BACKGROUND
The Centre for Financial Engineering (CFE) has the mission of nurturing a multidisciplinary center in Quantitative Finance, Financial Engineering and FinTech, and related areas, with international impacts for the quality of its people and research. One of its important mandates is to boost international research exchanges and collaboration. To fulfill this role, we initiate a monthly CUHK Distinguished Lectures in Quantitative Finance, where renown academic researchers and industrial leaders with global influence will be invited to share their areas of expertise. The event aims to provide a platform to carry out insightful and productive discussions, to stimulate future collaboration opportunities, and to promote quantitative finance/financial engineering/FinTech research in CUHK.
ORGANIZING COMMITTEE
Coordinator
Prof Nan Chen, Department of Systems Engineering and Engineering Management, Faculty of Engineering
Co-Coordinator
Prof Jay Cao, Department of Finance, Faculty of Business
Members
Prof Dohyun Ahn, SEEM
Prof Xuefeng Gao, SEEM
Prof Xuedong He, SEEM
Prof Lingfei Li, SEEM
Prof Chen Yang, SEEM
Prof. Gang Li, Finance
Prof. Xintong Zhan, Finance
Prof. Chao Ying, Finance
Prof. Xiaolu Tan, Mathematics
UPCOMING LECTURES
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PREVIOUS LECTURES
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| Jan 24, 2025 (Friday, HKT) | Speaker: Professor Neng Wang (Cheung Kong Graduate School of Business) Title: The Economics of Corporate Risk Management: New Framework and Directions Date & Time: Jan 24, 2025 (Friday) 5:00– 6:00 pm (HKT) Abstract: Standard corporate risk management teachings and prac>ces are rooted in the classic noarbitrage asset-pricing paradigm, such as the Black-Scholes formula for op>on pricing and its associated delta hedging strategies. However, these models inherently assume perfect capital markets and fric>onless firm opera>ons, as outlined in the Modigliani-Miller framework, which implies no economic value is added through risk management. In reality, firms encounter various fric>ons, and capital markets are far from perfect. For instance, issuing equity can be costly due to adverse selec>on and other reasons. To incorporate these considera>ons, I cast corporate risk management within a broader framework of dynamic, state-con>ngent corporate decision-making and valua>on. In this framework, corporate liquidity—comprising cash reserves, internally generated cash flows, and credit lines—along with leverage management, emerges as a cri>cal component of dynamic state-con>ngent risk management. Finally, I offer prac>cal recommenda>ons for applying the proposed theore>cal framework to enhance corporate risk management strategies in real-world seQngs. Biography: Neng Wang is Dean's Distinguished Chair Professor of Finance and Senior Vice Dean of Faculty and Research at CKGSB. He is also a Senior Research Fellow at Asian Bureau of Financial and Economics Research (ABFER). Prior to joining CKGSB, he was the Chong Khoon Lin Professor of Real Estate and Finance at Columbia Business School from 2007 to 2023 and a Senior Research Fellow at the National Bureau of Economic Research (NBER) from 2009 to 2023. He also served as the Honorary Dean and Academic Director of the School of Finance, Shanghai University of Finance and Economics (SUFE) from 2009 to 2022. He has widely published in leading economics, finance, and business journals, held editorial positions at journals including the Journal of Finance and Management Science, and won various research excellence awards. His research interests include corporate finance, risk management, macroeconomics, climate economics and sustainability, asset pricing, contract theory, international finance, financial institutions, entrepreneurial finance, household finance, and FinTech. He received his B.S. in Physical Chemistry from Nanjing University, China in 1992, M.S. in Chemistry from California Institute of Technology (Caltech) in 1995, M.A. in International Relations from the University of California, San Diego (UCSD) in 1997, and Ph.D. in Finance from the Graduate School of Business at Stanford University in 2002. He was born in 1973 in Anhui, China. Venue: Online via Zoom Meeting ID: 938 6408 6266 Passcode: 310095 Poster Here Video Here |
| Dec 17, 2024 (Tuesday, HKT) | Speaker: Professor Fan Jianqing (Princeton University) Title: Structural Deep Learning in Conditional Asset Pricing Date & Time: Dec 17 2024 (Tue) 4:30 pm - 5:30 pm (HKT) Abstract: We propose a period-by-period machine learning (ML) framework to estimate time-varying risk premia and asset pricing functions in factor pricing models. We develop a rigorous asymptotic theory to interpret the output of the ML procedures used in asset pricing. Our approach enables an economic interpretation by decomposing return predictions into risk-related components and mispricing while allowing for flexible, nonlinear, and time-varying models. One of our empirical findings reveals a time-varying correlation between the equity risk premium and macroeconomic variables, particularly real consumption growth. This dynamic relationship sheds light on the long-standing equity premium puzzle. These results show that our method not only enhances predictive performance but also provides critical insights into the dynamic relationship between firm characteristics, risk exposures, and asset returns. (Joint with Tracy Ke, Yuan Liao, and Andreas Neuhierl Biography: Jianqing Fan, Academician of Academia Sinica and Foreign member of the Royal Flemish Academy of Science, is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and former Chairman of the Department of Operations Research and Financial Engineering at Princeton University, where he directs both the financial econometrics lab and statistics lab. He previously held professorships at UNC-Chapel Hill, and UCLA. He has authored or co-authored over 300 articles on financial econometrics, statistical machine learning, analysis of Big Data, and various aspects of theoretical and methodological statistics and machine learning. His finance work focuses on the analysis of high-frequency data, empirical asset pricing, option pricing, portfolio theory, risk assessment, high-dimensional data, and time series. He is a joint-editor of Journal of American Statistical Association, and was the joint-editor of Journal of Business and Economics Statistics, Journal of Econometrics, and Annals of Statistics, and has served as associate editor of Econometrica, Management Science, and Journal of Financial Econometrics. His published work has been recognized by the 2000 COPSS Presidents’ Award, the 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellowship in 2009, P.L. Hsu prize in 2013, Guy Medal in Silver in 2014, Noether Distinguished Scholar Award in 2018, and IMS Le Cam Award and Lecture in 2021. He is an Elected Fellow of the American Association for Advancement of Science, the Society of Financial Econometrics, the Institute of Mathematical Statistics, and the American Statistical Association, and a past President of the Institute of Mathematical Statistics. Venue: Online via Zoom Meeting ID: 963 0978 3812 Passcode: 415280 Poster Here Video Here |
