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分离波动率面板中的系统性与个体动态AI 解读

Realized volatilities observed across several assets show a common secular trend and some idiosyncratic pattern which we accommodate by extending the class of Multiplicative Error Models (MEMs). In our model, the common trend is estimated nonparametrically, while the idiosyncratic dynamics are assumed to follow univariate MEMs. Estimation theory based on seminonparametric methods is developed for this class of models for large cross-sections and large time dimensions. The…

半参数向量乘法误差模型AI 解读

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…

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互补自动建模方法的比较：RETINA 与 PcGetsAI 解读

In Perez-Amaral, Gallo, and White (2003, Oxford Bulletin of Economics and Statstics 65, 821–838), the authors proposed an automatic predictive modeling tool called relevant transformation of the inputs network approach (RETINA). It is designed to embody flexibility (using nonlinear transformations of the predictors of interest), selective search within the range of possible models, control of collinearity, out-of-sample forecasting ability, and computational simplicity. In…