Multiscale Wavelet Portfolio Optimization under Market Noise: A Comparative Study of Markowitz and Black–Litterman Models

Ivan I. Burin, Alexey Yu. Domnikov

Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russia

Abstract

Amidst the growing algorithmization and high volatility of modern financial markets, classical portfolio optimization models frequently face the problem of critically noisy price series. Traditional covariance matrix calculations conflate short-term market noise with long-term fundamental trends. This leads to instability in optimal asset weights during rebalancing and significantly reduces the efficiency of an investor's capital allocation. This study aims to conduct a comparative statistical evaluation of an investment portfolio by integrating multiscale wavelet analysis into the classical Markowitz and Black-Litterman models. The research hypothesis posits that applying wavelet filtering to the covariance matrix will eliminate high-frequency noise, increase the reliability of risk assessment, and consequently improve the portfolio's return and stability metrics compared to traditional and heuristic methods. Various approaches to constructing the covariance matrix within portfolio optimization models are tested on the Russian and US markets. The methodology proposed in this paper for constructing the covariance matrix is based on the Discrete Wavelet Transform (DWT). This method allows asset returns to be decomposed into time-frequency components, successfully excluding high-frequency noise from risk calculations. Numerical experiments demonstrate that portfolios utilizing wavelet covariance at optimal decomposition scales show improvements in key performance indicators, including an increase in the Sharpe and Sortino ratios. The algorithm has a statistically significant positive impact on the overall portfolio return at a comparable level of risk when compared to classical models. The theoretical significance lies in expanding the toolkit of modern portfolio theory through digital signal processing methods. The practical significance of the work involves the creation of a robust filtering algorithm that quantitative analysts can apply to develop resilient investment strategies under real-market conditions.

Keywords

portfolio optimization; wavelet analysis; covariance matrix; Daubechies wavelet; wavelet denoising; asset allocation; Markowitz; Black-Litterman.

JEL classification

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About Authors

Ivan Ivanovich Burin

Master's Student, Department of Banking and Investment Management, Institute of Economics and Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID https://orcid.org/0009-0009-5569-5333 e-mail: vano.burin@gmail.com

Alexey Yurievich Domnikov

Doctor of Economics, Professor, Department of Banking and Investment Management, Institute of Economics and Management, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID https://orcid.org/0000-0002-6260-9423 e-mail: a.y.domnikov@urfu.ru

For citation

Burin, I.I., Domnikov, A.Yu. (2026). Multiscale Wavelet Portfolio Optimization under Market Noise: A Comparative Study of Markowitz and Black–Litterman Models. Journal of Applied Economic Research, Vol. 25, No. 3, 1022-1048. https://doi.org/10.15826/vestnik.2026.25.3.034

Article info

Received March 24, 2026; Revised June 18, 2026; Accepted July 9, 2026.

DOI: http://dx.doi.org/10.15826/vestnik.2026.25.3.034

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