ANVESHAK-International Journal of Management

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Measurement Models for Market Risk Management in Nigeria


Affiliations
1 Department of Actuarial Science, Lagos State University of Science and Technology, (LASUSTECH), Lagos, Nigeria
2 Department of Actuarial Science and Insurance, University of Lagos, Nigeria
     

   Subscribe/Renew Journal


Market risk is connected with the price fluctuations and other market factor movements on four of the most important economic markets: Market of debt securities, stock market, currency market and commodity market. A major factor behind the rapid development of risk measurement and management is the high level of instability in the market in which firms operate. A volatile environment exposes firms to greater market risk and therefore provides an incentive for firms to find new and better ways of modeling the risk. This paper generally analyses the performance of different models which try to solve, estimate, measure and manage various market risks in order to derive parameters which aid good decision making.


Keywords

Insurance Market Risk, Risk Management, Value at Risk (VaR), Volatility
Subscription Login to verify subscription
User
Notifications
Font Size


  • Alexander, C. (2001). Market models: A guide to financial data analysis. John Wiley & Sons, Chichester. ISBN 9780471899754

  • Almgren, R., & Chriss, N. (2000). Optimal execution of portfolio transactions. Journal of Risk 3 (Winter), 2000, pp. 5–39. https://doi.org/10.21314/JOR.2001.041

  • Ama, G. N. (2009). Corporate risk reporting practices and their determinants: A study of selected quoted firms in Nigeria. Nigeria Research Journal of Accountancy (NRJA), 2009, Vol. 1, pp. 81–96.

  • Anghelache, C., Voineagu, V., Culetu, D., & Baltac, A. G. (2013). Methods, theories and models to measure market risk of the portfolio of shares. Romanian Statistical Review nr. 8.

  • Asika, N. (2004). Research methodology: A Process approach. Mukugamu & Brothers Enterprises, Lagos.

  • Banjo, A. K. (2019). Insurance Fundamentals (4th Ed.). Lagos, Nigeria: Dekinban Ventures Ltd. ISBN: 987-32959-5-0

  • Berkowitz, J. (2001). Testing density forecasts, with applications to risk management. Journal of Business and Economic Statistics, 2001, Vol. 19, pp. 465–474. https://doi.org/10.1198/07350010152596718

  • Blaauw, A. J. (2009). Market risk management in Nigeria. United bank for Africa (UBA) Plc. Accessed from www.ubagroup.com on 01/02/2022. 10:00 GMT

  • Danu, M. (2014). Dimension of market risk. Studies and Scientific Researches, Economics Edition, issue 19. Accessed from https:// EconPapers.repec.org/article on 20/02/2022 01:06 GMT https://doi.org/10.29358/sceco.v0i19.248

  • Dowd, K. (2002). An introduction to market risk management. John Wiley & Sons Ltd, Chichester.

  • Elton, E. J., & Gruber, M. J. (1995). Modern portfolio theory and investment analysis. Fifth Edition. New York: John Wiley and Sons, Chichester.

  • Emilia, M. (2010). Modelsfor analysis and assessment of market risks in banking. University of National Economy, Bulgaria. Facta Universitatis Economic Journal, 2010, Vol. 7, No 4, pp. 395–410.

  • Enyi, P., & Adebawo, O. (2014). Impact of risk exposures on the market value of Nigerian Banks (2006-2012). America International Journal of Contemporary Research, 4(10).

  • Hubbard, D. (2009). The failure of risk management. John Willey & and Sons, pp. 46.

  • Jamshidian, F., & Zhu, Y. (1997). Scenario simulation: Theory and Methodology. Finance and Stochastics, 1997, Vol. 1, pp, 43–67. https://doi.org/10.1007/s007800050016

  • Kallestrap, R. (2012). The dynamics of bank and sovereign credit risk. [PhD Thesis] at the Copenhagen Business School and the Central Bank of Denmark. Retrieved from National Banken.dk/c1256BE9…strup_phd_Thesis_2012.pdf 9/12/21,10:10GMT

  • Kato, T., & Yoshiba, T. (2000). Model risk and its control. Bank of Japan Institute for Monetary and Economic Studies: Discussion Paper No. 2000-E-15.

  • Langrin, R. B., & Roach, K. (2009). Measuring the effects of concentration and risk on bank returns: Evidence from a panel individual loan portfolio in Jamaica. Business, Finance and Economics in Emerging Economies, 2009, Vol. 4, No. 1, pp. 74–119.

  • Mandelbrot, B., & Richard, H. (2006). The misbehavior of Market: A fractal view of financial turbulence.

  • Navarrete, E. (2006). Practical calculation of expected and unexpected losses in operational risk by simulation methods. Scalar Consulting, October 2006, Vol. 1, No. 1, pp. 1-12.

  • Scaillet, O. (2000). Non-parametric estimation and sensitivity analysis of expected shortfall. Mimeo. IRES, Catholic University of Louvain.

  • Zhang, Y. (2006). A portfolio theory of market risk load. Causality Actuarial Society Forum.


Abstract Views: 221

PDF Views: 0




  • Measurement Models for Market Risk Management in Nigeria

Abstract Views: 221  |  PDF Views: 0

Authors

Adeola Kudirat Banjo
Department of Actuarial Science, Lagos State University of Science and Technology, (LASUSTECH), Lagos, Nigeria
Omotayo Johncally Abere
Department of Actuarial Science and Insurance, University of Lagos, Nigeria

Abstract


Market risk is connected with the price fluctuations and other market factor movements on four of the most important economic markets: Market of debt securities, stock market, currency market and commodity market. A major factor behind the rapid development of risk measurement and management is the high level of instability in the market in which firms operate. A volatile environment exposes firms to greater market risk and therefore provides an incentive for firms to find new and better ways of modeling the risk. This paper generally analyses the performance of different models which try to solve, estimate, measure and manage various market risks in order to derive parameters which aid good decision making.


Keywords


Insurance Market Risk, Risk Management, Value at Risk (VaR), Volatility

References





DOI: https://doi.org/10.15410/aijm%2F2022%2Fv11i2%2F171517