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Can Spanned Term Structure Factors Drive Stochastic Yield Volatility?

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  • Jens H. E. Christensen
  • Jose A. Lopez
  • Glenn D. Rudebusch

Abstract

The ability of the usual factors from empirical arbitrage-free representations of the term structure?that is, spanned factors?to account for interest rate volatility dynamics has been much debated. We examine this issue with a comprehensive set of new arbitrage-free term structure specifications that allow for spanned stochastic volatility to be linked to one or more of the yield curve factors. Using U.S. Treasury yields, we find that much realized stochastic volatility cannot be associated with spanned term structure factors. However, a simulation study reveals that the usual realized volatility metric is misleading when yields contain plausible measurement noise. We argue that other metrics should be used to validate stochastic volatility models.

Suggested Citation

  • Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2014. "Can Spanned Term Structure Factors Drive Stochastic Yield Volatility?," Working Paper Series 2014-3, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfwp:2014-03
    DOI: 10.24148/wp2014-03
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    References listed on IDEAS

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    1. Koopman, Siem Jan & Mallee, Max I. P. & Van der Wel, Michel, 2010. "Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 329-343.
    2. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2014. "Do Central Bank Liquidity Facilities Affect Interbank Lending Rates?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 136-151, January.
    3. Jens H. E. Christensen & Glenn D. Rudebusch, 2012. "The Response of Interest Rates to US and UK Quantitative Easing," Economic Journal, Royal Economic Society, vol. 122(564), pages 385-414, November.
    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    5. Duffee, Gregory R, 1999. "Estimating the Price of Default Risk," The Review of Financial Studies, Society for Financial Studies, vol. 12(1), pages 197-226.
    6. Collin-Dufresne, Pierre & Goldstein, Robert S. & Jones, Christopher S., 2009. "Can interest rate volatility be extracted from the cross section of bond yields?," Journal of Financial Economics, Elsevier, vol. 94(1), pages 47-66, October.
    7. Gregory R. Duffee, 2002. "Term Premia and Interest Rate Forecasts in Affine Models," Journal of Finance, American Finance Association, vol. 57(1), pages 405-443, February.
    8. Jagannathan, Ravi & Kaplin, Andrew & Sun, Steve, 2003. "An evaluation of multi-factor CIR models using LIBOR, swap rates, and cap and swaption prices," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 113-146.
    9. Joost Driessen, 2005. "Is Default Event Risk Priced in Corporate Bonds?," The Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 165-195.
    10. Ruslan Bikbov & Mikhail Chernov, 2011. "Yield Curve and Volatility: Lessons from Eurodollar Futures and Options," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 66-105, Winter.
    11. Michael D. Bauer & Glenn D. Rudebusch & Jing Cynthia Wu, 2012. "Correcting Estimation Bias in Dynamic Term Structure Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 454-467, April.
    12. Torben G. Andersen & Luca Benzoni, 2010. "Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification Test for Affine Term Structure Models," Journal of Finance, American Finance Association, vol. 65(2), pages 603-653, April.
    13. Jacobs, Kris & Karoui, Lotfi, 2009. "Conditional volatility in affine term-structure models: Evidence from Treasury and swap markets," Journal of Financial Economics, Elsevier, vol. 91(3), pages 288-318, March.
    14. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2010. "Inflation Expectations and Risk Premiums in an Arbitrage-Free Model of Nominal and Real Bond Yields," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 143-178, September.
    15. Dong-Hyun Ahn & Robert F. Dittmar, 2002. "Quadratic Term Structure Models: Theory and Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 15(1), pages 243-288, March.
    16. Pierre Collin‐Dufresne & Robert S. Goldstein, 2002. "Do Bonds Span the Fixed Income Markets? Theory and Evidence for Unspanned Stochastic Volatility," Journal of Finance, American Finance Association, vol. 57(4), pages 1685-1730, August.
    17. Cheridito, Patrick & Filipovic, Damir & Kimmel, Robert L., 2007. "Market price of risk specifications for affine models: Theory and evidence," Journal of Financial Economics, Elsevier, vol. 83(1), pages 123-170, January.
    18. Hautsch, Nikolaus & Ou, Yangguoyi, 2012. "Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2988-3007.
    19. Dai, Qiang & Singleton, Kenneth J., 2002. "Expectation puzzles, time-varying risk premia, and affine models of the term structure," Journal of Financial Economics, Elsevier, vol. 63(3), pages 415-441, March.
    20. Ruslan Bikbov & Mikhail Chernov, 2009. "Unspanned Stochastic Volatility in Affine Models: Evidence from Eurodollar Futures and Options," Management Science, INFORMS, vol. 55(8), pages 1292-1305, August.
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    Cited by:

    1. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    2. Shin, Minchul & Zhong, Molin, 2017. "Does realized volatility help bond yield density prediction?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 373-389.
    3. Jens H. E. Christensen & Jose A. Lopez & Glenn D. Rudebusch, 2016. "Pricing Deflation Risk with US Treasury Yields," Review of Finance, European Finance Association, vol. 20(3), pages 1107-1152.
    4. Takamizawa, Hideyuki, 2022. "How arbitrage-free is the Nelson–Siegel model under stochastic volatility?," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 205-223.
    5. Monfort, Alain & Pegoraro, Fulvio & Renne, Jean-Paul & Roussellet, Guillaume, 2017. "Staying at zero with affine processes: An application to term structure modelling," Journal of Econometrics, Elsevier, vol. 201(2), pages 348-366.
    6. Christensen, Jens H.E. & Fischer, Eric & Shultz, Patrick J., 2021. "Bond flows and liquidity: Do foreigners matter?," Journal of International Money and Finance, Elsevier, vol. 117(C).
    7. Doshi, Hitesh & Jacobs, Kris & Liu, Rui, 2024. "Modeling volatility in dynamic term structure models," Journal of Financial Economics, Elsevier, vol. 161(C).

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    More about this item

    Keywords

    arbitrage-free Nelson-Siegel model; term structure modeling; interest rate risk; model validation;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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