英文标题：
《Measuring multiscaling in financial time-series》
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作者：
Riccardo Junior Buonocore, Tomaso Aste, Tiziana Di Matteo
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最新提交年份：
2015
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英文摘要：
  We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analysing the multi/uni-scaling behaviour of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range [2,5]. We discuss the right aggregation horizon to mitigate this bias.
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中文摘要：
我们讨论了金融时间序列中多尺度的起源，并研究了如何最好地量化它。我们的方法是通过分析具有已知特性的合成时间序列的多/单标度行为来分离测量多重分形的不同来源。我们使用合成时间序列的结果来解释真实日志返回时间序列的多重分形度量。主要发现是，收益率的聚合范围会对多重分形的度量产生强烈的偏差效应。当收益分布具有指数在[2,5]范围内的幂律尾时，这种效应可能变得尤为重要。我们讨论了减轻这种偏见的正确聚合范围。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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