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Fourier methods for analyzing piecewise constant volatilities

机译:分段常数波动的傅立叶方法

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We develop procedures for testing whether a sequence of independent random variables has constant variance. If this is fulfilled, the modulus of a Fourier-type transformation of the volatility process is identically equal to one. Our approach takes advantage of this property considering a canonical estimator for the modulus under the assumption of piecewise identically distributed zero mean observations. Using block-wise variance estimation, we introduce several test statistics resulting from different weight functions. All of them are given by simple explicit formulae. We prove the consistency of the corresponding tests and compare them to alternative procedures on extensive Monte Carlo experiments. According to the results, our proposals offer fairly high power, particularly in the case of multiple structural breaks. They also allow for an adequate estimation of the change point positions. We apply our procedure to gold mining data and also briefly discuss how it can be modified to test for the stationarity of other distributional parameters.
机译:我们开发了测试独立随机变量序列是否具有恒定方差的程序。如果满足这一要求,则波动率过程的傅立叶型变换的模量等于1。我们的方法利用这种性质,考虑了在分段相同分布的零均值观测值假设下模量的典范估计量。使用逐块方差估计,我们介绍了由不同权重函数得出的几个测试统计量。所有这些都由简单的明确公式给出。我们证明了相应测试的一致性,并将它们与广泛的蒙特卡洛实验中的替代程序进行了比较。根据结果​​,我们的建议提供了相当高的力量,尤其是在多个结构性中断的情况下。它们还允许对更改点位置进行适当的估计。我们将程序应用于金矿开采数据,还简要讨论了如何修改它以测试其他分布参数的平稳性。

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