BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Mathematical Finance - ECPv5.7.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Mathematical Finance
X-ORIGINAL-URL:https://www.math.ttu.edu/mathematicalfinance
X-WR-CALDESC:Events for Mathematical Finance
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20241103T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20240419T140000
DTEND;TZID=America/Chicago:20240419T140000
DTSTAMP:20260604T184734
CREATED:20231115T154042Z
LAST-MODIFIED:20240408T172908Z
UID:1241-1713535200-1713535200@www.math.ttu.edu
SUMMARY:Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach
DESCRIPTION:Speaker: Prof. Abderrahim Taamouti\, Management School\, University of Liverpool \nAbstract: The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns\, and it rules out\, by construction\, systemic risk\, which can negatively affect its out-of-sample performance. In the present work\, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme market scenarios\, we simulate a large number of returns via a Monte Carlo method. This is done by obtaining probabilistic return forecasts through a distributional machine learning approach in a big data setting and then combining them with a fitted copula to generate return scenarios. Based on a large-scale comparative analysis conducted on the US market\, the backtesting results demonstrate the superiority of our proposed portfolio selection approach against several popular benchmark strategies in terms of both profitability and minimizing systemic risk. This outperformance is robust to the inclusion of transaction costs.
URL:https://www.math.ttu.edu/mathematicalfinance/event/portfolio-selection-under-non-gaussianity-and-systemic-risk-a-machine-learning-based-forecasting-approach/
LOCATION:via Zoom
CATEGORIES:Seminars,Spring 2024
ATTACH;FMTTYPE=image/png:https://www.math.ttu.edu/mathematicalfinance/wp-content/uploads/2023/11/taamouti.png
END:VEVENT
END:VCALENDAR