Investigating Volatility Spillover between the Energy Market and the Sectoral Stock Markets in Malaysia: Evidence from VHAR-Type Models DOI: https://doi.org/10.33093/ijomfa.2026.7.1.19
Main Article Content
Abstract
This study examines the realised volatility spillover effects between Malaysia's energy market and other sectoral indices on the Kuala Lumpur Stock Exchange from September 2018 to December 2024. To address the limitations of current volatility modelling approaches, this study employs the Vector Heterogeneous Autoregressive (VHAR) model combined with the Realised Range-Based Volatility (RRV) measure to capture heterogeneous market behaviours at daily, weekly, and monthly time horizons. High-frequency data from September 2018 to December 2024 (earlier data are unavailable for some sectors) were collected from the financial data provider Bloomberg. The findings reveal that the energy sector exhibits strong volatility persistence, with past volatility having a significant influence on current volatility levels. More importantly, this study documents spillover effects. While the energy sector experiences limited volatility transmission from other sectors, it exerts substantial influence on the volatility of most other sectors, notably demonstrating adverse long-term effects but positive short- and medium-term impacts. The healthcare sector appears to be uniquely immune to the energy market volatility contagion. A comparative analysis confirms that the VHAR-RRV model substantially outperforms traditional Vector Autoregressive (VAR) models and modestly surpasses VHAR models using standard realised volatility measures. These results offer valuable insights into portfolio diversification strategies, risk management practices, and policy formulation in Malaysia and similar emerging markets, where energy sector dynamics have a significant impact on broader market stability.
Article Details
References
Aït-Sahalia, Y., Mykland, P. A., & Zhang, L. (2005). How often to sample a continuous-time process in the presence of market microstructure noise. The Review of Financial Studies, 18(2), 351–416. https://doi.org/10.1093/rfs/hhi016
Alfeus, M., Harvey, J., & Maphatsoe, P. (2024). Improving realised volatility forecast for emerging markets. Journal of Economics and Finance. https://doi.org/10.1007/s12197-024-09701-x
Alves, R., de Brito, D. S., Medeiros, M. C., & Ribeiro, R. M. (2023). Forecasting large realized covariance matrices: the benefits of factor models and shrinkage [arXiv preprint arXiv:2303.16151]. https://arxiv.org/abs/2303.16151
Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885–905. https://doi.org/10.2307/2527343
Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. The Review of Economics and Statistics, 89(4), 701–720. https://doi.org/10.1162/rest.89.4.701
Ariyon, M., Sukendi, S., Putra, R. M., Kausarian, H., & Santika, B. (2023). Comparison of oil and gas fiscal policies in Southeast Asian Countries: Indonesia, Malaysia and Brunei Darussalam. In BIO Web of Conferences (Vol. 70, p. 06007). EDP Sciences. https://doi.org/10.1051/bioconf/20237006007
Azad, N. F., & Serletis, A. (2024). Oil price uncertainty and consumer sentiment in advanced economies. The Energy Journal, 45(6), 159–175. https://doi.org/10.1177/01956574241281163
Baek, C., & Park, M. (2021). Sparse vector heterogeneous autoregressive modeling for realized volatility. Journal of the Korean Statistical Society, 50(2), 495–510. https://doi.org/10.1007/s42952-020-00090-5
Bandi, F. M., & Russell, J. R. (2006). Separating microstructure noise from volatility. Journal of Financial Economics, 79(3), 655–692. https://doi.org/10.1016/j.jfineco.2005.01.005
Bank Negara Malaysia. (2016). Annual report 2016. https://www.bnm.gov.my/-/default-basic-87
Bergsli, L. Ø., Lind, A. F., Molnár, P., & Polasik, M. (2022). Forecasting volatility of Bitcoin. Research in International Business and Finance, 59, 101540. https://doi.org/10.1016/j.ribaf.2021.101540
