Abstract
In this study, we apply a rolling window approach to wavelet-filtered (denoised) S&P500 returns (2000–2020) to obtain time varying Hurst exponents. We analyse the dynamics of the Hurst exponents by applying statistical tests (e.g., for stationarity, Gaussianity and self-similarity), a recurrence quantification analysis (RQA) and a wavelet multi-resolution analysis (MRA). Moreover, we discuss the implications of Hurst dynamics in terms of market efficiency, long memory, multifractal properties and financial crises predictability. Besides, we display academic literature by applying a bibliometric- and referring citation network analysis, state research streams and critically elaborate on the impact and future prospects.
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