| Nov 29, 2024 (Friday, HKT) | Speaker: Professor Runhuan Feng (Tsinghua University) Title: Tokenomics of DeFi Insurance Date & Time: Nov 29 2024 (Tue) 4:30 pm - 5:30 pm (HKT) Venue: Room 201, Cheng Yu Tung Building, The Chinese University of Hong Kong Abstract: Within the decentralized finance (DeFi) industry, effective risk management and insurance play pivotal roles, particularly due to the industry's self-regulatory nature and susceptibility to fraud and scams. The evolution of crypto assets and blockchain technology presents distinctive challenges and prospects for insurance and risk sharing. This presentation will encompass an exploration of three focal areas within DeFi insurance, spanning tokenomics, decentralized pricing, and risk sharing. Biography: Feng is a Chair Professor in the School of Economics and Management at Tsinghua University, where he is also the Director of the China Center for Insurance and Risk Management. He is a Fellow of the Society of Actuaries and a Chartered Enterprise Risk Analyst. Prior to joining Tsinghua, he was a tenured Professor, the State Farm Companies Foundation Endowed Professor at the University of Illinois at Urbana-Champaign, the Faculty Lead for the Finance and Insurance sector at the Discovery Partnership Institute of the University of Illinois System. Feng has published close to fifty papers on a variety of topics on insurance, risk sharing, retirement, financial technology and authored several books, including An Introduction to the Computational Risk Management of Equity-Linked Insurance and Decentralized Insurance: Technical Foundation of Business Models. He is currently a Co-Editor of North American Actuarial Journal and the Executive-Editor-in-Chief of Risk Sciences. He won numerous international awards including the 2019 Global Association of Risk Management Professionals’ Best Paper Prize for Quantitative Methods in Finance, and the 2022 Jeffrey Haywood Prize by the Institute and Faculty of Actuaries. Feng is a strong advocate for academic-industry collaboration. He currently serves as a Co-Chair of the Research Executive Committee at the Society of Actuaries. He previously served as a Chair of the Education and Research Section Council. He served as an external consultant for the State University Annuitants Association and the Illinois General Assembly and performed actuarial analyses for pension obligation bonds, which was used later as the basis of several legislative proposals. Feng’s research has received attention by various media outlets, including Thomas Reuters, Crain’s Chicago Business, China’s CCTV, etc. Meeting ID: 983 5074 1594 Passcode: 170189 Poster Here Video Here |
| Nov 26, 2024 (Tuesday, HKT) | Speaker: Professor Álvaro Cartea (University of Oxford) Title: Unintended Consequences of Artificial Intelligence in Financial Markets Date & Time: Nov 26 2024 (Tue) 4:30 pm - 5:30 pm (HKT) Venue: Room 402 Yasumoto International Academic Park, The Chinese University of Hong Kong Abstract: How can artificial intelligence and learning algorithms affect the integrity of market? We discuss three lines of research: Spoofing and quote-based manipulation. Market making learning algorithms will find optimal strategies that manipulate the limit order book. Manipulation occurs to induce mean reversion in inventory to an optimal level and to execute round-trip trades with limit orders at a higher probability than was otherwise likely to occur; spoofing is a special case when the market maker prefers that manipulative limit orders are not filled. Market making. We show that algorithms can tacitly collude to extract rents and we show that that tick size (coarseness of price grid) in the limit order book matters: a large tick size obstructs competition, while a smaller tick size lowers trading costs for liquidity takers, but slows the speed of convergence to an equilibrium. Breaking anonymity in a limit order book. Empirically, we find that liquidity providers use excessively large limit orders to break the anonymity of limit orders by signaling themselves to other liquidity providers. Importantly, we find that liquidity providers respond differently to signaled limit orders. Specifically, they avoid trading with each other and focus on picking-off soft flow from retail limit orders. We use a model of the limit order book to show that signaling occurs in a collusive equilibrium. In equilibrium, signaling occurs so that colluding firms can identity each other in an anonymous book to avoid sniping each other. This allows colluding firms to share profitable flow from retail limit orders. Biography: Álvaro Cartea is Professor of Mathematical Finance in the Mathematical Institute, University of Oxford and director of the Oxford-Man Institute of Quantitative Finance. Álvaro's work is at the interface of finance, mathematics, and data science. He has worked extensively on high-frequency and algorithmic trading, and more recently his work focuses on algorithmic collusion and on the unintended consequences of using artificial intelligence in financial markets. Meeting ID: 983 5074 1594 Passcode: 170189 Poster Here Video Here |
| Nov 1, 2024 (Friday, HKT) | Speaker: Professor Mao Ye (Cornell University) Title: Bridging the Gap Between Financial Engineering and Finance Communities: Opportunities and Challenges Led by the Big Data Date & Time: Nov 1 2024 (Tue) 8:30p.m. – 9:30p.m (Hong Kong Time) 8:30a.m – 9:30a.m. (New York Time) Abstract: Big data is revolutionizing the finance industry and has the potential to shape future practices in finance significantly. As the Wall Street Journal writes: “Today, the ultimate Wall Street status symbol is a trading floor comprising Carnegie Mellon Ph. D.s, not Wharton M.B.A.s.” (Rogow, G., “Meet the New Kings of Wall Street,” Wall Street Journal, May 21, 2017). The big data revolution creates many opportunities for collaboration between OR/Financial Engineering and finance researchers. As a researcher in the finance community, I would like to discuss several topics that may benefit from collaboration with the OR/Financial Engineering community, such as high-frequency trading, algorithmic trading, quantitative investing, and the pricing strategies of electronic platforms. Biography: Mao Ye is an Associate Professor of Finance at Cornell Johnson Graduate School of Management. Before he joined Cornell University in 2022, he was an Associate Professor of Finance at the University of Illinois, Urbana-Champaign. His research specializes in market microstructure, machine learning, and big data, with his work published in leading journals such as the Journal of Finance, Journal of Financial Economics, and the Review of Financial Studies. He is a Research Associate at the National Bureau of Economic Research (NBER), a Faculty Fellow at the National Center for Supercomputing Applications (NCSA), and a Fellow for the Program in the Law and Economics of Capital Markets at Columbia Law School. He was an associate editor of Management Science. Ye delivered the keynote address, "Big Data in Finance," at the NBER Summer Institute in 2018 and returned to give another keynote, "The Next Chapter of Big Data in Finance," at the 2024 NBER Summer Institute. Mao Ye earned his Ph.D. from Cornell University in 2011. While a student at Cornell, he was elected to the university's Board of Trustees, becoming the first trustee (校董) from Mainland China at any Ivy League institution. In 2016, he was named Educator of the Year by the University of Illinois at Urbana-Champaign. In 2019, Ye served as the commencement speaker at Renmin University of China. Venue: Online via Zoom Meeting ID: 991 0310 5681 Passcode: 892840 Poster Here Video Here |