Blomkvist, M., Dimic, N., & Vulanovic, M. (2023). Oil price uncertainty and IPOs. The Energy Journal, 44(6), 21–42. https://doi.org/10.5547/01956574.44.6.mblo
Brianzoni, S., Campisi, G., & Pacelli, G. (2025). Heterogeneous traders: Endogenous uncertainty in financial markets. Nonlinear Dynamics, 113(22), 31801–31813. https://doi.org/10.1007/s11071-025-11680-5
Bubak, V., Kocenda, E., & ZikeS, F. (2011). Volatility transmission in emerging European foreign exchange markets. Journal of Banking & Finance, 35(11), 2829–2841. https://doi.org/10.1016/j.jbankfin.2011.03.012
Bui, H. Q., Tran, T., Pham, T. T., Nguyen, H. L.-P., & Vo, D. H. (2022). Market volatility and spillover across 24 sectors in Vietnam. Cogent Economics & Finance, 10(1), 2122188. https://doi.org/10.1080/23322039.2022.2122188
Bursa Malaysia. (2024). Bursa Malaysia sectorial index series factsheet. https://www.bursamalaysia.com/sites/5d809dcf39fba22790cad230/assets/67612977cd34aa35f57b1328/BM_Sectorial_Index_Series_Factsheet_Nov24.pdf
Charles, A., Chua, C. L., Darné, O., & Suardi, S. (2021). Oil price shocks, real economic activity and uncertainty. Bulletin of Economic Research, 73(3), 364–392. https://doi.org/10.1111/boer.12252
Chen, C. W. S., Watanabe, T., & Lin, E. M. H. (2023). Bayesian estimation of realized GARCH-type models with application to financial tail risk management. Econometrics and Statistics, 28, 30–46. https://doi.org/10.1016/j.ecosta.2021.03.006
Christensen, K., & Podolskij, M. (2007). Realized range-based estimation of integrated variance. Journal of Econometrics, 141(2), 323–349. https://doi.org/10.1016/j.jeconom.2006.06.012
Clements, A., & Preve, D. P. A. (2021). A practical guide to harnessing the HAR volatility model. Journal of Banking & Finance, 133, 106285. https://doi.org/10.1016/j.jbankfin.2021.106285
Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174–196. https://doi.org/10.1093/jjfinec/nbp001
Corsi, F., Mittnik, S., Pigorsch, C., & Pigorsch, U. (2008). The Volatility of realized volatility. Econometric Reviews. https://doi.org/10.1080/07474930701853616
Degiannakis, S., Filis, G., Klein, T., & Walther, T. (2022). Forecasting realized volatility of agricultural commodities. International Journal of Forecasting, 38(1), 74–96. https://doi.org/10.1016/j.ijforecast.2019.08.011
Energy Institute. (2024). Statistical review of world energy. https://www.energyinst.org/statistical-review/
Fostering Effective Energy Transition 2023. (2023). World Economic Forum. https://www.weforum.org/publications/fostering-effective-energy-transition-2023/
FTSE Russell. (2025). FTSE Bursa Malaysia KLCI: Indicative index weight data as at closing on 30 June 2025 [Fact sheet]. London Stock Exchange Group. https://research.ftserussell.com/analytics/factsheets/home/DownloadConstituentsWeights/?indexdetails=FBMKLCI
Gerlach, R., & Wang, C. (2016). Forecasting risk via realized GARCH, incorporating the realized range. Quantitative Finance, 16(4), 501–511. https://doi.org/10.1080/14697688.2015.1079641
Gerlach, R., & Wang, C. (2022). Bayesian semi-parametric realized conditional autoregressive expectile models for tail risk forecasting. Journal of Financial Econometrics, 20(1), 105–138. https://doi.org/10.1093/jjfinec/nbaa002
Goswami, G. G., Yahya, M., Atique, M. A., & Uddin, G. S. (2025). Impact of financial and energy market uncertainties on ASEAN-5 markets. Eurasian Economic Review. https://doi.org/10.1007/s40822-025-00327-w
Ha, L. T. (2023). An application of Bayesian vector heterogeneous autoregressions to study network interlinkages of the crude oil and gold, stock, and cryptocurrency markets during the COVID-19 outbreak. Environmental Science and Pollution Research, 30(26), 68609–68624. https://doi.org/10.1007/s11356-023-27069-z
Han, Q. (2025). Understanding price momentum, market fluctuations, and crashes: Insights from the extended Samuelson model. Financial Innovation, 11(1), 56. https://doi.org/10.1186/s40854-024-00743-y
