金融时间序列的表观多重分形

J. Bouchaud, M. Potters, M. Meyer
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引用次数: 146

摘要

摘要:本文提出了一个完全可解的金融时间序列模型,该模型模拟了金融数据中已知的长期波动相关性。虽然我们的模型在构造上是渐近的“单分形”,但由于有限时间尺度上的缓慢交叉现象,它显示出明显的多尺度。我们的研究结果表明,在金融数据中可能很难区分明显的和真实的多重分形行为。我们的模型还为相关随机变量的和导出了一组新的稳定定律。
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Apparent multifractality in financial time series
Abstract:We present a exactly soluble model for financial time series that mimics the long range volatility correlations known to be present in financial data. Although our model is asymptotically `monofractal' by construction, it shows apparent multiscaling as a result of a slow crossover phenomenon on finite time scales. Our results suggest that it might be hard to distinguish apparent and true multifractal behavior in financial data. Our model also leads to a new family of stable laws for sums of correlated random variables.
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