Portfolio Optimization Under Conditional Value at Risk and Tail isk: Based on Two-Stage Stochastic Linear Programming Model

• Main Authors: Yu-Ren Lu, 盧俞任
• Other Authors: Kuo-Hwa Chang
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/61408191884791332618

碩士 === 中原大學 === 工業與系統工程研究所 === 97 === In response to the high degree uncertainty of financial market, how to estimate the potential loss of portfolio is the most important topic concerned by investors when they are pursuing the maximum of reward. Due to the return rate and risk of asset in stock are full of uncertainty, investors will face more complex investment problems than usual. In this thesis, we apply the modified Conditional Value-at-Risk (CVaR) model to be uncertainty to select the portfolio with riskless. Although CVaR be used in finance perform well, the portfolio that select by it is still risky. Therefore, in order to avoid any intensity fluctuates of risk, we structure our model based on safety-first model to control downside risk. In this thesis we compare two relative linear programming models for solving the single period portfolio selection problem. The first model is considered a stochastic linear programming (SLP) model by using minimization of Conditional Value-at-Risk as objective function, and constrain is structured by safety-first model. The second is based on safety-first model using original data. However, test results of ours performances are better than market and the safety-first model with original data.