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Nonextensive statistical mechanics distributions and dynamics of financial observables from the nonlinear stochastic differential equations

机译:非线性随机微分方程的非可观统计力学分布和可观察金融动力学

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We present nonlinear stochastic differential equations, generating processes with the q-exponential and q-Gaussian distributions of the observables, i.e. with the long-range power-law autocorrelations and 1/f ~β power spectral density. Similarly, the Tsallis q-distributions may be obtained in the superstatistical framework as a superposition of different local dynamics at different time intervals. In such approach, the average of the stochastic variable is generated by the nonlinear stochastic process, while the local distribution of the signal is exponential or Gaussian one, conditioned by the slow average. Further we analyze relevance of the generalized and adapted equations for modeling the financial processes. We model the inter-trade durations, the trading activity and the normalized return using the superstatistical approaches with the exponential and normal distributions of the local signals driven by the nonlinear stochastic process.
机译:我们提出了非线性随机微分方程,生成了具有可观察值的q指数和q高斯分布的过程,即具有长距离幂律自相关和1 / f〜β功率谱密度。类似地,可以在超统计框架中获得Tsallis q分布,作为在不同时间间隔的不同局部动力学的叠加。在这种方法中,随机变量的平均值是通过非线性随机过程生成的,而信号的局部分布是指数或高斯分布的,其条件是慢平均值。进一步地,我们分析了用于建模财务过程的广义方程和适应方程的相关性。我们使用非线性统计过程驱动的局部信号的指数分布和正态分布,使用超统计方法对交易时间,交易活动和归一化收益进行建模。

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