IDEAS home Printed from https://ideas.repec.org/a/kap/apfinm/v19y2012i3p259-292.html
   My bibliography  Save this article

Empirically Effective Bond Pricing Model and Analysis on Term Structures of Implied Interest Rates in Financial Crisis

Author

Listed:
  • Takeaki Kariya
  • Jingsui Wang
  • Zhu Wang
  • Eiichi Doi
  • Yoshiro Yamamura

Abstract

In his book (1993) Kariya proposed a government bond (GB) pricing model that simultaneously values individual fixed-coupon (non-defaultable) bonds of different coupon rates and maturities via a discount function approach, and Kariya and Tsuda (Financ Eng Japanese Mark 1:1–20, 1994 ) verified its empirical effectiveness of the model as a pricing model for Japanese Government bonds (JGBs) though the empirical setting was limited to a simple case. In this paper we first clarify the theoretical relation between our stochastic discount function approach and the spot rate or forward rate approach in mathematical finance. Then we make a comprehensive empirical study on the capacity of the model in view of its pricing capability for individual GBs with different attributes and in view of its capacity of describing the movements of term structures of interest rates that JGBs imply as yield curves. Based on various tests of validity in a GLS (Generalized Least Squares) framework we propose a specific formulation with a polynomial of order 6 for the mean discount function that depends on maturity and coupon as attributes and a specific covariance structure. It is shown that even in the middle of the Financial Crisis, the cross-sectional model we propose is shown to be very effective for simultaneously pricing all the existing JGBs and deriving and describing zero yields. Copyright Springer Science+Business Media, LLC. 2012

Suggested Citation

  • Takeaki Kariya & Jingsui Wang & Zhu Wang & Eiichi Doi & Yoshiro Yamamura, 2012. "Empirically Effective Bond Pricing Model and Analysis on Term Structures of Implied Interest Rates in Financial Crisis," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 19(3), pages 259-292, September.
  • Handle: RePEc:kap:apfinm:v:19:y:2012:i:3:p:259-292
    DOI: 10.1007/s10690-011-9149-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10690-011-9149-1
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10690-011-9149-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ross Williams, 2013. "Introduction," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 46(4), pages 460-461, December.
    2. Pierre Collin‐Dufresne & Bruno Solnik, 2001. "On the Term Structure of Default Premia in the Swap and LIBOR Markets," Journal of Finance, American Finance Association, vol. 56(3), pages 1095-1115, June.
    3. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    4. David Heath & Robert Jarrow & Andrew Morton, 2008. "Bond Pricing And The Term Structure Of Interest Rates: A New Methodology For Contingent Claims Valuation," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 13, pages 277-305, World Scientific Publishing Co. Pte. Ltd..
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Takeaki Kariya & Yoko Tanokura & Hideyuki Takada & Yoshiro Yamamura, 2016. "Measuring Credit Risk of Individual Corporate Bonds in US Energy Sector," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 23(3), pages 229-262, September.
    2. Takeaki Kariya & Yoshiro Yamamura & Koji Inui, 2019. "Empirical Credit Risk Ratings of Individual Corporate Bonds and Derivation of Term Structures of Default Probabilities," JRFM, MDPI, vol. 12(3), pages 1-29, July.
    3. Takeaki Kariya & Yoshiro Yamamura & Yoko Tanokura & Zhu Wang, 2015. "Credit Risk Analysis on Euro Government Bonds-Term Structures of Default Probabilities," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 22(4), pages 397-427, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chiarella, Carl & Kang, Boda & Nikitopoulos, Christina Sklibosios & Tô, Thuy-Duong, 2013. "Humps in the volatility structure of the crude oil futures market: New evidence," Energy Economics, Elsevier, vol. 40(C), pages 989-1000.
    2. Virmani, Vineet, 2014. "Model Risk in Pricing Path-dependent Derivatives: An Illustration," IIMA Working Papers WP2014-03-22, Indian Institute of Management Ahmedabad, Research and Publication Department.
    3. Munk, Claus & Sorensen, Carsten, 2004. "Optimal consumption and investment strategies with stochastic interest rates," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1987-2013, August.
    4. Max F. Schöne & Stefan Spinler, 2017. "A four-factor stochastic volatility model of commodity prices," Review of Derivatives Research, Springer, vol. 20(2), pages 135-165, July.
    5. Makushkin, Mikhail & Lapshin, Victor, 2023. "Dynamic Nelson–Siegel model for market risk estimation of bonds: Practical implementation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 69, pages 5-27.
    6. Oldrich Alfons Vasicek & Francisco Venegas-Martínez, 2021. "Models of the Term Structure of Interest Rates: Review, Trends, and Perspectives," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(2), pages 1-28, Abril - J.
    7. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
    8. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    9. Duffie, Darrell, 2003. "Intertemporal asset pricing theory," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 11, pages 639-742, Elsevier.
    10. 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.
    11. Andreea Oprea, 2022. "The Use of Principal Component Analysis (PCA) in Building Yield Curve Scenarios and Identifying Relative-Value Trading Opportunities on the Romanian Government Bond Market," JRFM, MDPI, vol. 15(6), pages 1-37, May.
    12. Bueno-Guerrero, Alberto & Moreno, Manuel & Navas, Javier F., 2016. "The stochastic string model as a unifying theory of the term structure of interest rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 217-237.
    13. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2021. "Accuracy of deep learning in calibrating HJM forward curves," Digital Finance, Springer, vol. 3(3), pages 209-248, December.
    14. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2020. "Accuracy of Deep Learning in Calibrating HJM Forward Curves," Papers 2006.01911, arXiv.org, revised May 2021.
    15. Chiara Sabelli & Michele Pioppi & Luca Sitzia & Giacomo Bormetti, 2014. "Multi-curve HJM modelling for risk management," Papers 1411.3977, arXiv.org, revised Oct 2015.
    16. Leo Krippner, 2013. "A tractable framework for zero lower bound Gaussian term structure models," Reserve Bank of New Zealand Discussion Paper Series DP2013/02, Reserve Bank of New Zealand.
    17. Jappelli, Ruggero & Pelizzon, Loriana & Subrahmanyam, Marti G., 2023. "Quantitative easing, the repo market, and the term structure of interest rates," SAFE Working Paper Series 395, Leibniz Institute for Financial Research SAFE.
    18. Leo Krippner, 2009. "A theoretical foundation for the Nelson and Siegel class of yield curve models," Reserve Bank of New Zealand Discussion Paper Series DP2009/10, Reserve Bank of New Zealand.
    19. Renato França & Raquel M. Gaspar, 2023. "On the Bias of the Unbiased Expectation Theory," Mathematics, MDPI, vol. 12(1), pages 1-20, December.
    20. Leo Krippner, 2011. "Modifying Gaussian term structure models when interest rates are near the zero lower bound," CAMA Working Papers 2011-36, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:apfinm:v:19:y:2012:i:3:p:259-292. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.