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.

Prof Nan Chen, Department of Systems Engineering and Engineering Management, Faculty of Engineering
Prof Jay Cao, Department of Finance, Faculty of Business
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


May 05, 2023 (Friday, HKT) Speaker: Prof. Yuying Li, University of Waterloo
Title: Optimally Dynamically Allocation Using NN Without Dynamic Programming
Date & Time: May 05, 2023 (Friday) 16:30 – 17:30 pm (HKT)
Abstract: We propose a data driven learning method to compute stochastic optimal asset allocation strategies without dynamic programming (DP). Our proposed neural network (NN) Policy Function Approximation (PFA) framework learns the optimal dynamic policies, without using DP, directly from data. Traditionally, computing finite time horizon discrete dynamic optimal controls is based on dynamic programming (DP), e.g., PDE or reinforcement learning. Using DP, computing a value function at each rebalancing time requires maximizing a conditional expectation. While DP requires computing a high dimensional conditional expectation, our proposed approach achieves efficiency by solving a low dimensional control directly based on a single optimization problem. For outperforming over a dynamic benchmark utilizing multiple assets, we consider different benchmark outperforming objectives, including information ratio IO and tracing differences, and contrast outperformance assessed based on the testing performance of the terminal wealth distribution. We validate and compare computed optimal strategies using simulations from synthetic models, as well as block resampling data directly from market.

This talk is based on joint work with Peter Forsyth, Chendi Ni, Univ of Waterloo, and Pieter Van Staden, National Australia Bank, Melbourne
Biography: Yuying Li is a Chinese-Canadian professor of computer science in the David R. Cheriton School of Computer Science at the University of Waterloo in Canada. Her research interests include mathematical optimization, scientific computing, data mining, and tail risk in computational finance.

After earning a bachelor's degree in mathematics in 1982 from Sichuan University, Li completed her PhD study at the University of Waterloo, in 1988. Her dissertation, An Efficient Algorithm for Nonlinear Minimax Problems, was supervised by Andrew Conn. She worked as a researcher at Cornell University before returning to Waterloo as a faculty member. Prof. Li was the winner of the Leslie Fox Prize for Numerical Analysis in 1993.
Venue: Lecture Theatre 5 Yasumoto International Academic Park (YIA), CUHK
Poster 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
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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
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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
Poster Here
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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
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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
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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.
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 Link)
Meeting ID: 969 2688 7078
Passcode: 667591
Poster Here
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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
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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
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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
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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
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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
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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
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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
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Stephanie Tam 3943 9561 or

Centre for Financial Engineering, The Chinese University of Hong Kong


Centre for Financial Engineering,
4th Floor, Academic Building No. 1,
The Chinese University of Hong Kong,
Sha Tin, Hong Kong
Tel: +852 3943 9561