Dynamic Spillovers and Portfolio Construction: A TVP-VAR Analysis of the S&P 500, SSE, ESG ETFs, and Commodities

Authors

  • RIHAB BELGUITH University of Sfax, Faculty of Business and Economic Sciences, Sfax-Tunisia Corresponding Author

DOI:

https://doi.org/10.47654/v30y2026i1p186-221

Keywords:

Dynamic connectedness, TVP-VAR Framework, portfolio optimization, Hedging effectiveness, ESG, Equity Markets, Crude Oil

Abstract

Purpose - This study explores the dynamic return spillovers and portfolio implications of key global financial assets, including U.S. and Chinese equities, crude oil, and ESG-focused investments, with the aim of analyzing whether investors in the S&P 500 and Shanghai Stock Exchange can mitigate portfolio risk through strategic allocation to ESG and commodity assets. This research provides a quantitative framework for investors, portfolio managers, and policymakers to make evidence-based decisions on risk diversification, hedging strategies, and performance enhancement in both equity and multi-asset portfolios under conditions of financial and economic uncertainty.

Design/methodology/approach - A Time-Varying Parameter Vector Autoregressive (TVP-VAR) model is applied to examine evolving interdependencies among the S&P 500 Index, Shanghai Stock Exchange Composite Index, West Texas Intermediate (WTI) crude oil, and the SPDR S&P 500 ESG ETF (EFIV). Four dynamic portfolio optimization strategies—Minimum Variance, Minimum Correlation, Minimum Connectedness, and Risk Parity—are implemented and evaluated under varying market conditions.

Findings - Results indicate that the S&P 500 and EFIV consistently act as net transmitters of volatility, while WTI and SSE function predominantly as receivers. Portfolios optimized using Minimum Connectedness and Correlation strategies demonstrate superior cumulative returns, whereas those using Minimum Variance and Risk Parity approaches achieve better risk-adjusted performance. Bivariate hedging analyses highlight the effectiveness of ESG assets, especially in equity pairings.

Practical implications - Findings provide valuable insights for institutional investors and portfolio managers seeking to optimize diversification, manage risk, and incorporate ESG principles in asset allocation strategies, particularly under conditions of global financial uncertainty.

Originality/value - This study contributes to the literature by integrating ESG-focused instruments within a dynamic connectedness framework and demonstrating their role in portfolio risk mitigation and performance enhancement. Specifically, it introduces a novel combination of TVP-VAR modeling with multiple dynamic portfolio optimization strategies, demonstrating how ESG assets can systematically mitigate portfolio risk and enhance performance, offering new guidance for evidence-based decision-making in financial markets.

References

Akin, I., Akin, M., Ozturk, Z., & Satiroglu, H. (2024). Exploring fluctuations and interconnected movements in stock, commodity, and cryptocurrency markets. British Actuarial Journal, 29, e13. https://doi.org/10.1017/S1357321724000126

AlGhazali, A., Belghouthi, H. E., Mensi, W., McIver, R., & Kang, S. H. (2024). Oil price shocks, sustainability index, and green bond market spillovers and connectedness during bear and bull market conditions. Economic Analysis and Policy,84, 1470-1489. https://doi.org/10.1016/j.eap.2024.10.016

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084

Attarzadeh, A., Isayev, M., & Irani, F. (2024). Dynamic interconnectedness and portfolio implications among cryptocurrency, gold, energy, and stock markets: A TVP-VAR approach. Sustainable Futures, 8, Article 100375. https://doi.org/10.1016/j.sftr.2024.100375

Bekun, F. V., Gyamfi, B. A., Olasehinde-Williams, G., & Yadav, A. (2024). Revisiting the foreign direct investment–CO₂ emissions nexus within the N-EKC framework: Evidence from South Asian countries. Sustainable Futures, 8, 100357. https://doi.org/10.1016/j.sftr.2023.100357

Bekun, F. V., Uzuner, G., Meo, M. S., & Yadav, A. (2025). Another look at energy consumption and environmental sustainability target through the lens of the load capacity factor: Accessing evidence from MINT economies. Natural Resources Forum, 49(3), 2349–2366. 10.1111/1477-8947.12481

