A Simple Test for Causality in Volatility

摘要：

　　An early development in testing for causality (technically,Granger non-causality) in the conditional variance (or volatility) associated with financial returns,was the portmanteau statistic for non-causality in variance of Cheng and Ng (1996).A subsequent development was the Lagrange Multiplier (LM) test of non-causality in the conditional variance by Hafner and Herwartz (2006),who provided simulations results to show that their LM test was more powerful than the portmanteau statistic.While the LM test for causality proposed by Hafner and Herwartz (2006) is an interesting and useful development,it is nonetheless arbitrary.In particular,the specification on which the LM test is based does not rely on an underlying stochastic process,so that the alternative hypothesis is also arbitrary,which can affect the power of the test.The purpose of the paper is to derive a simple test for causality in volatility that provides regularity conditions arising from the underlying stochastic process,namely a random coefficient autoregressive process,and for which the (quasi-) maximum likelihood estimates have valid asymptotic properties under the null hypothesis of non-causality.The simple test is intuitively appealing as it is based on an underlying stochastic process,is sympathetic to Grangers (1969,1988) notion of time series predictability,is easy to implement,and has a regularity condition that is not available in the LM test.

关键词： Random coefficient stochastic process Simple test Granger non-causality Regularity conditions Asymptotic properties Conditional volatility

作者: Chia-Lin Chang Michael McAleer

作者单位: Department of Applied Economics and Department of Finance National Chung Hsing University,Taiwan Department of Quantitative Finance National Tsing Hua University,Taiwan

会议类型: 国际会议

会议名称: ICEFS2017(International Conference on Economics, Finance and Statistics 2017) (2017经济、金融与统计国际会议)

会议地点: 香港

会议语种:英文

页码: 19-23

在线出版日期: 2017-01-14（万方平台首次上网日期，不代表论文的发表时间）