IDEAS home Printed from https://ideas.repec.org/a/sae/emffin/v11y2012i2p189-229.html
   My bibliography  Save this article

Risk Management in Trading and Investment Portfolios

Author

Listed:
  • Mazin A.M. Al Janabi

Abstract

The aim of this article is to bridge the gap in equity trading risk management literatures and particularly from the perspective of emerging and illiquid markets, such as in the context of the Gulf Cooperation Council (GCC) financial markets. In this article, we demonstrate a practical approach for the measurement and control of market risk exposure for financial trading portfolios that contain several illiquid equity securities during the unwinding (close-out) period. This approach is based on the renowned concept of Value-at-Risk (VaR) along with the development of an optimisation risk algorithm utilising matrix-algebra technique. Our thorough asset market risk modelling-algorithm can simultaneously handle VaR analysis under normal and severe market conditions, besides it takes into account the effects of illiquidity and short-sales of traded equity securities. In order to illustrate the proper use of VaR and stress-testing methods, real-world structured modelling techniques of trading risk management are presented for the GCC financial markets. To this end, comprehensive simulation case studies are accomplished with the objective of constructing a realistic framework for trading risk measurement and control in addition to the instigation of a risk optimisation algorithm-process for the computation of maximum authorised VaR risk-budgeting limits. JEL Classification : C10, C13, G20, G28

Suggested Citation

  • Mazin A.M. Al Janabi, 2012. "Risk Management in Trading and Investment Portfolios," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 11(2), pages 189-229, August.
    • Handle: RePEc:sae:emffin:v:11:y:2012:i:2:p:189-229
    DOI: 10.1177/0972652712454516
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0972652712454516
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0972652712454516?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
    ---><---

    References listed on IDEAS

    as
    1. René Garcia & Éric Renault & Georges Tsafack, 2007. "Proper Conditioning for Coherent VaR in Portfolio Management," Management Science, INFORMS, vol. 53(3), pages 483-494, March.
    2. Matthew Pritsker, 1997. "Evaluating Value at Risk Methodologies: Accuracy versus Computational Time," Journal of Financial Services Research, Springer;Western Finance Association, vol. 12(2), pages 201-242, October.
    3. Robert Engle, 2001. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 157-168, Fall.
    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. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    2. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, September.
    3. Laurent El Ghaoui & Maksim Oks & Francois Oustry, 2003. "Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach," Operations Research, INFORMS, vol. 51(4), pages 543-556, August.
    4. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2013. "A comparison of the original and revised Basel market risk frameworks for regulating bank capital," Journal of Economic Behavior & Organization, Elsevier, vol. 85(C), pages 249-268.
    5. Torben G. Andersen & Tim Bollerslev & Peter Christoffersen & Francis X. Diebold, 2007. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Chapters, in: The Risks of Financial Institutions, pages 513-544, National Bureau of Economic Research, Inc.
    6. Tully, Edel & Lucey, Brian M., 2007. "A power GARCH examination of the gold market," Research in International Business and Finance, Elsevier, vol. 21(2), pages 316-325, June.
    7. Siddiqi, Hammad, 2007. "Rational Interacting Agents and Volatility Clustering: A New Approach," MPRA Paper 2984, University Library of Munich, Germany.
    8. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2012. "When more is less: Using multiple constraints to reduce tail risk," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2693-2716.
    9. Mínguez, R. & Conejo, A.J. & García-Bertrand, R., 2011. "Reliability and decomposition techniques to solve certain class of stochastic programming problems," Reliability Engineering and System Safety, Elsevier, vol. 96(2), pages 314-323.
    10. Liviu-Stelian BEGU & Silvia Spataru & Erika Marin, 2012. "Investigating The Evolution Of Ron/Eur Exchange Rate: The Choice Of Appropriate Model," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 1(2), pages 23-39, DECEMBER.
    11. Rockstuhl, Sebastian & Wenninger, Simon & Wiethe, Christian & Ahlrichs, Jakob, 2022. "The influence of risk perception on energy efficiency investments: Evidence from a German survey," Energy Policy, Elsevier, vol. 167(C).
    12. Abdelkader Derbali & Slaheddine Hallara & Aida Sy, 2016. "Athen's game of chicken or the conditional dependence between the Greek banks," International Journal of Economics and Accounting, Inderscience Enterprises Ltd, vol. 7(1), pages 1-26.
    13. Ewing, Bradley T. & Malik, Farooq, 2016. "Volatility spillovers between oil prices and the stock market under structural breaks," Global Finance Journal, Elsevier, vol. 29(C), pages 12-23.
    14. Allen, David E. & Amram, Ron & McAleer, Michael, 2013. "Volatility spillovers from the Chinese stock market to economic neighbours," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 238-257.
    15. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2014. "Bank regulation and international financial stability: A case against the 2006 Basel framework for controlling tail risk in trading books," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 107-130.
    16. Francois John Nana, 2020. "Foreign official holdings of US treasuries, stock effect and the economy: a DSGE approach," The B.E. Journal of Macroeconomics, De Gruyter, vol. 20(1), pages 1-28, January.
    17. Shehu Usman Rano Aliyu, 2012. "Does inflation have an impact on stock returns and volatility? Evidence from Nigeria and Ghana," Applied Financial Economics, Taylor & Francis Journals, vol. 22(6), pages 427-435, March.
    18. Matthew Pritsker, 2001. "The hidden dangers of historical simulation," Finance and Economics Discussion Series 2001-27, Board of Governors of the Federal Reserve System (U.S.).
    19. Ñíguez, Trino-Manuel & Perote, Javier, 2017. "Moments expansion densities for quantifying financial risk," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 53-69.
    20. Shuang Xiao & Guo Li & Yunjing Jia, 2017. "Estimating the Constant Elasticity of Variance Model with Data-Driven Markov Chain Monte Carlo Methods," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-23, February.

    More about this item

    Keywords

    Economic capital; emerging markets; financial engineering; financial risk management; GCC financial markets; portfolio management; stress testing; value-at-risk;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

    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:sae:emffin:v:11:y:2012:i:2:p:189-229. 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: SAGE Publications (email available below). General contact details of provider: http://www.ifmr.ac.in .

    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.