April 15 at 4 pm (CEST).
\nPresenters: Blanka Horvath and Issa Zacharia (King\u2019s College London)
\nTitle: An Optimal Transport Approach to Market Regime Clustering
\nAbstract: The problem of rapid and automated detection of distinct market regimes is a topic of great interest to financial mathematicians and practitioners alike. In this paper, we outline an unsupervised learning algorithm clusters a given time-series – corresponding to an asset or index – into a suitable number of temporal segments (market regimes). This method – the principle of which is inspired by the well-known k-means algorithm – clusters said segments on the space of probability measures with finite p-th moment. On this space, our choice of metric is the p-Wasserstein distance. We compare our Wasserstein-kmeans approach with a more traditional implementation of the kmeans algorithm by generating clusters in Euclidean space via the first N raw moments of each log-return segment instead (moment-kmeans). We compare the two approaches initially on real data and validate the performance of either algorithm by studying the maximum mean discrepancy between, and within, clusters. We show that the Wasserstein-kmeans algorithm vastly outperforms the moment-based approach on both real and synthetic data. In particular, the Wasserstein-kmeans algorithm performs well, even when the distribution associated to each regime is non-Gaussian.<\/p>\n","protected":false},"excerpt":{"rendered":"
April 15 at 4 pm (CEST). Presenters: Blanka Horvath and Issa Zacharia (King\u2019s College London) Title: An Optimal Transport Approach to Market Regime Clustering Abstract: The problem of rapid and automated detection of distinct market regimes is a topic of great interest to financial mathematicians and practitioners alike. In this paper, we outline an unsupervised […]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[13],"tags":[],"_links":{"self":[{"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/posts\/1466"}],"collection":[{"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/comments?post=1466"}],"version-history":[{"count":2,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/posts\/1466\/revisions"}],"predecessor-version":[{"id":1480,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/posts\/1466\/revisions\/1480"}],"wp:attachment":[{"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/media?parent=1466"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/categories?post=1466"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/tags?post=1466"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}