Work detail

Wavelet portfolio optimization: Investment horizons, stability in time and rebalancing

Author: Mgr. Tomáš Kvasnička
Year: 2015 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 97
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/151162/
Abstract: The main objective of the thesis is to analyse impact of wavelet covariance estimation in the context
of Markowitz mean-variance portfolio selection. We use a rolling window to apply maximum overlap
discrete wavelet transform to daily returns of 28 companies from DJIA 30 index. In each step, we
compute portfolio weights of global minimum variance portfolio and use those weights in the out-ofsample
forecasts of portfolio returns. We let rebalancing period to vary in order to test influence of
long-term and short-term traders. Moreover, we test impact of different wavelet filters including
Haar, D4 and LA8. Results reveal that only portfolios based on the first scale wavelet covariance
produce significantly higher returns than portfolios based on the whole sample covariance. The
disadvantage of those portfolios is higher riskiness of returns represented by higher Value at Risk and
Expected Shortfall, as well as higher instability of portfolio weights represented by shorter period
that is required for portfolio weights to significantly differ. The impact of different wavelet filters is
rather minor. The results suggest that all relevant information about the financial market is
contained in the first wavelet scale and that the dynamics of this scale is more intense than the
dynamics of the whole market.

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