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首页> 外文期刊>Information Technology and Management Science >Stock Market Structural Changes Discovering Helical Structure of Volatility Wave Fourier Image/ Akciju tirgu struktūras izmai?u atklā?ana, izpētot volatilitātes vi??u Furjē attēla spirālveida struktūru/ Выявление структурных изменений фондовых рынков, исследующих спиралевидную структуру Фурье образа волн волатильности
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Stock Market Structural Changes Discovering Helical Structure of Volatility Wave Fourier Image/ Akciju tirgu struktūras izmai?u atklā?ana, izpētot volatilitātes vi??u Furjē attēla spirālveida struktūru/ Выявление структурных изменений фондовых рынков, исследующих спиралевидную структуру Фурье образа волн волатильности

机译:股市结构变化发现波动的螺旋结构傅立叶图

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This paper explores an alternative volatility estimation approach discovering the helical structure of Fourier coefficients of volatility wave. Volatility wave is calculated by using wavelet decomposition with consequent logarithmic variance indicator estimation for each decomposed part of the signal and subsequent volatility matrix transform in a specified way. Further, using discrete Fourier transform the Fourier image of obtained volatility wave is analyzed. The Fourier image coefficients of the transformed volatility indicator have a clear helical (spiral) structure that evolves in time. This brings a new understanding of volatility and its evolution process from signal theory (and wavelet theory) perspective. We have found some regularity in the volatility evolution process. The minimum total distance indicator between Fourier coefficients is proposed as a measure of such regularity. This indicator has a nature of volatility lower bound.
机译:本文探索了另一种波动率估计方法,该方法发现了波动波的傅立叶系数的螺旋结构。波动率波是通过使用小波分解并随后对信号的每个分解部分进行对数方差指标估计并随后以指定方式进行波动率矩阵变换来计算的。此外,使用离散傅立叶变换对获得的波动波的傅立叶图像进行分析。转换后的波动指标的傅立叶图像系数具有清晰的螺旋(螺旋)结构,该结构会随时间变化。这从信号理论(和小波理论)的角度带来了对波动率及其演变过程的新认识。我们在波动率的演变过程中发现了一些规律性。提出了傅立叶系数之间的最小总距离指示器作为这种规律性的量度。该指标具有波幅下限的性质。

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