Multiscale Transmission of Volatility Between Short and Long-Term Investment Preferences: Evidence from Malaysia's KLCI
Keywords:
Volatility Spillovers, Wavelet Decomposition, Multivariate Stochastic Volatility (MSV), Malaysian Stock Market
Abstract
This study examines how short-term and long-term investments in Malaysia’s stock market influence each other through volatility spillovers. Using daily data from the KLCI index from 2014 to 2024, the research applies a combination of wavelet analysis and the Dynamic Granger Causality Multivariate Stochastic Volatility (DGC-t-MSV) model. This method allows us to study market behavior across different time periods and frequencies. The results show that there are significant volatility spillovers between short-term and long-term investments, with stronger effects coming from long-term (institutional) investments to short-term (retail) investments. Both types of investments also show strong persistence in volatility, meaning that once volatility appears, it tends to last. These findings are important for investors who need to manage risks and returns more effectively. They also provide useful insights for policymakers who aim to improve the stability of financial markets. By introducing a new approach to analyzing volatility spillovers, this research contributes to a better understanding of risk management in emerging markets like Malaysia.
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References
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Nishimura, Y., Tsutsui, Y., and Hirayama, K. (2018). Do international investors cause stock market spillovers? Comparing responses of cross-listed stocks between accessible and inaccessible markets. Economic Modelling, 69, 237.
Percival, D. B., & Walden, A. T. (2000). Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
Peters, E. E. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. John Wiley & Sons.
Rua, A. and Nunes, L.C. (2012). A wavelet-based assessment of market risk: The emerging markets case. The Quarterly Review of Economics and Finance, 52(1), 84–92.
Sahoo, S., & Kumar, S. (2023). Volatility spillover among the sectoral indices of the Indian capital market: Evidence from the COVID period. Indian Journal of Finance, 17(9), 41–57.
Tian, S. and Hamori, S. (2016). (2016). Time-varying price shock transmission and volatility spillover in foreign exchange, bond, equity, and commodity markets: Evidence from the United States, The North American Journal of Economics and Finance, 38, 163-171, https://doi.org/10.1016/j.najef.2016.09.004.
Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). John Wiley & Sons.
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Wang, Y., and Wu, C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?. Energy Economics, 34, 2167.
Wang, J., Zeng, R., & Wang, L. (2024). Volatility Spillover Between the Carbon Market and Traditional Energy Market Using the DGC-t-MSV Model. Mathematics, 12(23), 3789. https://doi.org/10.3390/math12233789
Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258–273.
Wu, Y., Zhang, C., Yang, Y., Yang, X., Yun, P. and Cao, W. (2020). What Happened to the CER Market? A Dynamic Linkage Effect Analysis. IEEE Access, 8, 62322-62333.
Yilmaz, K. (2010). Return and volatility spillovers among the East Asian equity markets. Journal of Asian Economics, 21(3), 304–313.
Younis, I., Yousaf, I., Shah, W.U., and Longsheng, C. (2023). Risk transmission between the equity market of China and its trading partners: new evidence from various financial crises. International Journal of Emerging Markets.
Yu, L., Yaacob, M.H., and Buqi, D. (2023). Multiscale Spillover Transmission in China's Investment Preferences Using the Dynamic Stochastic Volatility Framework. Information Sciences Letters, 12(7).
Zhang, J. and He, Q. (2021). Dynamic Cross-Market Volatility Spillover Based on MSV Model: Evidence from Bitcoin, Gold, Crude Oil, and Stock Markets. Complexity, 1.
Zhang, W., He, X. and Hamori, S. (2022). Volatility spillover and investment strategies among sustainability-related financial indexes: Evidence from the DCC-GARCH-based dynamic connectedness and DCC-GARCH t-Copula approach. International Review of Financial Analysis, 83, 102223.
Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126–139.
Alfreedi, A. A. (2019). Shocks and Volatility Spillover Between Stock Markets of Developed Countries and GCC Stock Markets. Journal of Taibah University for Science, 13(1), 1–9.