| Mar 22, 2024 (Friday, HKT) | Speaker: Professor Luitgard Veraart (London School of Economics and Political Science) Title: Systemic Risk in Markets with Multiple Central Counterparties Date & Time: Mar 22 2024 (Friday) 5:00pm-6:00pm (Hong Kong Time), 9:00am-10:00am (London Time) Abstract: We provide a framework for modelling risk and quantifying payment shortfalls in cleared markets with multiple central counterparties (CCPs). Building on the stylised fact that clearing membership is shared among CCPs, we develop a modelling framework that captures the interconnectedness of CCPs and clearing members. We illustrate stress transmission mechanisms using simple examples as well as empirical evidence based on calibrated data. Furthermore, we show how stress mitigation tools such as variation margin gains haircutting by one CCP can have spillover effects on other CCPs. The framework can be used to enhance CCP stress-testing, which currently relies on the ``Cover 2'' standard requiring CCPs to be able to withstand the default of their two largest clearing members. We show that who these two clearing members are can be significantly affected if one considers higher-order effects arising from interconnectedness through shared clearing membership. Looking at the full network of CCPs and shared clearing members is therefore important from a financial stability perspective. This is joint work with Iñaki Aldasoro. Biography: Luitgard Veraart is a Professor in the Department of Mathematics at the London School of Economics and Political Science. Her research interests are in financial mathematics and statistics in finance. One special focus of her research is on problems related to risk management in financial markets and systemic risk. She is the co-winner of the 2019 Adams Prize for achievement in the field of Mathematics of Networks. From 01/2024-12/2025, she serves as the Program Director of the SIAM Activity Group on Financial Mathematics and Engineering. Prior to joining LSE in 2010, she has been an Assistant Professor of Financial Mathematics at the Karlsruhe Institute of Technology and a Postdoctoral Research Associate at the Bendheim Centre for Finance, Princeton University. She has a PhD in Mathematics from the University of Cambridge. Venue: Online via Zoom Meeting ID: 930 9516 6393 Passcode: 333823 Poster Here Video Here |
| Nov 17, 2023 (Friday, HKT) | Speaker: Prof Ciamac C. Moallemi (Columbia University) Title: The Economics of Automated Market Making and Decentralized Exchanges Date & Time: Nov 17, 2023 (Friday) 9:00-10:00 pm (Hong Kong Time), 8:00 - 9:00 am (New York Time) Abstract: Automated market making (AMM) protocols such as Uniswap have recently emerged as an alternative to the most common market structure for electronic trading, the limit order book. Relative to limit order books, AMMs are both more computationally efficient and do not require the participation of active market making intermediaries such as high frequency traders. As such, AMMs have emerged as the dominant market mechanism for trust-less decentralized exchanges (DEXs) implemented through smart contracts on programmable blockchain platforms such as Ethereum. In cryptocurrency markets, the aggregate trading volume on the Uniswap DEX exceeds that of the much better known Coinbase centralized exchange. We develop a model the underlying economics of AMMs from the perspective of their passive liquidity providers (LPs). Our central contribution is a ``Black-Scholes formula for AMMs''. Like the Black-Scholes formula, we consider the return to LPs once market risk has been hedged. We identify the main adverse selection cost incurred by LPs, which we call ``loss-versus-rebalancing'' (LVR, pronounced ``lever''). LVR captures costs incurred by AMM LPs due to stale prices that are picked off by better informed arbitrageurs. In a continuous time Black-Scholes setting, we are able to derive closed-form expressions for this adverse selection cost. Qualitatively, we highlight the main forces that drive AMM LP returns, including asset characteristics (volatility), AMM characteristics (curvature / marginal liquidity, fee structure), and blockchain characteristics (block rate). Quantitatively, we illustrate how our model's expressions for LP returns match actual LP returns for the Uniswap v2 WETH-USDC trading pair. Our model provides tradable insight into both the ex ante and ex post assessment of AMM LP investment decisions. LVR can also inform the design of the next generation of DEX market mechanisms --- in fact, in the short time since our work has been released, ``LVR mitigation'' has already emerged as the dominant challenge among practitioners in the AMM protocol designer community. This talk is joint work with Jason Milionis (Columbia CS), Tim Roughgarden (Columbia CS / a16z crypto), and Anthony Zhang (Chicago Booth). Biography: Ciamac C. Moallemi is William von Mueffling Professor of Business in the Decision, Risk, and Operations Division and the director of the Briger Family Digital Finance Lab at the Graduate School of Business at Columbia University, where he has been since 2007. A high school dropout, he received S.B. degrees in Electrical Engineering & Computer Science and in Mathematics from the Massachusetts Institute of Technology (1996). He studied at the University of Cambridge, where he earned a Master of Advanced Study degree in Mathematics (Part III of the Mathematical Tripos), with distinction (1997). He received a Ph.D. in Electrical Engineering from Stanford University (2007). Prior to his doctoral studies, he developed quantitative methods in a number of entrepreneurial ventures: as a partner in a $200 million fixed-income arbitrage hedge fund and as the director of scientific computing at an early-stage drug discovery start-up. He holds editorial positions at the journals Operations Research and Management Science. He is a past recipient of the British Marshall Scholarship (1996), the Benchmark Stanford Graduate Fellowship (2003), first place in the INFORMS Junior Faculty Paper Competition (2011), and the Best Simulation Publication Award of the INFORMS Simulation Society (2014). Aside from his academic work, he regularly consults for fintech companies. His research interests are in the development of mathematical and computational tools for optimal decision making under uncertainty, with a focus on applications areas including market microstructure, quantitative and algorithmic trading, and blockchain technology. Venue: Online via Zoom Meeting ID: 930 9279 5991 Passcode: 696208 Poster Here Video Here |
