Volatility spillovers from the Chinese stock market to economic neighbours

Abstract

This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China\u27s increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential effects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA-GARCH, and multivariate VARMA-AGARCH models are used to test for constant conditional correlations and volatility spillover effects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacific Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover effects from China to related markets during the GFC. We undertook some additional analysis for this period featuring an exploration of whether there was any spillover effect in the mean equations as well as in the variance equations. We used a bimean equation to model the conditional mean in the individual markets plus an ARMA model to capture volatility spillovers from China to the five markets considered. This augmented model showed much greater evidence of spillovers. We also suspected that the correlations were not constant and applied a moving window of 120 days of daily observations to explore time-varying conditional and fitted correlations. There was evidence of non-constant correlations and even a period of negative correlations between the US and China at the height of the GFC. This is presumably because the GFC was initially a US phenomenon, before spreading to developed markets around the globe and it was not a Chinese phenomenon

Volatility spillover effect in Western Balkans

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Non-Parametric Multiple Change Point Analysis of the Global Financial Crisis

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How the Quantitative Easing Affect the Spillover Effects between the Metal Market and United States Dollar Index?

“…Second, in regard to the issue of the correlative relationship between two types of markets or commodities, the types of markets or commodities that are used to explore the return and volatility spillover effects can be classified into the following cases. Past literature has focused on the return and volatility spillover effects between several stock markets (see Allen, Amrama, and McAleer 2013;Lee 2013;Singh, Kumar, and Pandey 2010 and so on); between several exchange rate markets (see Antonakakis 2012;Kitamura 2010;McMillan and Speight 2010 and so on); between several bond markets (see Skintzi and Refenes 2006); between the stock market and exchange rate market (see Zhao 2010); between the stock market and bond market (see Dean, Faff, and Loudon 2010); between the money market and interest rate market (see Zaghini and Colarossi 2009) and between the stock market and commodity market (see Mensi, Beljid, Boubaker, and Managi 2013).…”