SEMIPARAMETRIC VECTOR MEM
针对金融中非负值时间序列（如成交量、波动率）的联合动态分析，提出半参数向量乘性误差模型，用广义矩估计避免指定误差分布，模拟显示比逐方程估计更有效，实证表明联合建模能揭示波动率间的完全相互依赖。
SUMMARY Financial time series are often non‐negative‐valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector multiplicative error model (vMEM; the element‐by‐element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d. innovation process) is a suitable strategy. Its parameters can be estimated by generalized method of moments, bypassing the problem of specifying a multivariate distribution for the errors. Simulated results show the gains in efficiency relative to an equation‐by‐equation approach. A vMEM on several measures of volatility justifies a joint approach revealing full interdependence. Copyright © 2012 John Wiley & Sons, Ltd.