Hatem, G., Zeidan, J., Goossens, M., & Moreira, C. (2022). Normality Testing Methods and the Importance of Skewness and Kurtosis in Statistical Analysis. BAU Journal - Science and Technology, 3(2). https://doi.org/10.54729/KTPE9512
Hoque, M. E., & Batabyal, S. (2022). Carbon futures and clean energy stocks: Do they hedge or safe haven against the climate policy uncertainty? Journal of Risk and Financial Management, 15(9), Article 9. https://doi.org/10.3390/jrfm15090397
Jahan-Parvar, M. R., & Zikes, F. (2023). When Do Low-Frequency Measures Really Measure Effective Spreads? Evidence from Equity and Foreign Exchange Markets. The Review of Financial Studies. https://doi.org/10.1093/rfs/hhad028
Kambouroudis, D. S., McMillan, D. G., & Tsakou, K. (2021). Forecasting realized volatility: The role of implied volatility, leverage effect, overnight returns, and volatility of realized volatility. Journal of Futures Markets. https://doi.org/10.1002/fut.22241
Kesumah, F. S. D., & Azhar, R. (2025). Volatility spillover and risk measurement of Southeast Asian financial markets. BAR - Brazilian Administration Review, 22(2). https://doi.org/10.1590/1807-7692bar2025240148
Kim, D., & Baek, C. (2020). Factor-augmented HAR model improves realized volatility forecasting. Applied Economics Letters, 27(12), 1002–1009. https://doi.org/10.1080/13504851.2019.1657554
Kim, Y. G., & Baek, C. (2024). Bayesian vector heterogeneous autoregressive modelling. Journal of Statistical Computation and Simulation, 94(6), 1139–1157. https://doi.org/10.1080/00949655.2023.2281644
Kirby, C. (2025). Using Daily Stock Returns to Estimate the Unconditional and Conditional Variances of Lower-Frequency Stock Returns. Risks, 13(10), 190. https://doi.org/10.3390/risks13100190
Kocaarslan, B., Soytas, M. A., & Soytas, U. (2020). The asymmetric impact of oil prices, interest rates and oil price uncertainty on unemployment in the US. Energy Economics, 86, 104625. https://doi.org/10.1016/j.eneco.2019.104625
Lee, S., & Baek, C. (2023). Volatility changes in cryptocurrencies: Evidence from sparse VHAR-MGARCH model. Applied Economics Letters, 30(11), 1496–1504. https://doi.org/10.1080/13504851.2022.2064417
Liao, Y., & Anderson, H. M. (2019). Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices. Journal of Banking & Finance, 99, 252–274. https://doi.org/10.1016/j.jbankfin.2018.12.005
Liu, H., & Yin, B. (2025). An Integrated Framework for Assessing Livestock Ecological Efficiency in Sichuan: Spatiotemporal Dynamics, Drivers, and Projections. Sustainability, 17(16), 7415. https://doi.org/10.3390/su17167415
Lu, X., Ma, F., Wang, J., & Liu, J. (2022). Forecasting oil futures realized range-based volatility with jumps, leverage effect, and regime switching: New evidence from MIDAS models. Journal of Forecasting, 41(4), 853–868. https://doi.org/10.1002/for.2837
Lyócsa, Š., Molnár, P., & Výrost, T. (2021). Stock market volatility forecasting: Do we need high-frequency data? International Journal of Forecasting, 37(3), 1092–1110. https://doi.org/10.1016/j.ijforecast.2020.12.001
Mabro, R. (2001). Les dimensions politiques de l'OPEP. Politique étrangère, 403-417.
Martens, M., & Van Dijk, D. (2007). Measuring volatility with the realized range. Journal of Econometrics, 138(1), 181–207. https://doi.org/10.1016/j.jeconom.2006.05.019
Merton, R. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8(4), 323–361. https://doi.org/10.3386/w0444
Min, H. (2022). Examining the impact of energy price volatility on commodity prices from energy supply chain perspectives. Energies, 15(21), Article 21. https://doi.org/10.3390/en15217957
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), 213–239. https://doi.org/10.1016/S0927-5398(97)00007-8
Nardo, M., Ossola, E., & Papanagiotou, E. (2022). Financial integration in the EU28 equity markets: Measures and drivers. Journal of Financial Markets, 57, 100633. https://doi.org/10.1016/j.finmar.2021.100633
National Energy Policy, 2022-2040 (2022). Ministry of Economy Malaysia. https://ekonomi.gov.my/sites/default/files/2022-09/National_Energy_Policy_2022-2040.pdf.
