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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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... goal of this study is to assess the influence of large-scale political and economic events and international sanctions on the importance of the Russian stock market as a news (or shock) propagator within a network of six stock markets, each of which is represented by a stock index, namely the DJIA (US; symbol: dji), FTSE (UK; ftse), Euro Stoxx 50 (euro area; sx5e), Nikkei 225 (Japan; n225), SSE Com- posite (China; ssec), these five being dubbed the "systemic five" economies in IMF (2011b, 2012) spillover reports, and RTS (Russia; rts). The network is defined as a directed weighted graph with the stock indices as nodes and weights given by shock spillovers; see Figure 1 and the explanations below. ...
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... methodological core of our study can be depicted as shown in Figure 1. This is a weighted graph, also known as a network, with nodes representing markets (more specifically, stock market indices) and edges with weights representing direct, or one-time, spillovers of return-to-volatility and volatility-to-volatility shocks between markets; spillovers are updated on a daily basis. ...
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... With n = 5, the decomposition of forecast error variance is acceptably settled. This procedure is then applied for every t, resulting in a sequence of spillover matrices, which are the adjacency matrices for the network shown in Figure 1. ...
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... plot in Figure 10 of residuals in the case of a volatility shock in the Russian market reveals the following: The series of regression residuals is, with a few notable exceptions, close to zero, which shows that (i) there is little difference between the long- term average (1998 through 2015) and the period beginning with 2014, and (ii) propa- gation values with respect to volatility spillovers follow the direct spillovers closely. Particular exceptions are: ...
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... Following Schmidbauer, Rösch, and Uluceviz (2013), Schmidbauer, Rösch, Uluceviz, and Erkol (2016) and Schmidbauer, Rösch, and Uluceviz (2017), we may use the network structure of the connectedness matrix to discuss the issue of shock propagation. Let denote the connectedness matrix on date as before. ...
... If the mentioned relationship is strong, a stronger effect is noticed (Jing et al., 2003;Smeets, 2018). An analysis of the impact of the sanctions on Russia as a result of the illegal annexation of Crimea and Sevastopol in 2014 suggested that the sanctions reduced the significance of the Russian market but increased its significance as a propagator of volatility shocks in international financial markets (Schmıdbauer et al., 2016). Increased volatility in the Russian financial market and reductions in the rates of return (Stone, 2017), as effects of the sanctions, were also noticed. ...
The aim of this paper is to analyze the current impact of the implementation of the European Union sanctions related to the Ukrainian War on the abnormal rates of return on the stock prices of companies listed on the stock exchanges. It was hypothesized that the implementation of the European Union sanctions related to the Ukrainian War is causing the varied abnormal rates of return on the stock prices of companies listed on the stock exchanges, taking into consideration the type of sector and the geographical proximity to the military conflict. The analysis used panel data event studies prepared using the daily rates of return on the stock prices of companies listed on the stock exchanges. Data were collected from the Refinitiv Eikon database. The models included divisions according to the type of sector and the geographical proximity to the military conflict. The results show that the reactions of subsectors varied. The significant impact was in the vicinity of Russia and Ukraine.
... İncelenen konular; hisse senedi, döviz piyasalarının bağlanmışlığından (Diebold ve Yılmaz 2015), ülkelerin reel ve finans kesimleri arasındaki bağlanmışlığa kadar çok çeşitlidir (Uluceviz ve Yılmaz, 2020;2021). Bu çalışmada; avro/USD doları 3 döviz çiftinin yanı sıra gelişmekte olan Avrupa (Orta, Doğu ve Güneydoğu Avrupa) ülkelerinden Avrupa Birliği (AB) üyesi olan Polonya, Çekya, Macaristan, Romanya, Bulgaristan ile AB üyesi olmayan Türkiye ve Rusya'nın para birimleri ile USD arasında oluşturulan döviz çiftlerinin (USD temel para birimi olacak şekilde) oynaklık bağlanmışlığı ve şok yayma kapasitelerini gösteren yayılma değerleri (Schmidbauer v.d., 2013;2016; 2006-2024 yılları arasında günlük frekansta incelenmiştir. 4 İktisat yazınında yalnızca Orta ve Doğu Avrupa ülke para birimleri ile Diebold-Yılmaz yöntemi kullanılan az sayıda benzer çalışma (örneğin Kocenda ve Moravcova, 2019;Albrecht ve Kocenda, 2023;Bubak v.d., 2011) bulunmakla birlikte mevcut çalışmadaki sayıda ülke veya para biriminin seçildiği ve yayılma değerlerinin incelendiği bir çalışmaya rastlanmamıştır. Analiz sonucunda; oluşturulan ağda avro (EUR) en güçlü bağlanmışlık yayıcısı olarak öne çıkmıştır. ...
... Bu bölüm, Diebold-Yılmaz Bağlanmışlık Endeksi yönteminin ve uzantılarının bu çalışmada kullanıldığı biçimiyle kısa bir özetini sunmaktadır (Diebold ve Yılmaz, 2009;2012;2014). Ayrıca, yöntemin geliştirildiği alanlardan birinin çalışmadaki uygulamasına dair kısa bir özet de içermektedir (Schmidbauer vd., 2013;2016;. ...
