A Simple Test for Causality in Volatility
Chia-Lin Chang, Michael McAleer
Available Online January 2017.
- https://doi.org/10.2991/icefs-17.2017.3How to use a DOI?
- Random coefficient stochastic process, Simple test, Granger non-causality, Regularity conditions, Asymptotic properties, Conditional volatility.
- 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 Granger's (1969, 1988) notion of time series predictability, is easy to implement, and has a regularity condition that is not available in the LM test.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chia-Lin Chang AU - Michael McAleer PY - 2017/01 DA - 2017/01 TI - A Simple Test for Causality in Volatility BT - 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017) PB - Atlantis Press SP - 19 EP - 23 SN - 2352-5428 UR - https://doi.org/10.2991/icefs-17.2017.3 DO - https://doi.org/10.2991/icefs-17.2017.3 ID - Chang2017/01 ER -