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Tracking Scaling Effects in Mutual Funds Return Time Series

机译:跟踪共同基金回报时间序列中的规模效应

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摘要

Data coming from many fields of science and technology, ranging from hydrology through network traffic to economics, show long range dependence and self-similarity. These properties result in significant consequences and usually require a redefinition of well grounded assumptions and theories. In the case of financial markets, the classical models which often assume that the dynamics of economic time series is described by the random walk, may incorrectly evaluate the investment risk. Therefore, it is important to understand the dynamics of returns generated by different financial instruments. In this work, we tested fifteen different mutual funds investing in stocks through a stock exchange. We found that the distribution of funds daily returns cannot be described by the random walk. Furthermore, using several different method, we provide empirical evidence, that the daily returns of the analysed funds may exhibit long-range correlations and fractal behaviour.
机译:从水文到网络流量再到经济学,来自科学和技术许多领域的数据都显示出长期依赖性和自相似性。这些特性会导致严重的后果,通常需要重新定义有充分根据的假设和理论。在金融市场的情况下,经典模型(通常假设经济时间序列的动态由随机游动描述)可能会错误地评估投资风险。因此,重要的是要了解不同金融工具产生的收益动态。在这项工作中,我们测试了通过证券交易所投资股票的15种不同的共同基金。我们发现,资金每日收益的分布不能用随机游动来描述。此外,使用几种不同的方法,我们提供了经验证据,表明所分析基金的日收益可能表现出长期相关性和分形行为。

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