| Nov 10, 2023 (Friday, HKT) | Speaker: Dr Jiequn Han (Flatiron Institute, Simons Foundation) Title: Solving Multi-Agent Games Using Deep BSDE Methods and Fictitious Play Date & Time: 8:30 - 9:30 pm (Hong Kong Time), 7:30am - 8:30am (New York time) Abstract: In fields like finance, economics, and management science, agents often engage in multi-agent interactions within game-based frameworks for decision-making. However, computing the Nash equilibria of such games remains a substantial challenge due to the high dimensionality of the state space. This talk presents "Deep Fictitious Play," an innovative approach that intergrates deep learning with traditional probabilistic and simulation methods to effectively address these computational challenges. This advancement offers new tools for a systematic exploration of the interplay between individual decision-making processes and emergent collective behaviors across diverse settings. Biography: Jiequn Han is a Research Scientist in the Center for Computational Mathematics, Flatiron Institute, Simons Foundation. His research draws inspiration from various disciplines of science and is devoted to solving high-dimensional problems arising from scientific computing. His current research interests mainly focus on machine learning based methods for partial differential equations, multiscale modeling, and high-dimensional controls/games. He holds a Ph.D. in Applied Mathematics from Princeton University, a B.S. in Computational Mathematics and a B.A. in Economics from Peking University. Venue: Online via Zoom Meeting ID: 959 4176 5062 Passcode: 835784 Poster Here |
| Aug 3, 2023 (Saturday, HKT) | Speaker: Prof. Agostino Capponi (Columbia University) Title: Contemporary Practices of Machine Learning and Data Science in Financial Markets Date & Time: 4pm - 5pm (Hong Kong Time) Venue: Cheng Yu Tung Building CYT214, The Chinese University of Hong Kong Abstract: We provide an overview of the recently published book "Machine Learning and Data Science for Financial Markets: A Guide to Contemporary Practices', co-edited by Agostino Capponi and Charles-Albert Lehalle. We begin by discussing robo-advising and automated wealth management, together with its role in guiding investors allocation and consumption decisions. We then discuss the role of machine learning in risk intermediation, including derivative hedging, portfolio construction, market making, as well the interplay of reinforcement learning and mean-field games. We then discuss the role of modern data science techniques such as nowcasting and alternative data structures, the ethics of algorithms, and regulatory aspects of artificial intelligence in finance. Along the way, we provide key examples and illustrations to accompany concepts with use cases and financial engineering problems. Biography: Agostino Capponi is an Associate Professor in the IEOR Department at Columbia University, where he is also a member of the Data Science Institute. Agostino's research interests are in financial technology, market microstructure, and financial networks. Agostino's research has been funded by major public agencies and private corporations, including the NSF, DARPA, U.S. Department of Energy, IBM, and JP Morgan. Agostino is an Editor of Management Science in the Finance Department, co-editor of Mathematics and Financial Economics, and financial engineering area editor of Operations Research Letters. Agostino is a member of the Council of the Bachelier Finance Society, and served as Chair of the SIAM Activity group in Financial Mathematics and Engineering and of the INFORMS Finance Section. Agostino is a recipient of the NSF CAREER Award, and of the JP Morgan AI Faculty Research Award. Agostino is a fellow of the Crypto and Blockchain Economic Research Forum, an external research fellow of the Fintech@Cornell initiative, and an academic fellow of the Luohan Academy established by the Alibaba Group. Venue: Online via Zoom Meeting ID: 993 7407 9619 Passcode: 177554 Poster Here Video Here |
| Feb 17, 2023 (Friday, HKT) | Speaker: Prof. Rama Cont (Oxford University) Title: A model-free approach to continuous-time finance Date & Time: Feb 17, 2023 (Friday) 5:00 – 6:00 pm (HKT) Abstract: We present a pathwise approach to continuous-time finance based on causal functional calculus. Our framework does not rely on any probabilistic concept. We introduce a definition of continuous-time self-financing portfolios, which does not rely on any integration concept and show that the value of a self-financing portfolio belongs to a class of nonanticipative functionals, which are pathwise analogs of martingales. We show that if the set of market scenarios is generic in the sense of being stable under certain operations, such self-financing strategies do not give rise to arbitrage. We then consider the problem of hedging a path-dependent payoff across a generic set of scenarios. Applying the transition principle of Rufus Isaacs in differential games, we obtain a pathwise dynamic programming principle for the superhedging cost. We show that the superhedging cost is characterized as the solution of a path-dependent equation. For the Asian option, we obtain an explicit solution. Biography: Rama Cont is Professor of Mathematics and Chair of Mathematical Finance at Oxford University. He has held previous positions at Columbia University, Imperial College London, Ecole Polytechnique and Sorbonne University, and has served as advisor to IMF, ECB, CME, ICE Clear, Norges Bank, Bovespa and the US Office of Financial Research. His research focuses on stochastic processes and mathematical modeling in finance, with a focus on market instabilities and systemic risk. He is a recipient of the Louis Bachelier Prize (2010) and the Royal Society Award for Excellence in Interdisciplinary Research (2017), and was elected Fellow of the Society for Industrial and Applied Mathematics (SIAM) in 2017 for his 'contributions to stochastic analysis and mathematical modeling in finance.' Venue: Online via zoom ( Zoom Link) Meeting ID: 922 0689 6799 Passcode: 156866 Poster Here Video Here |
