IDEAS home Printed from https://ideas.repec.org/a/wsi/ijtafx/v08y2005i02ns0219024905002949.html
   My bibliography  Save this article

Affine Processes, Arbitrage-Free Term Structures Of Legendre Polynomials, And Option Pricing

Author

Listed:
  • CAIO IBSEN RODRIGUES DE ALMEIDA

    (IBMEC Business School, Av. Rio Branco 108, 17th floor, 20040-001, Rio de Janeiro, RJ, Brazil)

Abstract

Multivariate Affine term structure models have been increasingly used for pricing derivatives in fixed income markets. In these models, uncertainty of the term structure is driven by a state vector, while the short rate is an affine function of this vector. The model is characterized by a specific form for the stochastic differential equation (SDE) for the evolution of the state vector. This SDE presents restrictions on its drift term which rule out arbitrages in the market. In this paper we solve the following inverse problem: Suppose the term structure of interest rates is modelled by a linear combination of Legendre polynomials with random coefficients. Is there any SDE for these coefficients which rules out arbitrages? This problem is of particular empirical interest because the Legendre model is an example of factor model with clear interpretation for each factor, in which regards movements of the term structure. Moreover, the Affine structure of the Legendre model implies knowledge of its conditional characteristic function. From the econometric perspective, we propose arbitrage-free Legendre models to describe the evolution of the term structure. From the pricing perspective, we follow Duffie et al. [22] in exploring their conditional characteristic functions to obtain a computational tractable method to price fixed income derivatives.

Suggested Citation

  • Caio Ibsen Rodrigues De Almeida, 2005. "Affine Processes, Arbitrage-Free Term Structures Of Legendre Polynomials, And Option Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 161-184.
  • Handle: RePEc:wsi:ijtafx:v:08:y:2005:i:02:n:s0219024905002949
    DOI: 10.1142/S0219024905002949
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219024905002949
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1142/S0219024905002949?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. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    2. 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.
    3. Svensson, Lars E O, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992-4," CEPR Discussion Papers 1051, C.E.P.R. Discussion Papers.
    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. Almeida, Caio & Vicente, José, 2008. "The role of no-arbitrage on forecasting: Lessons from a parametric term structure model," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2695-2705, December.
    2. Almeida, Caio & Ardison, Kym & Kubudi, Daniela, 2014. "Approximating Risk Premium on a Parametric Arbitrage-free Term Structure Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 34(2), November.
    3. Varga, Gyorgy, 2009. "Teste de Modelos Estatísticos para a Estrutura a Termo no Brasil [Test of Term Structure Models for Brazil]," MPRA Paper 20832, University Library of Munich, Germany.

    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. Atsushi Inoue & Barbara Rossi, 2021. "A new approach to measuring economic policy shocks, with an application to conventional and unconventional monetary policy," Quantitative Economics, Econometric Society, vol. 12(4), pages 1085-1138, November.
    2. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    3. Boutabba, Mohamed Amine & Rannou, Yves, 2022. "Investor strategies in the green bond market: The influence of liquidity risks, economic factors and clientele effects," International Review of Financial Analysis, Elsevier, vol. 81(C).
    4. Grochola, Nicolaus, 2023. "The influence of negative interest rates on life insurance companies," ICIR Working Paper Series 53/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    5. Ioannis A. Venetis & Avgoustinos Ladas, 2023. "Co-movement and global factors in sovereign bond yields," Bulletin of Applied Economics, Risk Market Journals, vol. 10(2), pages 17-45.
    6. Mohamed Amine Boutabba & Yves Rannou, 2020. "Investor strategies and Liquidity Premia in the European Green Bond market," Post-Print hal-02544451, HAL.
    7. J. Arismendi-Zambrano & R. Azevedo, 2020. "Implicit Entropic Market Risk-Premium from Interest Rate Derivatives," Economics Department Working Paper Series n303-20.pdf, Department of Economics, National University of Ireland - Maynooth.
    8. Gauthier, Geneviève & Simonato, Jean-Guy, 2012. "Linearized Nelson–Siegel and Svensson models for the estimation of spot interest rates," European Journal of Operational Research, Elsevier, vol. 219(2), pages 442-451.
    9. Memmel, Christoph & Heckmann, Lotta, 2025. "Modeling the term structure," Discussion Papers 07/2025, Deutsche Bundesbank.
    10. 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.
    11. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    12. Michal Dvorák & Zlatuše Komárková & Adam Kucera, 2019. "The Czech Government Yield Curve Decomposition at the Lower Bound," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 69(1), pages 2-36, February.
    13. Lajos Horváth & Piotr Kokoszka & Jeremy VanderDoes & Shixuan Wang, 2022. "Inference in functional factor models with applications to yield curves," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 872-894, November.
    14. Eder, Armin & Keiler, Sebastian & Pichl, Hannes, 2013. "Interest rate risk and the Swiss solvency test," Discussion Papers 41/2013, Deutsche Bundesbank.
    15. Manousopoulos, Polychronis & Michalopoulos, Michalis, 2009. "Comparison of non-linear optimization algorithms for yield curve estimation," European Journal of Operational Research, Elsevier, vol. 192(2), pages 594-602, January.
    16. Anastasios Demertzidis & Vahidin Jeleskovic, 2021. "Empirical Estimation of Intraday Yield Curves on the Italian Interbank Credit Market e-MID," JRFM, MDPI, vol. 14(5), pages 1-23, May.
    17. Alfaro, Rodrigo & Piña, Marco, 2023. "Estimates of the US Shadow-Rate," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(1).
    18. Kang, Kyu Ho, 2015. "The predictive density simulation of the yield curve with a zero lower bound," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 51-66.
    19. Koo, B. & La Vecchia, D. & Linton, O., 2019. "Nonparametric Recovery of the Yield Curve Evolution from Cross-Section and Time Series Information," Cambridge Working Papers in Economics 1916, Faculty of Economics, University of Cambridge.
    20. Zura Kakushadze & Willie Yu, 2020. "Machine Learning Treasury Yields," Bulletin of Applied Economics, Risk Market Journals, vol. 7(1), pages 1-65.

    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:wsi:ijtafx:v:08:y:2005:i:02:n:s0219024905002949. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijtaf/ijtaf.shtml .

    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.