Financial Management Perspective

Financial Management Perspective

Comparison and Evaluation of Portfolio Insurance Strategies Using Bootstrap Block Simulation (Case Study: Parsian Insurance)

Document Type : Original Article

Authors
Ramin Bashir Khodaparasti 1
Samad Moslehi 2
1 Assistant Prof, Department of Business Management, Urmia University, Urmia, Iran
2 MSc. in Biostatistic, Tarbiat Modares University, Tehran, Iran.
10.52547/jfmp.9.25.147
Abstract
In this study, performance of four portfolio insurance strategies based on Stop-loss Portfolio(SL), Synthetic Put Portfolio Option (SPP), Constant Proportion Portfolio(CPPI) and Dynamic Value at Risk(D-VaR) were compared and based on the Omega performance criterion with threshold values of 1 to 4 percent and also empirical Value at Risk were evaluated. According to daily data of Tehran Stock Exchange index for 10 years, the sample includes 2467 observations from the first of April 2009 to the end of March 2019. Using the software R version 3.4.4, comparison of portfolio insurance strategies based on bootstrap block simulation were compared. The results showed that SPP and CPPI strategies with higher omega criteria had better performance than SL's strategy and also, the D-VaR strategy at level of 90% based on estimated the average and volatility from the parametric model after the Bootstrap simulation process and according to the omega performance criterion and empirical value at risk has more effective than other strategies in hedging portfolio toward the other strategies.
Keywords
: Portfolio Insurance Strategies
omega ratio
Bootstrap
  1. Setayeh, M., Tagizadeh, T. & Poormoosa, A. (2009). Feasibility of using technical analysis indices in predicting stock price trend in Tehran Stock Exchange. Strategic management research, Insight Quarterly, 42(16), 155-177.
  2. Dreher, W.A. (1988). Does portfolio insurance ever make sense? The Journal of Portfolio Management, 14(4), 25-32.
  3. Mlynarovič, V. (2011). Portfolio insurance strategies and their applications. Ekonomický časopis (Journal of Economics), 59(4), 355-367.
  4. Dichtl, H. & Drobetz, W. (2011). Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products. Journal of Banking & Finance, 35(7), 1683-1697.
  5. Shen, S.y, & Wang, A.M. (2001). On stop-loss strategies for stock investments. Applied Mathematics and Computation, 119(2-3), 317-337.
  6. Do, B.H. & Faff, R.W. (2004). Do futures‐based strategies enhance dynamic portfolio insurance? Journal of Futures Markets: Futures, Options, and Other Derivative Products, 24(6), 591-608.
  7. Philippe, B., & Jean-Luc, P. (2005). Portfolio insurance strategies: OBPI versus CPPI. Journal of Finance, 26(1), 5-32.
  8. Jiang, C., Y. Ma, and Y. An. (2009). The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis. International Review of Financial Analysis, 18(4), 185-197.
  9. Annaert, J., Van Osselaer, S. & Verstraete, B. (2009). Performance evaluation of portfolio insurance strategies using stochastic dominance criteria. Journal of Banking & Finance, 33(2), 272-280.
  10. Bertrand, P. & Prigent, J. (2011). Omega performance measure and portfolio insurance. Journal of Banking & Finance, 35(7), 1811-1823.
  11. Yao, Y. & Li. L. (2016). Portfolio insurance with a dynamic risk multiplayer based on price fluctuation. European Journal of Research and Reflection in Management Sciences, 4(2).
  12. Dichtl, H., Drobetz,W. & Wambach, M. (2017). A bootstrap-based comparison of portfolio insurance strategies. The European Journal of Finance, 23(1), 31-59.
  13. Heidari, H. & Molabahrami, A. (2012). Portfolio Optimization Using Multivariate GARCH Models: Evidence from Tehran Stock Exchange. Financial Research Journal, 12(30), 35-56.
  14. Allahi, F. & Hashemi, M. (2012). Analyzing and compared two OBPI and CPPI portfolio insurance strategies by Omega performance. Third Conference on Mathematical Finance and Applications, Semnan University, 11(33), 1-17.
  15. Asgharpur, H., et al. (2014). Stock Optimal Portfolio Selection in a VaR Framework: Comparison the MS-GARCH and Bootstrapping Methods. Journal of Research in Economic Modeling, 5(17), 87-122.
  16. Huu Do, B. (2002). Relative performance of dynamic portfolio insurance strategies: Australian evidence. Accounting & Finance, 42(3), 279-296.
  17. Cont, R. & Tankov, P. (2009). Constant proportion portfolio insurance in the presence of jumps in asset prices. Mathematical Finance: An International Journal of Mathematics, Statistics and Financial Economics, 19(3), 379-401.
  18. Philippe, J. (2001). Value at risk: the new benchmark for managing financial risk. NY: McGraw-Hill Professional.
  19. Wurtz, D., Y. Chalabi, and L. Luksan. (2006). Parameter estimation of ARMA models with GARCH/APARCH errors an R and SPlus software implementation. Journal of Statistical Software, 55, 28-33.
  20. Box, G.E., et al. (2015). Time series analysis: forecasting and control. 2015: John Wiley & Sons. 5 edition.
  21. Kazemi, H., Schneeweis, T. & Gupta, B. (2004). Omega as a performance measure. Jourbal of Performance Measurment, 8, 16-25.
  22. Good, P. (2013). Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer Science & Business Media.