2021/09/30 by Simon Rudkin, Wanling Qiu, Rudkin, Simon +4 · 1 citation
Computer Science · Economics, Econometrics and Finance · #FOS: Economics and business #General Finance (q-fin.GN) #Topological and Geometric Data Analysis #q-fin.GN
paper · pdf · doi:10.48550/arxiv.2110.00098
arxiv created 2021/09/30 · openalex publication_date 2021/09/30 · arxiv updated 2021/10/04 · openalex created_date 2022/10/01 · openalex updated_date 2026/08/01
Norms of Persistent Homology introduced in topological data analysis are seen as indicators of system instability, analogous to the changing predictability that is captured in financial market uncertainty indexes. This paper demonstrates norms from the financial markets are significant in explaining financial uncertainty, whilst macroeconomic uncertainty is only explainable by market volatility. Meanwhile, volatility is insignificant in the determination of norms when uncertainty enters the regression. Persistence norms therefore have potential as a further tool in asset pricing, and also as a means of capturing signals from financial time series beyond volatility.