IDEAS home Printed from https://ideas.repec.org/p/apc/wpaper/2017-116.html
   My bibliography  Save this paper

A unified framework for pricing credit and equity derivatives

Author

Listed:
  • Andrea De Martino

    (Barcelona GSE)

  • Edward Manuel Ruiz Crosby

    (BCRP)

  • Roberto Stagni

    (Barcelona GSE)

Abstract

This master project scrutinizes the underlying theoretical arguments within Bayraktar and Yang's (2011) model and tests if it is robust with newer 2017 data. We demonstrate all the related strong mathematical foundations to understand their model. We also observe that the matching between the observed and estimated data is not as good as expected with the Ford Motor Company data yet being better for the SPX Index data. Thus we claim for the model to be improved with more recent research.

Suggested Citation

  • Andrea De Martino & Edward Manuel Ruiz Crosby & Roberto Stagni, 2017. "A unified framework for pricing credit and equity derivatives," Working Papers 116, Peruvian Economic Association.
    • Handle: RePEc:apc:wpaper:2017-116
    as

    Download full text from publisher

    File URL: http://perueconomics.org/wp-content/uploads/2018/01/WP-116.pdf
    File Function: Application/pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fouque,Jean-Pierre & Papanicolaou,George & Sircar,Ronnie & Sølna,Knut, 2011. "Multiscale Stochastic Volatility for Equity, Interest Rate, and Credit Derivatives," Cambridge Books, Cambridge University Press, number 9780521843584.
    2. Vadim Linetsky, 2006. "Pricing Equity Derivatives Subject To Bankruptcy," Mathematical Finance, Wiley Blackwell, vol. 16(2), pages 255-282, April.
    3. Peter Carr & Vadim Linetsky, 2006. "A jump to default extended CEV model: an application of Bessel processes," Finance and Stochastics, Springer, vol. 10(3), pages 303-330, September.
    4. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
    5. E. Bayraktar, 2008. "Pricing Options on Defaultable Stocks," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(3), pages 277-304.
    6. E. Papageorgiou & R. Sircar, 2008. "Multiscale Intensity Models for Single Name Credit Derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(1), pages 73-105.
    7. Azusa Takeyama & Nick Constantinou & Dmitri Vinogradov, 2012. "A Framework for Extracting the Probability of Default from Stock Option Prices," IMES Discussion Paper Series 12-E-14, Institute for Monetary and Economic Studies, Bank of Japan.
    8. Hannah Dyrssen & Erik Ekström & Johan Tysk, 2014. "Pricing Equations In Jump-To-Default Models," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-13.
    9. Hull, John & White, Alan, 1995. "The impact of default risk on the prices of options and other derivative securities," Journal of Banking & Finance, Elsevier, vol. 19(2), pages 299-322, May.
    10. Rogers, L. C. G. & Stummer, Wolfgang, 2000. "Consistent fitting of one-factor models to interest rate data," Insurance: Mathematics and Economics, Elsevier, vol. 27(1), pages 45-63, August.
    11. Carl Chiarella & Mark Craddock & Nadima El-Hassan, 2000. "The Calibration of Stock Option Pricing Models Using Inverse Problem Methodology," Research Paper Series 39, Quantitative Finance Research Centre, University of Technology, Sydney.
    12. Akira Yamazaki, 2013. "Exponential L�vy Models Extended by a Jump to Default," Applied Mathematical Finance, Taylor & Francis Journals, vol. 20(3), pages 211-228, July.
    13. Bo Young Chang & Greg Orosi, 2016. "Equity Option-Implied Probability of Default and Equity Recovery Rate," Staff Working Papers 16-58, Bank of Canada.
    14. Freixas, Xavier & Laeven, Luc & Peydró, José-Luis, 2015. "Systemic Risk, Crises, and Macroprudential Regulation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262028697, December.
    15. Tsz Kin Chung & Yue Kuen Kwok, 2014. "Equity-credit modeling under affine jump-diffusion models with jump-to-default," Journal of Financial Engineering (JFE), World Scientific Publishing Co. Pte. Ltd., vol. 1(02), pages 1-25.
    Full references (including those not matched with items on IDEAS)

