IDEAS home Printed from https://ideas.repec.org/a/ora/journl/v1y2012i2p635-640.html
   My bibliography  Save this article

The Role Of Value At Risk In The Management Of Asset And Liabilities

Author

Listed:
  • Zapodeanu Daniela

    (University of Oradea, Faculty of Economic Sciences)

  • Cociuba Mihai

    (University of Oradea, Faculty of Economic Sciences)

  • Petria Nicolae

    (Lucian Blaga University Sibiu, Faculty of Economics)

Abstract

ALM is the management of risk at enterprise level, the models used in ALM can be static or dynamic: single period-static models, multiple period static model, single period stochastic model, multi period stochastic model. While single period-static don't incorporate the dynamic of the economical changes the multiple period-static models are an extension of the single period-static model, the most common used are multi-period stochastic which model the evolution of financial series in time and the assets and liabilities using different types of probability distributions (Student, GED). Highly correlated with ALM is the Value at Risk which can be used as and function to be minimized in ALM models. In the Value at Risk methodology the estimation models are classified as: parametric, nonparametric, semi-parametric; we present the parametric models (GARCH models) used in Value at Risk and the connections that can be established between ALM models and Value at Risk. We present the Conditional Value-at-risk and offer and example on how to calculate CVaR .

Suggested Citation

  • Zapodeanu Daniela & Cociuba Mihai & Petria Nicolae, 2012. "The Role Of Value At Risk In The Management Of Asset And Liabilities," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 635-640, December.
    • Handle: RePEc:ora:journl:v:1:y:2012:i:2:p:635-640
    as

    Download full text from publisher

    File URL: http://anale.steconomiceuoradea.ro/volume/2012/n2/096.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Szego, Giorgio, 2005. "Measures of risk," European Journal of Operational Research, Elsevier, vol. 163(1), pages 5-19, May.
    2. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Carlo Acerbi & Dirk Tasche, 2002. "Expected Shortfall: A Natural Coherent Alternative to Value at Risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 379-388, July.
    5. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    6. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    7. M. I. Kusy & W. T. Ziemba, 1986. "A Bank Asset and Liability Management Model," Operations Research, INFORMS, vol. 34(3), pages 356-376, June.
    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. Gonzalo Cortazar & Alejandro Bernales & Diether Beuermann, 2005. "Methodology and Implementation of Value-at-Risk Measures in Emerging Fixed-Income Markets with Infrequent Trading," Finance 0512030, University Library of Munich, Germany.
    2. Köksal, Bülent, 2009. "A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns," MPRA Paper 30510, University Library of Munich, Germany.
    3. Carol Alexander & Emese Lazar & Silvia Stanescu, 2011. "Analytic Approximations to GARCH Aggregated Returns Distributions with Applications to VaR and ETL," ICMA Centre Discussion Papers in Finance icma-dp2011-08, Henley Business School, University of Reading.
    4. Carl H. Korkpoe & Peterson Owusu Junior, 2018. "Behaviour of Johannesburg Stock Exchange All Share Index Returns - An Asymmetric GARCH and News Impact Effects Approach," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 68(1), pages 26-42, January-M.
    5. Marcel Bräutigam & Marie Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," Working Papers hal-02176276, HAL.
    6. Jamal Bouoiyour & Refk Selmi, 2016. "Bitcoin: a beginning of a new phase?," Economics Bulletin, AccessEcon, vol. 36(3), pages 1430-1440.
    7. Nieto, María Rosa, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Feng, Zhen-Hua & Wei, Yi-Ming & Wang, Kai, 2012. "Estimating risk for the carbon market via extreme value theory: An empirical analysis of the EU ETS," Applied Energy, Elsevier, vol. 99(C), pages 97-108.
    9. Marcel, Bräutigam & Marie, Kratz, 2019. "Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH(p, q) processes," ESSEC Working Papers WP1909, ESSEC Research Center, ESSEC Business School.
    10. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    11. Bouoiyour, Jamal & Selmi, Refk, 2015. "Bitcoin Price: Is it really that New Round of Volatility can be on way?," MPRA Paper 65580, University Library of Munich, Germany.
    12. Bouoiyour, Jamal & Miftah, Amal & Selmi, Refk, 2014. "Do Financial Flows raise or reduce Economic growth Volatility? Some Lessons from Moroccan case," MPRA Paper 57258, University Library of Munich, Germany.
    13. Waldemar Tarczyński & Sebastian Majewski & Małgorzata Tarczyńska-Łuniewska & Agnieszka Majewska & Grzegorz Mentel, 2021. "The Impact of Weather Factors on Quotations of Energy Sector Companies on Warsaw Stock Exchange," Energies, MDPI, vol. 14(6), pages 1-14, March.
    14. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    15. Altaf Muhammad & Zhang Shuguang, 2015. "Impact Of Structural Shifts on Variance Persistence in Asymmetric Garch Models: Evidence From Emerging Asian and European Markets," Romanian Statistical Review, Romanian Statistical Review, vol. 63(1), pages 57-70, March.
    16. Ding, Jing & Jiang, Lei & Liu, Xiaohui & Peng, Liang, 2023. "Nonparametric tests for market timing ability using daily mutual fund returns," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    17. Gerard H. Kuper & Daan P. van Soest, 2006. "Does Oil Price Uncertainty Affect Energy Use?," The Energy Journal, , vol. 27(1), pages 55-78, January.
    18. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    19. Theodore Panagiotidis, 2010. "Market efficiency and the Euro: the case of the Athens stock exchange," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(3), pages 237-251, July.
    20. Nadia Dridi & Fathi Ayachi, 2022. "The determinants of EURO/TND exchange rate volatility in Tunisia," Economics Bulletin, AccessEcon, vol. 42(4), pages 1981-1997.

    More about this item

    Keywords

    asset-liability models; Value-at-risk; Conditional Value-at-risk; GARCH;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:ora:journl:v:1:y:2012:i:2:p:635-640. 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: Catalin ZMOLE (email available below). General contact details of provider: https://edirc.repec.org/data/feoraro.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.