Estimating the time Varying Components of international stock markets' risk
Estimating the time Varying Components of international stock markets' risk
复制标题
估计国际股票市场风险的时间变化成分
DOI：
10.1080/13518479500000013
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
K. Giannopoulos
中科院分区：
文献类型：
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作者：
K. Giannopoulos
本研究采用另一种评估证券风险的方法。许多作者认为，证券回报不是均匀的，而是随时间变化的。他们观察到，大的变化之后往往会有更大的变化，因此，在大的变化之后，波动性必然是可预测的高。这种证券波动现象被称为聚类，对证券定价和风险管理具有重要意义。目前用于捕获聚类效应和预测未来波动的最流行的技术是属于自回归条件异方差（ARCH）模型家族的技术。本文的主要目的是研究这种波动率模型是否不仅可以用来捕捉证券回报总风险的时间变化，还可以用来捕捉其系统和非系统成分。利用每周的本地股市数据，通过二元GARCH-M模型估计了世界指数的时变beta。这里使用的GARCH-M参数化是SIM的动态规范。这个动态规范的假设不能被13个本地投资组合中的11个所拒绝。研究结果表明，系统和非系统的对应物也随着时间的推移而变化。然而，在一些市场，这些风险变化可能会延迟一些时间。这表明，为某些股票市场回报计算的一些低相关系数可能不是由于这些国家之间商业周期的差异，而可能是由于对世界市场发展的不同步反应造成的。这一发现应该对许多投资决策，如投资组合选择，市场时机和风险对冲具有重要意义。
In this study an alternative approach for assessing securities' risk is applied. Various authors have argued that security returns are not homoskedastic but exhibit variation over time. They have observed that large changes tend be followed by more large changes in either direction, and so volatility must be predictably high after large changes. This phenomenon of securities' volatility, referred to as clustering, has important implications for security pricing and risk management. Among the most popular techniques currently used to capture the clustering effect and to forecast future volatilityare those belonging to the family of Autoregressive Conditional Heteroskedastic (ARCH) models. The main aim of this paper is to investigate whether such volatility modelling can be used to capture the time variation not only in the total risk of a security return but also its systematic and unsystematic components. Using weekly local stock market data, the time varying beta with the World Index has been estimated via a bivariate GARCH-M model. The GARCH-M parameterization used here is a dynamic specification of the SIM. The hypothesis that this dynamic specification holds cannot be rejected for 11 out of 13 local portfolios. The results provide evidence that both the systematic and the non-systematic counterparts are also changing over time. However, in some markets those risk changes may take place with some delay. This suggests that some of the low correlation coefficients computed for certain stock market returns may not be due to differences in business cycles among those countries, but may be caused by the non-synchronous response to world market developments. This finding should have important implications in many investment decisions such as portfolio selection, market timing and risk hedging.