Stock Market Linkages: Evidence From The US, China And India During The Subprime Crisis

2017

Global Business Review

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The study attempts to capture static (long-run) as well as short-run time-varying co-movement among the US, frontier and Brazil, Russia, India and China (BRIC) equity markets (Morgan Stanley Capital International (MSCI) indices) in a multivariate framework after the recent global financial crisis, that is, during the easy money policy regime adopted by the emerged nations. The study employs Johansen cointegration and VAR ( p) ADCC-MVGARCH (1,1) models ranging from August 2010 to August 2015. Apart from this, efficient tests of causality inspired from Hill (2007, Journal of Applied Econometrics, 22(4), 747–765) are also employed to account for dynamic interactions between the co-movement coefficients. The Johansen cointegration model does not support the existence of a stochastic trend among the variables. However, asymmetric dynamic conditional correlation-multivariate generalized autoregressive conditional heteroskedastic model (ADCC-MVGARCH (1,1) model) results indicate time-varying co-movement among the underlying stock markets. The highest level of co-movement has been observed between the US and BRIC equity markets. On the other hand, co-movement between the US and frontier markets is found to be the lowest among others. On an average, 1-dollar long position in the US equity market should be shorted by 0.52 and 0.48 cents in the frontier and BRIC equity markets, respectively, across the sample time period. The efficient causality tests report indirect impact of co-movement between the US-frontier markets on the BRIC-frontier markets’ co-movement. The results critically support construction of a portfolio comprising stocks from the US, frontier and BRIC equity markets considering long-run and short-run co-movement among the variables.

Section: Resultsmentioning

confidence: 99%

“…(say, X, Y and an auxiliary variable Z ), placing a strong reliance on causality chains (Salamaliki & Venetis, 2013;Singh & Kaur, 2015). The tests help in analysing impact of one variable on another as 'direct' or 'indirect' through an auxiliary variable.…”

mentioning

confidence: 99%

Co-movement among US, Frontier and BRIC Equity Markets after the Financial Crisis

2017

Global Business Review

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“…Overall, the results report bi-directional causality relationships between all the co-movement coefficients at the 5 and 10 per cent significance levels. Hill (2007) proposed efficient causality tests and came out with a recursive parametric representation test procedure for the investigation of multi-steps ahead causation in a trivariate VAR framework (say, X, Y and an auxiliary variable Z), placing a strong reliance on causality chains (Salamaliki and Venetis, 2013; Singh and Kaur, 2015). The tests help in analyzing impact of one variable on another as “Direct” or “Indirect” through an auxiliary variable.…”

Section: Empirical Findings and Discussionmentioning

confidence: 99%

A revisit to how linkages fuel dependent economic policy initiatives

2017

IJLMA

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Purpose The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a). Design/methodology/approach Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used. Findings Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system. Originality/value The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

“…Suppose there are two variables, and the possible spillover shock impacts from the first variable to the second and vice versa are 2 0,12 a and 2 0,21 a respectively. The total of the latter can be regarded as the total spillover impact, whereas the average of the same represents as an index value, calculated N step ahead forecasted variances ( [9]). Figure 1 is the graphical presentation of the financial stress index and the volatility index across the years 2009 to 2015.…”

Section: Empirical Frameworkmentioning

confidence: 99%

On the Linkages between India VIX and US Financial Stress Index

2016

TEL

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The present study is the first of its kind accounting for linkages among India VIX and US financial stress index by employing vector autoregression model (VAR), Granger causality test, generalized impulse response functions, variance decomposition analysis (VDA) and Diebold and Yilmaz's (2009) spillover index highlighting the impact of cross market variations on each other. The span of monthly data ranges from 2009 to 2015, particularly after the global financial crisis. The results report a unidirectional causality running from the US financial stress to the Indian equity market implied volatility. When a shock is subject to the US financial stress, then the response of implied volatility in the Indian equity market is positive initially, approaching zero after a few months. On an average, 32% of the variations are accounted by cross market shocks whereas rest of the variations are as a result of own market shocks. The contribution of the US financial stress to forecasted error variances in the Indian equity market implied volatility increases over a period of 10 months to 25% approximately. The results have strong implications for the Indian equity market investors.