Examining the Effects of Intersectoral Uncertainty Transmission Using a Time-Varying Model

Document Type : Research Paper

Authors

1 PhD., Department of Financial Management, Qom Branch, Islamic Azad University, Qom, Iran.

2 Associate Prof., Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Assistant Prof., Department of Financial Management & Accounting, Qom Branch, Islamic Azad University, Qom, Iran.

10.22059/frj.2023.359630.1007466

Abstract

Objective
The impact of financial and economic shocks and uncertainty is not always limited to the target market and may spread to other markets as well. Empirical research results, such as those by Jurado et al. (2015) and Gabor and Gabota (2020), indicate that the contagion of cross-sectoral uncertainty and the significance of these uncertainties are not constant over time and may change. Traditional time series regression models assume that a relationship with fixed coefficients can be applied across different time periods. The misleading results of this unrealistic assumption have led to the development of dynamic models that better reflect the realities of the external world. The state-space approach is a modeling method for dynamic systems that predicts and analyzes system behavior under these modeling conditions. One of the applications of this approach is to account for structural instability in parameters and to allow coefficients to vary over time. Models of this type are known as time-varying parameter (TVP) models. This research aims to study the reaction of the financial, housing, and macroeconomic sectors in Iran to each other's shocks, with a focus on the effects of uncertainty contagion.
 
Methods
The present study is applied in terms of purpose and correlational analysis in terms of nature and method. It is post-event and utilizes past information. In this study, a library method was used to collect theoretical sources, while an archival method was employed to gather the data needed for hypothesis testing. To examine changes in cross-sectoral uncertainty contagion, the time-varying parameter vector autoregression model (TVP-VAR) is used with monthly data from January 2008 to December 2020. In this context, uncertainty indicators are calculated using GARCH models and then tested using the TVP-VAR approach, along with an analysis of variance of the generalized prediction error of total dynamic connectedness, as well as the directional dynamic connectedness of the indicator pairs.
 
Results
The research results indicate that the primary source of uncertainty is the macroeconomic sector, which acts as the main source and transmitter of uncertainty to the other financial and housing sectors. Additionally, the housing sector is a net recipient of uncertainty from the other two sectors. The findings suggest that the contagion of uncertainty between the financial and housing sectors is bidirectional and conditionally dynamic, while the contagion of uncertainty from the macroeconomic sector to the financial and housing sectors is unidirectional.
 
Conclusion
According to the results, the contagion of cross-sectoral uncertainty and the significance of these uncertainties are not constant and change over time. Therefore, identifying the different channels of contagion between markets and pinpointing the source of contagion can help in selecting policies that reduce vulnerability and enhance the performance of asset portfolio risk management.

Keywords

Main Subjects

References

 
Ansari Samani, H., & Heydarpoor, H. (2019). Investigation the financial risk contagion between Iran and selected financial partners. Journal of Econometric Modelling, 4(1), 93-119. doi: 10.22075/jem.2019.17656.1297 (in Persian)
Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness Based on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84.
Bala, D. A. & Takimoto, T.  (2017). Stock market's volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach. Borsa Istanbul Review, 17(1), 25-48.
Diebold, F. X. & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134.
Engel, C. (2011). Dictator games: A meta study. Experimental economics, 14, 583-610.
Fallah Shams, M. & Banisharif, A. (2021). Investigating the Financial Risk Spillover in Banks Accepted in Tehran Stock Exchange Market through MGARCH Approach. Financial Research Journal, 23(1), 87-107. doi: 10.22059/frj.2020.304816.1007033 (in Persian)
Gabauer, D. & Gupta, R. (2020). Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach. Structural Change and Economic Dynamics 52, 167–173.
González-Rivera, G., Lee, T. H., & Mishra, S. (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood. International Journal of forecasting, 20(4), 629-645.
Hoseini, A., Jahangiri, K., Heydari, H. & Ghaemi asl, M. (2019). Study of Shock and Volatility Spillovers among Selected Indices of the Tehran Stock Exchange Using Asymmetric BEKK-GARCH Model. Journal of Applied Economics Studies in Iran, 8(29), 123-155. doi: 10.22084/aes.2018.15376.2578 (in Persian)
Khalifa, A. A., Hammoudeh, S. & Otranto, E. (2014). Patterns of volatility transmissions within regime. International Review of Economics & Finance, 29, 512-524.
Koop, G. & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116.
Liow, K. H., Liao, W. C. & Huang, Y. (2018). Dynamics of international spillovers and interaction: Evidence from financial market stress and economic policy uncertainty. Economic Modelling, 68, 96-116.
Maghsoud, H., Vakilifard, H., & Torabi, T. (2020). Factor Variability Test in Stock Return Forecasting Using Dynamic Model Averaging (DMA). Financial Engineering & Securities Management, 11(45), 639- 660. (in Persian)
Mohammadinejad Pashaki, M., Sadeghi Sharif, S. & Eghbalnia, M. (2023). Investigating and Analyzing the Spillover Effects among Stock, Currency, Gold, and Commodity Markets: VARMA-BEKK-AGARCH Approach. Financial Research Journal, 25(1), 88-109. doi: 10.22059/frj.2022.332526.1007248 (in Persian)
Seyedhosseini, S.M. & Ebrahimi, S.B. (2013). Modeling and Evaluation of Volatility Transmission Using Multivariate GARCH، Case study: Iran, Emirates, Oil Global Price Index. Journal of Securities Exchange, 6(21), 137- 157. (in Persian)
Shahiki Tash, M.N., Azazi, M.E. & Gholami Bimorgh, L. (2013). Calculating Value at Risk (VAR) in the Tehran Stock Exchange. First National Conference on Accounting and Management. (in Persian)
Tehrani, M., Boghosian, A. & Mojtaba Mirlohi, S. (2021). Spillover between Tehran Stock Exchange and International Oil Market. Financial Research Journal, 23(3), 466-481. doi: 10.22059/frj.2021.312616.1007087 (in Persian)
Trung, N. B. (2019). The spillover effects of US economic policy uncertainty on the global economy: A global VAR approach. The North American Journal of Economics and Finance, 48, 90-110.Yin, L., & Han, L. (2014). Spillovers of macroeconomic uncertainty among major economies. Applied Economics Letters, 21(13), 938-944.
Financial Research Journal
Volume 26, Issue 4
2024
Pages 836-853

Files

Share

How to cite

Statistics