Research on Optimal Portfolio Based on Multifractal Features

TL;DR

This paper introduces a novel mean-detrended cross-correlation portfolio model that incorporates multifractal features and nonstationarity to improve optimal portfolio selection in volatile markets.

Contribution

The paper proposes the M-DCCP model, integrating detrended cross-correlation analysis into portfolio optimization, addressing limitations of traditional models in nonstationary markets.

Findings

M-DCCP outperforms traditional mean-variance models in empirical tests.

The model adapts better to market fluctuations and investor preferences.

Improves portfolio performance in Chinese A-share market.

Abstract

Providing optimal portfolio selection for investors has always been one of the hot topics in academia. In view of the traditional portfolio model could not adapt to the actual capital market and can provide erroneous results. This paper innovatively constructs a mean-detrended cross-correlation portfolio model (M-DCCP model), This model is designed to embed detrended cross-correlation between different simultaneously recorded time series in the presence of nonstationary into the reward-risk criterion. We illustrate the model's effectiveness by selected five composite indexes (SSE 50, CSI 300, SSE 500, CSI 1000 and CSI 2000) in China A-share market. The empirical results show that compared with traditional mean-variance portfolio model (M-VP model), the M-DCCP model is more conducive for investors to construct optimal portfolios under the different fluctuation exponent preference and time scales preference, so as to improve portfolio's performance.