Computer
Statistical analysis of extreme values
Statistical analysis of extreme values
A model for portfolio selection with order of expected returns
Computers and Operations Research
Relative risk aversion and wealth dynamics
Information Sciences: an International Journal
Portfolio algorithm based on portfolio beta using genetic algorithm
Expert Systems with Applications: An International Journal
Fuzzy portfolio selection based on value-at-risk
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A systematic design for coping with model risk
Expert Systems with Applications: An International Journal
Generating effective defined-contribution pension plan using simulation optimization approach
Expert Systems with Applications: An International Journal
Integrated framework of risk evaluation and risk allocation with bounded data
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Value-at-risk (VaR) has become a popular risk measure since it was adopted by the Bank for International Settlements and US regulatory agencies in 1988. The VaR concept has also been further extended to the portfolio value-at-risk (PVaR) measure used for managing risks and returns under a multiple-asset portfolio. Precise prediction of PVaR provides better evaluation criteria in areas such as investment decision-making and risk management. The two issues concerned with portfolio risk are efficient set selection and volatility forecasting. Most of the statistical portfolio selection models are based on linear functions under specific assumptions. Due to the fat-tailed distribution in most real financial time-series data, extreme value theory (EVT) is powerful in determining the VaR of a portfolio by concentrating on estimating the shape of the fat-tailed probability distribution. However, using EVT to evaluate the portfolio's volatility is very difficult, because each asset within the portfolio has its own distinct peak threshold value. This study introduces an evolutionary portfolio volatility forecasting model to optimize portfolios under their maximum expected returns subject to a risk constraint. We use a genetic algorithm (GA) to extract the best portfolio set and most suitable peak threshold in order to estimate the portfolio's VaR by means of EVT.