Work detail

On multifractality and predictability of financial time series

Author: Mgr. Michael Heller
Year: 2021 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 141
Awards and prizes:
Link:
Abstract: The aim of this thesis is to examine an empirical relationship between multifractality of financial time series and its returns. We approach the multifractality
of a given time series as a measure of its complexity. Multifractal financial time
series exhibit repeating self-similar patterns. Multifractality could be a good
predictor of stock returns or a factor which can be used in asset pricing. We
expected that capturing the complexity of a given time series by a model, a
positive or a negative risk premia for investing into “more multifractal assets”
could be found. Daily prices of 31 stock indices and daily returns of 10-years
US government bonds were downloaded. All the data were recorded between
2012 and 2021. After estimation the multifractal spectra, applying MF-DFA
method, of all stock indices, we ordered all stock indices from the lowest to
the most multifractal. Then, we constructed a “multifractal portfolio” holding
a long position in the 7 most multifractal and holding a short position in the
7 least multifractal stock indices. Fama-MacBeth regression with market risk
premia and multifractal variable as independent variables was applied. Multifractality in all examined financial time series was found. We also found a very
low negative risk premia for holding “a multifractal portfolio”. These results
suggest that “more multifractal assets” are connected with lower returns and
with lower risks in average. According to our results, an investor seeking a
lower risk and lower return in average should hold a long position in “more
multifractal assets” and hold a short position in “less multifractal assets” with
respect to the market.

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