Quantitative Finance Program Webinars

Quantitative Finance Program Webinars

Organizers: Prof. Stan Uryasev (), Prof. Pawel Polak (), Mr. Rui Ding ()

Zoom Link and Password: Request by e-mailing to Mrs. Laurie Dalessio (laurie.dalessio@stonybrook.edu)

Spring 2022 Semester

Vladimir Bugera | 4 February | Analytics and Big Data in Real Estate

Speaker: Vladimir

Title: Analytics and Big Data in Real Estate

Date/Time: 4 Feb.2022, 10:00-11:00 AM

Abstract: The real estate industry accounts for a significant part of Gross Domestic Product for any country. The United States has the most developed and most dynamic real estate market in the world. The real estate industry is naturally data-driven. Significant technological advancements happen today in leveraging analytics and big data for real estate. We will review the process of how data is used in real estate transactions today and will discuss several problem formulations for data science and mathematical programming applications.

Speaker Bio: Dr. Bugera is the founder and Data Scientist of Big Data Realty, an analytics real estate company in Miami, FL. Additionally, Vladimir is a faculty member at College of Communications at the University of Miami, where he teaches data analytics for communications and journalism students, and the Commissioner of Public Utilities Commission in the City of North Miami Beach. Prior to his career in real estate analytics, Dr. Bugera spent 15+ years with financial companies in various managerial roles leading development of business intelligence analytics and risk management capabilities. Vladimir holds Ph.D. in Quantitative Finance and M.Sc. in Industrial Engineering from the University of Florida, as well as M.Sc. and B.Sc. in Applied Mathematics and Physics from Moscow Institute of Physics and Technology.

Dirk Tasche | 18 February | Calibrating Sufficiently

Speaker: Dirk Tasche

Title: Calibrating Sufficiently

Date/Time: 18 Feb.2022, 10:00-11:00 AM

Abstract: When probabilistic classifiers are trained and calibrated, the so-called grouping loss component of the calibration loss can easily be overlooked. Grouping loss refers to the gap between observable information and information actually exploited in the calibration exercise. We investigate the relation between grouping loss and the concept of suffi ciency, identifying comonotonicity as a useful criterion for su fficiency. We revisit the probing reduction approach of Langford and Zadrozny (2005, 'Estimating Class Membership Probabilities using Classifier Learners') and find that it produces an estimator of probabilistic classifiers that reduces grouping loss. Finally, we discuss Brier curves as tools to support training and 'sufficient' calibration of probabilistic classifiers.

Speaker Bio Dirk Tasche is a senior risk manager in the banks division of the Swiss Financial Market Authority FINMA. His duties primarily consist of reviews of banks’ internal rating systems and the regulatory recognition of rating agencies. Before joining FINMA, he worked for the Prudential Regulation Authority (PRA), Lloyds Banking Group and Fitch Ratings in London and the Deutsche Bundesbank in Frankfurt. Dirk holds a doctorate in probability theory from Berlin University of Technology. He has published a number of papers on quantitative risk management and machine learning.

Vadim Omelchenko | 4 March | Optimal Balancing of Wind Parks with Virtual Power Plants

Speaker: Vadim Omelchenko

Title: Optimal Balancing of Wind Parks with Virtual Power Plants

Date/Time: 4 Mar.2022, 10:00-11:00 AM

Abstract: In this paper, we explore the optimization of virtual power plants (VPP), consisting of a portfolio of biogas power plants and a battery whose goal is to balance a wind park while maximizing their revenues. We operate under price and wind production uncertainty and in order to handle it, methods of machine learning are employed. For price modeling, we take into account the latest trends in the field and the most up-to-date events affecting the day-ahead and intra-day prices. The performance of our price models is demonstrated by both statistical methods and improvements in the profits of the virtual power plant. Optimization methods will take price and imbalance forecasts as input and conduct parallelization, decomposition, and splitting methods in order to handle sufficiently large numbers of assets in a VPP. The main focus is on the speed of computing optimal solutions of large-scale mixed-integer linear programming problems, and the best speed-up is in two orders of magnitude enabled by our method which we called Gradual Increase.

