Purpose: The paper analyzed the volatility spillovers between the global commodities (crude oil and gold) and the Indian stock market to find out whether the COVID-19 pandemic caused a structural break. It sought to determine whether the post-pandemic period was a new normal of risk transmission or a phase of returning to the long-run equilibrium. Design/Methodology/Approach : A multivariate GJR-GARCH dynamic conditional correlation (DCC) model was used to analyze the daily data from January 2016 to June 2025. A two-stage structural break analysis was used to achieve robustness: An independent samples t-test was applied to detect mean changes, and the Bai and Perron endogenous breakpoint test was used to differentiate between a permanent rupture and a transitory shock. Results : Although preliminary t-tests revealed that the post-pandemic correlation changes in the Nifty Energy and Auto index were significant compared to the pre-pandemic period, the Bai–Perron test showed that these changes were temporary stress rather than structural shifts. The aggregate market and the financial sector were resilient. Notably, the effectiveness of hedging was low (less than 2.5%), but the Auto sector experienced a certain decline in the protective power of Gold. Practical

Implications: Investors need to be aware that although market structures might be resilient, protective hedges might fail in periods of stress. The conventional methods of diversification by Gold and Crude Oil require dynamic reconsideration, especially in energy-sensitive industries. Originality/

Value: The current research has empirically distinguished between volatility stress and structural rupture at a sectoral level, which offers distinct evidence of resilience of the Indian market against non-financial global shocks.

1. Agarwalla, M., Sahu, T. N., & Jana, S. S. (2025). How far are the crude oil price and inflation relevant to forecast the stock prices? Evidence from India. Rajagiri Management Journal, 19(2), 113–129. https://doi.org/10.1108/RAMJ-03-2024-0064
2. Agyei, S. K., Junior, P. O., Bossman, A., & Arhin, E. Y. (2022). Situated information flow between food commodity and regional equity markets: An EEMD‐based transfer entropy analysis. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2022/3938331
3. Ampountolas, A. (2023). The effect of COVID-19 on cryptocurrencies and the stock market volatility: A two-stage DCC-EGARCH model analysis. Journal of Risk and Financial Management, 16(1), 25. https://doi.org/10.3390/jrfm16010025
4. Arshad, Nhung Vu, T. H., Warn, T. S., & Ying, L. M. (2023). The hedging ability of gold, silver and bitcoin against inflation in ASEAN countries. Asian Academy of Management Journal of Accounting and Finance, 19(1), 121–153. https://doi.org/10.21315/aamjaf2023.19.1.5
5. Asl, M. G., Canarella, G., & Miller, S. M. (2020). Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies. SSRN. https://doi.org/10.2139/ssrn.3660959
6. Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659
7. Baid, C., Baid, D., & Bhuyan, A. K. (2024). Exploring organizational performance: A multidimensional construct. Prabandhan: Indian Journal of Management, 17(4), 29–42. https://doi.org/10.17010/pijom/2024/v17i4/173426
8. Basak, S., & Pavlova, A. (2013). A model of financialization of commodities. SSRN. https://doi.org/10.2139/ssrn.2201600
9. Behera, H. K., & Yadav, I. S. (2019). Explaining India's current account deficit: A time series perspective. Journal of Asian Business and Economic Studies, 26(1), 117–138. https://doi.org/10.1108/jabes-11-2018-0089
10. Bekaert, G., Harvey, C. R., & Ng, A. (2002). Market integration and contagion. SSRN. https://doi.org/10.2139/ssrn.302797
11. Bhat, S., & Tandon, D. (2024). Covid-19 pandemic and volatility spillover effects on Foreign Exchange rates. Indian Journal of Finance, 18(6), 8–25. https://doi.org/10.17010/ijf/2024/v18i6/173966
12. Bouri, E., Das, M., Gupta, R., & Roubaud, D. (2018). Spillovers between bitcoin and other assets during bear and bull markets. Applied Economics, 50(55), 5935–5949. https://doi.org/10.1080/00036846.2018.1488075
13. Bunnag, T. (2024). The importance of gold's effect on investment and predicting the world gold price using the ARIMA and ARIMA-GARCH model. Ekonomikalia Journal of Economics, 2(1), 38–52. https://doi.org/10.60084/eje.v2i1.155
14. Cavallari, M., & Canuto, O. (2013). Monetary policy and macroprudential regulation: Whither emerging markets (Policy Research Working Paper No. 6310). World Bank. Group. https://doi.org/10.1596/1813-9450-6310
