Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China)
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China)
- Department of Industrial Engineering, University of Toronto (Canada)
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.
- OSTI ID:
- 22689529
- Journal Information:
- Environmental Research, Vol. 152; Other Information: Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0013-9351
- Country of Publication:
- United States
- Language:
- English
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