| Dec 01, 2022 (Thursday, HKT) | Speaker: Prof. Jaksa Cvitanic, California Institute of Technology Title: Principal-agent problems in financial markets Date & Time: Dec 01, 2022 (Thursday) 12:00 – 1:00 pm (HKT) Abstract: In this talk, I will present the benchmark continuous-time models for contracting between a principal and an agent. Next, I will talk about the extension of the classical models to the case in which the agent controls not only the drift, but also the volatility vector of the output process. Mathematically, this requires results from the theory of second-order BSDE’s. Then, I will show how to apply this methodology to finding the asset pricing equilibrium and optimal contracts in a market with delegated portfolio management. This talk is jointly organized with Hong Kong-Singapore Joint Seminar Series in Financial Mathematics/Engineering. Biography: Jaksa Cvitanic works in the fields of mathematical finance, financial engineering, and financial economics. He received a PhD in Statistics from Columbia University in 1992. He was an Assistant and Associate Professor of Statistics at Columbia University until 1999. From 1999 to 2005 he was a Professor of Mathematics and Economics at the University of Southern California. He is currently Richard N. Merkin Professor of Mathematical Finance at the California Institute of Technology and the president of the Bachelier Finance Society. He received the American Statistical Association Scholastic Excellence Award (1992). He has been or was a co-editor of “Finance and Stochastics” and “Mathematical Finance”, and has served on the editorial boards of several other journals. He has co-authored two books, “Introduction to the Economics and Mathematics of Financial Markets” and “Contract Theory in Continuous Time Models”, and numerous scientific articles. Venue: Online via zoom ( Zoom Link) Meeting ID: 992 2139 1061 Passcode: 343536 Poster Here Video Here |
| May 28, 2022 (Saturday, HKT) | Speaker: Prof. Thaleia Zariphopoulou (The University of Texas at Austin) Title: Human-machine interaction models and stochastic optimization Date & Time: May 28, 2022 (Saturday) 9:00 - 10:15 am (HKT) / May 27, 2022 (Friday) 8:00 - 9:15 pm (CDT) Abstract: I will introduce a model of human-machine interaction (HMI) in portfolio choice (robo-advising). Modeling difficulties stem from the limited ability to quantify the human’s risk preferences and describe their evolution, but also from the fact that the stochastic environment, in which the machine optimizes, adapts to real-time incoming information that is exogenous to the human. Furthermore, the human’s risk preferences and the machine’s states may evolve at different scales. This interaction creates an adaptive cooperative game with both asymmetric and incomplete information exchange between the two parties. As a result, challenging questions arise on, among others, how frequently the two parties should communicate, what information can the machine accurately detect, infer and predict, how the human reacts to exogenous events, how to improve the inter-linked reliability between the human and the machine, and others. Such HMI models give rise to new, non-standard optimization problems that combine adaptive stochastic control, stochastic differential games, optimal stopping, multi-scales and learning. Biography: Thaleia Zariphopoulou is the holder of the Presidential Chair of Mathematics and the V.F. Neuhaus Professorship of Finance at the University of Texas at Austin. Previously, she was the Laun Professor at the University of Wisconsin, Madison and from 2009-2012, the first holder of the Oxford-Man Chair in Quantitative Finance at the University of Oxford. Her area of expertise is Financial Mathematics and Stochastic Optimization. She has published extensively in the areas of investments and valuation in incomplete markets, and introduced novel approaches to indifference valuation and dynamic risk preferences. She has served very actively the community of Financial Mathematics. She sits on the editorial board of eleven academic journals and monograph series, and she is the Editor of the SIAM Series in Financial Mathematics. She has served in various prize committees and panels. She has also been the Vice-Chair (2007-2010) of the SIAG Activity Group in Financial Mathematics and Engineering, and has served as Vice-President (2004-2006) and President (2006-2008) of the Bachelier Finance Society. In 2012, she was elected SIAM Fellow and in 2014, she was an invited speaker at the International Congress of Mathematicians in Seoul. Venue: Online via zoom ( Zoom Link) Meeting ID: 991 4953 4273 Passcode:749860 Poster Here Video Here |
| Apr 23, 2022 (Saturday, HKT) | Speaker: Prof. Xin Guo, University of California, Berkeley Title: Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach Date & Time: Apr 23, 2022 (Saturday) 10:00 - 11:15 am (HKT) / Apr 22, 2022 (Friday) 7:00 - 8:15 pm (PST) Abstract: Multi-agent reinforcement learning (MARL) has attracted significant attention and research interests due to its wide range of applications. One of the challenges for MARL is designing efficient learning algorithms for a large system in which each agent has only limited or partial information of the entire system. While exciting progress has been made for decentralized MARL for social networks and team video games, much less is known for decentralized MARL of self-driving vehicles, ride-sharing, and data and traffic routing. We propose a mathematical framework to study such an MARL with a network of states. The theoretical analysis consists of three key components: the reformulation of the MARL system as a networked Markov decision process with teams of agents, the Bellman equation for the value function and the appropriate Q-function on the probability measure space, and the exponential decay property of the team Q-function. This theoretical analysis paves the way for a new algorithm where the actor-critic approach with over-parameterized neural networks is proposed. The convergence and sample complexity is established and shown to be scalable with respect to the sizes of both agents and states. Biography: Xin Guo is the Coleman Fung Chair professor at the college of engineering, UC Berkeley. Prior to that, she was an associate professor at the school of ORIE, Cornell University, and a research staff member at the mathematics department of IBM T. J. Watson Research center. Her research interests are in stochastic processes, stochastic controls and games, and mathematics of machine learning, with applications to financial engineering and medical data analysis. She is the co-editor-in-chief for Frontier of Mathematical Finance, and on the editorial board of several leading journals of controls, mathematical finance, and operations research. Venue: Online via zoom ( Zoom Link) Meeting ID: 973 2418 4128 Passcode: 614888 Poster Here Video Here |
