How Does the Russian-Ukrainian War Rock Stock and Commodity Markets? Fresh Insights from Joint Network-Connectedness Analysis

ABSTRACTThe outbreak of the war in Ukraine has had a profound and far-reaching impact on the global economy, with notable repercussions observed in stock markets, and particularly pronounced effects evident in commodities markets. This paper examines the connectedness network among 27 NATO stock markets, Russian stock market and a set of three commodity indices (energy, precious metals, and agricultural commodities) over the period 2017-2023. The empirical strategy consists of time and time-frequency connectedness metrics. The empirical results reveal that the connectedness structure has shifted during the Russian-Ukrainian conflict. Moreover, hit by a series of Western sanctions, Russia’s stock market appears to be the most isolated of the considered markets during the war period. Furthermore, the energy, agricultural and precious metals commodities seem to be efficient hedging instruments for investors in the stock markets of the NATO countries during the war period.KEYWORDS: NATO stock marketsRussian stock marketcommodity marketsRussian-Ukrainian warJEL CLASSIFICATION: C01D53F51Q02 Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1. The choice of the investigated NATO countries was subject to the availability of comprehensive data sets.2. The first case was officially reported to the country office of the World Health Organization in China on December 31, 2019. Hence, we have selected January 1, 2020 as the starting point for the COVID period, aligning with the initial documentation of the outbreak.3. Tian et al. (Citation2023) present a compelling analysis regarding the impacts of the conflict in Ukraine.4. It is not possible to use the methodology of Barunik and Krehlik (Citation2018) because the war period is rather short and also it is not possible to decompose the total effect on different frequency bands corresponding to the short- and long-term.5. The Diebold and Yilmaz (Citation2012) allows to compute the connectedness level while overcoming the inadequacies of potentially order-dependent outcomes due to the Cholesky factorization in the original work by Diebold and Yilmaz (Citation2009).6. We refer interested readers to Jacomy et al. (Citation2014) for more details about the anatomy of ForceAtlas2 algorithm.7. For more details on government sanctions and measures taken by major corporations and organisations around the world against Russia, we refer interested readers to https://graphics.reuters.com/UKRAINE-CRISIS/SANCTIONS/byvrjenzmve/8. To evaluate the robustness of our findings, we performed additional analyses by manipulating the lengths of the rolling windows (150 and 300 days) and forecast horizons (25, 50, and 100 days ahead). Interestingly, we consistently observed that the total time-varying connectedness indices displayed similar temporal patterns, irrespective of the chosen window length or forecast horizon. While these specific results were not included in this article, the authors are prepared to provide them upon reasonable request.9. It should be noted that CND, NRW, WRS and CZC (respectively DAN, RMN, SVN, RUS, GRK and HNG) are broadly net volatility transmitters to (respectively net volatility receivers from) all other commodities, except for very short time periods in which they become net volatility receivers (respectively net volatility transmitters).