Abstract：
:This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
journal_name
Environ Resjournal_title
Environmental researchauthors
Lu F,Qiao H,Wang S,Lai KK,Li Ydoi
10.1016/j.envres.2016.07.015subject
Has Abstractpub_date
2017-01-01 00:00:00pages
351-359eissn
0013-9351issn
1096-0953pii
S0013-9351(16)30298-5journal_volume
152pub_type
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