Equity Portfolio Optimization Using Mean-CVaR Method Considering Symmetric and Asymmetric Autoregressive Conditional Heteroscedasticity

Document Type : Research Paper

Authors

1 Prof. Department of Finance Management, Faculty of Management, University of Tehran, Tehran, Iran.

2 PhD Candidate, Department of Finance-Banking, Faculty of Management, University of Tehran, Tehran, Iran.

3 MSc., Department of Finance Management, Faculty of Management, University of Tehran, Tehran, Iran.

Abstract

Objective: Risk management is one of the most important areas of study in finance, and its vital role in the field has attracted the attention of managers and investors in in various sectors of the industry. Especially in recent years, with the onset of financial crises, the importance and necessity of accurate studies in this area has doubled. The main purpose of this study is to provide a model for a more accurate measurement of equity portfolio risk.
Methods: To conduct this research, adjusted closing prices of a sample of thirty listed companies have been used. CVaR is the main model and four other models are formulated using different methods of variance modeling. The first method calculates conditional value at risk using constant variance and in the other three methods, variance is modeled on GARCH, E-GARCH and T-GARCH approaches.
Results: Ultimately, the results have been evaluated using appropriate statistical tests, namely paired t test and Wilcoxon signed rank test. The results obtained from both tests suggest that the method used to model variance has a significant effect on attaining a better optimal portfolio.
Conclusion: Considering results of the research, which approve the tested hypothesis, one can conclude, taking into account the heteroscedasticity in Iranian capital market, would result in a better optimized portfolio. Moreover, the results illustrated that the use of CVaR model, for risk measurement, rather than previously used traditional models, can be effective in improving the performance, in optimizing stock portfolios, significantly.

Keywords

References

Alexander, C. (2008). Market Risk Analysis.Vol 4, Value at Risk. West Sussex: John Wiley & Sons.
Alexander, C. (2008). Market Risk Analysis.Vol 2, Practical Financial Econometrics. West Sussex: John Wiley & Sons
Baumol, W.J. (1963). An expected gain-confidence limit criterion for portfolio selection. Management Science, 10, 174-182.
Feyzabad, A. (2008). Review and analysis of calculation methods of Value at Risk models in Tehran Stock Market. Tehran, University of Tehran, Management Faculty, MSc Thesis. (in Persisan)
Xiong, J.X. (2010). Mean-Variance Versus Mean-Conditional Value-at-Risk Optimization: The Impact of Incorporating Fat Tails and Skewness into the Asset Allocation Decision.
Rockafellar, R.T., & Uryasev, S. (1999). Optimization of Conditional Value-at-Risk. Available in: https://www.ise.ufl.edu/uryasev/files/2011/11/CVaR1_JOR.pdf.
Rockafellar, R.T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443-1471. 
Financial Research Journal
Volume 22, Issue 2
2020
Pages 149-159

Files

Share

How to cite

Statistics