Nhlapho, W., Atemkeng, M., & Ndogmo, J.-C. (2025). An attention-guided hybrid statistical and deep learning modeling for enhanced time series forecasting: A case study of South African telecommunication companies. Scientific African, 30, e02950. https://doi.org/10.1016/j.sciaf.2025.e02950
Niako, N., Melgarejo, J. D., Maestre, G. E., & Vatcheva, K. P. (2024). Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM. BMC Medical Research Methodology, 24(1). https://doi.org/10.1186/s12874-024-02448-3
Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1), 61–65.
Robiyanto, R., Frensidy, B., Setyawan, I. R., & Huruta, A. D. (2021). A different view on ASEAN capital market integration. Economies, 9(4), 141. https://doi.org/10.3390/economies9040141
Securities Commission Malaysia. (2021). Capital Market Masterplan 3 | Securities Commission Malaysia. https://www.sc.com.my/cmp3
Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of Economic Perspectives, 17(1), 83–104. https://doi.org/10.1257/089533003321164967
Shin, A. J., Park, M., & Baek, C. (2022). Sparse vector heterogeneous autoregressive model with nonconvex penalties. Communications for Statistical Applications and Methods, 29(1), 53–64. https://doi.org/10.29220/CSAM.2022.29.1.053
Souropanis, I., & Vivian, A. (2023). Forecasting realized volatility with wavelet decomposition. Journal of Empirical Finance, 74, 101432–101432. https://doi.org/10.1016/j.jempfin.2023.101432
Symitsi, E., Symeonidis, L., Kourtis, A., & Markellos, R. (2018). Covariance forecasting in equity markets. Journal of Banking & Finance, 96, 153–168. https://doi.org/10.1016/j.jbankfin.2018.08.013
Tang, Y., Ma, F., Wahab, M. I. M., & Wei, Y. (2022). Does the US stock market information matter for European equity market volatility: A multivariate perspective? Applied Economics, 54(58), 6726–6743. https://doi.org/10.1080/00036846.2022.2081663
Tang, Y., Ma, F., Zhang, Y., & Wei, Y. (2022). Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model. International Journal of Finance & Economics, 27(4), 4770–4783. https://doi.org/10.1002/ijfe.2399
Tang, Y., Xiao, X., Wahab, M. I. M., & Ma, F. (2020). The role of oil futures intraday information on predicting US stock market volatility. Journal of Management Science and Engineering, 6(1), 64–74. https://doi.org/10.1016/j.jmse.2020.10.004
Wang, C., Gerlach, R., & Chen, Q. (2023). A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures. Quantitative Finance, 23(2), 309–334. https://doi.org/10.1080/14697688.2022.2157322
Wang, Z., & Liu, X. (2021). HAR model to examine the impact of daily, weekly, and monthly effect. 2021 International Conference on Computer, Blockchain and Financial Development (CBFD), 292–295. https://doi.org/10.1109/CBFD52659.2021.00065
Wen, D., He, M., Zhang, Y., & Wang, Y. (2022). Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach. Journal of Forecasting, 41(2), 230–251. https://doi.org/10.1002/for.2807
Wen, F., Zhao, Y., Zhang, M., & Hu, C. (2019). Forecasting realized volatility of crude oil futures with equity market uncertainty. Applied Economics, 51(59), 6411–6427. https://doi.org/10.1080/00036846.2019.1619023
Wu, X., Cui, H., & Wang, L. (2023). Forecasting oil futures price volatility with economic policy uncertainty: A CARR-MIDAS model. Applied Economics Letters, 30(2), 120–125. https://doi.org/10.1080/13504851.2021.1977232
Xie, X., & Clements, A. (2024). Predicting directional volatility: HAR model with machine learning integration. Applied Economics Letters, xx(x), 1–9. https://doi.org/10.1080/13504851.2024.2401512