International market links and volatility transmission

Corradi, Valentina; Distaso, Walter; Fernandes, Marcelo

doi:10.1016/j.jeconom.2012.03.003

Cited by 33 publications

(15 citation statements)

References 68 publications

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“…Koutmos and Booth (1995) found that the transmission of volatility is asymmetric and is more pronounced when news is bad and coming from either the US or UK market. Corradi et al (2012) examined the transmission of volatility between stock markets by testing the conditional independence of their volatility measure by investigating international stock market links using intra-day data from China, Japan, UK and US from January 2000 to December 2005.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Notably, these studies have not dealt with measures of both total and directional volatility spillovers, specifically within the generalized autoregressive framework in which the forecast error variance decompositions are invariant to the variable ordering (Diebold and Yilmaz, 2012). The approach falls within the class of realized measures robust to market structure noise and hence provides a consistent non-parametric estimator of the quadratic variation of the underlying diffusion process (Corradi et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic spillovers between Nigerian, South African and international equity markets

Fowowe

Shuaibu

2016

International Economics

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic spillovers between Nigerian, South African and international equity markets

Fowowe

Shuaibu

2016

International Economics

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“…The daily and weekly data had also been used for volatility transmission effects across equity markets. The respective studies also employed garch type models for conducting the test for volatility spillovers (Ng, 2000;Corradi et al, 2009).…”

Section: Literaturementioning

confidence: 99%

To Examine the Spillover effect between the KSE100 and S&P500 Index

Hasan

Ahmad

Naeem

et al. 2018

Universidad&Empresa

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The volatility spillover is defined as the transmission of instability from market to market. It occurs when the volatility price change in one market causes a lagged impact on volatility price in another market above the local effects of market. In this study the garchmodels are used to examine the possibility of volatility transmission between the KSE100 index (Pakistan) and S&P500 index (usa); in other words, to examine how the volatility in one market mayinfluence the other and vice versa. The egarchmodel is also applied and it was observed that our attempt to analyze symmetry and persistence in the KSE100 index and the S&P500 index volatility proved that there is clear evidence that shocks to the volatitlity of the KSE100 and S&P500 indexes have asymmetric and persistent effects. It is observed from the study that the shocks to the stock returns in one market do not transmit to the other; in other words, it appears that there is no spillover effect between the two stock markets.

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“…A literatura sobre o comportamento de retornos de ativos em mercados financeiros tem explorado tópicos como reversãoà média (Lehmann, 1990, Ferreira & Santa-Clara, 2011, efeito calendário (French, 1980, Doyle & Chen, 2009, heteroscedasticidade condicional (Nelson, 1991, Silvennoinen & Teräsvirta, 2009, estatísticas de razão de variância (Lo & MacKinlay, 1988, Kim, 2009, não-linearidades (Scheinkman & LeBaron, 1989, Corradi et al, 2012, entre outros. Em geral, mercados emergentes tendem a apresentar possibilidades de arbitragem de ganhos maiores do que em mercados desenvolvidos, podendo ser encontradas algumas evidências mais fortes de previsibilidade (Chang et al, 2004).…”

Section: Revisão De Literaturaunclassified

Relações Entre Correlação Serial e Volatilidade: Existe o Efeito LeBaron no Brasil?

Ely

2014

Braz. Rev. Fin.

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This paper examines the relation between serial correlation and volatility of the Ibovespa index returns and extends the empirical evidence of the LeBaron effect for higher orders of serial correlation. We employ an exponential general autoregressive conditional heteroskedastic model to estimate volatility and an automatic variance ratio statistic to calculate serial correlation. The results support some stylized facts from behavioral finance and help us to explain evidences from empirical studies. We show that (i) serial correlation in weekly returns are negative related with volatility, (ii) this negative relation is found in daily returns only if we use first order serial correlation, and (iii) the effect for weekly returns was not intensified by the 2008 crisis, but a positive relation between volatility and serial correlation for daily returns was identified during that time.

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International market links and volatility transmission

Cited by 33 publications

References 68 publications

Dynamic spillovers between Nigerian, South African and international equity markets

Dynamic spillovers between Nigerian, South African and international equity markets

To Examine the Spillover effect between the KSE100 and S&P500 Index

Relações Entre Correlação Serial e Volatilidade: Existe o Efeito LeBaron no Brasil?

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