Functional data analysis for volatility
Functional data analysis for volatility
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DOI：
10.1016/j.jeconom.2011.08.002
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发表时间：
2011-12
影响因子：
6.3
通讯作者：
H. Müller;Rituparna Sen;U. Stadtmüller
中科院分区：
文献类型：
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作者：
H. Müller;Rituparna Sen;U. Stadtmüller
我们介绍了一个功能波动率的过程中建模的波动轨迹在金融市场中的高频观察，并描述功能表示和基于数据的恢复过程中的重复观察。它的渐近性质的研究，作为观察到的交易频率的增加，补充模拟和应用程序的标准普尔500指数的日内波动模式的分析。建议的波动率模型被发现是有用的，以确定反复出现的波动模式，并成功地预测未来的波动率，通过应用功能回归和预测技术。
We introduce a functional volatility process for modeling volatility trajectories for high frequency observations in financial markets and describe functional representations and data-based recovery of the process from repeated observations. A study of its asymptotic properties, as the frequency of observed trades increases, is complemented by simulations and an application to the analysis of intra-day volatility patterns of the S&P 500 index. The proposed volatility model is found to be useful to identify recurring patterns of volatility and for successful prediction of future volatility, through the application of functional regression and prediction techniques.