Systemic risk, as one of the challenges of the financial system, has attracted special attention from policymakers, investors, and researchers. Identifying and assessing systemic risk is crucial for enhancing the financial stability of the banking system. In this regard, this article uses the Conditional Value at Risk method to evaluate the systemic risk of simulated data and Iran's banking system. In this method, the conditional mean and conditional variance are modeled using Autoregressive Moving Average and Generalized Autoregressive Conditional Heteroskedasticity models, respectively. The data studied includes the daily stock prices of 17 Iranian banks from April 8, 2019, to May 1, 2023, which contains missing values in some periods. The Kalman filter approach has been used for interpolating the missing values. Additionally, Vine copulas  with a hierarchical tree structure have been employed to describe the nonlinear dependencies and hierarchical risk structure of the returns of the studied banks. The results of these calculations indicate that Bank Tejarat has the highest systemic risk, and the increase in systemic risk, in addition to causing financial crises, has adverse effects on macroeconomic performance. These results can significantly help in predicting and mitigating the effects of financial crises and managing them effectively.