Asai, M., McAleer, M. and Yu, J. (2006). Multivariate Stochastic Volatility: A Review. Econometric Reviews, 25(2–3), 145–175.
Beirne, J., Caporale, G. M., Schulze-Ghattas, M., & Spagnolo, N. (2013). Volatility spillovers and contagion from mature to emerging stock markets. Review of International Economics, 21(5), 1060–1075.
Belhassine, O. (2020). Volatility spillovers and hedging effectiveness between the oil market and Eurozone sectors: A tale of two crises. Research in International Business and Finance, 53, 101195.
Brooks, C. (2008). Introductory Econometrics for Finance (2nd ed.). Cambridge University Press.
Chen, Y., Xu, J. and Miao, J. (2023). Dynamic volatility contagion across the Baltic dry index, iron ore price and crude oil price under the COVID-19: A copula-VAR-BEKK-GARCH-X approach. Resources Policy, 81, 103296.
Chen, Y., Shen, S. & Wang, C. (2025). Climate risks and stock market volatility spillover: new insights from wavelet and causality methods. Econ Change Restruct 58, 37 https://doi.org/10.1007/s10644-025-09877-0
Diebold, F.X., and Yilmas, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting. Volume 28, Issue 1, 57-66. https://doi.org/10.1016/j.ijforecast.2011.02.006.
Dutt, T., & Humphery-Jenner, M. (2013). Stock return volatility, operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly. Journal of Banking & Finance, 37(3), 999–1017.
Gao, Y., Li, Y. and Wang, Y. (2021). Risk spillover and network connectedness analysis of China’s green bond and financial markets: Evidence from financial events of 2015–2020. North American Journal of Economics and Finance, 57, 101386.
Gee, C. S., & Karim, M. Z. A. (2010). Volatility spillovers of the major stock markets in ASEAN-5 with the U.S. and Japanese stock markets. International Research Journal of Finance and Economics, 44, 156–168.
Hanif, W., Mensi, W., Gubareva, M. and Teplova, T. (2023). Impacts of COVID-19 on dynamic return and volatility spillovers between rare earth metals and renewable energy stock markets. Resources Policy, 80, 103196.
Khalfaoui, R., Boutahar, M., and Boubaker, H. (2015). Analyzing volatility spillovers and hedging between oil and stock markets: Evidence from wavelet analysis. Energy Economics, 49, 540.
Lestari, N.P., Rofik, M. and Utami, Y. (2023). Riding or challenging the waves: Uncovering the volatility of Southeast Asian stock markets amidst global uncertainties. Journal of Eastern European and Central Asian Research, 10(5), 841-854.
Li, Y., Yaacob, M. H., & Du, B. (2023). Multiscale spillover transmission in China’s investment preferences using a dynamic stochastic volatility framework. Information Sciences Letters, 12(7), 3283–3298.
Liu, T., He, X., Nakajima, T., and Hamori, S. (2020). Influence of fluctuations in fossil fuel commodities on electricity markets: Evidence from spot and futures markets in Europe. Energies, 13, 1900.
Liu, X., An, H., Huang, S. and Wen, S. (2017). The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH-BEKK model. Physica A: Statistical Mechanics and its Applications, 465, 374-383.
Long, W., Guo, Y., and Wang, Y. (2021). Information spillover features in global financial markets: A systematic analysis. Research in International Business and Finance, 57, 101395.
Mala, R., & Reddy, M. (2007). Measuring stock market volatility in an emerging economy. International Research Journal of Finance and Economics, 8, 126–133.
Merton, R. C., & Bodie, Z. (1995). A conceptual framework for analyzing the financial environment. In The Global Financial System: A Functional Perspective (Harvard Business School Press).
Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica, 41(5), 867–887. https://doi.org/10.2307/1913811
Müller, U. A., Dacorogna, M. M., Davé, R. D., Olsen, R. B., Pictet, O. V., & von Weizsäcker, J. E. (1997). Volatilities of different time resolutions - Analyzing the dynamics of market components. Journal of Empirical Finance, 4(2–3), 213–239. https://doi.org/10.1016/S0927-5398(97)00007-5
Naeem, M.A., Hamouda, F., Karim, S., and Vigne, S.A. (2023). Return and volatility spillovers among global assets: Comparing health crisis with geopolitical crisis. International Review of Economics and Finance, 87, 557.
Nishimura, Y., Tsutsui, Y., and Hirayama, K. (2018). Do international investors cause stock market spillovers? Comparing responses of cross-listed stocks between accessible and inaccessible markets. Economic Modelling, 69, 237.
Percival, D. B., & Walden, A. T. (2000). Wavelet Methods for Time Series Analysis. Cambridge: Cambridge University Press.
Peters, E. E. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. John Wiley & Sons.
Rua, A. and Nunes, L.C. (2012). A wavelet-based assessment of market risk: The emerging markets case. The Quarterly Review of Economics and Finance, 52(1), 84–92.
Sahoo, S., & Kumar, S. (2023). Volatility spillover among the sectoral indices of the Indian capital market: Evidence from the COVID period. Indian Journal of Finance, 17(9), 41–57.
Tian, S. and Hamori, S. (2016). (2016). Time-varying price shock transmission and volatility spillover in foreign exchange, bond, equity, and commodity markets: Evidence from the United States, The North American Journal of Economics and Finance, 38, 163-171, https://doi.org/10.1016/j.najef.2016.09.004.
Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). John Wiley & Sons.
Tsionas, M.G., Phillipas, D. and Phillipas, N. (2022). Multivariate stochastic volatility for herding detection: Evidence from the energy sector. Energy Economics, 109, 105964.
Wang, Y., and Wu, C. (2012). Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?. Energy Economics, 34, 2167.
Wang, J., Zeng, R., & Wang, L. (2024). Volatility Spillover Between the Carbon Market and Traditional Energy Market Using the DGC-t-MSV Model. Mathematics, 12(23), 3789. https://doi.org/10.3390/math12233789
Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258–273.
Wu, Y., Zhang, C., Yang, Y., Yang, X., Yun, P. and Cao, W. (2020). What Happened to the CER Market? A Dynamic Linkage Effect Analysis. IEEE Access, 8, 62322-62333.
Yilmaz, K. (2010). Return and volatility spillovers among the East Asian equity markets. Journal of Asian Economics, 21(3), 304–313.
Younis, I., Yousaf, I., Shah, W.U., and Longsheng, C. (2023). Risk transmission between the equity market of China and its trading partners: new evidence from various financial crises. International Journal of Emerging Markets.
Yu, L., Yaacob, M.H., and Buqi, D. (2023). Multiscale Spillover Transmission in China's Investment Preferences Using the Dynamic Stochastic Volatility Framework. Information Sciences Letters, 12(7).
Zhang, J. and He, Q. (2021). Dynamic Cross-Market Volatility Spillover Based on MSV Model: Evidence from Bitcoin, Gold, Crude Oil, and Stock Markets. Complexity, 1.
Zhang, W., He, X. and Hamori, S. (2022). Volatility spillover and investment strategies among sustainability-related financial indexes: Evidence from the DCC-GARCH-based dynamic connectedness and DCC-GARCH t-Copula approach. International Review of Financial Analysis, 83, 102223.
Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126–139.
Published
2026-02-28
How to Cite
Yaacob, M. H., Yu, L., & Samsuddin, A. A. (2026). Multiscale Transmission of Volatility Between Short and Long-Term Investment Preferences: Evidence from Malaysia’s KLCI. Information Management and Business Review, 18(1(J), 28-39. https://doi.org/10.22610/imbr.v18i1(J).4812
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Copyright (c) 2026 Mohd Hasimi Yaacob, Li Yu, Azyulail Azra Samsuddin
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