| Apr 01, 2022 (Friday, HKT) | Speaker: Prof. Raymond Kan, University of Toronto Title: In-sample and Out-of-sample Sharpe Ratios of Multi-factor Asset Pricing Models Date & Time: Apr 01, 2022 (Fri) 8:30 – 9:30 pm (HKT) Abstract: For many multi-factor asset pricing models proposed in the literature, their implied tangency portfolios have substantially higher sample Sharpe ratios than that of the value-weighted market portfolio. In contrast, such high Sharpe ratio is rarely delivered by professional fund managers. One reason that real world investors cannot attain the high sample Sharpe ratios of the multi-factor models is estimation risk. In this paper, we study the effect of estimation risk on the out-of-sample Sharpe ratio of a multi-factor asset pricing model by obtaining the finite sample distribution of the out-of-sample Sharpe ratio conditional on the observed in-sample Sharpe ratio. For an investor who does not know the mean and covariance matrix of the factors in a model, the out-of-sample Sharpe ratio of an asset pricing model is substantially worse than its in-sample Sharpe ratio. After taking into account of estimation risk, many of the multi-factor asset pricing models no longer outperform the value-weighted market portfolio. This is joint work with Xiaolu Wang and Xinghua Zheng. Biography: Raymond Kan is the National Bank Financial Professor in Capital Markets at Rotman School of Management, University of Toronto, where he is a faculty since 1992. His research interests include empirical asset pricing, portfolio management and computational statistics. His research has been published in Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Econometrica and Biometrika. He currently serves as the chair of the Business Panel of Hong Kong Research Grant Council. Venue: Online via zoom ( Zoom Link) Meeting ID: 942 0291 3265 Passcode: 712335 Poster Here Video Here |
| Feb 26, 2022 (Saturday, HKT) | Speaker: Prof. Agostino Capponi, Columbia University Discussant: Prof. Andreas Park, University of Toronto Title: The Evolution of Blockchain: from Lit to Dark Date & Time: Feb 26, 2022 (Saturday) 9:00 – 10:30 am (HKT) Abstract: Transactions submitted through the blockchain peer-to-peer network may leak out exploitable information. We study the economic incentives behind the adoption of blockchain dark venues, where users' transactions are observable only by miners on these venues. We show that miners may not fully adopt dark venues to preserve rents extracted from arbitrageurs, hence creating execution risk for users. The dark venue neither eliminates frontrunning risk nor reduces transaction costs. It strictly increases payoff of miners, weakly increases payoff of users, and weakly reduces arbitrageurs' profits. Our main implications are empirically supported. We show that a 1\% increase in the probability of being frontrun raises users' adoption rate of the dark venue by 0.6\%. Arbitrageurs' cost-to-revenue ratio increases by a third in the presence of a dark venue. Biography: Speaker: Agostino Capponi is an Associate Professor in the IEOR Department at Columbia University, where he is also a member of the Data Science Institute. Agostino's research interests are in financial technology, market microstructure, and financial networks. Agostino's research has been funded by major public agencies and private corporations, including the NSF, DARPA, U.S. Department of Energy, IBM, and JP Morgan. Agostino is an Editor of Management Science in the Finance Department, co-editor of Mathematics and Financial Economics, and financial engineering area editor of Operations Research Letters. Agostino is a member of the Council of the Bachelier Finance Society, and served as Chair of the SIAM Activity group in Financial Mathematics and Engineering and of the INFORMS Finance Section. Agostino is a recipient of the NSF CAREER Award, and of the JP Morgan AI Faculty Research Award. Agostino is a fellow of the Crypto and Blockchain Economic Research Forum, an external research fellow of the Fintech@Cornell initiative, and an academic fellow of the Luohan Academy established by the Alibaba Group. Discussant: Andreas Park is a Professor of Finance at the University of Toronto, appointed to the Rotman School of Management and the Department of Management at UTM. He currently serves as the Research Director at the FinHub, Rotman’s Financial Innovation Lab, he is the co-founder of LedgerHub, the University of Toronto’s blockchain research lab. He has served as a lab economist for the Blockchain stream at the Creative Destruction Lab. Andreas teaches courses on payments innovation, decentralized finance, and financial market trading, and his current research focuses on the economic impact of technological transformations such as blockchain technology. He recently co-authored a design proposal for a central-bank issued digital currency, commissioned by the Bank of Canada. Andreas received his doctorate from the University of Cambridge. His work has been published in top journals in economics and finance including Econometrica, the Journal of Finance, the Journal of Financial Economics, and the Journal of Financial and Quantitative Analysis. Venue: Online via zoom Zoom ID: ( Zoom Link) Meeting ID: 969 2688 7078 Passcode: 667591 Poster Here Video Here Lecture slides and paper |
| Jan 14, 2022 (Friday) | Speaker: Prof. Paul Glasserman, Columbia University Title: W-shaped Implied Volatility Curves and The Gaussian Mixture Model Date & Time: Jan 14, 2022 (Friday) 9:00 – 10:15 pm (HKT) Abstract: The number of crossings of the implied volatility function with a fixed level is bounded above by the number of crossings of the risk-neutral density with the density of a log-normal distribution with the same mean as the forward price. It is bounded below by the number of convex payoffs priced equally by the two densities. We discuss the implications of these bounds for the implied volatility in the N-component Gaussian mixture model, with particular attention to the possibility of W-shaped smiles. We show that the implied volatility in this model crosses any level at most 2(N − 1) times. We give monotonicity properties of the implied volatility under stochastic orderings of the location parameters and volatilities of the mixture components. For some of these results we make use of a novel convexity property of the Black-Scholes price at one strike with respect to the price at another strike. In particular,for a mixture of log-normals with the same mean as the forward price, we prove that the implied volatility is always U-shaped and bounded above and below by the volatilities of the mixture components. The combined constraints from density crossings and extreme strike asymptotics restrict the allowed shapes of the implied volatility. As an application we discuss a symmetric N = 3 Gaussian mixture model which generates three possible smile shapes: U-shaped, W-shaped and an oscillatory shape with two minima and two maxima. This is joint work with Dan Pirjol. Biography: Prof. Paul Glasserman is the Jack R. Anderson Professor of Business at Columbia Business School, and he currently chairs the Financial and Business Analytics Center within Columbia University's Data Science Institute. Paul's research interests include quantitative finance and other applications of stochastic models. In 2011-2012, he was on leave from Columbia, working at the Office of Financial Research in the U.S. Treasury Department, where he continues to serve as a part-time consultant. His work with the OFR has included research on stress testing, financial networks, systemic importance indicators, contingent capital, and counterparty risk. Paul's publications include the book Monte Carlo Methods in Financial Engineering, which received the 2006 Lanchester Prize and the 2005 I-Sim Outstanding Publication Award. Paul is also a past recipient of the Erlang Prize in Applied Probability and Risk Magazine's Quant of the Year Award. Venue: Online via zoom ( Zoom Link) Meeting ID: 989 6455 9063 Passcode: 353229 Poster Here Video Here |
