Multivariate GARCH Models for the Greater China Stock Markets (original) (raw)
This paper examines the transmission of equity returns and volatility among Asian equity markets and investigates the differences that exist in this regard between the developed and emerging markets. Three developed markets (Hong Kong, Japan and Singapore) and six emerging markets (Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand) are included in the analysis. A multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model is used to identify the source and magnitude of spillovers. The results generally indicate the presence of large and predominantly positive mean and volatility spillovers. Nevertheless, mean spillovers from the developed to the emerging markets are not homogenous across the emerging markets, and own-volatility spillovers are generally higher than cross-volatility spillovers for all markets, but especially for the emerging markets.
Empirical Research on Shanghai Stock Index Based on GARCH Model
International Journal of Latest Research in Engineering and Technology, 2018
The stock market has been affected by many factors, leading to the stock market is unpredictable, which makes stocks have high-risk and high-yield characteristics. Studying the stock market's Shanghai stock Index is the key to reducing risks and increasing profits. This article analyzes the characteristics of the daily yield series by collecting the daily closing price of the Shanghai stock Index from the daily closing price of June 3, 2013 to June 29, 2018, and using Eviews statistical analysis software to analyse the nature of the sequence, the time series model GARCH(1,1) is initially fitted. The empirical results show that the GARCH(1,1) model has a good fitting effect on the time series of the logarithmic price of the Shanghai Stock Index.
A multivariate GARCH analysis of equity returns and volatility in Asian equity markets
2001
This paper examines the transmission of equity returns and volatility among Asian equity markets and investigates the differences that exist in this regard between the developed and emerging markets. Three developed markets (Hong Kong, Japan and Singapore) and six emerging markets (Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand) are included in the analysis. A multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model is used to identify the source and magnitude of spillovers. The results generally indicate the presence of large and predominantly positive mean and volatility spillovers. Nevertheless, mean spillovers from the developed to the emerging markets are not homogenous across the emerging markets, and own-volatility spillovers are generally higher than cross-volatility spillovers for all markets, but especially for the emerging markets.
Flexible multivariate GARCH modeling with an application to international stock markets
Review of Economics and Statistics, 2003
This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1,1) model. Our method is numerically feasible for large-scale problems, produces positive semide nite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the new estimator with a number of existing ones.
Stock returns in emerging markets and the use of GARCH models
Applied Economics Letters, 2011
We use the Hinich portmanteau bicorrelation test to detect for the adequacy of using GARCH (Generalized Autoregressive Conditional Heteroscedasticity) as the data-generating process to model conditional volatility of stock market index rates of return in 13 emerging economies. We find that a GARCH formulation or any of its variants fail to provide an adequate characterization for the underlying process of the 13 emerging stock market indices. We also study whether there exist evidence of ARCH effects, over windows of 200, 400 and 800 observations, using Engle's LM (Lagrange Multiplier) test, and find that there exist long periods of time with no evidence of ARCH effects. The results suggest that policymakers should use caution when using autoregressive models for policy analysis and forecast because the inadequacy of GARCH models has strong implications for the pricing of stock index options, portfolio selection and risk management. Specially, measures of spillover effects and output volatility may not be accurate when using GARCH models to evaluate economic policy.
An Empirical Research on Chinese Stock Market Volatility Based on Garch
International Journal of Latest Research in Engineering and Technology, 2018
Stock market volatility is a major issue in the modern financial field. As China's stock market is immature and volatile, it is particularly important to study the volatility of China's stock market. This paper selects Shanghai Composite Index gains from January 7, 2013 to December 29, 2017, to make an empirical research on stock market volatility based on GARCH model. The results show that there is volatility clustering, durative and leverage effects in stock market. The volatility is largely affected by the past volatility, especially in Chinese stock market. Its influence reaches 0.927.
An Empirical Analysis of Stock Price Risk in Chinese Growth Enterprises Market -A GARCH-VaR Approach
The aim of Growth Enterprises Market (GEM) is to provide financing channels to burgeoning and high-technology companies which cannot be listed in the main board. GEM is a supplement to the main board. As an emerging securities market, GEM shows a unique volatility compared with the main board. The volatility of GEM has connections and differences with the main board market. Studying the price volatility of GEM contributes directly to the healthy growth of GEM and the main board. This paper investigates the risk characteristics of GEM and provides several measures to deal with the risk. In this paper, VaR based on GARCH model is utilized for empirical tests. Therefore, this paper studies the characteristics and the extent of volatility risk of GEM stock price systematically.
2008
We propose a new multivariate factor GARCH model, the GICA-GARCH model , where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one.