Bhuyan, R., & Roubaud, D. (2022). How does investors’ attention influence equity trading and performance? Evidence from listed Indian companies. Advances in Decision Sciences, 26, 77–101. https://doi.org/10.47654/v26y2022i5p77-101

Chatziantoniou, I., & Gabauer, D. (2021). EMU risk-synchronisation and financial fragility through the prism of dynamic connectedness. The Quarterly Review of Economics and Finance, 79, 1–14. https://doi.org/10.1016/j.qref.2020.12.003

Chiu, J., Chung, H., & Ho, K.-Y. (2014). Fear sentiment, liquidity, and trading behavior: Evidence from the index ETF market. Review of Pacific Basin Financial Markets and Policies, 17(3), 1-25. https://doi.org/10.1142/S0219091514500179.

Christoffersen, P., Errunza, V., Jacobs, K., & Jin, X. (2014). Correlation dynamics and international diversification benefits. International Journal of Forecasting, 30(3), 807–824. https://doi.org/10.1016/j.ijforecast.2014.01.001

Cogley, T., & Sargent, T. J. (2005). Drift and volatilities: Monetary policies and outcomes in the post WWII U.S. Review of Economic Dynamics, 8(2), 262–302. https://doi.org/10.1016/j.red.2004.09.002

Daugaard, D. (2020). Emerging new themes in environmental, social and governance investing: A systematic literature review. Accounting & Finance, 60(2), 1501–1530. https://doi.org/10.1111/acfi.12479

Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012

Doumenis, Y., Izadi, J., Dhamdhere, P., & Koufopoulos, D. (2021). A critical analysis of volatility surprise in Bitcoin cryptocurrency and other financial assets. Risks. https://doi.org/10.3390/risks9110207

Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157–170. https://doi.org/10.2307/2327101

Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate GARCH models. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487

Erdas, M. L., & Caglar, A. E. (2018). Analysis of the relationships between Bitcoin and exchange rate, commodities and global indexes by asymmetric causality test. Eastern Journal of European Studies.

Fiorillo, P., Meles, A., Pellegrino, L. R., & Verdoliva, V. (2024). Geopolitical risk and stock price crash risk: The mitigating role of ESG performance. International Review of Financial Analysis. https://doi.org/10.1016/j.irfa.2023.102958

Fung, J. K. W., Lam, F. Y. E., & Tse, Y. (2024). The impact of ESG rating on hedging downside risks: Evidence from a weight-tilted Hang Seng index. Journal of Risk and Financial Management, 17(2), 57. https://doi.org/10.3390/jrfm17020057

Gabauer, D. (2021). Dynamic measures of asymmetric and pairwise spillovers within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, Article 100680. https://doi.org/10.1016/j.mulfin.2021.100680

Ghani, M., Ma, F., & Huang, D. (2025). Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures. International Journal of Finance and Economics. https://doi.org/10.1002/ijfe.2745

Hoang, T.-H.-V., Wong, W.-K., & Zhu, Z. (2015). Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange. Economic Modelling. https://doi.org/10.1016/j.econmod.2015.06.021

Kayral, I. E., Jeribi, A., & Loukil, S. (2024). Are Bitcoin and gold a safe haven during COVID-19 and the 2022 Russia–Ukraine war? Journal of Risk and Financial Management, 16(4), 222. https://doi.org/10.3390/jrfm16040222

Ma, F., Wei, Y., Huang, D., & Zhao, L. (2013). Cross-correlations between West Texas Intermediate crude oil and the stock markets of the BRIC. Physica A: Statistical Mechanics and its Applications. https://doi.org/10.1016/j.physa.2013.06.061

Maillard, S., Roncalli, T., & Teïletche, J. (2010). The properties of equally weighted risk contribution portfolios. The Journal of Portfolio Management, 36(4), 60–70. https://doi.org/10.3905/jpm.2010.36.4.060

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974

Meira, E., Cunha, F. A. F. de S., Orsato, R. J., & Miralles-Quirós, J. L. (2022). The added value and differentiation among ESG investment strategies in stock markets. Business Strategy and the Environment, 32(4), 1816–1834.https://doi.org/10.1002/bse.3221

Mensi, W., Aslan, A., Vo, X. V., & Kang, S. H. (2023). Time-frequency spillovers and connectedness between precious metals, oil futures and financial markets: Hedge and safe haven implications. International Review of Economics and Finance. https://doi.org/10.1016/j.iref.2022.08.015

