Dynamic Conditional Correlation Analysis of Investor Herding: Evidence from the Indonesian and Malaysian Capital Markets

Document Type : Research Paper

Authors

1 Department of Accounting, Faculty of Economics, Batik Islamic University, Surakarta, Indonesia.

2 Department of Management, Faculty of Economics and Business, Satya Wacana Christian University, Salatiga, Indonesia.

Abstract

This paper applies the Dynamic Conditional Correlation (DCC) multivariate GARCH model by including a volatility index to examine herding behavior. We analyzed whether stock return dispersion to the market return has a time-varying conditional correlation in the Indonesian and Malaysian capital markets. We used daily returns data of blue-chip stock and LQ45 & KLCI index for the period January 2015 – December 2022 to capture herding effects among the investors of both capital markets. The main findings demonstrate herding behavior in bullish markets before the pandemic, but only for the LQ45 and not for the KLCI. Herding will result in lower market returns. We further establish that the volatility index exhibits significant positive changes in a bearish condition amidst the epidemic in both indexes. These findings suggest that while institutional and long-term investors dominate the market for blue-chip companies, they nonetheless make investment decisions based on the majority under specific circumstances. The partial presence of herding behavior and anxiety indices in the market provides support for the behavioral finance theory, which suggests that individuals may exhibit irrational behavior.

