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Modelling fluctuations of financial time series: from cascade process to stochastic volatility model

机译:金融时间序列波动建模:从级联过程到随机波动率模型

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In this paper, we provide a simple, "generic" interpretation of multifractal scaling laws and multiplicative cascade process paradigms in terms of volatility correlations. We show that in this context 1/f power spectra, as recently observed in reference [23], naturally emerge. We then propose a simple solvable "stochastic volatility" model for return fluctuations. This model is able to reproduce most of recent empirical findings concerning financial time series: no correlation between price variations, long-range volatility correlations and multifractal statistics. Moreover, its extension to a multivariate context, in order to model portfolio behavior, is very natural. Comparisons to real data and other models proposed elsewhere are provided.
机译:在本文中,我们就波动率相关性提供了一种简单的,“通用”的多分形标度定律和乘法级联过程范式的解释。我们显示在这种情况下,如参考文献[23]中最近观察到的,1 / f功率谱自然出现。然后,我们为收益波动提出了一个简单的可解“随机波动”模型。该模型能够重现有关金融时间序列的大多数最新经验发现:价格变化,长期波动率相关性和多重分形统计量之间没有相关性。而且,为了建模投资组合行为,将其扩展到多变量上下文是很自然的。提供了与真实数据和其他地方提出的其他模型的比较。

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