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首页> 外文会议>Conference on Independent Component Analyses, Wavelets, and Neural Networks Apr 22-25, 2003 Orlando, Florida, USA >Dimension Reduction and Multi-scaling Law through Source Extraction
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Dimension Reduction and Multi-scaling Law through Source Extraction

机译：通过源提取进行降维和多尺度定律

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摘要

Through the empirical analysis of financial return generating processes one may find features that are common to other research fields, such as internet data from network traffic, physiological studies about human heart beat, speech and sleep recorded time series, geophysics signals, just to mention well-known cases of study. In particular, long range dependence, intermittency, heteroscedasticity are clearly appearing, and consequently power laws and multi-scaling behavior result typical signatures of either the spectral or the time correlation diagnostics. We study these features and the dynamics underlying financial volatility, which can respectively detected and inferred from high frequency realizations of stock index returns, and show that they vary according to the resolution levels used for both the analysis and the synthesis of the available information. Discovering whether the volatility dynamics are subject to changes in scaling regimes requires the consideration of a model embedding scale-dependent information packets, thus accounting for possible heterogeneous activity occurring in financial markets. Independent component analysis result to be an important tool for reducing the dimension of the problem and calibrating greedy approximation techniques aimed to learn the structure of the underlying volatility.
机译：通过对财务收益产生过程的实证分析，人们可能会发现其他研究领域共有的特征，例如来自网络流量的互联网数据，有关人的心跳的生理研究，语音和睡眠记录的时间序列，地球物理学信号，研究案例。特别是，明显出现了远程依赖性，间歇性，异方差性，因此，功率定律和多尺度行为会导致频谱或时间相关性诊断的典型特征。我们研究了这些特征和潜在的金融波动性动力学，可以分别从股票指数收益的高频实现中检测和推断出这些特征，并表明它们根据用于分析和综合可用信息的解决方案级别而有所不同。要发现波动率动态是否会随比例尺制度的变化而变化，就需要考虑一种模型，该模型嵌入了比例尺相关的信息包，从而解决了金融市场中可能发生的异构活动。独立的成分分析结果将成为减少问题规模和校准贪婪近似技术的重要工具，以了解潜在波动率的结构。

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