Consistent with previous studies, the ongoing conflict between Russia and Ukraine bears resemblance to the 2014 conflict, which negatively impacted not only the Russian and Ukrainian economies but also affected the entire region, including the European Union. Jin et al. (2022) have highlighted the novel and detrimental impact of the current ongoing conflict on the financial market. In light of this impact, Adekoya et al. (2022) examined the oil’s connectedness with financial assets such as bonds, bitcoin, US Gold, and stocks during the Russia–Ukraine conflict using intraday data and the TVP-VAR model. They found stronger connectedness during the conflict with oil becoming a net transmitter.

Furthermore, Chortane and Pandey (2022) explored the impact of the Russia–Ukraine conflict on the global currency value against the US dollar. Using event and market model estimations, they confirmed a negative influence on the global currency value, with varying effects across regions. The ongoing geopolitical tension not only increases the risk of equity markets but also affects energy product supply and the tourism sector. Pandey and Kumar (2023) examined the impact of the Russia–Ukraine conflict on the global tourism sector using the event study model and found abnormal returns in the EU, Middle East, Pacific, and Africa.

Singh at al. (2022) investigated the effect of the conflict between Russia and Ukraine on investors’ perception of the energy, aerospace and defense, and environment, social, and governance sectors. They found increased attention towards the energy, aerospace, and defense sectors due to their growing sustainability role. Regarding the response of global financial markets to the Russia–Ukraine conflict­, Umar et al. (2022b) depicted that EU equities and Russian bonds are the net transmitters of shocks. Overall, this conflict has significant impact on the regional economy and the stability of the financial and economic system. Various studies have explored the correlation between domestic and regional stock markets in times of crises and conflicts, examining capital markets with other variables such as the Asia currency crisis and foreign exchange markets (Karanasos et al., 2022; Kubiczek and Tuszkiewicz, 2022). Additionally, previous research focused on examining the relationships of Asian stock markets before and during the GFC 2007-08 (Manopimoke et al., 2018).

In addition to the literature mentioned earlier, it is crucial to consider the trade boycott against Russia and its financial ramifications on the Russian economy. Western countries imposed sanctions and recalled multinational corporations operating­ in Russia, leading to a stagnant downgrade of the financial market. Consequently, the EU and other allies sought gradual independence from Russian energy products. Heilmann (2016) found significant negative effects of such regional boycotts, but response heterogeneity exists due to factors such as the lack of alternatives or high import costs for energy products. These boycotts have ultimately impacted the stock market, leading to negative price and return effects. Tosun and Eshraghi (2022) evalua­ted the response of firms remaining in Russia during these sanctions and boycotts, finding­ that investors’ portfolios underperformed leavers and the market benchmark, with investors applying a strong market fine on the companies with operations in Russia.

The impacts of the ongoing situation on stock markets have been widely ­investigated by policymakers, academics, investment institutions, and individuals­. Kollias et al. (2011) analyzed the impact of political risk and war threat on the debt and stock market, highlighting significant contagion effects on the stock and trading­ markets. Similarly, Bloom (2009) studied the exogenous impact of events like the GFC, 9/11 attack, and crude oil price shocks on the EU stock market, considering micro and macroeconomic variables at different uncertainty levels. Ahmad et al. (2013) examined the impact of the GFC 2007-08 on the European capital market, finding risk transmission to emerging market countries. In our study, we expect a similar risk transmission to European countries from Russia due to trading and investment partnerships among the countries. Several other studies have examined the economic situations during crises and their response on the stock market, such as Heiberger (2014) studying the impact of war crises and economic uncertainty on the stock index.

Yousaf et al. (2022b) conducted an analysis of the impact of the Russia–Ukraine conflict on the G20 and selected stock markets using the event study technique. The results show a significant and negative impact of the conflict on the event day and the post-event day. Regional analysis further confirms the negative effect on the EU and Asian regions, with variations observed across different countries. Similarly, Alam et al. (2022) investigated the impact of the Russian invasion crisis on five commodities in BRIC and G7 countries. Applying the TVP-VAR approach, the authors documented a strong connectedness among all commodities and markets. Furthermore, Patel et al. (2023) examined the spillover between green-dirty cryptocurrencies and socially responsible investment during the Ukraine conflict and identified significant variations in the connectedness between the pre- and during-conflict periods.

