IDEAS home Printed from https://ideas.repec.org/p/uts/rpaper/348.html
   My bibliography  Save this paper

A Consistent Framework for Modelling Basis Spreads in Tenor Swaps

Author

Abstract

The phenomenon of the frequency basis (i.e. a spread applied to one leg of a swap to exchange one oating interest rate for another of a different tenor in the same currency) contradicts textbook no-arbitrage conditions and has become an important feature of interest rate markets since the beginning of the Global Financial Crisis (GFC) in 2008. Empirically, the basis spread cannot be explained by transaction costs alone, and therefore must be due to a new perception by the market of risks involved in the execution of textbook "arbitrage" strategies. This has led practitioners to adopt a pragmatic "multi-curve" approach to interest rate modelling, which leads to a proliferation of term structures, one for each tenor. We take a more fundamental approach and explicitly model liquidity risk as the driver of basis spreads, reducing the dimensionality of the market for the frequency basis from observed spread term structures for every frequency pair down to term structures of two factors characterising liquidity risk. To this end, we use an intensity model to describe the arrival time of (possibly stochastic) liquidity shocks with a Cox Process. The model parameters are calibrated to quoted market data on basis spreads, and the improving stability of the calibration suggests that the basis swap market has matured since the turmoil of the GFC.

Suggested Citation

  • Yang Chang & Erik Schlogl, 2014. "A Consistent Framework for Modelling Basis Spreads in Tenor Swaps," Research Paper Series 348, Quantitative Finance Research Centre, University of Technology, Sydney.
  • Handle: RePEc:uts:rpaper:348
    as

    Download full text from publisher

    File URL: https://www.uts.edu.au/sites/default/files/rp348.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John C. Williams & John B. Taylor, 2009. "A Black Swan in the Money Market," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 58-83, January.
    2. Markus K. Brunnermeier & Lasse Heje Pedersen, 2009. "Market Liquidity and Funding Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 22(6), pages 2201-2238, June.
    3. John B. Taylor & John C. Williams, 2009. "A black swan in the money market," Proceedings, Federal Reserve Bank of San Francisco, issue jan.
    4. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    5. Erik Schlogl & Lutz Schlogl, 2000. "A square root interest rate model fitting discrete initial term structure data," Applied Mathematical Finance, Taylor & Francis Journals, vol. 7(3), pages 183-209.
    6. Erik Schlögl, 2001. "Arbitrage-Free Interpolation in Models of Market Observable Interest Rates," Research Paper Series 71, Quantitative Finance Research Centre, University of Technology, Sydney.
    7. 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..
    8. Masaaki Kijima & Keiichi Tanaka & Tony Wong, 2009. "A multi-quality model of interest rates," Quantitative Finance, Taylor & Francis Journals, vol. 9(2), pages 133-145.
    9. Miltersen, Kristian R & Sandmann, Klaus & Sondermann, Dieter, 1997. "Closed Form Solutions for Term Structure Derivatives with Log-Normal Interest Rates," Journal of Finance, American Finance Association, vol. 52(1), pages 409-430, March.
    10. Cairns, A.J.G. & Pritchard, D.J., 2001. "Stability of Models for the Term Structure of Interest Rates with Application to German Market Data," British Actuarial Journal, Cambridge University Press, vol. 7(3), pages 467-507, August.
    11. François-Louis Michaud & Christian Upper, 2008. "What drives interbank rates? Evidence from the Libor panel," BIS Quarterly Review, Bank for International Settlements, March.
    12. Masaaki Fujii & Yasufumi Shimada & Akihiko Takahashi, 2009. "A Market Model of Interest Rates with Dynamic Basis Spreads in the presence of Collateral and Multiple Currencies," CIRJE F-Series CIRJE-F-698, CIRJE, Faculty of Economics, University of Tokyo.
    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. Nauta, Bert-Jan, 2016. "A Model for the Valuation of Assets with Liquidity Risk," MPRA Paper 92493, University Library of Munich, Germany.
    2. Takahiro Hattori, 2017. "Does swap-covered interest parity hold in long-term capital markets after the financial crisis?," Discussion papers ron293, Policy Research Institute, Ministry of Finance Japan.
    3. Alfeus, Mesias & Grasselli, Martino & Schlögl, Erik, 2020. "A consistent stochastic model of the term structure of interest rates for multiple tenors," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    4. Hattori, Takahiro, 2022. "Does the swap-covered interest parity still hold in long-term capital markets after the financial crisis? Evidence from cross-currency basis swaps," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 224-240.
    5. Mesias Alfeus, 2019. "Stochastic Modelling of New Phenomena in Financial Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2019, January-A.

