IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/36300.html
   My bibliography  Save this paper

Calibration of factor models with equity data: parade of correlations

Author

Listed:
  • Baranovski, Alexander L.

Abstract

This paper describes the process of ML-estimating of the equity correlations which can be used as proxies for asset correlations. In a Gaussian framework the ML-estimators are given in closed form. On this basis the impact of the Lehman’s collapse on the dynamics of correlations is investigated: after the Lehman failure in September 2008 the rise in correlations took place across all economic sectors.

Suggested Citation

  • Baranovski, Alexander L., 2012. "Calibration of factor models with equity data: parade of correlations," MPRA Paper 36300, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:36300
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/36300/1/MPRA_paper_36300.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Timo Altmann & Thorsten Schmidt & Winfried Stute, 2008. "A Shot Noise Model For Financial Assets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 87-106.
    2. Duellmann, Klaus & Küll, Jonathan & Kunisch, Michael, 2010. "Estimating asset correlations from stock prices or default rates--Which method is superior?," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2341-2357, November.
    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. Kreis, Yvonne & Leisen, Dietmar P.J., 2018. "Systemic risk in a structural model of bank default linkages," Journal of Financial Stability, Elsevier, vol. 39(C), pages 221-236.
    2. Matteo Accornero & Giuseppe Cascarino & Roberto Felici & Fabio Parlapiano & Alberto Maria Sorrentino, 2018. "Credit risk in banks’ exposures to non‐financial firms," European Financial Management, European Financial Management Association, vol. 24(5), pages 775-791, November.
    3. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    4. Angelos Dassios & Xin Dong, 2014. "Stationarity of Bivariate Dynamic Contagion Processes," Papers 1405.5842, arXiv.org.
    5. García-Céspedes, Rubén & Moreno, Manuel, 2014. "Estimating the distribution of total default losses on the Spanish financial system," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 242-261.
    6. Moreno, Manuel & Serrano, Pedro & Stute, Winfried, 2011. "Statistical properties and economic implications of jump-diffusion processes with shot-noise effects," European Journal of Operational Research, Elsevier, vol. 214(3), pages 656-664, November.
    7. M. Dietsch & K. Düllmann & H. Fraisse & P. Koziol & C. Ott, 2016. "Support for the SME Supporting Factor - Multi-country empirical evidence on systematic risk factor for SME loans," Débats économiques et financiers 23, Banque de France.
    8. Düllmann, Klaus & Koziol, Philipp, 2013. "Evaluation of minimum capital requirements for bank loans to SMEs," Discussion Papers 22/2013, Deutsche Bundesbank.
    9. Henry Penikas, 2023. "IRB Asset and Default Correlation: Rationale for the Macroprudential Mark-Ups to the IRB Risk-Weights," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-27, March.
    10. Kai Kopperschmidt & Winfried Stute, 2009. "Purchase timing models in marketing: a review," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 93(2), pages 123-149, June.
    11. Oleksandra Putyatina & Jörn Sass, 2018. "Approximation for portfolio optimization in a financial market with shot-noise jumps," Computational Management Science, Springer, vol. 15(2), pages 161-186, June.
    12. Henry Penikas, 2020. "IRB Asset and Default Correlation: Rationale for the Macroprudential Add-ons to the Risk-Weights," Bank of Russia Working Paper Series wps56, Bank of Russia.
    13. Liang, Xiaoqing & Lu, Yi, 2017. "Indifference pricing of a life insurance portfolio with risky asset driven by a shot-noise process," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 119-132.
    14. Boudreault, Mathieu & Gauthier, Geneviève & Thomassin, Tommy, 2015. "Estimation of correlations in portfolio credit risk models based on noisy security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 334-349.
    15. Thorsten Schmidt, 2014. "Catastrophe Insurance Modeled by Shot-Noise Processes," Risks, MDPI, vol. 2(1), pages 1-22, February.
    16. Rainer Baule, 2021. "Credit risk in derivative securities: A simplified approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(5), pages 641-657, May.
    17. Byström, Hans, 2017. "The currency composition of firms' balance sheets, asset value correlations, and capital requirements," Global Finance Journal, Elsevier, vol. 34(C), pages 89-99.
    18. Shuang Li & Yanli Zhou & Yonghong Wu & Xiangyu Ge, 2017. "Equilibrium approach of asset and option pricing under Lévy process and stochastic volatility," Australian Journal of Management, Australian School of Business, vol. 42(2), pages 276-295, May.
    19. Byström, Hans, 2016. "The Currency Composition of Firms' Balance Sheets and its Effect on Asset Value Correlations and Capital Requirements," Working Papers 2016:1, Lund University, Department of Economics.
    20. Bernd Engelmann, 2024. "Spurious Default Probability Projections in Credit Risk Stress Testing Models," Papers 2401.08892, arXiv.org.

    More about this item

    Keywords

    intra/inter asset correlations; maximum likelihood estimation; single risk factor model; normal mixture; VAR of equity portfolio;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

    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:pra:mprapa:36300. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.