Risk measures-based cluster methods for finance

This paper performs an extensive comparison of cluster techniques for financial applications based on risk measures and returns as classification variables. We consider the cluster techniques and risk measures largely used in the literature. For the analysis, we use a database composed of daily returns of the U.S. equity market. As for financial applications, we consider capital determination, portfolio optimization, and asset pricing. We found that the number of clusters varies over the years. The years with the fewest clusters coincide with periods of instability, such as 2008 (Subprime Crisis) and 2015 (slowdown in United States domestic product). Overall, we observe that our data support the superiority of the Fanny and MC approaches. By construction, both techniques are more robust to the distinct probabilistic distribution of data, which is typically the case for financial data. Furthermore, our results highlight the practical utility of considering risk measures and returns as classification variables in financial applications.