Mensi, W., Hammoudeh, S., Al-Jarrah, I. M. W., & Kang, S. H. (2017). Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications. Energy Economics. https://doi.org/10.1016/j.eneco.2017.08.031

Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72(3), 821–852. https://doi.org/10.1111/j.1467-937X.2005.00353.x

Qian, X. (2020). Gold market price spillover between COMEX, LBMA and SGE. Journal of Economics and Finance, 44, 810–831. https://doi.org/10.1007/s12197-020-09517-5

Seok, S., Cho, H., & Ryu, D. (2024). Dual effects of investor sentiment and uncertainty in financial markets. The Quarterly Review of Economics and Finance, 95, 300-315. https://doi.org/10.1016/j.qref.2024.04.006

Sharma, I., Bamba, M., Verma, B., & Verma, B. (2024). Dynamic connectedness and investment strategies between commodities and ESG stocks: Evidence from India. Australasian Accounting, Business and Finance Journal. 10.14453/aabfj.v18i3.05

Sharpe, W. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119–138. https://doi.org/10.1086/294846

Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017

Smales, L. A. (2021). Geopolitical risk and volatility spillovers in oil and stock markets. The Quarterly Review of Economics and Finance, 80, 358–366. https://doi.org/10.1016/j.qref.2021.03.008

Wang, Y., Liu, X., & Wan, D. (2023). Stock market openness and ESG performance: Evidence from the Shanghai-Hong Kong Connect program. Economic Analysis and Policy, 78(C), 1306–1319. https://doi.org/10.1016/j.eap.2023.05.005

Wilksch, M., & Abramova, O. (2023). PyFin-sentiment: Towards a machine-learning-based model for deriving sentiment from financial tweets. International Journal of Information Management Data Insights, 3(1), 100171. https://doi.org/10.1016/j.jjimei.2023.100171

Xu, N., He, Z., Zhou, F., & Chen, J. (2023). Mechanisms underlying geopolitical shocks and stock price crash risk: Evidence from China. Emerging Markets Finance and Trade. 10.1080/1540496X.2023.2195535

Yadav, A. (2024). Promoting economic stability: The role of renewable energy transition in mitigating global volatility. International Journal of Energy Sector Management. https://doi.org/10.1108/IJESM-06-2024-0032

Yadav, A., & Asongu, S. A. (2025). The role of ESG performance in moderating the impact of financial distress on company value: Evidence of wavelet-enhanced quantile regression with Indian companies. Business Strategy and the Environment, 34, 2782–2798. https://doi.org/10.1002/bse.4118

Yadav, A., Bekun, F. V., Ozturk, I., Ferreira, P. J. S., & Karalinc, T. (2024). Unravelling the role of financial development in shaping renewable energy consumption patterns: Insights from BRICS countries. Energy Strategy Reviews, 54, Article 101434. 10.1016/j.esr.2024.101434

Yang, J., Agyei, S. K., Bossman, A., & Marfo-Yiadom, E. (2024). Energy, metals, market uncertainties, and ESG stocks: Analysing predictability and safe havens. North American Journal of Economics and Finance, 69, Article 101434. https://doi.org/10.1016/j.najef.2023.102030

Yao, S., & Luo, D. (2009). The economic psychology of stock market bubbles in China. The World Economy, 32(5), 667 – 691. https://doi.org/10.1111/j.1467-9701.2009.01176.x

Zeng, T., Yang, M., & Shen, Y. (2020). Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks. Economic Modelling, 90, 209–220. 10.1016/j.econmod.2020.05.003

Zheng, J., Wen, B., Jiang, Y., Wang, X., & Shen, Y. (2023). Risk spillovers across geopolitical risk and global financial markets. Energy Economics, 127, 107051. https://doi.org/10.1016/j.eneco.2023.107051

Published

2026-02-19

How to Cite

Belguith, R. (2026). Dynamic Spillovers and Portfolio Construction: A TVP-VAR Analysis of the S&P 500, SSE, ESG ETFs, and Commodities. Advances in Decision Sciences, 30(1), 186-221. https://doi.org/10.47654/v30y2026i1p186-221