IDEAS home Printed from https://ideas.repec.org/p/osf/frenxi/dxvnw.html
   My bibliography  Save this paper

An Efficient Lattice Algorithm For The Libor Market Model

Author

Listed:
  • Xiao, Tim

Abstract

The LIBOR Market Model has become one of the most popular models for pricing interest rate products. It is commonly believed that Monte-Carlo simulation is the only viable method available for the LIBOR Market Model. In this article, however, we propose a lattice approach to price interest rate products within the LIBOR Market Model by introducing a shifted forward measure and several novel fast drift approximation methods. This model should achieve the best performance without losing much accuracy. Moreover, the calibration is almost automatic and it is simple and easy to implement. Adding this model to the valuation toolkit is actually quite useful; especially for risk management or in the case there is a need for a quick turnaround.

Suggested Citation

  • Xiao, Tim, 2015. "An Efficient Lattice Algorithm For The Libor Market Model," FrenXiv dxvnw, Center for Open Science.
  • Handle: RePEc:osf:frenxi:dxvnw
    DOI: 10.31219/osf.io/dxvnw
    as

    Download full text from publisher

    File URL: https://osf.io/download/5d98ee29080409000b9244d3/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/dxvnw?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Martzoukos, Spiros H. & Trigeorgis, Lenos, 2002. "Real (investment) options with multiple sources of rare events," European Journal of Operational Research, Elsevier, vol. 136(3), pages 696-706, February.
    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. Xiao, Tim, 2017. "A New Model for Pricing Collateralized OTC Derivatives," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 24(4), pages 8-20.
    2. Lago, Jesus & De Ridder, Fjo & Vrancx, Peter & De Schutter, Bart, 2018. "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration," Applied Energy, Elsevier, vol. 211(C), pages 890-903.
    3. Xiao, Tim, 2012. "An Economic Examination of Collateralization in Different Financial Markets," MPRA Paper 47105, University Library of Munich, Germany.
    4. Xiao, Tim, 2013. "The Impact of Default Dependency and Collateralization on Asset Pricing and Credit Risk Modeling," MPRA Paper 47136, University Library of Munich, Germany.
    5. Tim Xiao, 2017. "A New Model for Pricing Collateralized Financial Derivatives," Post-Print hal-01800559, HAL.
    6. Kian Guan Lim, 2021. "Bermudan option in Singapore Savings Bonds," Review of Derivatives Research, Springer, vol. 24(1), pages 31-54, April.
    7. Zhongkai Liu & Tao Pang, 2016. "An efficient grid lattice algorithm for pricing American-style options," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 5(1), pages 36-55.

    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. Pringles, Rolando & Olsina, Fernando & Penizzotto, Franco, 2020. "Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method," Renewable Energy, Elsevier, vol. 151(C), pages 846-864.
    2. Jyh-Bang Jou & Tan (Charlene) Lee, 2011. "Mutually exclusive investment with technical uncertainty," Applied Economics, Taylor & Francis Journals, vol. 43(30), pages 4723-4728.
    3. Lukas, Elmar & Mölls, Sascha & Welling, Andreas, 2016. "Venture capital, staged financing and optimal funding policies under uncertainty," European Journal of Operational Research, Elsevier, vol. 250(1), pages 305-313.
    4. Julien Chevallier & Benoît Sévi, 2014. "On the Stochastic Properties of Carbon Futures Prices," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(1), pages 127-153, May.
    5. Andrea Gamba & Nicola Fusari, 2009. "Valuing Modularity as a Real Option," Management Science, INFORMS, vol. 55(11), pages 1877-1896, November.
    6. Haehl, Christian & Spinler, Stefan, 2018. "Capacity expansion under regulatory uncertainty:A real options-based study in international container shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 113(C), pages 75-93.
    7. Julien Chevallier & Stéphane Goutte, 2014. "The goodness-of-fit of the fuel-switching price using the mean-reverting Lévy jump process," Working Papers 2014-285, Department of Research, Ipag Business School.
    8. Fernando A. C. C. Fonte & Dalila B. M. M. Fontes, 2007. "Optimal investment timing using Markov jump price processes," FEP Working Papers 245, Universidade do Porto, Faculdade de Economia do Porto.
    9. Spiros H. Martzoukos & Nayia Pospori & Lenos Trigeorgis, 2024. "Corporate investment decisions with switch flexibility, constraints, and path-dependency," Review of Quantitative Finance and Accounting, Springer, vol. 62(3), pages 1223-1250, April.
    10. Pennings, Enrico & Sereno, Luigi, 2011. "Evaluating pharmaceutical R&D under technical and economic uncertainty," European Journal of Operational Research, Elsevier, vol. 212(2), pages 374-385, July.
    11. Charalambides, Marios & Koussis, Nicos, 2018. "A stochastic model with interacting managerial operating options and debt rescheduling," European Journal of Operational Research, Elsevier, vol. 267(1), pages 236-249.
    12. Yanyun Liu & Baiqing Sun, 2023. "Investment strategies of duopoly firms with asymmetric time-to-build under a jump-diffusion model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 98(3), pages 377-410, December.
    13. S H Martzoukos, 2009. "Real R&D options and optimal activation of two-dimensional random controls," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 843-858, June.
    14. Chen, Yu-Fu & Funke, Michael, 2012. "Global Warming and Fat Tailed-uncertainty: Rethinking the Timing and Intensity of Climate Policy," SIRE Discussion Papers 2012-41, Scottish Institute for Research in Economics (SIRE).
    15. Douglas A. Bodner & William B. Rouse, 2007. "Understanding R&D value creation with organizational simulation," Systems Engineering, John Wiley & Sons, vol. 10(1), pages 64-82, March.
    16. Pringles, Rolando & Olsina, Fernando & Garcés, Francisco, 2014. "Designing regulatory frameworks for merchant transmission investments by real options analysis," Energy Policy, Elsevier, vol. 67(C), pages 272-280.
    17. Schmit, T.M. & J., Luo & Conrad, J.M., 2011. "Estimating the influence of U.S. ethanol policy on plant investment decisions: A real options analysis with two stochastic variables," Energy Economics, Elsevier, vol. 33(6), pages 1194-1205.
    18. Lee, David, 2022. "Pricing Cancellation Product," MPRA Paper 114147, University Library of Munich, Germany.
    19. Pennings, H.P.G. & Sereno, L., 2010. "A Model for Evaluating Pharmaceutical R&D Investment Projects under Technical and Economic Uncertainties," ERIM Report Series Research in Management ERS-2010-009-STR, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    20. Lauren Chenarides & Mark Manfredo & Timothy J. Richards, 2021. "COVID‐19 and Food Supply Chains," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(1), pages 270-279, March.

    More about this item

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    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:osf:frenxi:dxvnw. 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: OSF (email available below). General contact details of provider: https://frenxiv.org .

    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.