جستجوی مقالات مرتبط با کلیدواژه « cvar » در نشریات گروه « مالی »

The issue of choosing a portfolio is a multi-objective issue; Therefore, the need to know the methods of solving portfolio selection models is of great importance. Ultra-innovative algorithms are new ideas that were introduced in this regard. The multi-objective SPEA2 algorithm is one of the algorithms that solves the portfolio optimization problem. The purpose of this study is to use the SPEA2 multi-objective algorithm to achieve the desired combination of petrochemical companies in the petrochemical portfolio. The objective functions of the problem under study include the two objectives of maximizing returns and minimizing risk. The statistical sample includes data of 900 days of 12 petrochemical companies allowed to operate from 1/12/94 to 12/12/98, which by transferring this data to MATLAB software, the logarithmic return each stock is calculated and is the input of SPEA2 algorithm. Then the SPEA2 algorithm is implemented for each of the models of Mean-Variance, Mean-Semi Variance, Mean-Absolute Deviation, Mean- Conditional Value at Risk and the weight of each stock and risk and return of each portfolio are calculated. Then, using SPSS software, the mean difference between risk and return of the models was tested. The results show that the returns obtained by SPEA2 algorithm under different risk models are not statistically significant; However, the portfolio risk created by the SPEA2 algorithm under the Conditional Value at Risk model is significantly different from other risk measures and shows more risk.

Ever since Harry Markowitz's groundbreaking paper on the mean-variance model was published in 1952, numerous efforts have been dedicated to exploring the applications and advancements of classical models. Following the development of financial markets, active portfolio optimization has become one of the most important topics in finance. This study aimed to examine active portfolio management, a critical and delicate choice for investors, particularly concerning overall portfolio risk. The determination of an optimal stock portfolio that offers both a substantial return rate and controlled risk is consistently a subject of keen interest for analysts, investors, and even portfolio managers.
 

Many methods have been developed to measure investment risk, and the price of risky assets changes rapidly and randomly due to the complexity of the financial market. A random interval is a suitable tool for describing uncertainty with randomness and imprecision. Given the uncertainty in financial markets, this study used stochastic intervals to describe the returns of risky assets and the tail sequence risk, called the interval-valued conditional value at risk (ICVaR). The interval value in this model is an extension of the classic portfolio model, which can comprehensively reflect the complexity of the financial market and the risk-taking behavior of investors.
 

Following the findings from the real data of 10 out of 30 large corporates listed on the Tehran Stock Exchange, the ICVaR model is interpretable and compatible with the practical scenario and can be used to choose the optimal portfolio at different levels of risk and depending on the risk-taking degree of the investor. The present study used the portfolio optimization approach under a new criterion of ICVaR through the closing price, the highest price, and the lowest price on each trading day. In this model, the return range of the risky asset is taken as a random variable with an interval value. Besides, CVaR with an interval value is used to describe the risk instead of the variance at a certain level of return.
 

Uncertainties induced by asset transactions affect the predictions of investment plans. To address such challenging uncertainties in this study, a stable stochastic optimization approach was presented based on the range of optimal solutions produced by the proposed model to determine different operational options. Finally, the model developed in this study showed that investors’ subjective risk preference or aversion can be described by observing the principle of portfolio diversification, which reflects an innovation different from the classic portfolio model. Furthermore, the worst possible case was optimized in all scenarios in the model by robust optimization. The findings indicated that a narrower range corresponds to a higher level of risk aversion among investors.

 Based on Markowitz theory of portfolio optimization, capital market is not predictable by any methods and the risk can only be diversified through portfolio formation and optimization. Recent works made huge developments in the basic model from modeling and risk measures perspectives. Spectral risk measures such as expected shortfall and value at risk are being used frequently as risk measures. In addition, researchers tend to consider uncertainty in risk and return evaluation via fuzzy, stochastic and robust modeling. However, a matter that has been neglected in many researches is portfolio management under uncertainty conditions. This paper propose a method for robust modeling of portfolio optimization and management using expected shortfall as risk measure and Bertsimas modeling as robust programming. The proposed model solved with artificial bee colony algorithm and results show a better performance of proposed model compared to classic methods in both the optimal portfolio formation and its management phase.

Formal portfolio optimization methodologies describe the dynamics of financial instruments price with Gaussian Copula (GC). Regardless of the skewness and kurtosis of assets return rate, optimization with GC underestimates the optimal CVaR of portfolio. In the present paper, we develop an approach to portfolio optimization by introducing Lévy processes. It focuses on describing the dynamics of assets’ log price with Variance Gamma copula (VGC) rather than GC. Doing a case study on three Indexes of Iran Stock Market, the best hedge positions of Total Index, Market Index and Industry Index with the performance function CVaR under VG model were calculated. The results indicate that (a) VG copula can efficiently overcome the shortcomings of Gaussian copula which underestimates the CVaR of portfolio; (b) optimal portfolio, VaR and CVaR keep stable each time one parameter of sample’s skewness or kurtosis was changed, but the optimal portfolio change significantly when the sample’s mean increases or decreases; (c) different copula lead to different optimal CVaR; and (d) fat-tailedness and kurtosis are extremely important in portfolio optimization framework.