报告人：Ruodu Wang (University of Waterloo)
时间：2020-12-25 14:00-15:00
地点：Online (Tencent Meeting)
Abastract: Goodhart’s law, named after British economist Charles Goodhart, states that “When a measure becomes a target, it ceases to be a good measure”. We discuss this law in the context of financial risk optimization in banking regulation, where the target measure is a regulatory risk measure. The two most important regulatory risk measures, Value-at-Risk (VaR) and Expected Shortfall (ES), have given rise to many debates over the past few years on their comparative advantages, where robustness issues become a crucial consideration. By introducing and analyzing the concept of robustness in optimization, we obtain a “second Goodhart’s law”: All risk measures cease to be good, but some risk measures are much worse than the others. In particular, VaR is seriously problematic in this regard, in sharp contrast to commonly used convex risk measures like ES. This talk is based on joint work with Paul Embrechts (ETH Zurich) and Alexander Schied (Waterloo).
Bio: Dr. Ruodu Wang is University Research Chair and Associate Professor of Actuarial Science at the University of Waterloo in Canada. He received his PhD in Mathematics (2012) from the Georgia Institute of Technology, after completing his Bachelor (2006) and Master’s (2009) degrees at Peking University. He holds editorial positions in leading journals in actuarial science and mathematical economics, including Co-Editor of the European Actuarial Journal, and Co-Editor of ASTIN Bulletin - The Journal of the International Actuarial Association. His scientific work has appeared in academic journals in various other fields, such as Management Science, Operations Research, The Annals of Statistics, Biometrika, The Annals of Applied Probability, and Mathematical Finance. He is an affiliated member of RiskLab at ETH Zurich. He received the Golden Jubilee Research Excellence Award from the Faculty of Mathematics at Waterloo in 2017 and a Discovery Accelerator Supplement Award from the Natural Sciences and Engineering Research Council in 2018.
Tencent Information:
Link: https://meeting.tencent.com/s/WQpfm8NLKSIn
ID: 130 336 215
Password: 201225
