Iranian Journal of Finance

Abstract Portfolio optimization has always been the main concern of investors. What differentiates different optimization models from each other is the risk measure. The main contribution of this paper is to provide a portfolio optimization model that considers systemic risk so that it can help investors make optimal investment decisions as a general model. For this purpose, two models are presented. In the first model, systemic and systematic risk were considered simultaneously, and in the second model, only systemic risk was considered. In the two mentioned models, delta conditional value at risk (∆CoVaR) and the Markowitz model are used respectively to measure systemic risk and a benchmark model. Also, the criteria used to compare the performance of the reviewed models include the ratio of reward-to-risk, along with the Sortino ratio and the Omega ratio. The problem of optimization and examination of the results was carried out on a selected sample, 38 companies listed in the Tehran Stock Exchange (TSE) from 2013 to 2023. The results of empirical analysis of out-of-sample data (during a period of 1198 days) show that based on all three mentioned criteria, the first proposed model shows the best performance among the three models. In addition, the performance of the second model is ranked second. In short, it can be said that considering systemic risk in portfolio optimization leads to better performance than the Markowitz model.

Abstract As a trading strategy, pairs trading is performed based on the arbitrage opportunities extracted from statistical models. It is an outcome of the distance between an asset pair and the equilibrium state. Consequently, selecting a pair with the potential to form long-term relationships and reverting to the mean is the main challenge associated with pair trading. Cointegration is one of the most famous statistical tests for selecting a pair's trading. The present study uses Empirical Mode Decomposition (EMD) to decompose the time series of an asset pair price into its constituent elements (intrinsic mode functions). This study examined the property of cointegration across different levels and the corresponding levels of 2- time series to find the cointegration pairs in different decomposition levels and finally examine the resulting profitability. To this end, the profitability of the pairs trading system related to 14 stocks of the Tehran Stock Exchange throughout 2012-2021 was investigated based on EMD. The results showed that the outputs are pretty noticeable for the first level of decomposition (the first intrinsic mode function), and the number of trading opportunities increased by more than two times compared to the normal pair trading with cointegration; the daily returns increased by four times; and the Sharpe ratio increased by about two times compared to the normal pairs trading. The system formed based on the second mode function also outperformed the normal cointegration, and the performance of the third intrinsic mode function is almost on par with that of cointegration. Moreover, the mean transaction duration decreased remarkably in the first and second mode functions.

Abstract Risk parity is perceived as one of the stock portfolio selection models that have received a lot of attention since the US financial crisis in 2008. The philosophy of this model is to allocate the same amount of portfolio risk between the constituent assets. In the present study, the combined portfolio selection model of relative robust risk parity is introduced, which uses the worst-case scenario approach on the covariance matrix parameter appearing in the robust risk model in portfolio robustness. According to historical data, several scenarios are considered for the covariance matrix. The objective function value of the hybrid model for each portfolio (feasible point) is the worst result (with most volatility) among the set of scenarios.  Finally, the model selects a portfolio for which the worst possible result has the least relative volatility. The research portfolio consists of 8 industries from Tehran Stock Exchange in the period 2011 to 2020. This portfolio has a higher Sharpe ratio than conventional models of mean-variance and weight parity, and is more resilient to market declines than the two models and produces less loss. Therefore, risk-averse investors are advised to use this stock portfolio selection model as a cover to face severe market declines.

Abstract one of the most important financial and investment issues is Portfolio selection, that seeks to allocate a predetermined capital (wealth) over one or multiple periods between assets and stocks in such a way that the wealth of investor (portfolio owner) is maximized and, Simultaneously, its risk minimized. In the paper, we first propose a mathematical programming model for Portfolio selection to maximize the minimum amount of Sharpe ratios of the portfolio in all periods (max-min problem). Then, due to the uncertain property of the input parameters of such a problem, a robust possibilistic programming model (based on necessity theory) has been developed, which is capable of adjusting the robust degree of output decisions to the uncertainty of the parameters. The proposed model was tested on 27 companies active in the Tehran stock market. In the end, the results of the model demonstrated the good performance of the robust possibilistic programming model.