AUTHOR=Veliu Denis , Shkurti Aranit , Martire Antonio Luciano 

TITLE=On transaction costs in minimum-risk portfolios: insights into risk parity and asset allocation

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=Volume 11 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1585187

DOI=10.3389/fams.2025.1585187

ISSN=2297-4687

ABSTRACT=In minimal risk portfolios, costs associated with transactions are essential in calculating the net performance. The transaction costs of maintaining such portfolios are predominantly negative due to the fact that traditional portfolio optimization strategies, which focus solely on risk and return, neglecting transaction costs typically incurred through rebalancing. This research analyzes the impact of costs associated with transactions while constructing minimum risk portfolios centered around risk parity models and provides a way to control those costs. We investigate the performance of portfolios under fixed and flexible costs and include these parameters in the optimization model to achieve more realistic results. Applying real-world data on conventional stock portfolios and highly volatile cryptocurrency markets, we demonstrate the performance of mean–variance optimization (M-V), risk parity with standard deviation (RP-Std), and risk parity with Conditional Value at Risk (RP-CVaR) through empirical data for both stock portfolios and cryptocurrencies. We found that potential transaction costs can cause portfolio returns to change by anywhere between 0.5 to 2% per year depending on how often one trades, and market conditions. By highlighting how crucial it is to incorporate transaction costs into the decision-making process, this study contributes to the expanding literature of research on portfolio optimization. For investors looking to create and manage their portfolios in a way that balances risk, return, and cost effectiveness, our findings offer useful insights. Future studies might investigate adaptive models that dynamically adapt to shifting cost structures and market situations, or they could generalize these findings to other asset classes.