Predictability of Volatility Homogenised Financial Time Series
Pawe{\l} Fiedor, Odd Magnus Trondrud

TL;DR
This paper evaluates the effectiveness of volatility homogenisation decomposition in financial time series by using entropy rate to measure predictability across a large dataset from the Warsaw Stock Exchange.
Contribution
It introduces an entropy-based measure to assess the predictability of decomposed financial time series, broadening the scope beyond specific forecasting methods.
Findings
Decomposed series show higher predictability than original series.
Entropy rate effectively quantifies predictability independent of forecasting method.
Large dataset enhances the robustness of the results.
Abstract
Modelling financial time series as a time change of a simpler process has been proposed in various forms over the years. One of such recent approaches is called volatility homogenisation decomposition, and has been designed specifically to aid the forecasting of price changes on financial markets. The authors of this method have attempted to prove the its usefulness by applying a specific forecasting procedure and determining the effectiveness of this procedure on the decomposed time series, as compared with the original time series. This is problematic in at least two ways. First, the choice of the forecasting procedure obviously has an effect on the results, rendering them non-exhaustive. Second, the results obtained were not completely convincing, with some values falling under 50% guessing rate. Additionally, only nine Australian stocks were being investigated, which further limits…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Stock Market Forecasting Methods · Market Dynamics and Volatility
