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The goal of the this paper is to investigate the shock spillover characteristics of the Russian stock market during different rounds of sanctions imposed as a reaction to Russia’s alleged role in the Ukrainian crisis. We consider six stock markets, represented by their respective stock indices, namely the US (DJIA), the UK (FTSE), the euro area (Eu...
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... 1 gives daily average returns and standard deviations of rts in each period. The series of daily simple returns in percent are plotted in Figure 2. It appears that the series are "somehow connected", and the goal of the present paper is to make the connectedness dynamics during the time of sanctions explicit. ...
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... The US stock market was a net volatility receiver in 2015 when oil prices historically plunged due to shale booming. Our findings are partially in line with Schmidbauer et al. (2016), who argue that Chinese and Russian stock markets contributed to the US stock market significantly. ...
Given the importance of stock market synchronization for international portfolio diversification, we estimate the degrees of co-movements among US, Chinese and Russian markets. By applying the TVP-VAR approach, we measure total and bivariate synchronization indices utilizing daily data from 1998 to 2021. Our analysis demonstrates that the total connectedness index (TCI) is 26.15% among the three markets. We find that the US market is the highest volatility contributor, whereas the Russian market is the highest receiver. Since stock market synchronization is exposed to geopolitical risk, at the second stage, we apply the Quantile-on-Quantile framework to measure the response of total and bilateral connectedness indices to geopolitical risk (GPR). The findings affirm our proposition that GPR impedes TCI when it has a bullish state and a higher quantile of GPR. The response of bilateral connectedness is negative towards GPR concerning US–China and US–Russian pairs. However, the degree of connectedness between Russian and Chinese stock markets is less responsive to GPR
... Various researchers have studied clusters of representative countries to examine the regional connectedness of their equity markets. These include studies conducted on East Asia ( (Schmidbauer et al. 2016) and a global sample of emerging markets (Yarovaya, Brzeszczyński, and Lau 2016). Whilst these researchers have specifically studied the equity markets, many authors have also examined the interconnectedness between equity markets and crude oil (Awartani and Maghyereh 2013;Husain et al. 2019;Maghyereh, Awartani, and Bouri 2016;Schmidbauer and Roesch 2013;Zhang 2017). ...
This paper investigates the interconnectedness between the GCC region, crude oil prices, and global equity markets of the US, Europe, and China. We use DCC-GARCH models and the Diebold and Yilmaz (2012) approach to examine the dynamic connectedness and the net directional flow of spillovers. Consistent with previous studies, we find that the US and European markets are net global contributors of return and volatility shocks, whilst the Chinese equity markets are gradually becoming influential. Meanwhile, the GCC equity markets have been anet recipient of shocks from oil prices. Our empirical results provide some important insights. Firstly, the net transmission of shocks from oil prices to the GCC markets has been reducing over time. Secondly, the total connectedness nearly doubled in response to the global pandemic. Thirdly, the Chinese stock markets are gradually transforming into net transmitters of spillovers to other global equity markets.
... Researchers have used various approaches to assess the impact of sanctions on Russian financial markets. Comparison of changes in and between financial variables in different periods before and after sanctions were imposed (Schmıdbauer et al. 2016;Tyll et al. 2018), use of a general index including all types of sanctions with different weights (Dreger et al. 2015;Kholodilin and Netšunajev 2019), and the incorporation of separate dummy variables for each sanction while researching the impact of a limited number of sanctions (Stone 2017). The application of the EGARCH model, the use of high-frequency daily data and the incorporation of economic, financial, and corporate sanctions as separate dummy variables in return and variance equations enable us to aptly assess the sanctions' impact on the returns and volatility of essential variables of Russian financial markets. ...
... Dreger et al. (2015), based on cointegrated vector autoregressive (VAR) models and daily data for the period from 1 January 2014 to 31 March 2015, claim that the depreciation of the ruble may be related to the decline of oil prices, while the sanctions affected only the conditional volatility of the exchange rate. Schmıdbauer et al. (2016), using a VAR model in daily returns for six representative stock markets (of the US, the UK, the EU, Japan, China, and Russia) from 3 March 1998 to 6 July 2015, argue that the sanctions reduced the importance of the Russian stock market as a propagator of return shocks but increased its significance as a propagator of volatility shocks in international financial markets. Applying a generalized autoregressive conditional heteroscedasticity (GARCH) model to data for the period between 1 January 2014 and 31 October 2014, Stone (2017) demonstrates that the flow of information on sanctions is associated with a decrease in the returns and an increase in the variance of Russian securities. ...
Russia's international comportment and geostrategic moves, particularly the invasion of Ukraine and the annexation of Crimea in 2014, caused a substantial change in its international economic and political relations. In response to Russia's invasion, the United States of America, the European Union, and their allies imposed a series of sanctions. In this study, by applying an exponential generalized autoregressive conditional heteroscedasticity model to daily logarithmic returns of the ruble exchange rate and the closing price index of the Russian Trading System, we analyze how the returns and volatility of the exchange rate and the stock price index responded to the sanctions and oil price changes. The estimation results show that the sanctions have a significant positive short-term impact on exchange rate returns. Economic sanctions have a significant negative long-term impact on the returns and variance of the exchange rate and a significant positive long-term impact on the returns of the stock price index. Financial sanctions have a positive/negative long-term impact on the returns of the exchange rate/stock price index and a positive long-term impact on the variance of the exchange rate and the stock price index. Corporate sanctions have a positive long-term impact on exchange rate returns.