Keywords

Main Subjects

References

Ah Mand, A., & Sifat, I. (2021). Static and regime-dependent herding behavior: An emerging market case study. Journal of Behavioral and Experimental Finance, 29, 100466. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S2214635021000101?via%3Dihub.
Alber, N., & Ezzat, E. (2021). The Impact of Herding Behavior on Stock Mispricing: The Case of Listed Companies at the Egyptian Exchange. European Journal of Business and Management Research, 6(4), 7–10. Retrieved from https://www.ejbmr.org/index.php/ejbmr/article/view/917/500.
Andrikopoulos, P., Kallinterakis, V., Leite Ferreira, M. P., & Verousis, T. (2017). Intraday herding on a cross-border exchange. International Review of Financial Analysis, 53, 25–36. Retrieved from  https://www.sciencedirect.com/science/article/abs/pii/S1057521917301023?via%3Dihub.
Arjoon, V., Bhatnagar, C. S., & Ramlakhan, P. (2020). Herding in the Singapore Stock Exchange. Journal of Economics and Business, 109, 105889. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0148619519300712?via%3Dihub.
Asadi, G. H., Tabrizi, H. A., & Farazmand, S. (2022). Exchange Rates, Gold Coin Prices, and Herding in the Stock Market. Iranian Economic Review, 26(2), 389–406. Retrieved from https://ier.ut.ac.ir/article_88170_a1fe7ac96dc71c8113b5a15fe155cd53.pdf.
Baddeley, M. (2015). Herding, Social Influences and Behavioural Bias in Scientific Research. EMBO Reports, 16(8), 902–905. Retrieved from https://www.embopress.org/doi/full/10.15252/embr.201540637.
Baig, A. S., Blau, B. M., Butt, H. A., & Yasin, A. (2022). Do Retail Traders Destabilize Financial Markets ? An Investigation Surrounding the COVID-19 Pandemic. Journal of Banking and Finance, 2022(November), 1–18. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC9355410/.
BenSaïda, A. (2017). Herding Effect on Idiosyncratic Volatility in U.S. Industries. Finance Research Letters, 23, 121–132. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1544612317300442?via%3Dihub.
Bikhchandani, S., & Sharma, S. (2001). Herd Behavior in Financial Markets. IMF Staff Papers, 47(3). Retrieved from https://www.imf.org/external/pubs/ft/staffp/2001/01/pdf/bikhchan.pdf.
Bo, C. C. (2023). A critical review of FOMO behaviour among young investors. Issues and Perspectives in Business and Social Sciences, 3(1), 14–18. Retrieved from https://journals.mmupress.com/index.php/ipbss/article/view/vol3iss1bo/vol3iss1bo.
Bohl, M. T., Branger, N., & Trede, M. (2017). The Case for Herding is Stronger than You Think. Journal of Banking and Finance, 85, 30–40. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0378426617301930.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327. Retrieved from https://public.econ.duke.edu/~boller/Published_Papers/joe_86.pdf.
Botsvadze, I. (2013). Herd Behavior in Equity Markets - The International Evidence. Journal of Business, 2(2), 41–46. Retrieved from https://jb.ibsu.edu.ge/jms/index.php/jb/article/view/61.
Braiek, S., & Jeribi, A. (2022). Cryptocurrencies’ Time to Shine in the World. Iranian Economic Review, 27(4),  1154-1198. Retrieved from https://ier.ut.ac.ir/article_94930_236956ca52f2196f2f9a42451d862311.pdf.
Brenner, M., & Galai, D. (1989). New Financial Instruments for Hedging Changes in Volatility. Financial Analysis Journal, 45(4), 61–65. Retrieved from https://pages.stern.nyu.edu/~mbrenner/research/FAJ_articleon_Volatility_Der.pdf.
Brooks, R. D., Faff, R. W., & McKenzie, M. D. (1998). Time-varying beta risk of Australian industry portfolios: A comparison of modelling techniques. Australian Journal of Management, 23(1), 1–22. Retrieved from https://journals.sagepub.com/doi/epdf/10.1177/031289629802300101.
Calzolari, G., Halbleib, R., & Parrini, A. (2014). Estimating GARCH-Type Models with Symmetric Stable Innovations: Indirect Inference versus Maximum Likelihood. Computational Statistics and Data Analysis, 76, 158–171. Retrieved from https://d-nb.info/1163950564/34.
Chae, J., & Lee, E. J. (2018). Distribution uncertainty and expected stock returns. Finance Research Letters, 25, 55–61. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1544612317303574.
Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An Examination of Herd Behavior in Equity Markets: An International Perspective. Journal of Banking and Finance, 24(10), 1651–1679. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0378426699000965.
Chen, Y. C., Wu, H. C., Zhang, Y., & Kuo, S. M. (2021). A transmission of beta herding during the subprime crisis in Taiwan’s market: DCC-MIDAS approach. International Journal of Financial Studies, 9(70), 1–16. Retrieved from https://www.mdpi.com/2227-7072/9/4/70.
Chiang, T. C., & Zheng, D. (2010). An Empirical Analysis of Herd Behavior in Global Stock Markets. Journal of Banking and Finance, 34(8), 1911–1921. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0378426609003409.
