How Modern Portfolio Theory Helps Investors Manage Risk in Volatile Markets

DOI

10.2991/978-94-6239-642-5_23How to use a DOI?

Keywords

Modern Portfolio Theory; Value-at-Risk; Volatile Markets

Abstract

Value-at-Risk (VaR) is a widely used risk management tool in financial markets to quantify potential portfolio losses at a given confidence level. Traditional VaR estimation methods, such as Variance-Covariance, Historical Simulation, and Monte Carlo Simulation, are not efficient when markets exhibit fat tails, volatility clustering, or structural breaks leading to continuous evolution in VaR estimation. This study adopted a mixed-methods design, combining a critical literature review on the transformation in Modern Portfolio Theory (MPT) with quantitative empirical analysis implemented in R. The reviewed empirical studies demonstrate a shift in MPT modelling algorithms toward adaptive ML and hybrid models, which improve VaR estimation, highlighting the growing need to address dynamic market risks in the stock market. Empirically, the study investigates four national equity indices: S&P 500 (United States), FTSE 100 (United Kingdom), Shanghai Composite (China), and Nikkei 225 (Japan). Daily data from January 2022 to October 2025 were analyzed. Risk exposures were assessed using 95% VaR, CVaR, HVaR, and Monte Carlo CVaR. CVaR captured large tail risks of 2.0–3.1%. Monte Carlo CVaR produced far smaller values, ranging from approximately 0.03% to 0.06% losses. FTSE appeared most stable, while Nikkei 225 and S&P 500 were highly vulnerable. Shanghai Composite displayed episodic but severe volatility. Overall, the findings confirm that modern downside risk metrics and adaptive estimation techniques strengthen MPT frameworks, enabling investors to mitigate losses and allocate capital prudently.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yifei Xu
PY  - 2026
DA  - 2026/04/29
TI  - How Modern Portfolio Theory Helps Investors Manage Risk in Volatile Markets
BT  - Proceedings of the 2026 11th International Conference on Financial Innovation and Economic Development (ICFIED 2026)
PB  - Atlantis Press
SP  - 223
EP  - 234
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-642-5_23
DO  - 10.2991/978-94-6239-642-5_23
ID  - Xu2026
ER  -