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

Authors

    Maryam Haghparast Department of Financial Management, NT.C., Islamic Azad University, Tehran, Iran
    Ali Saeedi * Department of Financial Management, NT.C., Islamic Azad University, Tehran, Iran a_saeedi@iau.ac.ir
    Moslem Peymany Foroushani Associate Professor, Department of Finance and Banking, Allameh Tabataba'i University, Tehran, Iran

Keywords:

Yield Curve, Islamic Treasury Bills, Return Rate Forecasting, Meta-Synthesis, Iranian Economy

Abstract

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.

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Published

2027-05-01

Submitted

2026-02-20

Revised

2026-06-10

Accepted

2026-06-17

Issue

Section

Articles

How to Cite

Haghparast, M., Saeedi, A., & Peymany Foroushani, M. (2027). 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. Business, Marketing, and Finance Open, 1-18. https://www.bmfopen.com/index.php/bmfopen/article/view/485

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