| Nov 26, 2021 (Friday) | Speaker: Prof. H. Mete Soner, Princeton University Title: Stochastic Optimal Control in High-dimensions Date & Time: Nov 26, 2021 (Friday) 9:00 – 10:15 pm (HKT) Abstract: Stochastic optimal control has been an effective tool in very diverse applications ranging from modern learning models of engineering to quantitative finance and financial economics. Although, they provide the much needed quantitative modeling, until recently they have been intractable in high-dimensional settings. However, several recent studies - using a Monte-Carlo type algorithm combined with deep neural networks proposed by Han, E and Jentzen - report impressive numerical results. At high level, this rather straightforward method can be formulated as empirical risk minimization and is seemingly quite flexible in its applicability. At the problem level, one needs to adjust this approach and provide large training sets for accurate results. In this talk I will outline this approach, discuss its properties and share two numerical experiments: a stylized utility maximization and a model-driven optimal stopping. Numerical results, while validating the power of the method in high dimensions, they also show the dependence of the dimension and the size of the training data. This is joint work with Max Reppen of Boston University and Valentin Tissot-Daguette from Princeton. Biography: H. Mete Soner is a Professor of Operations Research and Financial Engineering at Princeton University.He is also affiliated with the Bendheim Center of Finance and with the Program in Applied & Computation Mathematics. Prior to joining Princeton, he was a Professor of Mathematics and the Chair of Department of Mathematics at ETH Zürich. Prof. Soner’s research focuses on decisions under uncertainty, stochastic optimal control, Markov decision processes, nonlinear partial differential equations, probability theory, mathematical finance, and financial economics. His book Controlled Markov Processes and Viscosity Solutions (Coauthored with Wendell Fleming, Springer-Verlag 1993, 2nd Edition in 2006) has become one classical reference book in the area. He also authored or co-authored numerous articles on nonlinear partial differential equations, viscosity solutions, stochastic optimal control, and mathematical finance. Currently, he is Editor-in-Chief of SIAM Journal of Financial Mathematics (SIFIN), a Co-Editor of Mathematics and Financial Economics (MAFE), and an associate editor for Finance and Stochastics, Interfaces and Free Boundaries, Mathematics of Operations Research. During 2011-2016, he has been the Executive Secretary of the Bachelier Finance Society. In 2014, he received Alexander von Humbolt Foundation Research Award. In 2015, he was elected as a SIAM Fellow. Venue: Online via zoom ( Zoom Link) Meeting ID: 966 4946 1502 Passcode: 053557 Poster Here Video Here |
| Nov 20, 2021 (Saturday) | Speaker: Prof. Kay Giesecke, Infima Technologies and Stanford University Title:Deep Learning for MBS Prepayments Date & Time: Nov 20, 2021 (Saturday) 9:30 – 10:45 am (HKT) Abstract: Predictions of prepayment speeds are mission-critical for investors, dealers, originators and other participants in the $10T Agency MBS market. Legacy prediction technologies produce flawed and stale data wrecking returns and profits. We develop deep learning systems that set new accuracy and latency standards, delivering performance-boosting edges to market participants. Our systems harness data of unprecedented size and granularity, covering monthly records for tens of millions of borrowers across the US over two decades. By uncovering hidden nonlinear patterns in borrower behavior at the individual loan level, they improve prediction accuracy for MBS pool CPRs by a full order of magnitude relative to the market’s current “gold standard.” Our predictions are robust in all market environments including the pandemic. Rigorous significance tests offer deep insights into the variables influencing predictions. Biography: Kay Giesecke is the CEO and founder of Infima Technologies, a startup company building AI systems that empower fixed-income market participants to make better investment, trading and execution decisions. He is on leave from Stanford University, where he is a professor of Management Science and Engineering, the director of the Advanced Financial Technologies Laboratory, and the director of the Mathematical and Computational Finance Program. He serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk. He is a member of the Council of the Bachelier Finance Society. Prof. Giesecke is a financial technologist and engineer. Much of his work is driven by important applications in areas such as credit risk management, investment management, and, most recently, housing finance. He has published numerous articles in operations research, probability, and finance journals. He has coauthored five United States patents. He is an Editor of Management Science in the Finance Area and an Associate Editor for Mathematical Finance,Operations Research, SIAM Journal on Financial Mathematics, Finance and Stochastics, Mathematics and Financial Economics, Journal of Credit Risk, Journal of Risk, and so on. Prof. Giesecke has won the JP Morgan AI Faculty Research Award (2019), the SIAM Financial Mathematics and Engineering Conference Paper Prize (2014), the Fama/DFA Prize for the Best Asset Pricing Paper in the Journal of Financial Economics (2011), and the Gauss Prize of the Society for Actuarial and Financial Mathematics of Germany (2003). Venue: Online via zoom ( Zoom Link) Meeting ID: 993 4176 3918 Passcode: 306530 Poster Here Video Here |
