MIDAS regressions: Further results and new directions
MIDAS regressions: Further results and new directions
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DOI：
10.1080/07474930600972467
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发表时间：
2007-01-01
影响因子：
1.2
通讯作者：
Valkanov, Rossen
中科院分区：
文献类型：
--
作者：
Ghysels, Eric;Sinko, Arthur;Valkanov, Rossen
我们探索混合数据采样（此后MIDAS）回归模型。回归涉及以不同频率采样的时间序列数据。波动率和相关过程是我们的主要重点，尽管回归方法在宏观经济学和金融领域具有更广泛的应用。回归结合了有关估计波动率的最新发展和关于分布式滞后模型的不太关注文献。我们研究各种滞后结构，以分析回归并将其与现有模型相关联。我们还提出了MIDAS框架的几个新扩展。该论文凝结了一个经验部分，我们提供了有关风险回收权衡的新证据和新结果。我们还报告了有关微观结构噪声和波动性预测的经验证据。
We explore mixed data sampling (henceforth MIDAs) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider application's in macroeconomics and finance, among other areas. The regressions combine recent developments regarding estimation of volatility and a not-so-recent literature on distributed lag models. We study various lag structures to parameterize parsimoniously the regressions and relate them to existing models. We also propose several new extensions of the MIDAS framework. The paper condcludes with an empirical section where we provide futher evidence and new results on the risk-return trade-off. We also report empirical evidence on microstructure noise and volatility forecasting.