{"id":262,"date":"2019-01-24T09:46:29","date_gmt":"2019-01-24T08:46:29","guid":{"rendered":"https:\/\/www.iemest.eu\/liss\/?p=262"},"modified":"2021-01-13T09:52:40","modified_gmt":"2021-01-13T08:52:40","slug":"diversification-in-health-care-industry-in-us-market-an-empirical-time-series-analysis","status":"publish","type":"post","link":"https:\/\/www.iemest.eu\/life-safety-and-security\/2019\/01\/24\/diversification-in-health-care-industry-in-us-market-an-empirical-time-series-analysis\/","title":{"rendered":"Diversification in Health Care Industry in US market: an empirical time-series analysis"},"content":{"rendered":"\n<p>Alessandra Costa<\/p>\n\n\n\n<p><strong>Abstract<\/strong><\/p>\n\n\n\n<p>The US healthcare\nindustry represents the fastest growing of the US economy: since it accounts\nfor 17.2% of total GDP, there is no doubts about its attractiveness for\ninvestors and it could be useful to analyze the benefits that could arise from\ndiversification portfolio\u2019s strategies.<\/p>\n\n\n\n<p>Thus, by using high frequency time-series analysis, we focus on different indices and founds that are able to measure the stock price performances of healthcare industries and its sub-segments, in the US market. We show that models that take into account dynamic correlations among assets better performs respect to traditional models, and allows investors to minimize the variance of their financial portfolio, thus providing the usefulness of time series methods as management tool for investors.<\/p>\n\n\n\n<p><strong>References<\/strong><\/p>\n\n\n\n<p>[1] Ridic, Goran &amp; Gleason, Suzanne &amp; Ridic.\nComparisons of Health Care Systems in the United States, Germany and Canada. Materia\nsocio-medica 2012; 24. <\/p>\n\n\n\n<p>[2] Filkov, Vladimir &amp; Skiena, Steven &amp; Zhi,\nJizu. Analysis Techniques for Microarray Time-Series Data. Journal of\ncomputational biology: a journal of computational molecular cell biology 2002;\n9: 317-30.<\/p>\n\n\n\n<p>&nbsp;[3] Ernst,\nJason &amp; Bar-Joseph, Ziv. STEM: a tool for the analysis of short time series\ngene expression data. BMC bioinformatics 2006; 7. 191. <\/p>\n\n\n\n<p>[4] Huynh-Thu&nbsp;VA, Irrthum&nbsp;A,\nWehenkel&nbsp;L, Geurts&nbsp;P. Inferring Regulatory Networks from Expression\nData Using Tree-Based Methods. PLOS ONE 2010; 5(9): e12776. <\/p>\n\n\n\n<p>[5] Friston, K. J., Jezzard, P. and Turner, R.. Analysis\nof functional MRI time\u2010series. Hum. Brain Mapp 1994; 1: 153-171. <\/p>\n\n\n\n<p>[6] Richman, Joshua &amp; Moorman, Joseph. (2000).\nPhysiological Time-Series Analysis Using Approximate Entropy and Sample\nEntropy. American journal of physiology. Heart and circulatory physiology 2000;\n278. H2039-49. <\/p>\n\n\n\n<p>[7] William\nCheckley, Leonardo D Epstein, Robert H Gilman, Dante Figueroa, Rosa I Cama,\nJonathan A Patz, Robert E Black. (2000). Effects of EI Ni\u00f1o and ambient\ntemperature on hospital admissions for diarrheal diseases in Peruvian children.\nThe Lancet 2000; Volume 355, Issue 9202, Pages 442-450. <\/p>\n\n\n\n<p>[8] Guijarro\nR, Trujillo\u2010Santos J, Bernal\u2010Lopez MR, de Miguel\u2010D\u00edez J, Villalobos A, Salazar\nC, Fernandez\u2010Fernandez R, Guijarro\u2010Contreras A, G\u00f3mez\u2010Huelgas R, Monreal M.\nTrend and seasonality in hospitalizations for pulmonary embolism: a time\u2010series\nanalysis. J Thromb Haemost, 2015. <\/p>\n\n\n\n<p>[9] Preti,\nAntonio &amp; Lentini, Gianluca. &nbsp;Forecast models for suicide: Time-series\nanalysis with data from Italy. Chronobiology International 2016; 33: 1-12.<\/p>\n\n\n\n<p>[10] Hu, Y &amp; Y Guan, M &amp; Zhao, D &amp; Chen, L\n&amp; G Chen, F &amp; Song, J.. Time-series analysis of association between\nSO(2), NO(2) pollution and daily emergency room visits in Shijiazhuang.\nZhonghua yu fang yi xue za zhi Chinese journal of preventive medicine 2017; 51:\n358-361.<\/p>\n\n\n\n<p>[11] Chen, Mei-Ping &amp; Chen, Wen-Yi &amp; Tseng,\nTseng-Chan. <a href=\"https:\/\/ideas.repec.org\/a\/eee\/reveco\/v49y2017icp484-498.html\">Co-movements of returns in the healthcare sectors from the US, UK, and\nGermany stock markets: Evidence from the continuous wavelet analyses<\/a>. <a href=\"https:\/\/ideas.repec.org\/s\/eee\/reveco.html\">International Review of Economics &amp; Finance<\/a>, Elsevier 2017; vol. 49(C): pages 484-498.<\/p>\n\n\n\n<p>[12] Chen, Wen-Yi. Co-Movement of Healthcare Financing\nin OECD Countries: Evidence from Discrete Wavelet Analyses. Romanian journal of\neconomic forecasting 2016; 19: 40-56.<\/p>\n\n\n\n<p>[13] Engle, Robert F.,May. Dynamic Conditional Correlation: A\nSimple Class of Multivariate GARCH Models. UCSD Economics Discussion Paper\n2000; No. 2000-09. <\/p>\n\n\n\n<p>[14]\nBollerslev, Tim. <a href=\"https:\/\/ideas.repec.org\/a\/tpr\/restat\/v72y1990i3p498-505.html\">Modelling the Coherence in Short-run\nNominal Exchange Rates: A Multivariate Generalized ARCH Model<\/a>.&nbsp;<a href=\"https:\/\/ideas.repec.org\/s\/tpr\/restat.html\">The Review of Economics and\nStatistics<\/a>, MIT Press 1990; vol. 72(3), pages\n498-505.<\/p>\n\n\n\n<p>[15]\nEngle, Robert F., and Kenneth F. Kroner.. Multivariate Simultaneous Generalized\nArch. Econometric Theory 1995; vol 11, no. 1 (1995): 122-50.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Alessandra Costa Abstract The US healthcare industry represents the fastest growing of the US economy: since it accounts for 17.2% of total GDP, there is<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[65,63,66,64],"class_list":["post-262","post","type-post","status-publish","format-standard","hentry","category-minireview","tag-dcc","tag-forecast","tag-healthcare","tag-hfd"],"_links":{"self":[{"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/posts\/262","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/comments?post=262"}],"version-history":[{"count":1,"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/posts\/262\/revisions"}],"predecessor-version":[{"id":263,"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/posts\/262\/revisions\/263"}],"wp:attachment":[{"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/media?parent=262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/categories?post=262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iemest.eu\/life-safety-and-security\/wp-json\/wp\/v2\/tags?post=262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}