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英文文献：Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting-利用错误:一种改进波动性预测的简单方法
英文文献作者：Tim Bollerslev,Andrew J. Patton,Rogier Quaedvlieg
英文文献摘要：
We propose a new family of easy-to-implement realized volatility based forecasting models. The models exploit the asymptotic theory for high-frequency realized volatility estimation to improve the accuracy of the forecasts. By allowing the parameters of the models to vary explicitly with the (estimated) degree of measurement error, the models exhibit stronger persistence, and in turn generate more responsive forecasts, when the measurement error is relatively low. Implementing the new class of models for the S&P500 equity index and the individual constituents of the Dow Jones Industrial Average, we document significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors.
我们提出了一种新的易于实现的基于波动率的预测模型。该模型利用高频已实现波动率估计的渐近理论来提高预测的准确性。通过允许模型的参数随测量误差的(估计的)程度明确变化，模型表现出更强的持久性，并反过来在测量误差相对较低时产生更有响应性的预测。实现模型的新类标准普尔股票指数和道琼斯工业股票平均价格指数的个人组成,我们文档相比,结果预测的准确性显著改善现有的一些最受欢迎的预测模型,隐式地忽略了时态的变化实现波动率测量误差的大小。
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