Based on the existing literature, we observe a common theme of risk as a key factor in financial markets. Shocks in the market transmit associated risks, influencing the volatility and behavior of the stock market. In the context of the Russia–Ukraine tension, the Russian stock market faced heightened risk, leading to a herding effect amid the ongoing crisis. This study predicts that the Russian stock market may experience contagion and collapse if external shocks persist. Therefore, policymakers and financial market supervisors should prioritize risk assessment during crises. Scholars have empirically examined factors causing systemic financial risk. Boungou and Yatié (2022) studied the spillover effect of systemic risk on financial institutions, while Sohail et al. (2021) explored the scale of financial assets’ impact on systemic risk. Acemoglu et al. (2015) found that closely associated financial markets can increase constancy for small shocks but become a significant source of systematic risk beyond a certain threshold.

The financial world has witnessed global outbreaks like the COVID-19 pandemic, impacting the stock market with high-frequency shocks that affect the economy. Researchers have investigated the impact of the COVID-19 outbreak on the stock market. For instance, Abosedra et al. (2021) studied consumer expenditure and sentiment in the US during COVID-19 and found significant changes in consumer behavior with sentiment impact during each sub-period. Arfaoui et al. (2022) evaluated­ Sukuk and stock association in GCC economies during COVID-19 and observed asymmetries in shocks effects and spillover. Corbet et al. (2021) examined­ volatility spillover in Chinese financial markets during COVID-19. Similarly, Pandey and Kumari (2021) analyzed the impact of COVID-19 on 49 stock markets of developed and emerging countries and found a significant impact. Other studies­ also explored COVID-19’s influence on the US, Chinese, and Russian stock markets­, as well as on IT sectors, portfolio diversification, and volatility spillover.

During the crisis, excessive credit expansion was observed, leading to macroeconomic imbalances. Current crisis has several features unique to the Russian stock market, such as the herding effect in stock market, negative externalities in commodity market, and information mismatch regarding the economic policy uncertainty has contributed more uncertainty in the uprising of the systemic financial risks and risk contagion in the economy. Scholars are employing new econometric methods to investigate financial issues, including the network connectivity of different markets. The transmission of information during crises is closely related to increased uncertainty in the stock market. Researchers have studied information transmission and its impact on various markets like commodities, currency, bonds, and stocks. Studies on the network connectedness of major stock markets during global crises, such as the 2008–2009 financial crisis, China–India’s unique strategies, and the Russia–Ukraine tension, have been explored. The complex network estimations and high-dimensional data­set analysis are applied to examine the effects of crises on stock markets. Studies have shown that the effects of global financial crises vary significantly among different stock markets due to event characteristics. Risk assessment during crises is crucial for policymakers at the macro level, considering the strong correlations among financial assets and commodities. However, previous research focused more on pairwise correlation, neglecting the overall interconnectedness network and interaction correlation of financial risk with other markets, including commodity, metal, energy, and agriculture.

The global financial crisis has severely impacted financial markets worldwide, prompting numerous studies to examine the dynamic relationships across different financial markets. Researchers have investigated the dynamic relations between the US and Asia stock markets, as well as the interconnections among bond, gold, and stock markets, which vary over time and respond to market conditions, particularly during crises. Regional crises, such as the Asian currency exchange and European sovereign debt crises, have also caused financial instability­ and collapses of financial institutions. The ongoing conflict between Russia and Ukraine has further exacerbated the situation, leading to significant damage to the regional economy. Scholars have explored systemic risk in European countries, examining the dynamic return and cross-connectedness for financial assets and commodities, and observing volatility spillovers between US treasury bonds and emerging markets during crisis periods. This study contributes to the literature by elucidating the impact of the Russia–Ukraine conflict on the relationship between local stock markets. The empirical findings have important implications for various stakeholders, including market participants, corporations, and individuals interested in investing in Russia’s stock markets during current situation. Given Russia’s significant role as a major exporter of agricultural commodities, such as wheat, and its substantial contribution to global exports in conjunction with Ukraine, it is evident that conflicts and wars have detrimental effects on the economy’s health and development process, especially in the stock market.

