1), pages 313-351, 02.
  17. Liesenfeld, Roman & Richard, Jean-Francois, 2003. "Univariate and multivariate stochastic volatility models: estimation and diagnostics," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 505-531, September.
  18. E.K. Berndt & B.H. Hall & R.E. Hall, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 103-116 National Bureau of Economic Research, Inc.
  19. Garry J. Schinasi & T. Todd Smith & Charles Frederick Kramer, 2001. "Financial Implications of the Shrinking Supply of U.S. Treasury Securities," IMF Working Papers 01/61, International Monetary Fund.
  20. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
  21. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
  22. Maureen O'Hara, 2003. "Presidential Address: Liquidity and Price Discovery," Journal of Finance, American Finance Association, vol. 58(4), pages 1335-1354, 08.
  23. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
  24. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(02), pages 280-310, April.
  25. Gregory R. Duffee, 1998. "The Relation Between Treasury Yields and Corporate Bond Yield Spreads," Journal of Finance, American Finance Association, vol. 53(6), pages 2225-2241, December.
  26. Tim Bollerslev & Jonathan H. Wright, 2001. "High-Frequency Data, Frequency Domain Inference, And Volatility Forecasting," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 596-602, November.
  27. John M. Maheu & Tom McCurdy, 2000. "Volatility Dynamics Under Duration-Dependent Mixing," Econometric Society World Congress 2000 Contributed Papers 1427, Econometric Society.
  28. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(03), pages 318-334, September.
  29. Thomakos, Dimitrios D. & Wang, Tao, 2003. "Realized volatility in the futures markets," Journal of Empirical Finance, Elsevier, vol. 10(3), pages 321-353, May.
  30. McAleer, Michael & Chan, Felix & Marinova, Dora, 2007. "An econometric analysis of asymmetric volatility: Theory and application to patents," Journal of Econometrics, Elsevier, vol. 139(2), pages 259-284, August.
  31. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, 02.
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