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. 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.
    2. 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.
    3. 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.
    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. 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.
    2. 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.
    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. 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.
    5. Siddiqi, Hammad, 2007. "Rational Interacting Agents and Volatility Clustering: A New Approach," MPRA Paper 2984, University Library of Munich, Germany.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Rosa Ferrentino & Luca Vota, 2022. "A Mathematical Model for the Pricing of Derivative Financial Products: the Role of the Banking Supervision and of the Model Risk," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 11(1), pages 1-2.
    12. Viviana Fernandez, 2003. "Extreme Value Theory and Value at Risk," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 18(1), pages 57-85, June.
    13. Erdal Demirhan & Banu Demirhan, 2015. "The Dynamic Effect of ExchangeRate Volatility on Turkish Exports: Parsimonious Error-Correction Model Approach," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 62(4), pages 429-451, September.
    14. Suripto & Supriyanto, 2021. "The Effect of the COVID-19 Pandemic on Stock Prices with the Event Window Approach: A Case Study of State Gas Companies, in the Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 155-162.
    15. Farid Bagheri & Diego Reforgiato Recupero & Espen Sirnes, 2023. "Leveraging Return Prediction Approaches for Improved Value-at-Risk Estimation," Data, MDPI, vol. 8(8), pages 1-22, August.
    16. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    17. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    18. Buczyński Mateusz & Chlebus Marcin, 2018. "Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(2), pages 67-82, June.
    19. Allison Roehling, 2021. "Implications of exchange rate volatility for trade: Volatility measurement matters," Review of International Economics, Wiley Blackwell, vol. 29(5), pages 1486-1523, November.
    20. Silvia Stanescu & Radu Tunaru, 2013. "Quantifying the uncertainty in VaR and expected shortfall estimates," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 15, pages 357-372, Edward Elgar Publishing.

    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.