مطالب

This study aims at getting a better performance for optimal stock portfolios by modeling stocks prices dynamics through a continuous paths Levy process. To this end, the share prices are simulated using a multi-dimensional geometric Brownian motion model. Then, we use the results to form the optimal portfolio by maximizing the Sharpe ratio and comparing the findings with the outputs of the conventional model. To examine the robustness of the results, we have evaluated its performance for different investment horizons and various volumes of price information over a long period (approximately twenty years) in the Tehran Stock Exchange (TSE). Findings indicate that within the trading dates spanning the interval 24-Mar-2001 to 19-Sep-2020, the return of the portfolios obtained from applying this simulation scheme for maximization of Sharpe ratio is (244% on average) higher and their risk (standard deviation) are lower (1227% on average) than those realized by the conventional methods. Additionally, a comparison of the simulation approach with a performance of the actual market portfolios indicates that the Sharpe ratios of the simulation method are higher (0.055% on average) than those resulting from the total market performances. The results of the stochastic dominance test show that our proposed strategy has a first-order stochastic dominance (FSD) over the conventional one and market portfolios, that means at each level of cumulative distribution, the Sharpe ratio of our method is higher, and as FSD test makes no assumptions about the curvature of investors' utility functions, these results do not depend on the degree of risk aversion of investors, and as long as investors prefer a higher Sharpe ratio, they would be better off if they follow our proposed strategy.

As a mechanism to enhance financial system stability and a process that allows banks to change their role from traditional lenders to originators and distributors of loans, securitization reduces the dependence on customer deposits. Also, it expands lending capacity, manages banks credit risk, and transforms illiquid assets into saleable securities. In this research, GMM method in three formats is used for the 16 selected Iranian banks. Results show that real sector growth positively and significantly increase financial stability in the Iranian economy. This is because of the economic scale augmentation and its impact on creating new financial resources. Meanwhile, the non-performing loans ratio significantly diminishes banking stability as well as it lowers banks' capacity to generate revenues from intermediary activities. Moreover, return is affected by the inflationary conditions which heightens revenue making and equity factors in banks' balance sheets. In order to generate higher revenues and gain upper profits, banking resources are occasionally withdrawn to enter other financial markets. Loans to deposits ratio, representing the credit risk in banking systems, denotes that higher risk in credit areas exacerbates financial stability due to the higher probability of risk appetite in generating loans to the general public. Also, security size highlights that although it is expected that securitization augments the financial stability in the banking system, other indicators would also be influential on financial stability. In other words, the higher the security size, the bigger its impact on banking stability. Furthermore, Lending capacity augments as a result of risk management and transforming illiquid assets into saleable securities.

Corporate governance of banks is one of the most important structures required by banks to maintain the health and stability of banks, which can play an important role in managing banks' risk. This paper examines the effect of corporate governance on liquidity risk management, credit risk management, and total bank risk management. We used board structure effectiveness, transparency, and responsibility as corporate governance indicators. The financial ratio approach is also used to measure risk management. The period under review was 2006-2018. In addition to corporate governance criteria, other explanatory variables affecting banks' risk management have also been used. This paper used the performing unit root, cointegration, and F-Limmer tests to ensure panel estimation. Given the impact of past banks' risk management on current risk management, this variable has also been modeled as an explanatory variable. For this reason, the GMM method has been used to estimate the models in question. Given the importance of bank size in corporate governance on bank risk management, Banks are divided into large and small groups, so the effect of corporate governance in large and small banks has also been investigated on bank risk management. The results show that compliance with corporate governance criteria positively affects banks' risk management. However, due to weak corporate governance in large banks, corporate governance in large banks hurts bank risk management.