Analysis of Jakarta Composite Index Volatility and Macroeconomic Determinants on the Frequency of Initial Public Offerings in the Indonesia Stock Exchange

Basel Onn

doi:10.61536/escalate.v4i4.782

Authors

Basel Onn PADJADJARAN University Faculty of Economics and Business Jatinangor

DOI:

https://doi.org/10.61536/escalate.v4i4.782

Keywords:

IPO Frequency, Inflation, Initial Public Offering, Stock Market, Market Volatility

Abstract

This study examines the short-run and long-run relationships between Jakarta Composite Index (JCI) volatility, macroeconomic factors, and the frequency of Initial Public Offerings (IPOs) in Indonesia over the period 1995–2024. Using 30 annual observations from the Indonesia Stock Exchange, Investing.com, and the World Bank, the study applies the Autoregressive Distributed Lag (ARDL) model and Error Correction Model (ECM). The results indicate that JCI volatility and inflation have significant negative long-run relationships with IPO frequency, while the crisis dummy has a significant positive relationship. In contrast, the long-run coefficients for the CBOE Volatility Index (VIX) and JCI returns are not statistically significant. In the short run, changes in JCI volatility are positively and significantly associated with IPO frequency, whereas changes in the crisis dummy are negatively and significantly associated with IPO frequency. Changes in the VIX show negative associations, with the contemporaneous coefficient significant at the 10% level and the lagged coefficient significant at the 5% level. Changes in JCI returns do not show a statistically significant relationship with IPO frequency. The error correction term is negative and statistically significant, indicating an adjustment toward the estimated long-run equilibrium. Overall, the findings suggest that the relationships between market conditions and IPO frequency differ between the short and long run. However, the results should be interpreted in light of the limited annual observations and the operational definition of the crisis dummy.

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References

Alwago, W. O. (2023). The nexus between health expenditure, life expectancy, and economic growth: ARDL model analysis for Kenya. Regional Science Policy & Practice, 15(5), 1064–1085. https://doi.org/10.1111/rsp3.12588

Amorim, D. P. L., Camargos, M. A., & Ferreira, C. M. B. L. (2021). Macroeconomic factors and initial public offerings in Brazil. Global Business Review, 1–18. https://doi.org/10.1177/09721509211003696

Angelini, E., & Foglia, M. (2018). The relationship between IPO and macroeconomics factors: An empirical analysis from UK market. Annals of Economics and Finance, 19(1), 319–336.

Baker, M., & Wurgler, J. (2002). Market timing and capital structure. The Journal of Finance, 57(1), 1–32. https://doi.org/10.1111/1540-6261.00414

Brooks, C. (2008). Introductory econometrics for finance (2nd ed.). Cambridge University Press.

Ederington, L. H., & Guan, W. (2006). Measuring historical volatility. Journal of Applied Finance, 16(1). https://ssrn.com/abstract=926167

Enders, W. (2014). Applied econometric time series (4th ed.). John Wiley & Sons.

Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236

Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill.

Hashmi, S. M., & Chang, B. H. (2020). Asymmetric effect of exchange rate volatility on India's cross-border trade: Evidence from global financial crisis and multiple threshold nonlinear autoregressive distributed lag model. Australian Economic Papers, 60(1), 64–97. https://doi.org/10.1111/1467-8454.12194

Javangwe, K. Z., & Takawira, O. (2022). Exchange rate movement and stock market performance: An application of the ARDL model. Cogent Economics & Finance, 10(1), Article 2075520. https://doi.org/10.1080/23322039.2022.2075520

Mehmood, W., Mohd-Rashid, R., Ong, C. Z., & Abbas, Y. A. (2021). Factors driving IPO variability: Evidence from Pakistan Stock Exchange. Journal of Economics, Finance and Administrative Science, 26(52), 300–316. https://doi.org/10.1108/JEFAS-04-2021-0036

Narayan, P. K., & Smyth, R. (2006). What determines migration flows from low-income to high-income countries? An empirical investigation of Fiji-U.S. migration 1972–2001. Contemporary Economic Policy, 24(2), 332–342. https://doi.org/10.1093/cep/byj019

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616

Rydqvist, K., & Högholm, K. (1995). Going public in the 1980s: Evidence from Sweden. European Financial Management, 1(3), 287–315. https://doi.org/10.1111/j.1468-036X.1995.tb00021.x

Wooldridge, J. M. (2012). Introductory econometrics: A modern approach (5th ed.). Cengage Learning.