درخت حوزه‌های تخصصی

The absence of a comprehensive and localized model for the effective management of the cost of money in the country’s banking system, considering the cultural, economic, and structural complexities inherent in the banking industry, constitutes a fundamental challenge. Therefore, the present study aims to articulate a model for managing the cost of money in Iran’s banking industry through a grounded theory approach. This research is applied in purpose and conducted with a qualitative methodology based on grounded theory. The statistical population consists of distinguished professors in the fields of finance, accounting, and management, as well as senior banking executives including bank presidents and faculty members of reputable universities in the country, selected during the years 2023 and 2024. Sampling was carried out purposefully and through the snowball method until theoretical saturation was achieved. Data were collected through library studies and 11 semi-structured interviews with banking and academic experts possessing sufficient expertise and professional experience. The data were analyzed using Strauss and Corbin’s three-stage coding process with MAXQDA software, which resulted in the extraction of 95 basic concepts categorized into four main themes: socio-cultural-political factors, technical-structural factors, environmental and contextual factors, and factors related to the improvement of economic indicators. The findings indicate that comprehensive management of the cost of money requires simultaneous consideration of technological, structural, interactive, and policy-making components. The proposed conceptual model, by clarifying the relationships influencing resource mobilization and cost control in Iran’s banking industry, provides practical strategies for enhancing efficiency, strengthening economic governance, and developing banking services. Furthermore, factors such as interbank interest rates, economic risks, operational costs, inflation, and exchange rates were identified as decisive variables. Ultimately, the finalized model, informed by expert insights, delineates a pathway for reducing costs and improving the efficiency of the banking system.

This study evaluates the economic capital levels for Iranian banks and analyzes their behavior under various macroeconomic shocks by developing a Dynamic Stochastic General Equilibrium (DSGE) model that incorporates banking sector heterogeneity and macroeconomic shock transmission mechanisms. A comprehensive DSGE framework was constructed and calibrated using Iranian macroeconomic and banking data. The model implementation utilized Dynare software to analyze impulse response functions across technology, liquidity, and monetary policy shock scenarios, with particular emphasis on capital adequacy dynamics, bank bankruptcy and exit possibilities, and effects on economic welfare and financial stability. Simulation results indicate that current capital levels of Iranian banks are insufficient for achieving financial stability and welfare maximization, requiring increases to higher levels, that exceeding Basel III regulatory minimums to maintain adequate resilience against macroeconomic volatility and prevent bank bankruptcy and exit. Technology shocks generate expansionary effects on output, consumption, and investment variables while improving economic welfare, whereas liquidity shocks create contractionary pressures reducing these key economic indicators and welfare levels. Interest rate shocks produce moderate and largely temporary effects across macroeconomic variables, with limited long-term impact on economic welfare and minimal influence on bank bankruptcy probability. The shock analyses demonstrate the critical importance of macroeconomic conditions in bank capital management decisions, economic welfare preservation, and bank exit prevention, with liquidity shocks showing the most substantial explanatory power for capital requirement variations. The study contributes to both theoretical understanding and practical application by extending DSGE modeling to incorporate banking sector capital dynamics, bankruptcy mechanisms, and welfare effects within an emerging market context, thereby offering a more comprehensive framework for economic capital determination. Our findings suggest that Iranian banks need to maintain higher capital levels to provide adequate stability and risk absorption capacity for Iranian banks under diverse economic shock conditions.

In financial markets, a primary concern for investors is achieving enhanced returns while effectively managing portfolio risk. Leveraged trading is one potential strategy for increasing returns; however, the fundamental question is to find the optimal degree of leverage that yields the most efficient investment portfolio. This paper investigates the optimal leverage level of cryptocurrency futures within a diversified portfolio comprising digital assets, employing a constrained mean semi-variance model and the Invasive Weed Optimization (IWO) algorithm. The dataset, sourced from Coin Market Cap, consists of daily returns for 10 cryptocurrencies and 5 futures contracts over the period from 2022 to 2025. The proposed model incorporates allocation constraints, wherein each asset in the portfolio is subject to upper and lower bounds on its weight. Due to the imposed constraints, the problem is not solvable via traditional quadratic programming techniques, necessitating the application of the IWO algorithm as the optimization method. Empirical results reveal that incorporating futures into a cryptocurrency portfolio does not inherently enhance its performance. While leverage may increase expected returns, it simultaneously elevates portfolio risk. Consequently, based on the Sortino ratio, the overall risk-adjusted performance of the portfolio does not necessarily improve with the use of leveraged futures.