Hurst Exponent Dynamics | International Symposium on Forecasting Conference | Oxford University
Channel: VoglData
Date: 2022-08-20

This video contains my live conference talk at the 42nd International Symposium on Forecasting at the Oxford University given at July 11th 2022 about the Hurst exponent dynamics of cascadic wavelet filtered (denoised) S&P500 logarithmic returns (2000-2020) and their implication on market efficiency, long memory, multifractality and the predictability of financial crises. 

Time Steps:
00:00 - 00:15 Intro of Video
00:16 - 00:39 Welcome and Introduction
00:40 - 01:02 Content Presentation
01:03 - 03:34 Initial Situation and Past Research Results
03:35 - 05:03 Research Idea
05:04 - 06:32 Chaos Analysis Framework
06:33 - 07:24 Rolling Window & Bootstrapping Procedure
07:25 - 16:07 Empirical Results
16:08 - 18:07 Implications and Prospects
18:08 - 21:17 Questions & Answers

Content of the Presentation: 
My presentations elaborates on the meaning of Hurst exponents, namely, that instead of long memory, fractal trends are measured instead (contradicting Mandelbrot's conception). Further, the talk encompasses the generation of rolling window (time varying) Hurst exponent series based upon the cascadic level 12 wavelet filtered (denoised) S&P500 logarithmic return series (2000-2020). The Hurst exponent series are then analysed with a generalizable nonlinear analysis framework, which allows the determination of the underlying empirical data generating process. 

The framework includes standard statistical tests such as ADF (for stationarity), KS (for Gaussianity) and other nonlinear test statistics (e.g. BDS test), alongside a blockwise bootstrapping procedure as initial step. Second, a full recurrence quantification analysis (RQA) is conducted and combined with discrete wavelet transformations (DWTs) to elaborate on potential hidden sub-dynamics. Next, continuous wavelet transformations (CWTs) are calculated to elucidate potential frequency containments. Lastly, multifractal analysis via multifractal detrended fluctuation analysis (MFDFA) and power-law coherence tests are conducted. 

Subsequently, forecasting attempts via stochastic processes, neural networks (MLPs, LSTMs) and wavelet neural networks (WNNs) are conducted to forecast the next Hurst exponents, which then are incorporated in a multifractal Brownian motion (MBM) to determine the next S&P500 returns. 

Results and implications are discussed and potential financial crises predictability elaborated on. A major insight is that financial markets are complex systems, potentially chaotic and that the efficient market hypothesis is dead.

Note that the presentation is part of a current research paper, which is under review and can be found on my SSRN account linked in the banner of my channel. I will link the journal publication, once it is formally published.


ISF-Conference:
https://isf.forecasters.org/


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