Pre-Shrinkage: Improved Volatility Forecasting Using Biased Time-Series

R. Quaedvlieg
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引用次数: 0

Abstract

We propose to model and forecast realized covariances by estimating reduced form models on 'pre-shrunk' time-series. By adapting established linear and non-linear shrinkage techniques to high-frequency volatility estimates we construct an alternative time-series that is biased, but offers an expected Frobenius norm improvement with respect to the latent covariance matrix. Both parameter estimates and forecasts are based on the pre-shrunk series. We document statistically and economically significant forecast improvements based on statistical loss functions with respect to both the standard and shrunk realized covariance measures, for cross-sectional dimensions ranging from one to over a hundred. The forecasts also lead to improved global minimum variance portfolios, which do not inherently favour either series. The pre-shrunk models compare favourably to alternative measurement-error alleviating techniques.
预收缩:利用有偏时间序列改进的波动率预测
我们建议通过估计“预收缩”时间序列上的简化形式模型来建模和预测实现的协方差。通过将已建立的线性和非线性收缩技术应用于高频波动率估计,我们构建了一个有偏差的替代时间序列,但相对于潜在协方差矩阵提供了预期的Frobenius范数改进。参数估计和预测都是基于预收缩序列。我们记录了统计损失函数对标准和缩小的实现协方差度量的统计和经济上显著的预测改进,横截面尺寸范围从1到100以上。预测也导致改进的全球最小方差组合,这本身并不有利于任何一个系列。预收缩模型与其他测量误差缓解技术相比具有优势。
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