Bu çalışma, Avro ve gelişmekte olan yedi Doğu, Orta ve Güneydoğu Avrupa ülkesi para birimleri arasındaki oynaklık bağlanmışlığını Diebold-Yılmaz bağlanmışlık endeksi çerçevesinde incelemektedir. 2006-2024 arasında günlük veri kullanılarak yapılan analiz sonucunda avronun diğer yedi para birimine doğru güçlü bir bağlanmışlık kaynağı olduğu bulunmuştur. Gelişen ekonomileri ile paralel olarak Polonya zlotisi ve Çek korunası avroyu takip eden diğer önemli bağlanmışlık kaynaklarındandır. Türkiye ve Rusya, Avrupa Birliği üyesi olmasalar da, büyük yerel şoklardan etkilendikleri dönemlerde Türk lirası ve Rus rublesi kanalıyla bağlanmışlık kaynakları olarak davranır. Macar forinti, Romen leyi ve Bulgar levası görece düşük bağlanmışlık etkilerine sahiptir. İncelenen para birimlerinin şok yayma kapasiteleri de benzer sıralamayı izler. Çek korunası, görece güçlü bir şok yayıcısı olmasının yanı sıra şok yayma değerleri en düşük standart sapma değerine sahip para birimidir.
... This paper focuses on BIST as the primary subject of investigation, analyzing the volatility connectedness among a chosen set of its subindicesspecifically, banks, industrials, and services-utilizing a standard DYCI approach and its extensions as detailed in Schmidbauer et al. (2013Schmidbauer et al. ( , 2016Schmidbauer et al. ( , 2017. Our threevariable model is parsimonious enough, and in line with earlier literature, important subindices are selected so that possible indirect spillover effects between multiple subindices are excluded by design. ...
... As an extension, we analyse propagation values as computed in Schmidbauer et al. (2013Schmidbauer et al. ( , 2016Schmidbauer et al. ( , 2017. Propagation values serve as metrics for assessing the significance of nodes in a network as shock propagators, essentially quantifying the eigenvector centrality of network nodes (For additional perspectives on centrality measures within the network literature, refer to, for instance, Newman, 2010.) ...
... In this section, we provide a brief introduction to DYCI methodology, which was developed in Diebold and Yılmaz (2009, 2012, 2014, and its extension by Schmidbauer et al. (2013Schmidbauer et al. ( , 2016Schmidbauer et al. ( , 2017. To save space, we present only the equations pertinent to our empirical analysis (for a more comprehensive understanding, interested readers are encouraged to consult the referenced papers). ...
This study examines the volatility connectedness among banks, industrials, and services subindices of Borsa Istanbul using the Diebold-Yılmaz connectedness index methodology. The findings indicate that the banks index typically acts as a net receiver of connectedness from industrials and services indices. If the banks index is considered a proxy for the financial side of the Turkish economy while the other two represent the real side, this result aligns with earlier observations on the connectedness between the real and financial sides of economies. Specifically, it suggests that when a proxy for the real side incorporates financial variables, the real side tends to be a net source of connectedness most of the time. As shock propagators, industrials play a dominant role, and the banks index often moves in the opposite direction to the other two sectors. Key Words: Real and Financial Sectors, Financial Connectedness, Volatility, Borsa Istanbul. JEL Classification: C32, E44, G10.
... The global economic crisis is the leading cause of the rise in oil prices, another main economic problem. [21] explained the different equity markets and found the interconnectedness of the US, UK, and EU markets that played a critical role in strengthening their respective currencies and exchange rates, distinguishing them from other economies. These three markets' interconnectedness and coordination have fostered their strong financial positions. ...
The growing trend of interdependence between the international stock markets indicated the amalgamation of risk across borders that plays a significant role in portfolio diversification by selecting different assets from the financial markets and is also helpful for making extensive economic policy for the economies. By applying different methodologies, this study undertakes the volatility analysis of the emerging and OECD economies and analyzes the co-movement pattern between them. Moreover, with that motive, using the wavelet approach, we provide strong evidence of the short and long-run risk transfer over different time domains from Malaysia to its trading partners. Our findings show that during the Asian financial crisis (1997–98), Malaysia had short- and long-term relationships with China, Germany, Japan, Singapore, the UK, and Indonesia due to both high and low-frequency domains. Meanwhile, after the Global financial crisis (2008–09), it is being observed that Malaysia has long-term and short-term synchronization with emerging (China, India, Indonesia), OECD (Germany, France, USA, UK, Japan, Singapore) stock markets but Pakistan has the low level of co-movement with Malaysian stock market during the global financial crisis (2008–09). Moreover, it is being seen that Malaysia has short-term at both high and low-frequency co-movement with all the emerging and OECD economies except Japan, Singapore, and Indonesia during the COVID-19 period (2020–21). Japan, Singapore, and Indonesia have long-term synchronization relationships with the Malaysian stock market at high and low frequencies during COVID-19. While in a leading-lagging relationship, Malaysia’s stock market risk has both leading and lagging behavior with its trading partners’ stock market risk in the selected period; this behavior changes based on the different trade and investment flow factors. Moreover, DCC-GARCH findings shows that Malaysian market has both short term and long-term synchronization with trading partners except USA. Conspicuously, the integration pattern seems that the cooperation development between stock markets matters rather than the regional proximity in driving the cointegration. The study findings have significant implications for investors, governments, and policymakers around the globe.
... 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.
