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Quasi-maximum likelihood estimation of GARCH models in the presence of missing values

机译:存在缺失值时GARCH模型的拟最大似然估计

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This work presents a new method to deal with missing values in financial time series. Previous works are generally based in state-space models and Kalman filter and few consider ARCH family models. The traditional approach is to bound the data together and perform the estimation without considering the presence of missing values. The existing methods generally consider missing values in the returns. The proposed method considers the presence of missing values in the price of the assets instead of in the returns. The performance of the method in estimating the parameters and the volatilities is evaluated through a Monte Carlo simulation. Value at risk is also considered in the simulation. An empirical application to NASDAQ 100 Index series is presented.
机译:这项工作提出了一种新方法来处理财务时间序列中的缺失值。先前的工作通常基于状态空间模型和卡尔曼滤波器,很少考虑ARCH族模型。传统方法是将数据绑定在一起并执行估计,而不考虑缺失值的存在。现有方法通常考虑返回中的缺失值。所提出的方法考虑了资产价格而不是收益中缺失价值的存在。通过蒙特卡洛模拟评估该方法在估计参数和挥发度方面的性能。模拟中还考虑了风险价值。介绍了对纳斯达克100指数系列的经验应用。

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