CRETA Workshop on Advanced Econometrics 17_ Prof. Wolfgang Härdle (12 月 5 日) 開始報名！！
CRETA is honored to invite Professor Wolfgang Härdle from Humboldt-Universität zu Berlin as a visitor on December 05. During his visit, Prof. Härdle will lecture on TEDAS – Tail Event Driven ASset Allocation on CRETA Workshop on Advanced Econometrics 17. The workshop is due to take place on Dec. 5 (Friday) at Nietzsche, GIS NTU Convention Center (集思台大會議中心尼釆廳；台北市羅斯福路四段 85 號 B1). All participants are welcomed!
Please be sure to register your attendance online ( http://www.creta.org.tw/?news_3=172 ) by noon, Dec. 01 (Mon.).
*Date: December 5 (Friday), 15:00 pm – 16:30 pm
*Venue: Nietzsche, GIS NTU Convention Center
集思台大會議中心尼釆廳 (台北市羅斯福路四段 85 號 B1)
*Topic: TEDAS – Tail Event Driven ASset allocation
(當天將開放現場繳交台灣經濟計量學會 2015 年年度會費)
Professor Härdle is currently Ladislaus von Bortkiewicz chair professor of statistics at the School of Business and Economics, Humboldt-Universität zu Berlin and also the director of C.A.S.E. – Center for Applied Statistics & Economics. Professor Härdle’s research interests focus on the dimension reduction techniques, computational statistics and quantitative finance. His research articles have been published in several prestigious journals, such as Journal of the American Statistical Association, Journal of Econometrics, Journal of Financial Econometrics, Journal of Empirical Finance, Journal of Forecasting, Journal of Risk and Insurance and Quantitative Finance.
Portfolio selection and risk management are very actively studied topics in quantitative finance and applied statistics. They are closely related to the dependency structure of portfolio assets or risk factors. The correlation structure across assets and opposite tail movements are essential to the asset allocation problem, since they determine the level of risk in a position. Correlation alone is not informative on the distributional details of the assets. By introducing TEDAS -Tail Event Driven ASset allocation, one studies the dependence between assets at different quantiles. In a hedging exercise, TEDAS uses adaptive Lasso based quantile regression in order to determine an active set of negative non-zero coefficients. Based on these active risk factors, an adjustment for intertemporal correlation is made. Finally, the asset allocation weights are determined via a Cornish-Fisher Value-at-Risk optimization. TEDAS is studied in simulation and a practical utility-based example using hedge fund indices.
December 5 (Friday ) Nietzsche, GIS NTU Convention Center (集思台大會議中心尼釆廳)
16:00-16:30: Tea Time and Discussion
*Lecture in English