Previous studies utilized the event study estimation approach to conduct similar analyses (Umar et al., 2022a). However, the event study approach estimation solely provides the event’s impact during the event window. In this study, we examine the response of the Russian economy to the crisis that arose after the start of conflict with Ukraine. For this purpose, we adopted the Time-Varying Parameter Vector Autoregressive (TVP VAR) model proposed by Antonakakis and Gabauer (2017), which extends the work of Diebold and Yilmaz (2009, 2014). The TVP VAR model offers several advantages over other methodologies. Firstly, it is less susceptible to outliers, ensuring more robust estimations. Secondly, it avoids parameter estimation bias caused by arbitrary window size selection. Additionally, this estimation method does not result in data loss since it employs the Kalman filter method for determining variance and covariance matrices. Lastly, the TVP VAR model introduces a novel approach that enables the investigation of high-frequency data, such as intraday and daily data (Antonakakis et al., 2020). Below, we present the equations that define the TVP VAR model. Let W_t be a vector with (n × 1) elements, representing n sectors. We can express the TVP VAR model as follows:

W_t = X_t W_{t –1} + ε_t where ε_t ~ n (0, P_t), (1)

X_t = X_{t –1} + θ_t where θ_t ~ n (0, Q_t), (2)

where W_{t –1} denotes the lag of the dependent variable, and X_t represents the time-varying n × n element coefficient matrix, where ε_t and θ_t are disturbances explained by the (n × 1) and (n² × n) vectors respectively. On the other hand, P_t and Q_t are (n × n), and (n² × n²) matrices, respectively, representing the time-varying variance and covariance matrices of the disturbance terms ε_t and θ_t.

To facilitate analysis, we use the World representation theorem to transform the TVP VAR model into TVP VMA (Time-Varying Vector Moving Average), enabling the application of generalized forecast error and variance decomposition (hereafter referred to as GFEVD). By doing so, variance decomposition can provide insights into the transformation of forecast errors using a moving average representation.

Furthermore, the H-step-ahead forecast partitions the error variation of a variable into distinct components represented by shocks in the system. This approach is captured by the following equation:

$W_t=\sum _{i=1}^p{X}_{it}{W}_{t-i}+{\epsilon }_t=\sum _{i=0}^{\mathrm{8}}{a}_{jt}+{\epsilon }_{t-j}$. (3)

Using the GFEVD approach, we can develop four different connectedness procedures, i.e., total connectedness TO others, total connectedness from others, net connectedness, and average connectedness (interconnectedness). In the below equations, i and j represent variables, δ^g_ij,t (h) shows spillover impact from variable i to j and vice versa in the case of δ^g_ij,t (h).

$T{o}_{jt};C_{i\to j,t}^g(h)=\sum _{i=1,i\ne j}^N{\delta }_{ij,t}^g(h)$, (4)

${\text{From}}_{jt};C_{i\leftarrow j,t}^g(h)=\sum _{i=1,i\ne j}^N{\delta }_{ji,t}^g(h)$, (5)

$\text{Net;}{\text{To}}_{jt}-{\text{From}}_{jt};=\sum _{i=1,i\ne j}^N{\delta }_{ji,t}^g(h)-\sum _{i=1,i\ne j}^N{\delta }_{ji,t}^g(h)$, (6)

$A{c}_t;=N^{-1}\sum _{j=1}^{N}T{o}_{jt}=N^{-1}\sum _{j=1}^N{\text{From}}_{jt}$. (7)

Similarly, graphic visualization is also provided to the structural connectedness tables where different sectors indices show nodes while the arrows show pairwise connectedness among indices.