Christie, W. G., & Huang, R. D. (1995). Following the Pied Piper: Do Individual Returns Herd around the Market? Financial Analysts Journal, 51(4), 31–37. Retrieved from https://www.tandfonline.com/doi/abs/10.2469/faj.v51.n4.1918.
Contessi, S., & Pace, P. De. (2021). The International Spread of COVID-19 Stock Market Collapses. Finance Research Letters, 42(C), 1–11. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC8450777/.
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339–350. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=236998.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1008. Retrieved from http://www.econ.uiuc.edu/~econ536/Papers/engle82.pdf.
Essa, M. S., & Giouvris, E. (2023). What is the Effect of VIX and (un)Expected Illiquidity on Sectoral Herding in US REITs during (Non)Crises? Evidence from a Markov Switching Model (2014–2022). Journal of Behavioral Finance, 0(0), 1–23. Retrieved from https://www.tandfonline.com/doi/full/10.1080/15427560.2023.2249155#d1e115.
Fabozzi, F. J., & Francis, J. C. (1979). Mutual Fund Systematic Risk for Bull and Bear Markets: An Empirical Examination. The Journal of Finance, 34(5), 1243–1250. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2586506.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383. Retrieved from https://people.hec.edu/rosu/wp-content/uploads/sites/43/2023/09/Fama-Efficient-capital-markets-1970.pdf.
Farbmacher, H., & Kögel, H. (2017). Testing Under a Special Form of Heteroscedasticity. Applied Economics Letters, 24(4), 264–268. Retrieved from https://www.tandfonline.com/doi/full/10.1080/13504851.2016.1181827.
Feng, L., & Shi, Y. (2017). A Simulation Study on the Distributions of Disturbances in the GARCH Model. Cogent Economics and Finance, 5(1), 1–19. Retrieved from https://www.tandfonline.com/doi/full/10.1080/23322039.2017.1355503#abstract.
Ferreruela, S., Kallinterakis, V., & Mallor, T. (2022). Cross-Market Herding: Do ‘Herds’ Herd with Each Other? Journal of Behavioral Finance, 1–45. Retrieved from https://zaguan.unizar.es/record/119011/files/texto_completo.pdf.
Financial Services Authority (OJK). (2022). Capital Market Fact Book 2022. Retrieved from https://ojk.go.id/en/Statistik/Pasar-Modal/Laporan-Tahunan/Pages/Capital-Market-Fact-Book-2022.aspx.
Giot, P., & Laurent, S. (2003). Value-at-Risk for Long and Short Trading Positions. Journal of Applied Econometrics, 18(6), 641–663. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/jae.710.
Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics; The McGraw-Hill Series Economics (5th Ed.). New York: McGraw-Hill.
Gupta, S., & Shrivastava, M. (2022). Herding and Loss Aversion in Stock Markets: Mediating Role of Fear of Missing Out (FOMO) in Retail Investors. International Journal of Emerging Markets, 17(7), 1720–1737. Retrieved from  https://www.emerald.com/ijoem/article-abstract/17/7/1720/146285/Herding-and-loss-aversion-in-stock-markets?redirectedFrom=fulltext.
Haghani, M., & Sarvi, M. (2019). ‘Herding’ in direction choice-making during collective escape of crowds: How likely is it and what moderates it? Safety Science, 115(February), 362–375. Retrieved from  https://www.sciencedirect.com/science/article/abs/pii/S0925753518309287.
Hirshleifer, D., & Hong Teoh, S. (2003). Herd Behaviour and Cascading in Capital Markets: A Review and Synthesis. European Financial Management, 9(1), 25–66. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=296081.
Hsieh, S. F. (2013). Individual and Institutional Herding and the Impact on Stock Returns: Evidence from Taiwan Stock Market. International Review of Financial Analysis, 29(C), 175–188. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S1057521913000045.
Hwang, S., & Salmon, M. (2004). Market Stress and Herding. Journal of Empirical Finance, 11(4), 585–616. Retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0927539804000507.
Jabalameli, F., Ghorbani, P., & Ahmadian, M. (2020). Risk Management in Oil Market: A Comparison between Multivariate GARCH Models and Copula-Based Models. Iranian Economic Review, 24(2), 489–513. Retrieved from https://ier.ut.ac.ir/article_76015_c38140a75f74c942b20db4a48c6bd01d.pdf.
Kumar, A., Badhani, K. N., Bouri, E., & Saeed, T. (2021). Herding behavior in the commodity markets of the Asia-Pacific region. Finance Research Letters, 41(C), 101813. Retrieved from https://www.researchgate.net/publication/346358389_Herding_behavior_in_the_commodity_markets_of_the_Asia-Pacific_region.
Lauridsena, J., & Kosfeld, R. (2011). Spurious Spatial Regression and Heteroscedasticity. Journal of Spatial Science, 56(1), 59–72. Retrieved from https://doi.org/10.1080/14498596.2011.567411.
Malkiel, B. G. (2003). The Efficient Market Hypothesis and Its Critics. Journal of Economic Perspectives, 17(1), 59–82. Retrieved from https://eml.berkeley.edu/~craine/EconH195/Fall_14/webpage/Malkiel_Efficient%20Mkts.pdf.
