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 no doubts about its attractiveness for investors and it could be useful to analyze the benefits that could arise from diversification portfolio’s strategies.
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.
References
[1] Ridic, Goran & Gleason, Suzanne & Ridic. Comparisons of Health Care Systems in the United States, Germany and Canada. Materia socio-medica 2012; 24.
[2] Filkov, Vladimir & Skiena, Steven & Zhi, Jizu. Analysis Techniques for Microarray Time-Series Data. Journal of computational biology: a journal of computational molecular cell biology 2002; 9: 317-30.
[3] Ernst, Jason & Bar-Joseph, Ziv. STEM: a tool for the analysis of short time series gene expression data. BMC bioinformatics 2006; 7. 191.
[4] Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLOS ONE 2010; 5(9): e12776.
[5] Friston, K. J., Jezzard, P. and Turner, R.. Analysis of functional MRI time‐series. Hum. Brain Mapp 1994; 1: 153-171.
[6] Richman, Joshua & Moorman, Joseph. (2000). Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy. American journal of physiology. Heart and circulatory physiology 2000; 278. H2039-49.
[7] William Checkley, Leonardo D Epstein, Robert H Gilman, Dante Figueroa, Rosa I Cama, Jonathan A Patz, Robert E Black. (2000). Effects of EI Niño and ambient temperature on hospital admissions for diarrheal diseases in Peruvian children. The Lancet 2000; Volume 355, Issue 9202, Pages 442-450.
[8] Guijarro R, Trujillo‐Santos J, Bernal‐Lopez MR, de Miguel‐Díez J, Villalobos A, Salazar C, Fernandez‐Fernandez R, Guijarro‐Contreras A, Gómez‐Huelgas R, Monreal M. Trend and seasonality in hospitalizations for pulmonary embolism: a time‐series analysis. J Thromb Haemost, 2015.
[9] Preti, Antonio & Lentini, Gianluca. Forecast models for suicide: Time-series analysis with data from Italy. Chronobiology International 2016; 33: 1-12.
[10] Hu, Y & Y Guan, M & Zhao, D & Chen, L & G Chen, F & Song, J.. Time-series analysis of association between SO(2), NO(2) pollution and daily emergency room visits in Shijiazhuang. Zhonghua yu fang yi xue za zhi Chinese journal of preventive medicine 2017; 51: 358-361.
[11] Chen, Mei-Ping & Chen, Wen-Yi & Tseng, Tseng-Chan. Co-movements of returns in the healthcare sectors from the US, UK, and Germany stock markets: Evidence from the continuous wavelet analyses. International Review of Economics & Finance, Elsevier 2017; vol. 49(C): pages 484-498.
[12] Chen, Wen-Yi. Co-Movement of Healthcare Financing in OECD Countries: Evidence from Discrete Wavelet Analyses. Romanian journal of economic forecasting 2016; 19: 40-56.
[13] Engle, Robert F.,May. Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. UCSD Economics Discussion Paper 2000; No. 2000-09.
[14] Bollerslev, Tim. Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, MIT Press 1990; vol. 72(3), pages 498-505.
[15] Engle, Robert F., and Kenneth F. Kroner.. Multivariate Simultaneous Generalized Arch. Econometric Theory 1995; vol 11, no. 1 (1995): 122-50.