Time-varying price shock transmission and volatility spillover in foreign exchange,bond, equity,and commodity markets: Evidence from the United States |
| |
| Affiliation: | 1. Energy and Sustainable Development (ESD), Montpellier Business School, Montpellier, France;2. Department of Finance and Accounting, University of Tunis El Manar, Tunis, Tunisia;3. Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman;4. Lebow College of Business, Drexel University, Philadelphia, United States;5. Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad, Pakistan;1. Business School, Hunan University, Changsha 410082, China;2. Center of Finance and Investment Management, Hunan University, Changsha 410082, China;3. School of Business, East China University of Science and Technology, Shanghai 200237, China;4. Center for Polymer Studies and Department of Physics, Boston University, Boston, MA, 02215, USA;1. Department of Quantitative Methods in Economics, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain;2. Department of Economic Theory, Universitat de Barcelona, Barcelona 08034, Spain;3. Complutense Institute for International Studies, Universidad Complutense de Madrid, Madrid 28223, Spain;1. School of Economics and Finance, Massey University, New Zealand;2. Montpellier Business School, France;3. South Ural State University, Russia |
| |
| Abstract: | We study the cross-market financial shocks transmission mechanism on the foreign exchange, equity, bond, and commodity markets in the United States using a time-varying structural vector autoregression model with stochastic volatility (TV-SVAR-SV). The price shocks are absorbed immediately in two or three days, suggesting that all markets are quite efficient. A slight mean reversion and an overshooting behavior are observed. Considering the volatility spillover effect, we highlight two properties of volatility shocks. First, the effects of the volatility shocks are released gradually. Reaching peak volatility spillover levels would require five to ten days. Second, the dynamics of volatility spillovers vary tremendously over time. Different types of markets respond to certain, but not all, extreme events. Our findings suggest the need to conduct investor monitoring of current events instead of using technical analysis based on historical data. Investors should also diversify their portfolios using assets that can respond to different and extreme shocks. |
| |
| Keywords: | Price shock transmission Volatility spillovers Time-varying structural vector autoregression model Stochastic volatility |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