One of the main advantages of the quantile model is that it can provide output at different levels (quantile τ) of Y_t conditional on X_t (Furno and Vistocco, 2018; Koenker and Bassett, 1978). It can be written as:

q_τ (Y_t conditional on X_t) = X_t α (τ). (8)

where q_τ shows the τ^th conditional quantile of Y_t having a range between 0 and 1 while X_t shows independent variables, similarly, α (τ) denotes dependency between X_t and τ^th conditional quantile of Y_t, which can be stated as:

$\hat{a}(\tau )=\underset{a(\tau )}{\mathrm{argmin}}\sum _{\tau =1}^T(\tau -1_{[Y_t<X_{t}a(\tau )]})|(Y_t<X_{t}a(\tau )|$. (9)

Thus the p^th order of the n-variable quantile VAR procedure is:

$Y_t=k(\tau )+\sum _{i=1}^p{a}_i(\tau )Y_{t-i}+e_t(\tau )\text{where}t=1,2,3,\dots T$, (10)

were Y_t is the dependent variable; k (τ) denotes the n-vector of constant; ε_t (τ) represents quantile (τ) of the error term; α_i (τ) is the lagged coefficients of the dependent valuable at quantile (τ) having i = 1, 2, ..., p. The coefficients of α ̂(τ) and k (τ) are computed given that the error terms are in line with population quantile constraint, i.e.

q_τ (ε_t (τ)|Y_{t– i}, ..., Y_{t– p}) = 0. (11)

The τ^th conditional quantile of dependent variable Y_t is provided below, which may be computed following equation by equation sequence at every quantile (τ).

$q_{\tau }\left(Y_t\right|Y_{t-i},\dots ,Y_{t-p})=k(\tau )+\sum _{i=1}^p\hat{a}(\tau )Y_{t-i}$. (12)

This study aims to investigate the Russian main and sectorial stock market indices, utilizing high-frequency hourly data collected between September 12, 2021 and April 29, 2022. This time frame was chosen to encompass both the period before the conflict and the period after its outbreak, allowing us to analyze the impact of the conflict on the Russian economy. The intraday hourly stock data, comprising a total of 2786 observations, were sourced from Bloomberg. We meticulously adopted the approach of utilizing the squares of return values as a proxy for volatility by following the seminal work of Hanif et al. (2023).

Table 1 presents the summary statistics of the Russian stock markets, calculated using the squared returns. A brief examination of the descriptive statistics reveals interesting findings. Specifically, MOEXBC exhibits the highest mean value (102.589), closely followed by IMOEX (16.692). Additionally, MOEXBC shows the greatest volatility in systemic risk, as indicated by its standard deviation of 145930.721. On the other hand, RTSTN displays the lowest mean (0.218) and the least volatility in market risk, evident from its standard deviation of 0.218. Furthermore, we conducted the Jarque–Bera test to assess the normality of all ten indices. Results indicate that all series demonstrate excess kurtosis, deviating from the normal distribution. The p-value provided in the table is significant for both the Jarque–Bera test and the measure of kurtosis.

Fig. 1 depicts the volatility of returns for the selected stock indices. In response to the crisis event in February 2022, the returns of the stock market seem to be unaffected as expected.

Fig. 1.

Return volatility of stock indices.

Source: Authors’ calculations.

Furthermore, Fig. 2 illustrates the correlation matrix of the Russian stock markets, demonstrating the unconditional correlation between the main and sectorial indices. The results indicate that most markets positively correlate with RTSCH and each other, suggesting a high degree of interconnectedness and market integration. Notably, the market pair RTSEU-RTSTL exhibits the highest correlation (0.98), followed closely by the pair RTSN-RTSMM (0.96). These findings are particularly intriguing, as they reveal a strong positive correlation between both main and sectorial indices during the sample period of our study. More remarkably, both main and sectorial indices exhibit a weakly negative correlation. Further analysis and interpretation of these correlations will be conducted to gain deeper insights into the dynamics of the Russian stock markets.

Fig. 2.

Pairwise correlation matrix.

Source: Authors’ calculations.

Fig. 3 examines the potential shocks on returns. Despite no evident shocks being observed, further investigation confirms the interconnectedness among the main and sectorial indices during the crisis period. Additionally, the spikes observed before the event could possibly be attributed to other factors such as the impact of the COVID-19 pandemic.

Fig. 3.

Dynamic total connectedness.

Source: Authors’ calculations.

The findings from the VAR return spillover analysis for the selected markets are presented in Tables 2, 3, and 4, respectively. We explore bear, normal, and bull market conditions using the 5th, 50th, and 95th quantiles. Each spillover system in these tables comprises three paths: the unidirectional spillover of each variable, the total spillover both from and to individual series, and the net directional connectedness from a specific series. These tables (2–4) also demonstrate a significant spillover effect among different indices, with substantial transmitting and receiving behaviors observed during bear, normal, and bull market conditions.