| Oct 29, 2021 (Friday, HKT) | Speaker: Prof. Xuezhong(Tony) He, University of Technology Sydney Title:The Microstructure of Endogenous Liquidity Provision Date & Time: Oct 29, 2021 (Friday) 3:00 – 4:00 pm (HKT) Abstract: We analyze a nonlinear rational expectations equilibrium model with an ex post endogenous liquidity provision decision. Speed and information technology advantages allow endogenous liquidity providers (ELPs) to switch between limit and market orders after observing private information. This significantly influences the adverse selection faced by designated market makers (DMMs), thereby generating a gap between liquidity supply from DMMs and liquidity demand by informed traders. As a result, endogenous liquidity provision leads to two equilibrium regimes with nonlinear price impacts and the possibility of market breaks. These breaks occur when ELPs switch from liquidity provision to liquidity consumption as a consequence of unexpected shocks. Increasingly similar information among ELPs reduces the risk of systematic liquidity withdrawals but intensifies the DMMs’ adverse selection cost. Our model is relevant to various plausible settings and can help to explain a variety of financial market outcomes. Biography: Tony He has been a Professor in Finance at University of Technology Sydney (UTS) since 2010. He has been a co-editor of Journal of Economic Dynamics and Control (an ABDC A* journal) since 2013. Prof. Tony He received his PhD in Finance in 2010 from UTS and PhD in Applied Mathematics in 1995 from Flinders University, the two fundamental disciplines that underpin his areas of teaching and research. Tony is an internationally recognized expert in asset pricing, financial market modelling, market microstructure, and nonlinear dynamics in finance and economics. His international research profile is attested by his publications in the field of finance and economics, invited contributions to the prestigious Handbook of Financial Markets and Handbook of Computational Economics, numerous keynote talks in the international conferences, and a number of competitively national and international research grants. Venue: Online via zoom ( Zoom Link) Meeting ID: 961 5691 5444 Passcode: 580897 Poster Here Video Here |
| Oct 08, 2021 (Friday, HKT) | Speaker: Prof. Kerry Back, Rice University Title: Optimal Transport and Risk Aversion in Kyle's Model of Informed Trading Date & Time: Oct 08, 2021 (Friday) 20:30 – 21:45 pm (HKT) Abstract: We establish connections between optimal transport theory and the dynamic version of the Kyle model, including new characterizations of informed trading profits via conjugate duality and Monge-Kantorovich duality. We use these connections to extend the model to multiple assets, general distributions, and risk-averse market makers. With risk-averse market makers, liquidity is lower, assets exhibit short-term reversals, and risk premia depend on market maker inventories, which are mean reverting. We illustrate the model by showing that implied volatilities predict stock returns when there is informed trading in stocks and options and market makers are risk averse. Biography: Kerry Back is the J. Howard Creekmore Professor of Finance in the Jones Graduate School of Business and a Professor of Economics in the School of Social Sciences at Rice University. Previously, he served on the faculties of Northwestern University, Indiana University, Washington University in St. Louis, and Texas A&M University. He has served as a co-editor of the Review of Financial Studies, as a co-editor of Finance and Stochastics, and as an associate editor of the Journal of Finance and several other academic journals. Currently, he teaches asset pricing theory to PhD students in the Jones School and Department of Economics and quantitative finance to MBA students in the Jones School and to Masters of Data Science students in the Department of Computer Science at Rice University Venue: Online via zoom ( Registration Link) Meeting ID: 990 6057 6705 Passcode: 592394 Poster Here Video Here |
| July 16, 2021 (Friday, HKT) | Speaker: Prof. Steven Kou, Boston University Title: Bitcoin Mining and Electricity Consumption Date & Time: July 16, 2021 (Friday) 20:30 – 21:45 pm (HKT) Abstract: We develop a dynamic equilibrium Bitcoin mining model to characterize miners’ optimal entry and exit strategies with technology innovation. The model can be calibrated to the co-movements of computing power, electricity consumption of Bitcoin network, and mining revenue. We find substantial new miners entering and leaving the mining market, and the ratio of the electricity consumption to the mining revenue fluctuated around a fixed constant. Biography: Steven Kou is a Questrom Professor in Management and Professor of Finance at Boston University. He teaches courses on FinTech and quantitative finance. Currently he is a co-area-editor for Operations Research and a co-editor for Digital Finance, and has served on editorial boards of many journals, such as Management Science, Mathematics of Operations Research, and Mathematical Finance. He is a fellow of the Institute of Mathematical Statistics and won the Erlang Prize from INFORMS in 2002. Some of his research results have been incorporated into standard MBA textbooks and have implemented in commercial software packages and terminals. Venue: Online via zoom ( Registration Link and Zoom Link) Meeting ID: 919 2244 2067 Passcode: 308525 Poster Here Video Here |
| June 15, 2021 (Tuesday, HKT) | Speaker: Prof. Xunyu Zhou, Columbia University Title: Curse of Optimality, and How We Break It Date & Time: June 15, 2021 ((Tuesday) 10:00 – 11:15 am (HKT) Abstract: We strive to seek optimality, but often find ourselves trapped in bad "optimal" solutions that are either local optimizers, or too rigid to leave any room for errors, or simply based on wrong models. A way to break this "curse of optimality" is to engage exploration through randomization. Exploration broadens search space, provides flexibility, and facilitates learning via trial and error. We review some of the latest developments of this exploratory approach in continuous time and spaces, along with applications to dynamic portfolio choice. Biography: Xunyu Zhou is the Liu Family Professor of Financial Engineering and the Director of Nie Center for Intelligent Asset Management at Columbia University. He was the Nomura Professor of Mathematical Finance at University of Oxford and Choh-Ming Li Professor of Financial Engineering at the Chinese University of Hong Kong before joining Columbia in 2016. His research covers stochastic control, dynamic portfolio selection, asset pricing, behavioral finance, and time inconsistency. Currently his research focuses on continuous-time reinforcement learning and applications to optimization broadly and to wealth management specifically. He is a recipient of the Wolfson Research Award from The Royal Society, the Outstanding Paper Prize from SIAM, the Alexander von Humboldt Research Fellowship, and the Croucher Senior Research Fellowship. He was a Humboldt Distinguished Lecturer at Humboldt University and an Archimedes Lecturer at Columbia. He is both an IEEE Fellow and a SIAM Fellow. Xunyu Zhou received his PhD in Operations Research and Control Theory from Fudan University in 1989. Venue: Online via zoom ( Zoom Link) Meeting ID: 992 5333 4758 Passcode: 482109 Poster Here Video Here |
ENQUIRY
Stephanie Tam 3943 9561 or steptam@cuhk.edu.hk.