15. Chaudhuri, T. D., & Ghosh, I. (2016). Artificial neural network and time series modeling based approach to forecasting the exchange rate in a multivariate framework. arXiv. https://doi.org/10.48550/arxiv.1607.02093
16. Cheema, M. A., Faff, R. W., & Szulczyk, K. R. (2020). The 2008 global financial crisis and COVID-19 pandemic: How safe are the safe haven assets? SSRN. https://doi.org/10.2139/ssrn.3642945
17. Deshpande, P., Srivastava, A., Kulkarni, M., & Lenka, R. (2024). Impact of green transformational leadership on employee green behavior: A PLS-SEM approach. Prabandhan: Indian Journal of Management, 17(2), 24–43. https://doi.org/10.17010/pijom/2024/v17i2/173139
18. Dicle, M. F., & Levendis, J. D. (2017). Hedging market volatility with gold. Quantitative Finance and Economics, 1(3), 253–271. https://doi.org/10.3934/qfe.2017.3.253
19. Dixit, A., Jha, R., Baber, R., & Baber, P. (2024). The impact of artificial intelligence on digital employee engagement. Prabandhan: Indian Journal of Management, 17(9), 24–43. https://doi.org/10.17010/pijom/2024/v17i9/173940
20. Echaust, K., & Just, M. (2022). Is gold still a safe haven for stock markets? New insights through the tail thickness of portfolio return distributions. Research in International Business and Finance, 63, Article ID 101788. https://doi.org/10.1016/j.ribaf.2022.101788
21. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
22. Farid, S., Kayani, G. M., Naeem, M. A., & Shahzad, S. J. (2021). Intraday volatility transmission among precious metals, energy and stocks during the COVID-19 pandemic. Resources Policy, 72, Article ID 102101. https://doi.org/10.1016/j.resourpol.2021.102101
23. Forbes, K., & Rigobon, R. (1999). No contagion, only interdependence: Measuring stock market co-movements (NBER Working Paper No. 7267). National Bureau of Economic Research. https://doi.org/10.3386/w7267
24. Gencer, G., & Musoğlu, Z. (2014). The safe haven property of gold in Turkish financial markets: An investigation of the global financial crisis. Boğaziçi Journal Review of Social, Economic and Administrative Studies, 28(2), 75–89. https://doi.org/10.21773/boun.28.2.5
25. Hanif, W., & Khan, M. (2017). Hedging effectiveness of commodities in the stock portfolio: Empirical evidence from Pakistan Stock Exchange using multivariate GARCH models. South Asian Journal of Management Sciences, 11(2), 153–175. https://doi.org/10.21621/sajms.2017112.05
26. Harri, A., Nalley, L., & Hudson, D. (2009). The relationship between oil, exchange rates, and commodity prices. Journal of Agricultural and Applied Economics, 41(2), 501–510. https://doi.org/10.1017/s1074070800002959
27. Herrera, F. L., Salgado, R. J., & Ake, S. C. (2015). Volatility dependence structure between the Mexican Stock Exchange and the world capital market. Investigación Económica, 74(293), 69–97. https://doi.org/10.1016/j.inveco.2015.06.001
28. International Monetary Fund, Asia and Pacific Department. (2015). India: 2015 article IV consultation-staff report; Press release; and statement by the executive director for India. IMF Staff Country Reports, 2015(061). https://doi.org/10.5089/9781498341783.002
29. Jain, S., Puri, N., & Rabbani, M. R. (2025). Dynamic interconnectedness and risk transmission in green, clean energy, and sustainable markets. Indian Journal of Finance, 19(12), 29–48. https://doi.org/10.17010/ijf/2025/v19i12/175876
30. Kaminsky, G. L., Reinhart, C. M., & Vegh, C. A. (2005). Chapter 2: The unholy trinity of financial contagion. In M. Dungey, & D. N. Tambakis (eds.), Identifying international financial contagion: Progress and challenges (pp. 34–59). Oxford Academic. https://doi.org/10.1093/oso/9780195187182.003.0002
31. Kang, S. H., & Yoon, S.-M. (2013). Information transmission of volatility between WTI and brent crude oil markets. Environmental and Resource Economics Review, 22(4), 671–689. https://doi.org/10.15266/kerea.2013.22.4.671
32. Khilar, R. P., & Singh, S. (2025). Intent to invest in cryptocurrency amid uncertainty: A theory of planned behavior approach. Indian Journal of Finance, 19(12), 49–65. https://doi.org/10.17010/ijf/2025/v19i12/175877
33. Kodres, L. E., & Pritsker, M. (2002). A rational expectations model of financial contagion. The Journal of Finance, 57(2), 769–799. https://doi.org/10.1111/1540-6261.00441
34. Kumar, A., Mallick, S. S., Hota, S. L., Vasudevan, A., & Şimşek, S. A. (2024). Green marketing strategies: Enhancing brand image and consumer trust in sustainable development. Prabandhan: Indian Journal of Management, 17(12), 8–27. https://doi.org/10.17010/pijom/2024/v17i12/174054
