Structural Macroeconomic Transmission and the Indonesian Islamic Capital Market: Evidence from a Structural Vector Autoregression Before and During the COVID-19 Pandemic
Keywords
Islamic capital market; Indonesian Sharia Stock Index; Structural VAR; Macroeconomic transmission; COVID-19; Exchange rate.
Abstract
The Indonesian Islamic capital market has experienced rapid development over the last decade, yet its vulnerability to macroeconomic disturbances remains insufficiently understood. While previous studies have examined the effects of macroeconomic variables on Islamic stock markets, most rely on reduced-form econometric models that are unable to distinguish structural shocks or identify changes in transmission mechanisms during periods of economic disruption. This study investigates how Gross Domestic Product (GDP), the Bank Indonesia policy rate (BI Rate), and the Rupiah–US dollar exchange rate dynamically influence the Indonesian Sharia Stock Index (ISSI) before and during the COVID-19 pandemic. Monthly data covering the period 2015–2023 are analyzed using a Structural Vector Autoregression (SVAR) framework. The empirical analysis incorporates unit root tests, Johansen cointegration, Vector Error Correction Model (VECM), Granger causality, Impulse Response Functions (IRF), and Forecast Error Variance Decomposition (FEVD) to identify structural relationships and evaluate the dynamic responses of ISSI to macroeconomic shocks across two economic regimes. The findings reveal a substantial shift in the transmission mechanism following the COVID- 19 outbreak. Prior to the pandemic, ISSI fluctuations were largely explained by internal market dynamics, whereas macroeconomic variables mainly affected the long-run equilibrium. During the pandemic, exchange rate shocks emerged as the largest contributor to ISSI volatility (27.24%), followed by the BI Rate (21.13%) and GDP (17.97%), indicating a structural transition toward greater macroeconomic dependence. The IRF results further demonstrate that ISSI responds asymmetrically to macroeconomic shocks and requires several periods to converge to a new equilibrium. This study contributes to the Islamic finance literature in three ways. First, it applies a Structural VAR framework to identify structural macroeconomic transmission in the Indonesian Islamic capital market. Second, it provides empirical evidence that the COVID-19 pandemic fundamentally reshaped the transmission mechanism from internally driven market dynamics toward macroeconomically driven fluctuations. Third, it proposes the Structural Macroeconomic Transmission for Islamic Capital Market Stability (SMT-Sharia) model as a conceptual framework to support macroprudential policy and enhance the resilience of Islamic capital markets in emerging economies.
References
- Ajmi, A. N., Hammoudeh, S., Nguyen, D. K., & Sarafrazi, S. (2014). How strong are the causal relationships
- between Islamic stock markets and conventional financial systems? Evidence from linear and nonlinear
- tests. Journal of International Financial Markets, Institutions and Money, 28, 213–227.
- https://doi.org/10.1016/j.intfin.2013.11.004
- Ashraf, B. N. (2020). Stock markets’ reaction to COVID-19: Cases or fatalities? Research in International
- Business and Finance, 54, 101249. https://doi.org/10.1016/j.ribaf.2020.101249
- Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied
- Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659
- Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C., & Viratyosin, T. (2020). The Unprecedented
- Stock Market Impact of COVID-19 (Working Paper No. 26945). National Bureau of Economic Research.
- https://doi.org/10.3386/w26945
- Bernanke, B. S. (1986). Alternative explanations of the money-income correlation. Carnegie-Rochester
- Conference Series on Public Policy, 25, 49–99. https://doi.org/10.1016/0167-2231(86)90037-0
- Christiano, L. J. (2012). Christopher A. Sims and Vector Autoregressions. The Scandinavian Journal of
- Economics, 114(4), 1082–1104. https://doi.org/10.1111/j.1467-9442.2012.01737.x
- Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance,
- 25(2), 383–417. https://doi.org/10.2307/2325486
- Kilian, L., & Lütkepohl, H. (2017). Structural Vector Autoregressive Analysis. Cambridge University Press.
- https://doi.org/10.1017/9781108164818
- Lütkepohl, H. (2007). New introduction to multiple time series analysis: With ... 36 tables (1. ed., corr. 2. print).
- Springer.
- Naifar, N. (2016). Do global risk factors and macroeconomic conditions affect global Islamic index dynamics? A
- quantile regression approach. The Quarterly Review of Economics and Finance, 61, 29–39.
- https://doi.org/10.1016/j.qref.2015.10.004
- Perron, P. (1989). The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis. Econometrica, 57(6),
- 1361–1401. https://doi.org/10.2307/1913712
- Sims, C. (1986). Are forecasting models usable for policy analysis? Quarterly Review, 10(Win), 2–16.
- Yarovaya, L., Elsayed, A. H., & Hammoudeh, S. (2021). Determinants of Spillovers between Islamic and
- Conventional Financial Markets: Exploring the Safe Haven Assets during the COVID-19 Pandemic.
- Finance Research Letters, 43, 101979. https://doi.org/10.1016/j.frl.2021.101979