|
|
|
Sort Order |
|
|
|
Items / Page
|
|
|
|
|
|
|
| Srl | Item |
| 1 |
ID:
100261
|
|
|
|
|
| Publication |
2010.
|
| Summary/Abstract |
This paper tests the volatility spillovers between the Shanghai market and the main developed stock markets abroad, namely New York, Tokyo, London, and Frankfurt, as well as between the Shanghai market and the emerging stock markets, namely Hong Kong, and Korea. The tests cover the period of 11 July 2005-12 July 2007 and 1 August 2007-10 July 2009, since the financial crisis started from July 2007. To account for asymmetries in the volatility transmission mechanism, we have used the GARCH and EGARCH model. The main findings are as follows. First, the degree of the Leverage Effect of the Shanghai market changes in the opposite direction from most of the other sample markets after the financial crisis began in July 2007. This change implies that investors in the Shanghai markets began to pay close attention to bad news. Secondly, reciprocal spillovers exist for some market pairs before the crisis, as the Shanghai A-share market has unidirectional influence on the emerging markets. After the onset of the financial crisis, the stock markets in developed countries have bidirectional influences on each other. The financial crisis brings the developed markets closer together than before, but they do not receive volatility spillover from the Shanghai A-share market. The financial crisis also influences the Asian emerging markets so as to decrease the volatility transmission that comes from the Shanghai A-share market. The Chinese mainland market is still not a vital security market in the world, although it has become the third largest one. Thirdly, the risk-precautionary role of the Shanghai B-share market is not as significant as expected.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
ID:
117678
|
|
|
|
|
| Publication |
2013.
|
| Summary/Abstract |
This article examines the return, volatility, upside risk and downside risk spillover effects from crude oil prices and the US$/INR exchange rate to the major Indian industrial sectors using Hong's (2001) approach. We make use of the generalised autoregressive conditional heteroskedasticity (GARCH) class of models based on the generalised error distribution (GED) to estimate extreme upside and downside Value-at-Risk (VaR). Our empirical results provide evidence of a significant return spillover effect from the crude oil market to the energy, FMCG and pharmaceutical (pharma) sectors and from the US$/INR exchange rate to the pharma sector. We also find evidence of significant volatility spillover from crude oil prices to the energy, fast-moving consumer goods (FMCG) and multinational corporation (MNC) sectors and from the US$/INR exchange rate to the energy and pharma sectors. Moreover, we find significant upside risk spillover from crude oil prices and the US$/INR exchange rate to all the industrial sectors (except for the energy sector with respect to the US$/INR exchange rate); significant downside risk spillover from crude oil prices to the energy, FMCG, pharma and MNC sectors and from the US$/INR exchange rate to only the pharma sector. In addition, we observe two-way downside risk spillover between crude oil prices and the US$/INR exchange rate.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|