35. Kumari, N., & Singh, D. (2018). Impact of organizational culture on employee performance. Prabandhan: Indian Journal of Management, 11(6), 53–63. https://doi.org/10.17010/pijom/2018/v11i6/128442
36. Lovreta, L., & López Pascual, J. (2020). Structural breaks in the interaction between bank and sovereign default risk. SERIEs, 11(4), 531–559. https://doi.org/10.1007/s13209-020-00219-z
37. Mahajan, S., & Mahajan, P. (2021). Impact of COVID-19 on stock market and gold returns in India. Eurasian Journal of Business and Economics, 14(27), 29–46. https://doi.org/10.17015/ejbe.2021.027.02
38. Makhija, R., Aggarwal, S., & Singh, Y. (2025). AI-driven hybrid learning for sustainable development: A bibliometric analysis and systematic literature review. Prabandhan: Indian Journal of Management, 18(9), 29–53. https://doi.org/10.17010/pijom/2025/v18i9/174842
39. Marçal, E. F., & Valls Pereira, P. L. (2009). Testing the hypothesis of contagion using multivariate volatility models. SSRN. https://doi.org/10.2139/ssrn.1373152
40. Meyer, D. F., Sanusi, K. A., & Hassan, A. (2018). Analysis of the asymmetric impacts of oil prices on food prices in oil-exporting developing countries. Journal of International Studies, 11(3), 82–94. https://doi.org/10.14254/2071-8330.2018/11-3/7
41. Mohapatra, A. K., Mohanty, D., Sardana, V., & Shrivastava, A. (2024). The ripple effect: Influence of exchange rate volatility on Indian sectoral indices. Indian Journal of Finance, 18(2), 8–24. https://doi.org/10.17010/ijf/2024/v18i2/173519
42. Morales-Zumaquero, A., & Sosvilla-Rivero, S. (2014). Real exchange rate volatility, financial crises and exchange rate regimes. Applied Economics, 46(8), 826–847. https://doi.org/10.1080/00036846.2013.859382
43. Paramati, S. R., Gupta, R., & Roca, E. (2015). Stock market interdependence between Australia and its trading partners: Does trade intensity matter? Applied Economics, 47(49), 5303–5319. https://doi.org/10.1080/00036846.2015.1047088
44. Pinho, C., & Maldonado, I. (2022). Commodity and equity markets: Volatility and return spillovers. Commodities, 1(1), 18–33. https://doi.org/10.3390/commodities1010003
45. Shaeri, K., & Katircioğlu, S. (2018). The nexus between oil prices and stock prices of oil, technology and transportation companies under multiple regime shifts. Economic Research-Ekonomska Istraživanja, 31(1), 681–702. https://doi.org/10.1080/1331677x.2018.1426472
46. Shakil, M. H., Mustapha, I. M., Tasnia, M., & Saiti, B. (2018). Is gold a hedge or a safe haven? An application of ARDL approach. Journal of Economics, Finance and Administrative Science, 23(44), 60–76. https://doi.org/10.1108/jefas-03-2017-0052
47. Sharma, A. M., Sethi, M., Vidyasagar, A., & Ravichandran, S. S. (2024). Blockchain in the lens of populism: A short communication of research evidence. Prabandhan: Indian Journal of Management, 17(2), 62–73. https://doi.org/10.17010/pijom/2024/v17i2/173329
48. Siemaszkiewicz, K. (2021). Safe haven instruments – A comparison between the global financial crisis and the COVID-19 pandemic. Econometrics, 25(4), 1–16. https://doi.org/10.15611/eada.2021.4.01
49. Singh, R. K., Sharma, T., Singh, Y., Kumar, A., & Kumar, S. (2025). Modeling asymmetric volatility connectedness and spillover dynamics in emerging forex markets through the lens of a TVP-VAR framework. Indian Journal of Finance, 19(10), 8–38. https://doi.org/10.17010/ijf/2025/v19i10/175677
50. Sonia, & Narwal, K. P. (2023). Volatility spillover in the Indian commodity market: A comparative assessment of various commodity segments. Indian Journal of Finance, 17(2), 54–67. https://doi.org/10.17010/ijf/2023/v17i2/169193
51. Suresh, G., & Loang, O. K. (2024). The rationality conundrum: Exploring herd mentality among individual investors in the Indian stock market. Indian Journal of Finance, 18(6), 26–45. https://doi.org/10.17010/ijf/2024/v18i6/173967
52. Thuy, V., Oanh, T. T., & Ha, N. T. (2024). The roles of gold, US dollar, and bitcoin as safe-haven assets in times of crisis. Cogent Economics & Finance, 12(1), Article ID 2322876. https://doi.org/10.1080/23322039.2024.2322876
53. Yadav, S. (2016). Integration of exchange rate and stock market : Evidence from the Indian stock market. Indian Journal of Finance, 10(10), 56–63. https://doi.org/10.17010/ijf/2016/v10i10/103015
54. Yu-Xin, C., Yun-Lu, G., & Guo-Yang, S. (2021). The gold price and the economic policy uncertainty dynamics relationship: The continuous wavelet analysis. Economic Computation and Economic Cybernetics Studies and Research, 55(1), 105–116. https://doi.org/10.24818/18423264/55.1.21.07