{"id":4446,"date":"2025-06-09T07:09:39","date_gmt":"2025-06-09T07:09:39","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/06\/09\/2506-05354\/"},"modified":"2025-06-09T07:09:39","modified_gmt":"2025-06-09T07:09:39","slug":"2506-05354","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/06\/09\/2506-05354\/","title":{"rendered":"Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series"},"content":{"rendered":"<p>    Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2506.05354v1 Announce Type: cross<br \/>\nAbstract: Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=sum_{tau<t local log-likelihood with exponentially weakening weights of the old values. in practice such moving estimates can be found by ema average some parameters like absolute central moments updated m_ we will focus here on its applications for alpha-stable distribution which also influences hurst exponent hence used adaptive estimation. application shown financial data as djia time series beside standard estimation evolution center and scale parameter there is estimated allowing to continuously evaluate market stability tails having behavior controlling probability potentially dangerous extreme events.><\/t>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Jarek Duda<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2506.05354\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series arXiv:2506.05354v1 Announce Type: cross Abstract: Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,182,183,112],"tags":[2014,2891,15],"class_list":["post-4446","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-econ-em","category-stat-me","category-stat-ml","tag-estimator","tag-moving","tag-time"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4446"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=4446"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/4446\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=4446"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=4446"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=4446"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}