{"id":947,"date":"2016-07-05T10:56:31","date_gmt":"2016-07-05T09:56:31","guid":{"rendered":"http:\/\/mathfinance.sns.it\/?p=947"},"modified":"2016-07-05T10:56:31","modified_gmt":"2016-07-05T09:56:31","slug":"anirban-chakraborti-sectoral-co-movements-and-volatilities-of-indian-stock-market-an-analysis-of-daily-returns-data","status":"publish","type":"post","link":"http:\/\/mathfinance.sns.it\/index.php\/anirban-chakraborti-sectoral-co-movements-and-volatilities-of-indian-stock-market-an-analysis-of-daily-returns-data\/","title":{"rendered":"Anirban Chakraborti, \u201cSectoral co-movements and volatilities of Indian stock market: An analysis of daily returns data\u201d"},"content":{"rendered":"<p style=\"text-align: center;\">Wednesday\u00a0July\u00a06 2016<br \/>\n11:30<br \/>\nScuola Normale Superiore<br \/>\nAula Fermi<\/p>\n<p style=\"text-align: center;\"><strong>Anirban Chakraborti<\/strong><br \/>\nJawaharlal Nehru University, New Delhi,\u00a0India<\/p>\n<p style=\"text-align: center;\"><strong>Abstract<br \/>\n<\/strong><\/p>\n<p style=\"text-align: center;\">First, we review the techniques of decomposing aggregate correlation matrices to study co-movements in financial data. We apply the techniques to daily return time series from the Indian stock market. Secondly, we use the multi-dimensional scaling methods to visualise the dynamic evolution of the stock market. This method helps to differentiate sectors in the market in the form of clusters. The other objective is to detect periods of instability in the market. Finally, our aim is to decompose the aggregate volatility into sectoral components. Such a mapping allows us to study impact of different sectors on the market behaviour and vice versa.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Wednesday\u00a0July\u00a06 2016 11:30 Scuola Normale Superiore Aula Fermi Anirban Chakraborti Jawaharlal Nehru University, New Delhi,\u00a0India Abstract First, we review the techniques of decomposing aggregate correlation matrices to study co-movements in financial data. We apply the techniques to daily return time series from the Indian stock market. Secondly, we use the multi-dimensional scaling methods to visualise [&hellip;]<\/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\/947"}],"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=947"}],"version-history":[{"count":1,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/posts\/947\/revisions"}],"predecessor-version":[{"id":948,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/posts\/947\/revisions\/948"}],"wp:attachment":[{"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/media?parent=947"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/categories?post=947"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/mathfinance.sns.it\/index.php\/wp-json\/wp\/v2\/tags?post=947"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}