| 杂志 | Journal of Time Series Analysi
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| 作者 | Muyi Li, Wai Keung Li, Guodong Li |
| 正文 | We propose a new volatility model, which is called the mixture memory GARCH (MM-GARCH) model. The MM-GARCH model has two mixture components, of which one is a short memory GARCH and the other is the long memory FIGARCH. The new model, a special ARCH(∞) process with random coefficients, possesses both the properties of long memory volatility and covariance stationarity. The existence of its stationary solution is discussed. A dynamic mixture of the proposed model is also introduced. Other issues, such as the EM algorithm as a parameter estimation procedure, the observed information matrix which is relevant in calculating the theoretical standard errors, and a model selection criterion are also investigated. Monte Carlo experiments demonstrate our theoretical findings. Empirical application of the MM-GARCH model to the daily S&P 500 index illustrates its capabilities. |
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| 关键词： | long memory in volatility, covariance stationarity, mixture ARCH(∞), EM algorithm. |