McKenzie, M. D., Mitchell, H., Brooks, R. D., & Faff, R. W. (2001). Power arch modelling of commodity futures data on the London Metal Exchange. International Journal of Phytoremediation, 21(1), 22–38. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/13518470123011.
Mertzanis, C., & Allam, N. (2018). Political Instability and Herding Behaviour: Evidence from Egypt’s Stock Market. Journal of Emerging Market Finance, Retrieved from 17(1), 29–59. Retrieved from https://journals.sagepub.com/doi/abs/10.1177/0972652717748087.
Nieto, B., Orbe, S., & Zarraga, A. (2014). Time-varying market beta: Does the estimation methodology matter? Sort, 38(1), 13–42. Retrieved from https://www.raco.cat/index.php/SORT/article/download/277216/365148.
Ogata, H. (2012). Optimal Portfolio Estimation for Dependent Financial Returns with Generalized Empirical Likelihood. Advances in Decision Sciences, 2012, 1–8. Retrieved from https://www.emis.de/journals/HOA/ADS/Volume2012/973173.pdf.
Park, B. -J., & Kim, M. -J. (2017). A Dynamic Measure of Intentional Herd Behavior in Financial Markets. MPRA Paper, 82025(October). Retrieved from https://mpra.ub.uni-muenchen.de/82025/1/MPRA_paper_82025.pdf.
Putra, A. A., Rizkianto, E., & Chalid, D. A. (2017). The Analysis of Herding Behavior in Indonesia and Singapore Stock Markets. 11th International Conference on Business and Management Research (ICBMR 2017), 36(November), 197–206. Retrieved from https://www.atlantis-press.com/proceedings/icbmr-17/25887772.
Rahman, E., & Ermawati. (2020). Analysis of Herding Behavior in the Stock Market: A Case Study of the ASEAN-5 and the U.S. Bulletin of Monetary Economics and Banking, 23(3), 297–318. Retrieved from https://bulletin.bmeb-bi.org/cgi/viewcontent.cgi?article=1044&context=bmeb.
Rice, G., Wirjanto, T., & Zhao, Y. (2020). Tests for Conditional Heteroscedasticity of Functional Data. Journal of Time Series Analysis, 41(6), 733–758. Retrieved from https://onlinelibrary.wiley.com/doi/full/10.1111/jtsa.12532.
Rizal, N. A., & Damayanti, M. K. (2019). Herding Behavior in the Indonesian Islamic Stock Market. Journal of Islamic Monetary Economics and Finance, 5(3), 673–690. Retrieved from https://www.jimf-bi.org/JIMF/article/view/1079/781.
Robiyanto, R. (2018). DCC-GARCH Application in Formulating a Dynamic Portfolio between Stocks in the Indonesia Stock Exchange with Gold. Indonesian Capital Market Review, 10(1). Retrieved from https://scholarhub.ui.ac.id/icmr/vol10/iss1/3/.
Rout, B., Mohanty, A., & Kacharia, A. K. (2017). Bull Vs Bear Market- an Investment Game Analysis Using Moving Average Method. International Journal of Research-Granthaalayah, 5(11), 425–443. Retrieved from https://www.granthaalayahpublication.org/journals/granthaalayah/article/view/IJRG17_A12_834/2259.
Samitas, A., Kampouris, E., & Polyzos, S. (2022). COVID-19 Pandemic and Spillover Effects in Stock Markets: A Financial Network Approach. International Review of Financial Analysis, 80(C), 102005. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC8694798/.
Scharfstein, D. S., & Stein, J. C. (1990). Herd Behavior and Investment. American Economic Review, 80(3), 465–479. Retrieved from https://stein.scholars.harvard.edu/sites/g/files/omnuum5951/files/stein/files/aer1990_0.pdf.
Shahraki, J., Shahraki, A. S., Ahmadi, N. A., & Radmehr, Z. (2021). The Economic Assessment of the Production and Technical Efficiency of Bakeries (Focus on Social Factor Using Stochastic Frontier Analysis). Iranian Economic Review, 25(3), 383–395. Retrieved from https://ier.ut.ac.ir/article_84135_527189c8f186ea30543e74d2d4397680.pdf.
Sharpe, W. F. (1964). Capital Asset Price: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, 19(3), 425–442. Retrieved from https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.1964.tb02865.x.
Stojanovski, T. (2015). Volatility and Kurtosis of Daily Stock Returns at MSE. UTMS Journal of Economics, 6(2), 209–221. Retrieved from https://www.utmsjoe.mk/files/Vol.%206%20No.%202/UTMSJOE-2015-0602-003-Ivanovski-Stojanovski-Narasanov.pdf.
Tsionas, M. G., Philippas, D., & Philippas, N. (2022). Multivariate stochastic volatility for herding detection: Evidence from the energy sector. Energy Economics, 109(C), 105964. Retrieved from https://www.gfli.gr/wp-content/uploads/2022/07/Multivariate-stochastic-volatility-for-herding-detection-Evidence-from-the-energy-sector-2022.pdf.
Zhang, H., & Giouvris, E. (2022). Measures of Volatility, Crises, Sentiment, and the Role of U.S. ‘Fear’ Index (VIX) on Herding in BRICS (2007–2021). Journal of Risk and Financial Management, 15(3), 134. Retrieved from https://www.mdpi.com/1911-8074/15/3/134.
Iranian Economic Review
Volume 29, Issue 4
December 2025
Pages 1205-1233

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