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Enhancing portfolio risk management: a comparative study of parametric, non-parametric, and Monte Carlo methods, with VaR and percentile ranking

Author

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  • Aris Shokri
  • Alexios Kythreotis

Abstract

In this paper, we propose a methodology to effectively manage portfolio risk and allocate capital. By taking a scientific, proactive approach, and understanding the risk associated with each asset before creating a portfolio, it is possible to minimise overall portfolio risk by distributing capital in a diverse and systematic manner. To achieve this, we suggest combining value-at-risk (VaR) with other statistical measures like the percentile rank and the empirical rule. Through this research, we found that this combination can significantly reduce potential portfolio losses when compared with an equally weighted portfolio. The results are based on an analysis of 30,200 daily historical prices between January 2011 and December 2022, using three different methods: historical (non-parametric), variance-covariance (parametric), and Monte Carlo. These findings underscore the importance of proactively managing risks along with allocating capital and highlight the benefits of using a data-driven, systematic approach to portfolio management.

Suggested Citation

  • Aris Shokri & Alexios Kythreotis, 2024. "Enhancing portfolio risk management: a comparative study of parametric, non-parametric, and Monte Carlo methods, with VaR and percentile ranking," International Journal of Business and Emerging Markets, Inderscience Enterprises Ltd, vol. 16(3), pages 411-428.
    • Handle: RePEc:ids:ijbema:v:16:y:2024:i:3:p:411-428
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