    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. Nan Chen & S. G. Kou, 2009. "Credit Spreads, Optimal Capital Structure, And Implied Volatility With Endogenous Default And Jump Risk," Mathematical Finance, Wiley Blackwell, vol. 19(3), pages 343-378, July.
    2. Matthew Lorig, 2011. "Pricing Derivatives on Multiscale Diffusions: an Eigenfunction Expansion Approach," Papers 1109.0738, arXiv.org, revised Apr 2012.
    3. Tim Leung & Peng Liu, 2012. "Risk Premia And Optimal Liquidation Of Credit Derivatives," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(08), pages 1-34.
    4. Dean Fantazzini & Stephan Zimin, 2020. "A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 19-69, March.
    5. Xu, Ruxing, 2011. "A lattice approach for pricing convertible bond asset swaps with market risk and counterparty risk," Economic Modelling, Elsevier, vol. 28(5), pages 2143-2153, September.
    6. Kay Giesecke & Dmitry Smelov, 2013. "Exact Sampling of Jump Diffusions," Operations Research, INFORMS, vol. 61(4), pages 894-907, August.
    7. Tahir Choulli & Catherine Daveloose & Michèle Vanmaele, 2020. "A martingale representation theorem and valuation of defaultable securities," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1527-1564, October.
    8. Hilscher, Jens, 2007. "Is the corporate bond market forward looking?," Working Paper Series 800, European Central Bank.
    9. Dassios, Angelos & Li, Luting, 2020. "Explicit asymptotic on first passage times of diffusion processes," LSE Research Online Documents on Economics 103087, London School of Economics and Political Science, LSE Library.
    10. Linda Allen & Anthony Saunders, 2004. "Incorporating Systemic Influences Into Risk Measurements: A Survey of the Literature," Journal of Financial Services Research, Springer;Western Finance Association, vol. 26(2), pages 161-191, October.
    11. Shican Liu & Yanli Zhou & Benchawan Wiwatanapataphee & Yonghong Wu & Xiangyu Ge, 2018. "The Study of Utility Valuation of Single-Name Credit Derivatives with the Fast-Scale Stochastic Volatility Correction," Sustainability, MDPI, vol. 10(4), pages 1-21, March.
    12. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    13. Tsung-Kang Chen & Hsien-Hsing Liao & Chia-Wu Lu, 2011. "A flow-based corporate credit model," Review of Quantitative Finance and Accounting, Springer, vol. 36(4), pages 517-532, May.
    14. Duffie, Darrell, 2005. "Credit risk modeling with affine processes," Journal of Banking & Finance, Elsevier, vol. 29(11), pages 2751-2802, November.
    15. Samuel Chege Maina, 2011. "Credit Risk Modelling in Markovian HJM Term Structure Class of Models with Stochastic Volatility," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2011.
    16. Houweling, Patrick & Hoek, Jaap & Kleibergen, Frank, 2001. "The joint estimation of term structures and credit spreads," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 297-323, July.
    17. Suresh M. Sundaresan, 2000. "Continuous‐Time Methods in Finance: A Review and an Assessment," Journal of Finance, American Finance Association, vol. 55(4), pages 1569-1622, August.
    18. Abbassi, Puriya & Brownlees, Christian & Hans, Christina & Podlich, Natalia, 2017. "Credit risk interconnectedness: What does the market really know?," Journal of Financial Stability, Elsevier, vol. 29(C), pages 1-12.
    19. Xiao, Tim, 2013. "A Simple and Precise Method for Pricing Convertible Bond with Credit Risk," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(4), pages 259-277.
    20. 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.

    More about this item

    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:apc:wpaper:2017-116. 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: Nelson Ramírez-Rondán (email available below). General contact details of provider: https://edirc.repec.org/data/peruvea.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.