Beyond VaR and CVaR: Topological Risk Measures in Financial Markets

Amit Kumar Jha

Abstract: This paper introduces a novel approach to financial risk assessment by incorporating topological data analysis (TDA), specifically cohomology groups, into the evaluation of equities portfolios. The study aims to go beyond traditional risk measures like Value at Risk (VaR) and Conditional Value at Risk (CVaR), offering a more nuanced understanding of market complexities. Using last one year daily real-world closing price return data for three equities Apple, Microsoft and Google , we developed a new topological riskmeasure, termed Topological VaR Distance (TVaRD). Preliminary results indicate a significant change in the density of the point cloud representing the financial time series during stress conditions, suggesting that TVaRD may offer additional insights into portfolio risk and has the potential to complement existing risk management tools.

Date: 2023-10, Revised 2023-10
New Economics Papers: this item is included in nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2310.14604 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2310.14604

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().