درخت حوزه‌های تخصصی

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.

Technological progress has transformed the payment industry. Although alternative payment instruments such as bank cards share some characteristics with banknotes, they are not direct competitors to cash. First, cash possesses unique attributes—its functionality without electrical power and its inherent anonymity. Second, statistical data indicate that the ratio of cash to M1 has stabilized at approximately 10% in recent years, primarily due to its substitution with demand deposits. In this paper, we model the demand for banknotes by incorporating technology-related characteristics alongside conventional theories of money demand. Our approach has important implications for central bank, particularly in liquidity management, currency issuance, effective management of physical currency, and continued investment in digital payment infrastructures. We adopt a macroeconomic perspective that considers banknote demand from both transactional and asset viewpoints. To this end, we estimate an autoregressive distributed lag (ARDL) model, which allows us to analyze both short-term and long-term coefficients within a framework of model uncertainty. A key focus of our study is the estimation of a payment system development index using logistic growth and Gompertz models, through which we assess its impact on cash demand. Our empirical evidence supports the notion of S-shaped growth in the adoption of payment technologies. The findings demonstrate that advancements in the payment sector have led to a significant decline in cash demand. Moreover, while increased real economic activity exerts a positive and significant influence on cash demand, a higher opportunity cost of holding cash has a pronounced negative impact.