Issue

Vol 38 No 2 (2023): May

Date Log

Submitted
Sep 23, 2022
Accepted
Mar 15, 2023
Published
May 2, 2023
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright

Upon acceptance of an article, authors transfer copyright to the JIEB as part of a journal publishing agreement, but authors still have the right to share their article for personal use, internal institutional use, and for any use permitted under the CC BY-SA license

Open Access

Articles are freely available to the public without any subscription with permitted reuse. For open access articles, permitted third party (re)use is defined by the following Creative Commons user licenses: Creative Commons Attribution (CC BY-SA).

Revisiting Financial Volatility in the Indonesian Islamic Stock Market: GARCH – MIDAS Approach

https://doi.org/10.22146/jieb.v38i2.5704
Nevi Danila
ndanila@psu.edu.sa
Finance Department, College of Business Administration, Prince Sultan University, Riyadh, Saudi Arabia

Corresponding Author(s) : Nevi Danila

ndanila@psu.edu.sa

Journal of Indonesian Economy and Business, Vol 38 No 2 (2023): May

Abstract

Introduction/main objectives: The aim of this research is to study the impact of macroeconomic variables on the Indonesian Islamic stock market’s volatility. Background issues: To predict the stock market’s volatility, daily or high-frequency data has been applied to the model’s explanatory variables with the same data frequency. However, when it comes to the macroeconomic variables as volatility drivers, the data is low-frequency, such as weekly, monthly, or quarterly. The current study uses a model which treats the data equally. Novelty: This study employs the mixed data sampling (MIDAS) model, which allows data from multiple frequencies to be included in the same model. This model can combine daily stock returns’ data with monthly or quarterly macro­economic data. Hence, this is the first paper to study the determinants of the volatility of Indonesia's Islamic stock index using GARCH-MIDAS. Research Methods: The Generalized Autoregressive Conditional Heteroscedasticity GARCH-MIDAS model captures the short-run from the long-term element of volatility; the findings show the asymmetry effect for the short-term element’s result. Finding/Results: Inflation does not influence long-term market volatility. Moreover, after the 2008 crisis, the study shows that inflation and short-term interest rates positively influenced market volatility. Conclusion: The positive effect of inflation suggests that stocks can function as inflation hedges for stock investors in the long run. Further, the positive impact of interest rates implies that Muslim investors use the conventional short-term interest rates as a benchmark for investment in Shari’ah-compliant instruments.

