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摘要翻译：
我们对高频金融时间序列进行了大小时间尺度分量的小波分解。以FTSE100指数为例，利用Haar基，证明了在期权溢价评估中，小波系数中的大部分($\simeq$99.6%)所定义的小尺度分量可以忽略不计。低通小波滤波提供的大量压缩信息的相关性与以下事实有关，即原始金融时间序列的非高斯统计结构在超过一个交易日的到期时间内基本保持不变。
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英文标题：
《Option Pricing from Wavelet-Filtered Financial Series》
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作者：
V. T. X. de Almeida and L. Moriconi
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最新提交年份：
2012
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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英文摘要：
We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most ($\simeq$ 99.6%) of the wavelet coefficients can be neglected for the purpose of option premium evaluation. The relevance of the hugely compressed information provided by low-pass wavelet-filtering is related to the fact that the non-gaussian statistical structure of the original financial time series is essentially preserved for expiration times which are larger than just one trading day.
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PDF链接：
https://arxiv.org/pdf/1103.3639
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