... To enhance our understanding of Q2, using connectedness tables obtained from the real-financial networks we set up to answer Q1, we compute propagation values as in Schmidbauer et al. (2013); Schmidbauer et al. (2016); Schmidbauer et al. (2017). The approach we follow is simply to compute a certain network centrality measure, a normed left eigenvector, of the network matrices we estimated to answer Q1. ...
... The network structure of the spillover matrix with respect to the propagation of shocks lends itself to a broader perspective, as elaborated in Schmidbauer et al. (2013); Schmidbauer et al. (2016); Schmidbauer et al. (2017). Let C again denote the spillover matrix for month t. ...
... Page 13 of 20 Fig. 7 Net connectedness from the real sector to each financial market more volatility into the network owing to a prospective financial market intervention? As we have outlined in Section 1 and elsewhere, DYCI methodology permits tracing the network consequences of a shock that hit either the real or the financial side of the economy at time t and settle at t + h, see, e.g., Schmidbauer et al. (2016) and Section 2.1. Namely, it focuses on one-time spillovers through utilization of raw connectedness tables. ...
Abstract We study macro-financial linkages and their importance within the Swiss economy from a network perspective. First, we investigate the real-financial connectedness in the Swiss economy, using the KOF economic barometer, obtained from real and financial variables, and, the real activity index (RAI), we distilled from a small set of real variables, as two alternative proxies for the real side. Whereas the KOF-barometer-based analysis shows that both sides transmit sizeable shocks to each other without one dominating the other, the RAI-based analysis shows that in the aggregate, the financial side turns out to be the net shock transmitter to the real sector. In the second part, we focus on the relative importance of financial markets as shock propagators using a network centrality measure. We find that 2008–2009 recession in Switzerland and the Swiss National Bank’s (SNB) exchange rate policy changes in 2011 and 2015 have significantly altered the way the shocks are transmitted across the two sides of the economy. During 2009–2011, stock, bond, and foreign exchange (FX) markets, in descending order, played important roles as shock propagators. Following the SNB’s 2015 policy decision to discontinue the lower bound for the EUR/CHF exchange rate, FX market has become equally important as the stock market but more important than the bond market as a shock propagator.
... Researchers have used various approaches to assess the impact of sanctions on Russian financial markets. Comparison of changes in and between financial variables in different periods before and after sanctions were imposed (Schmıdbauer et al. 2016;Tyll et al. 2018), use of a general index including all types of sanctions with different weights (Dreger et al. 2015;Kholodilin and Netšunajev 2019), and the incorporation of separate dummy variables for each sanction while researching the impact of a limited number of sanctions (Stone 2017). The application of the EGARCH model, the use of high-frequency daily data and the incorporation of economic, financial, and corporate sanctions as separate dummy variables in return and variance equations enable us to aptly assess the sanctions' impact on the returns and volatility of essential variables of Russian financial markets. ...
... Dreger et al. (2015), based on cointegrated vector autoregressive (VAR) models and daily data for the period from 1 January 2014 to 31 March 2015, claim that the depreciation of the ruble may be related to the decline of oil prices, while the sanctions affected only the conditional volatility of the exchange rate. Schmıdbauer et al. (2016), using a VAR model in daily returns for six representative stock markets (of the US, the UK, the EU, Japan, China, and Russia) from 3 March 1998 to 6 July 2015, argue that the sanctions reduced the importance of the Russian stock market as a propagator of return shocks but increased its significance as a propagator of volatility shocks in international financial markets. Applying a generalized autoregressive conditional heteroscedasticity (GARCH) model to data for the period between 1 January 2014 and 31 October 2014, Stone (2017) demonstrates that the flow of information on sanctions is associated with a decrease in the returns and an increase in the variance of Russian securities. ...
Russia’s international comportment and geostrategic moves, particularly the invasion of Ukraine and the annexation of Crimea in 2014, caused a substantial change in its international economic and political relations. In response to Russia’s invasion, the United States of America, the European Union, and their allies imposed a series of sanctions. In this study, by applying an exponential generalized autoregressive conditional heteroscedasticity model to daily logarithmic returns of the ruble exchange rate and the closing price index of the Russian Trading System, we analyze how the returns and volatility of the exchange rate and the stock price index responded to the sanctions and oil price changes. The estimation results show that the sanctions have a significant positive short-term impact on exchange rate returns. Economic sanctions have a significant negative long-term impact on the returns and variance of the exchange rate and a significant positive long-term impact on the returns of the stock price index. Financial sanctions have a positive/negative long-term impact on the returns of the exchange rate/stock price index and a positive long-term impact on the variance of the exchange rate and the stock price index. Corporate sanctions have a positive long-term impact on exchange rate returns.