Keywords

GARCH-MIDAS Islamic stock market interest rates inflation
Danila, N. (2023). Revisiting Financial Volatility in the Indonesian Islamic Stock Market: GARCH – MIDAS Approach. Journal of Indonesian Economy and Business, 38(2), 166 -. https://doi.org/10.22146/jieb.v38i2.5704
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Abbas, G., Bhowmik, R., Koju, L., & Wang, S. (2017). Cointegration and Causality Relationship Between Stock Market, Money Market and Foreign Exchange Market in Pakistan. Journal of Systems Science and Information, 5(1). https://doi.org/10.21078/jssi-2017-001-20
  2. Alam, M. M., Gazi, M., & Uddin, S. (2009). Relationship between Interest Rate and Stock Price: Empirical Evidence from Developed and Developing Countries.
  3. Alexakis, P., & Apergis, N. (1996). ARCH effects and cointegration: Is the foreign exchange market efficient? Journal of Banking and Finance, 20(4). https://doi.org/10.1016/0378-4266(95)00027-5
  4. Amendola, A., Candila, V., & Scognamillo, A. (2017). On the influence of US monetary policy on crude oil price volatility. Empirical Economics, 52(1). https://doi.org/10.1007/s00181-016-1069-5
  5. Ascarya. (2009). Toward Optimum Synergy of Monetary Policy in Dual Financial/Banking System. In Journal of Indonesian Economy and Business (Vol. 24, Issue 1).
  6. Asgharian, H., Hou, A. J., & Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH-MIDAS approach. Journal of Forecasting, 32(7), 600–612. https://doi.org/10.1002/for.2256
  7. Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial Economics, 18(2). https://doi.org/10.1016/0304-405X(87)90045-6
  8. Conrad, C., & Kleen, O. (2020). Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models. Journal of Applied Econometrics, 35(1), 19–45. https://doi.org/10.1002/jae.2742
  9. Conrad, C., & Loch, K. (2015). The variance risk premium and fundamental uncertainty. Economics Letters, 132. https://doi.org/10.1016/j.econlet.2015.04.006
  10. Conrad, C., Loch, K., & Rittler, D. (2014). On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets. Journal of Empirical Finance, 29, 26–40. https://doi.org/10.1016/j.jempfin.2014.03.009
  11. Cooper Maysami, R., & Sim Koh, T. (2000). A vector error correction model of the Singapore stock market.
  12. Damayanti, D., Affandi, Y., Sutarto, I. G., & Simatupang, M. S. (2020). Financial market development in Indonesia. www.bi.go.id/en/moneter/pasar-keuangan/snpppk/Contents/default.aspx
  13. Danila, N., Kamaludin, K., Sundarasen, S., & Bunyamin, B. (2021). Islamic index market sentiment: evidence from the ASEAN market. Journal of Islamic Accounting and Business Research, 12(3). https://doi.org/10.1108/JIABR-05-2020-0166
  14. Demirer, R., Gupta, R., Li, H., & You, Y. (2023). A note on financial vulnerability and volatility in emerging stock markets: evidence from GARCH-MIDAS models. Applied Economics Letters, 30(1). https://doi.org/10.1080/13504851.2021.1971613
  15. Dhaoui, A. (2015). Empirical Linkages between Trading Volume and Stock Markets Shocks: When Sentiments Drive Investors’ Behavior. Journal of Economic and Social Studies, 5(2), 105–126. https://doi.org/10.14706/jecoss15527
  16. Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776–797. https://doi.org/10.1162/REST_a_00300
  17. Fama, E. F. (1981). Stock Returns, Real Activity, Inflation, and Money. American Economic Association Stock Returns The American Economic Review, 71(4).
  18. Fang, T., Lee, T. H., & Su, Z. (2020). Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection. Journal of Empirical Finance, 58, 36–49. https://doi.org/10.1016/j.jempfin.2020.05.007
  19. Fornari, F., & Mele, A. (2013). Financial Volatility and Economic Activity. Journal of Financial Management, Markets and Institutions, 1(2), 155–196. https://doi.org/10.12831/75569
  20. Ghysels, E., Santa-Clara, P., & Valkanov, R. (2006). Predicting volatility: Getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131(1–2). https://doi.org/10.1016/j.jeconom.2005.01.004
  21. Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90. https://doi.org/10.1080/07474930600972467
  22. Gu, G., Zhu, W., & Wang, C. (2021). Time-varying influence of interest rates on stock returns: evidence from China. Economic Research-Ekonomska Istrazivanja . https://doi.org/10.1080/1331677X.2021.1966639
  23. Hasan, A., & Javed, M. T. (2009). An Empirical Investigation of the Causal Relationship among Monetary Variables and Equity Market Returns. The Lahore Journal of Economics, 14(1). https://doi.org/10.35536/lje.2009.v14.i1.a5
  24. Joubert, T. (2021). How does inflation affect the stock market? IG International Limited. https://www.ig.com/en/trading-strategies/how-does-inflation-affect-the-stock-market-210423
  25. Kim, K. H. (2003). Dollar exchange rate and stock price: Evidence from multivariate cointegration and error correction model. Review of Financial Economics, 12(3). https://doi.org/10.1016/S1058-3300(03)00026-0
  26. Kusumah, H., Asri, M., Setiawan, K., & Setiyono, B. (2022). The Relationship Between Asia Pacific Markets During the Financial Crisis: Var-Granger Causality Analysis. Journal of Indonesian Economy and Business, 37(2), 162–187. https://journal.ugm.ac.id/v3/jieb
  27. Majid, M. S. A., & Yusof, R. M. (2009). Long-run relationship between Islamic stock returns and macroeconomic variables: An application of the autoregressive distributed lag model. Humanomics, 25(2), 127–141. https://doi.org/10.1108/08288660910964193
  28. Moore, T., & Wang, P. (2014). Dynamic linkage between real exchange rates and stock prices: Evidence from developed and emerging Asian markets. International Review of Economics and Finance, 29. https://doi.org/10.1016/j.iref.2013.02.004
  29. Mukherjee, T. K., & Naka, A. (1995). Dynamic Relations Between Macroeconomic Variables and The Japanese Stock Market: An Application of a Vector Error Correction Model. Journal of Financial Research, 18(2). https://doi.org/10.1111/j.1475-6803.1995.tb00563.x
  30. Ndako, U. B., Salisu, A. A., & Ogunsiji, M. O. (2021). Geopolitical Risk and the Return Volatility of Islamic Stocks in Indonesia and Malaysia: A GARCH-MIDAS Approach. Asian Economics Letters. https://doi.org/10.46557/001c.24843
  31. Nieh, C. C., & Lee, C. F. (2001). Dynamic relationship between stock prices and exchange rates for G-7 countries. Quarterly Review of Economics and Finance, 41(4). https://doi.org/10.1016/S1062-9769(01)00085-0
  32. Pan, Z., & Liu, L. (2018). Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects. Physica A: Statistical Mechanics and Its Applications, 492. https://doi.org/10.1016/j.physa.2017.09.030
  33. Ratanapakorn, O., & Sharma, S. C. (2007). Dynamic analysis between the US stock returns and the macroeconomic variables. Applied Financial Economics, 17(5). https://doi.org/10.1080/09603100600638944
  34. SCHWERT, G. W. (1989). Why Does Stock Market Volatility Change Over Time? The Journal of Finance, 44(5), 1115–1153. https://doi.org/10.1111/j.1540-6261.1989.tb02647.x
  35. Smyth, R., & Nandha, M. (2003). Bivariate causality between exchange rates and stock prices in South Asia. Applied Economics Letters, 10(11). https://doi.org/10.1080/1350485032000133282
  36. Sokpo, J. T., Iorember, P. T., Terhemba Iorember, P., & Usar, T. (2017). Inflation and Stock Market Returns Volatility: Evidence from the Nigerian Stock Exchange 1995Q1-2016Q4: An E-GARCH Approach. International Journal of Econometrics and Financial Management, 5(2), 69–76. https://doi.org/10.12691/ijefm-5-2-6
  37. Uygur, U., & Taş, O. (2014). The impacts of investor sentiment on returns and conditional volatility of international stock markets. Quality and Quantity, 48, 1165–1179. https://doi.org/10.1007/s11135-013-9827-3
  38. Yu, X., Huang, Y., & Xiao, K. (2021). Global economic policy uncertainty and stock volatility: evidence from emerging economies. Journal of Applied Economics, 24(1), 416–440. https://doi.org/10.1080/15140326.2021.1953913
  39. Yunanto, M., & Medyawati, H. (2015). Monetary And Fiscal Policy Analysis: Which Is More Effective? Journal of Indonesian Economy and Business, 29(3). https://doi.org/10.22146/jieb.v29i3.6470
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 800 Download : 1472