The Research Department of Economics and Management of Tadbir Nikan Business, Marketing, and Finance Open 3092-6238 In Press 2027 05 01 A Meta-Synthesis of Studies on Forecasting the Yield Curve of Islamic Treasury Bills in the Iranian Economy and the Development of a Conceptual Forecasting Framework A Meta-Synthesis of Studies on Forecasting the Yield Curve of Islamic Treasury Bills in the Iranian Economy and the Development of a Conceptual Forecasting Framework 1 18 EN Journal Article 2026 02 20 <p>The yield curve is one of the most important indicators in debt market analysis, economic expectations assessment, and monetary policy formulation. With the expansion of Islamic Treasury Bill issuance in Iran, forecasting the term structure of returns on these securities has become increasingly important. However, most existing studies have focused on the application of individual models and have not provided a comprehensive framework for integrating different forecasting approaches. The present study aims to identify and synthesize the theoretical and empirical approaches related to yield curve forecasting and to develop a comprehensive conceptual framework for analyzing Islamic Treasury Bills in the Iranian economy. The research employed a meta-synthesis methodology based on a systematic review of 37 domestic and international studies. The concepts extracted from these studies were classified according to conceptual similarities and organized into analytical categories. The findings indicated that the yield curve forecasting literature can be categorized into several major domains, including structural parametric models, macro-financial models, dynamic time-series models, machine learning and hybrid models, evaluation criteria, and influential macroeconomic factors. The results suggest that parametric models such as the Nelson–Siegel model provide an appropriate framework for explaining the structure of the yield curve; however, under conditions of economic instability, they do not offer sufficient forecasting accuracy when used independently. In contrast, machine learning models and hybrid approaches demonstrate greater capability in identifying nonlinear relationships and improving forecasting performance. Accordingly, integrating structural models with macroeconomic variables and data-driven methods can provide an efficient framework for forecasting the yield curve of Islamic Treasury Bills in the Iranian economy.</p> https://www.bmfopen.com/index.php/bmfopen/article/download/485/347