    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. Yang Chang, 2014. "A Consistent Approach to Modelling the Interest Rate Market Anomalies Post the Global Financial Crisis," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 18, July-Dece.
    2. Yang Chang, 2014. "A Consistent Approach to Modelling the Interest Rate Market Anomalies Post the Global Financial Crisis," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2-2014, January-A.
    3. Mesias Alfeus, 2019. "Stochastic Modelling of New Phenomena in Financial Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2019, January-A.
    4. Guillermo Andrés Cangrejo Jiménez, 2014. "La Estructura a Plazos del Riesgo Interbancario," Documentos de Trabajo 12172, Universidad del Rosario.
    5. repec:uts:finphd:41 is not listed on IDEAS
    6. Filipović, Damir & Trolle, Anders B., 2013. "The term structure of interbank risk," Journal of Financial Economics, Elsevier, vol. 109(3), pages 707-733.
    7. Alexander Bechtel & Angelo Ranaldo & Jan Wrampelmeyer, 2023. "Liquidity Risk and Funding Cost," Review of Finance, European Finance Association, vol. 27(2), pages 399-422.
    8. Alfeus, Mesias & Grasselli, Martino & Schlögl, Erik, 2020. "A consistent stochastic model of the term structure of interest rates for multiple tenors," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    9. De Socio, Antonio, 2013. "The interbank market after the financial turmoil: Squeezing liquidity in a “lemons market” or asking liquidity “on tap”," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1340-1358.
    10. Dubecq, Simon & Monfort, Alain & Renne, Jean-Paul & Roussellet, Guillaume, 2016. "Credit and liquidity in interbank rates: A quadratic approach," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 29-46.
    11. Deuskar, Prachi & Gupta, Anurag & Subrahmanyam, Marti G., 2011. "Liquidity effect in OTC options markets: Premium or discount?," Journal of Financial Markets, Elsevier, vol. 14(1), pages 127-160, February.
    12. Cho-Hoi Hui & Tsz-Kin Chung & Chi-Fai Lo, 2013. "Using Interest Rate Derivative Prices to Estimate LIBOR-OIS Spread Dynamics and Systemic Funding Liquidity Shock Probabilities," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 20(2), pages 131-146, May.
    13. Marco Taboga, 2014. "What Is a Prime Bank? A Euribor–OIS Spread Perspective," International Finance, Wiley Blackwell, vol. 17(1), pages 51-75, March.
    14. Stefano Puddu & Andreas Waelchli, 2015. "TAF Effect on Liquidity Risk Exposure," IRENE Working Papers 15-07, IRENE Institute of Economic Research.
    15. Gerhart, Christoph & Lütkebohmert, Eva, 2020. "Empirical analysis and forecasting of multiple yield curves," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 59-78.
    16. Gallitschke, Janek & Seifried (née Müller), Stefanie & Seifried, Frank Thomas, 2017. "Interbank interest rates: Funding liquidity risk and XIBOR basis spreads," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 142-152.
    17. Nikolaos Karouzakis, 2021. "The role of time‐varying risk premia in international interbank markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5720-5745, October.
    18. Renne, Jean-Paul, 2016. "A tractable interest rate model with explicit monetary policy rates," European Journal of Operational Research, Elsevier, vol. 251(3), pages 873-887.
    19. Geršl, Adam & Lešanovská, Jitka, 2014. "Explaining the Czech interbank market risk premium," Economic Systems, Elsevier, vol. 38(4), pages 536-551.
    20. Bjork, Tomas, 2009. "Arbitrage Theory in Continuous Time," OUP Catalogue, Oxford University Press, edition 3, number 9780199574742.
    21. Frank De Jong & Joost Driessen & Antoon Pelsser, 2001. "Libor Market Models versus Swap Market Models for Pricing Interest Rate Derivatives: An Empirical Analysis," Review of Finance, European Finance Association, vol. 5(3), pages 201-237.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G1 - Financial Economics - - General Financial Markets
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:uts:rpaper:348. 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: Duncan Ford (email available below). General contact details of provider: https://edirc.repec.org/data/qfutsau.html .

    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.