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Faculty of Actuarial Science & Insurance Seminar with Georgios Sermpinis (University of Glasgow).
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This study examines the predictability of one thousand five hundred and twelve volatility models on the EUR/USD, GBP/USD and USD/JPY exchange rates, the DJIA and the FTSE 100 stock indices and the XAU/USD gold spot price in US dollar. A discrete false discovery controlling procedure is employed to identify the significant predictable models. Our results indicate significant differences in forecasting conditional variance. The most accurate models vary across the series, periods and measurement scales. Time-varying means, Integrated GARCH (IGARCH) and SV, as well as fat-tailed innovation distributions are the dominant specifications for the outperforming models compared to three benchmarks of ARCH (1), GARCH (1,1), and the volatility pool’s 90th percentile.
Where
Bayes Business School, 106 Bunhill Row
Room 2005
106 Bunhill Row, London EC1Y 8TZ, UK
Speakers
Georgios Sermpinis
Professor of Finance
University of Glasgow
Georgios Sermpinis is Professor of Finance at the Adam Smith Business School of the University of Glasgow. He holds a PhD in the Quantitative Finance and Forecasting from Liverpool John Moores University, a MSc in Banking and Finance from Forecasting from Liverpool John Moores University and a BSc (Hons) in Mathematics from the National and Kapodistrian University of Athens.
His mains interests are in the fields of Forecasting, Quantitative Finance, Machine Learning and Operations Research. He has published more than 30 papers in peer-review journals and his research has been cited more than 500 times. Georgios has offer consultancy for major banks such as Santander and Goldman Sachs. He has also co-author a book in Machine Learning and acts as editor for journals such as the Decision Support Systems and the Information Systems and Operational Research. Georgios has also edit special issues for the Quantitative Finance, the Journal of Forecasting, International Journal of Finance and Economics and Annals of Operations Research.