This paper investigates the relative importance of stock markets in a network consisting of the four BRIC (Brazil, Russia, India, China) markets, plus the USA. Each of these markets is represented by a stock index: Bovespa (Brazil), RTS (Russia), BSE Sentex (India), Shanghai Stock Index Composite (China), and Dow-Jones Industrial Average (USA), constituting the nodes of the network; edge weights are determined, on a daily basis, by return-to-volatility spillovers. The importance of a stock market as a risk spreader in the network can then be assessed by propagation values reflecting the network centrality of each node: the propagation value of a market measures the value of a shock to that market as seed for volatility creation in the network. This methodology has been developed only recently.
Within the BRIC group, Brazil and Russia are mainly raw material suppliers, while India and China are predominantly manufacturing economies. We hypothesize that the surge in crude oil prices beginning in late 2004 and causing liquidity shocks in Brazil and Russia has shifted the relative importance towards Brazil and Russia, and away from India and China. Indeed we find that structural breaks in the difference between propagation values of Brazil and Russia versus India and China confirm this hypothesis. Further structural breaks permit a detailed assessment of their respective relative stock market importance.
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Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference
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View project The goal of our project is to measure stock market connectedness and the contribution of shocks to the creation of volatility across the network of stock markets from an historical perspective.
View project Our goals in this project are:
(1) to develop new MGARCH specifications,
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(3) to apply them to real-world problems.
View project July 2017
This paper investigates the relative importance of stock markets in a network consisting of the four BRIC (Brazil, Russia, India, China) markets, plus the USA. Each of these markets is represented by a stock index: Bovespa (Brazil), RTS (Russia), BSE Sentex (India), Shanghai Stock Index Composite (China), and Dow-Jones Industrial Average (USA), constituting the nodes of the network; edge weights
... [Show full abstract] are determined, on a daily basis, by return-to-volatility spillovers. The importance of a stock market as a risk spreader in the network can then be assessed by propagation values reflecting the network centrality of each node: the propagation value of a market measures the value of a shock to that market as seed for volatility creation in the network. This methodology has been developed only recently. Within the BRIC group, Brazil and Russia are mainly raw material suppliers, while India and China are predominantly manufacturing economies. We hypothesize that the surge in crude oil prices beginning in late 2004 and causing liquidity shocks in Brazil and Russia has
shifted the relative importance towards Brazil and Russia, and away from India and China. Indeed we find that structural breaks in the difference between propagation values of Brazil and Russia versus India and China confirm this hypothesis. Further structural breaks permit a detailed assessment of their respective relative stock market importance. Read more April 2017 · International Journal of Emerging Markets
Purpose
The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times.
Design/methodology/approach
The stock price volatility is partly explained by volatility in crude oil price.
... [Show full abstract] The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.
Findings
For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries.
Originality/value
The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects. Read more June 2017 · Physica A: Statistical Mechanics and its Applications
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
... [Show full abstract] (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. Read more Article
Full-text available
December 2016 · Finance a Uver
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 (Euro Stoxx 50), Japan (Nikkei 225), China (SSE
... [Show full abstract] Composite) and Russia (RTS). Linking these markets together in a network on the basis of vector autoregressive processes, we can measure, among other things: (i) direct daily return and volatility spillovers from RTS to other market indices, (ii) daily propagation values quantifying the relative importance of the Russian stock market as a return or volatility shock propagator, and (iii) the amount of network repercussions after a shock. The last two are methodological innovations in this context. It turns out that distinct spillover patterns exist in different rounds of sanctions. Largescale sanctions, beginning in July 2014, rendered the consequences of shocks from Russia less predictable. While these sanctions reduced the importance of the Russian stock market as a propagator of return shocks, they also increased its importance as a propagator of volatility shocks, thus making the network more vulnerable with respect to volatility shocks from the Russian stock market. This is a form of backlash that the sanctioning economies have been facing. View full-text July 2017
BRICS, the acronym for an association of the states Brazil, Russia, India, China and South Africa, is an invented concept. Nevertheless, the member states have institutionalized, to some extent, their economic and political cooperation and thus gained more influence worldwide as an entity. The main purpose of the present paper is to investigate how the interconnectedness of the BRICS stock
... [Show full abstract] markets has evolved in the wake of intensifying links between the BRICS states. To that end, we construct a network with the stock markets of the five BRICS members (each represented by a stock index) as nodes and daily return-to-volatility spillovers defining edge weights. Daily stock market interconnectedness is then operationalized as the average share of volatility in a node (stock market) originating from the other nodes. As a methodological innovation, we show how to measure the concentration of the power to spread news and create volatility in the network. Using a similar network of stock markets of five systemically important economies (the “Systemic Five”: USA, UK, euro area, China, Japan) as benchmark, we find that the interconnectedness of the BRICS stock markets has increased from 2002 through 2008 and mostly stayed on a high level since then. “Power concentration” has tended to decline among the BRICS stock markets, making them more equal in this respect; this is in contrast to the “Systemic Five”. Read more Last Updated: 05 Jul 2022
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