The Russia–Ukraine conflict precipitated a sudden increase in the connectedness of financial markets, as indicated by Wang et al. (2022) and the global impact of this ongoing event, as mentioned by Umar et al. (2022b). Specifically, Table 2 focuses on the 5^th quantile, representing bear market events, where the total­ connectedness reaches 83.89%. Notably, (RTSFN) and (RTSCR) emerge as the strongest transmitters of spillover, with values of 92.26% and 91.48%, respectively, followed by (RTSOG) with a value of 90.8%.

Moreover, MOEXBC and IMOEX are the primary recipients of spillover from other indices, closely followed by RTSCH. Concurrently, all the selected sectoral indices exhibit significant effects from the event, as indicated by Boubaker et al., 2022 in the context of global indices.

Table 3 corresponds to the 50^th quantile, representing normal market events, where the total connectedness measures 72.75%. The Oil & Gas index (RTSOG) and Electric Utilities index (RTSEU) are identified as the most substantial transmitters of spillover, with values of 83.74% and 82.84%, respectively, followed by the financial index (RTSFN) with a value of 82.73%. Additionally, we observe that MOEXBC, IMOEX, Chemical’s index (RTSCH) and RTSTL are the primary recipients of spillover from other indices.

Table 4 illustrates the Bull market events (95^th quantile) with a total connectedness of 83.89%. The results reveal that the Electric Utilities index (RTSEU) and Oil & Gas index (RTSOG) are the most significant transmitters of spillover, with values of 92.57% and 90.29%, respectively, followed by RTSTN with a value of 87.73%. Furthermore, the remaining indices in this network system are observed as net recipients.

Fig. 5 presents a network diagram depicting the connectedness among the stock indices. The diagram offers insights into the spillover relationships among the stock indices, where seven indices are identified as transmitters of volatility, while three indices act as receivers of volatility shocks during the crisis period. The figure effectively portrays the intensity and direction of information flow at a lower quantile. The size of the nodes and the thickness of the edges represent the strength of linkages between pairs of indices.

Fig. 5.

The information’s intensity and direction at lower quantile.

Note: The figure shows network connectedness between 10 main & sectorial indices based on 10-step-ahead forecasting horizons, quantile VAR with 200 days rolling window, and Q = 0.05. Source: Authors’ calculations.

Notably, sectors such as oil & gas (RTSOG), electric utilities (RTSEU), metals & mining (RTSMM), financial (RTSFN), transport (RTSTN), telecom (RTSTL), and consumer goods & services (RTSCR) emerge as significant sources of volatility transmission to other indices, signifying their critical role in the economy. Conversely, the MOEXBC, IMOEX, and chemicals (RTSCH) sectors are identified as net recipients of spillover, indicating that they receive higher levels of volatility. Based on these findings, Boungou and Yatié (2022) recommend that investors and portfolio managers should consider favoring indices with potential volatility transmission.

Fig. 6 presents the network connectivity diagram for the median quantile, revealing the Metals & Mining index (RTSMM) and financial index (RTSFN) as bidirectional transmitters and receivers. The thin edges between indices indicate low volatility spillovers among the Russian sectoral indices. Similar results were obtained by Wang et al. (2022) using geopolitical risk and systemic risk for the Russian commodity markets during the crisis. In Fig. 7, the network connection for the high quantile is displayed, indicating a decrease in the number of transmitters and an increase in the number of receivers during the crisis period.

Fig. 6.

Network connection for median quantile and number of transmitters and receivers.

Note: The figure shows network connectedness between 10 main & sectorial indices based on 10-step-ahead forecasting horizons, quantile VAR with 200 days rolling window, and Q = 0.05. Source: Authors’ calculations.

Fig. 7.

Network connection for high quantile and number of transmitters and receivers.

Note: The figure shows network connectedness between 10 main & sectorial indices based on 10-step-ahead forecasting horizons, quantile VAR with 200 days rolling window, and Q = 0.05. Source: Authors’ calculations.