Philip Yu is an Assistant Dean (Taught Postgraduate Programmes), the Faculty of Science and an Associate Professor at the Department of Statistics and Actuarial Science of the University of Hong Kong (HKU). His research interests are broad and includes non-parametric inference, ranking methods, time series analysis, financial data analysis, risk management and statistical trading. He has a substantial volume of work on most of these topics, including two co-authored books and more than 100 papers in conference proceedings and international refereed journals such as Biometrika, Journal of Business and Economic Statistics, Statistics and Computing, Journal of Statistical Software, Expert Systems with Applications, and IEEE Transactions on Neural Networks and Learning Systems. With more than 20 years of research experience in computational statistics, his current research interests lie more in the area of AI and big data analytics.
This research develops a framework adopting conditional covariance modeling combined with various de-noising methods to estimate the portfolio VaR and proves the importance of DCC over the sample rolling method widely used in the industry.