... It was caused by large-scale Western sanctions against Russia in relation to the Ukraine Crisis in July 2014 and by a fall in oil prices. These external events rendered the Russian stock market and rouble more volatile and consequently less predictable (Obizhaeva, 2016;Schmidbauer, Rösch, Uluceviz, & Erkol, 2016). This recent intense difficulty for the Russian economy poses questions regarding the role of the internal drivers of economic prosperity during 2001-2014, with a particular focus on the contribution of human capital to such prosperity and the importance of skills development for Russian employees. ...
The acquisition and maintenance of human capital are considered key drivers of productivity and economic growth. However, recent literature shows that in the case of Russia, this relationship is not obvious, which raises a question concerning the nature of human capital accumulation, despite the significant expansion of tertiary education in this country. The existing literature, much of it relying on a theory of market imperfections, tends to explain low incidences of training by the lack of employer incentives to invest in the human capital of their employees. This dissertation adds to this view confirming the negative role of ‘bad’ jobs and social origins in obstructing employees from skills development in BRIC-like countries. Skills training in Russia is constrained by stratifying occupational forces comprising jobs with low requirements to skills development, which conserves the working population in generic labour. This reveals the phenomenon of skills polarisation ‘at the bottom’ in a late-industrial country, thus, contributing to the growing critique of the knowledge society theory. For those few workers who occupy ‘good’ jobs, skills training is strongly linked to personal-specific traits, such as qualifications and computer and language skills; and this is common in both Russia and India. However, in contrast to Russia, India is still forming their knowledge society. This is confirmed by the statistically significant impact of socio-demographic origins (e.g. age, household size, marital status, and religion) on the incidence of training, which reveals a crucial role of ascription in human capital acquisition in contemporary India. The present thesis contributes to the growing literature on structural prerequisites for successful advancement and the contradictory development of the BRIC countries.
http://repository.essex.ac.uk/21789
... Based on such models, it can be shown that, on a given day, not all markets in a network are equally important with respect to network volatility creation via return shocks, or equivalently, with respect to information transmission: a shock in a ''central'' market may spark massive repercussions throughout the network, while a shock in an isolated market may go almost unnoticed. This concept is used in Schmidbauer et al. [31] to investigate the impact of sanctions during the Ukrainian crisis on the Russian stock market. In the present study, our focus is not on the assessment of singular events, but rather on investigating periodic patterns of importance in a network of stock markets: Are there cycles in the relative importance of a stock market as news (or shock) propagator? ...
... The present study is thus an effort to investigate frequency aspects with respect to information transmission, focusing on a network consisting of three western stock markets, each represented by a stock index: DJIA (US), FTSE 100 (UK) and Euro Stoxx 50 (proxy for the euro area). To that end, departing from the Diebold-Yilmaz [32][33][34] connectedness framework, and extensions detailed in Schmidbauer et al. [31,35], we undertake the following steps: ...
... • Compute propagation values, which measure the relative importance of an asset market as a news propagator within a network of asset markets (see Schmidbauer et al. [31]). Each market is represented by a stock index, and markets are connected together via a vector autoregressive model, as suggested by Diebold and Yilmaz [32][33][34]. ...
Cycles in the behavior of stock markets have been widely documented. There is an increasing body of literature on whether stock markets anticipate business cycles or its turning points. Several recent studies assert that financial integration impacts positively on business cycle comovements of economies.
We consider three western equity markets, represented by their respective stock indices: DJIA (USA), FTSE 100 (UK), and Euro Stoxx 50 (euro area). Connecting these three markets together via vector autoregressive processes in index returns, we construct “propagation values” to measure and trace, on a daily basis, the relative importance of a market as a volatility creator within the network, where volatility is due to a return shock in a market.
A cross-wavelet analysis reveals the joint frequency structure of pairs of the propagation value series, in particular whether or not two series tend to move in the same direction at a given frequency. Our main findings are: (i) From 2001 onwards, the daily propagation values of markets have been fluctuating much less than before, and high frequencies have become less pronounced; (ii) the European markets are in phase at business cycle frequency, while the US market is not in phase with either European market; (iii) in 2008, the euro area has taken over the leading role. This approach not only provides new insight into the time-dependent interplay of equity markets, but it can also replicate certain findings of traditional business cycle research, and it has the advantage of using only readily available stock market data.
We examine the connectedness in the energy commodities sector and the Russian stock market over the period 2005-2020 using the variance decomposition approach. Our analysis identifies the booms and busts in the correspondence of political and war episodes that are related to spillover effects in the Russian economy, as well as the energy commodities markets. Our findings show that the Russian Oil & Gas and Metals & Mining sectors are net shock contributors of crude oil and have the highest spillovers to other Russian sectors. Furthermore, we disentangle the sources of spillovers that originated from the financial and energy commodity markets and find that a positive change in the energy commodity volatility spillover is associated with an increase in Russian geopolitical uncertainty. Finally, we show that the spread of COVID-19 increases the stock market volatility spillover, whereas it lowers the energy commodity volatility spillover.
