NUS Quantitative Finance Joint Seminar Series (Webinar)
NUS Quantitative Finance Joint Seminar Series (Webinar)
This webinar is jointly organised by Risk Management Institute and Centre for Quantitative Finance.
About the Speakers
Mathieu ROSENBAUM is a full professor at École Polytechnique, where he holds the chair “Analytics and Models for Regulation” and is co-head of the quantitative finance (El Karoui) master program. His research mainly focuses on statistical finance problems, regulatory issues and risk management of derivatives. He published more than 60 articles on these subjects in the best international journals. He is notably one of the most renowned experts on the quantitative analysis of market microstructure and high frequency trading.
On this topic, he co-organizes every two years in Paris the conference “Market Microstructure, Confronting Many Viewpoints”. He is also at the origin (with Jim Gatheral and Thibault Jaisson) of the development of rough volatility models. Mathieu Rosenbaum has collaborations with various financial institutions (investment banks, hedge funds, regulators, exchanges…), notably BNP-Paribas since 2004. He also has several editorial activities as he is one of the editors in chief of the journal “Market Microstructure and Liquidity“ and is associate editor for 10 other journals. He received the Europlace Award for Best Young Researcher in Finance in 2014, the European Research Council Grant in 2016 and the Louis Bachelier prize in 2020.
Agostino CAPPONI is an Associate Professor in the Department of Operations Research at Columbia University, and a member of the Data Science Institute. He also serves as a consultant at the U.S. Commodity Futures Trading Commission. Agostino‘s research has been recognized with the 2018 NSF CAREER award, the JP Morgan AI Research Faculty award, and several best paper award prizes. Agostino is editor of the Finance Department at Management Science. He also serves as editor of the Financial Engineering Department at Operations Research Letters and at the IESE Transactions. Agostino also serves on the editorial board of Operations Research, SIAM Journal in Financial Mathematics, Finance and Stochastics, Mathematics and Financial Economics, Stochastic Systems, and other journals of his field. Agostino’s research has been funded by NSF, DARPA, the US Department of Energy, Institute for New Economic Thinking, IBM, the Global Risk Institute, and other private agencies. Agostino serves as the chair of the SIAM Activity Group in Financial Engineering, and as the president of the INFORMS Finance Section.
| 8.00PM – 9.00PM
(Singapore Time Zone, GMT +8) |
Title: AHEAD : Ad-Hoc Electronic Auction Design
Prof Mathieu ROSENBAUM (Ecole Polytechnique) Abstract: We introduce a new matching design for financial transactions in an electronic market. In this mechanism, called ad-hoc electronic auction design (AHEAD), market participants can trade between themselves at a fixed price and trigger an auction when they are no longer satisfied with this fixed price. In this context, we prove that a Nash equilibrium is obtained between market participants. Furthermore, we are able to assess quantitatively the relevance of ad-hoc auctions and to compare them with periodic auctions and continuous limit order books. We show that from the investors’ viewpoint, the microstructure of the asset is usually significantly improved when using AHEAD. This is joint work with Joffrey Derchu, Philippe Guillot and Thibaut Mastrolia |
| 9.00PM -10.00PM
(Singapore Time Zone, GMT +8) |
Title: Personalized Robo-Advising: Enhancing Investment through Client Interaction
A/Prof Agostino CAPPONI (Columbia University) Abstract: Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel human-machine interaction framework, in which the robo-advisor solves an adaptive mean-variance portfolio optimization problem. The risk-return tradeoff dynamically adapts to the client’s risk profile, which depends on idiosyncratic characteristics as well as on market performance and varying economic conditions. We characterize the optimal level of personalization in terms of a tradeoff faced by the robo-advisor between receiving client information in a timely manner and mitigating the effect of behavioral biases in the risk profile communicated by the client. We argue that the optimal portfolio’s Sharpe ratio and return distribution improve if the robo-advisor counters the client’s tendency to reduce portfolio risk during economic contractions, when the market risk-return tradeoff is more favorable. |
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