Vadim works as a senior optimizer at Alpiq, where I develop optimization models. I also used to work in consulting and bank sector. He has a Ph.D. from Charles University in Prague (Faculty of Mathematics and Physics). His research area covers the following areas: stochastic optimization, mixed integer programming, and quantitative analysis.

Giovanni Faonte | 1 April | TBD

Speaker: Giovanni Faonte

Title: TBD

Date/Time: 1 Apr.2022, 10:00-11:00 AM

Abstract: TBD

Leonard MacLean and Yonggan Zhao | 15 April | TBD

Speaker: Leonard MacLean and Yonggan Zhao

Title: TBD

Date/Time: 15 Apr.2022, 10:00-11:00 AM

Abstract: TBD

Kiseop Lee | 29 April | TBD

Speaker: Kiseop Lee

Title: TBD

Date/Time: 29 Apr.2022, 10:00-11:00 AM

Abstract: TBD

Fall 2021 Semester

Cheng Peng, Drew Kouri, and Stan Uryasev | 3 September 2021 | Optimal Design of Experiments Focused on Distribution Tails

Speaker: Cheng Peng, Drew Kouri, and Stan Uryasev

Title: Optimal Design of Experiments Focused on Distribution Tails

Date/Time: 3 Sep. 2021, 10:00-11:00 AM

Abstract: This paper studies an optimal design problem with unsymmetric, possibly heavy tailed distributions. Uncertainties at the point of interest are estimated with quantiles. We provide upper bounds for quantiles by using Conditional Value-at-Risk. Quantile regression is used for the estimation of these upper bounds. We do not assume a large number of experiments (i.e., it is not an asymptotic convergence result). Although the design is focused on one tail of the distribution, allocation of tests is similar to the traditional C-optimal design. A case study on acoustic testing demonstrates the suggested methodology.

Bruno Dupire | 17 September 2021 | Functional Itô Calculus and the Characterization of Attainable Claims

Speaker: Bruno Dupire, Head of Quantitative Research at Bloomberg LP

Title: Functional Itô Calculus and the Characterization of Attainable Claims

Date/Time: 17 Sep. 2021, 10:00-11:00 AM

Abstract: We extend some results of the Itô calculus to functionals of the current path of a process to reflect the fact that often the impact of randomness is cumulative and depends on the history of the process, not merely on its current value. We express the differential of the functional in terms of adequately defined partial derivatives to obtain an Itô formula. We develop an extension of the Feynman-Kac formula to the functional case and an explicit expression of the integrand in the Martingale Representation Theorem. We establish that under certain conditions, even path dependent options prices satisfy a partial differential equation in a local sense. We exploit this fact to find an expression of the price difference between two models and compute variational derivatives with respect to the volatility surface. We also characterize which contingent claims can be attained by dynamic trading of the underlying or by a static position in European options or both.

Speaker Bio: Bruno Dupire is head of Quantitative Research at Bloomberg L.P., which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

Rafael Frongillo | 1 October 2021 | Toward a General Theory of Elicitation Complexity

Speaker: Rafael Frongillo

Title: Toward a General Theory of Elicitation Complexity

Date/Time: 1 Oct. 2021, 10:00-11:00 AM

Abstract: In statistics, finance, machine learning, economics, and several other domains, the question arises of which statistical functionals are elicitable, meaning they can be expressed as the minimizer of expected loss for some loss function. Several important functionals are not elicitable, however, including many risk and uncertainty measures of interest. To circumvent this negative result, several authors have proposed notions of "higher-order" elicitability, wherein a d-dimensional functional is elicited, and then condensed to recover the functional of interest. The smallest possible value of d is called the elicitation complexity. We will discuss several related definitions as part of a unifying framework, and give upper and lower bounds on elicitation complexity for a broad class of risk and uncertainty measures. The talk will conclude with several interesting open problems.

Gary Kazantsev | 15 October 2021 | Machine Learning in Finance

Speaker: Gary Kazantsev, Head of Quant Technology Strategy, Office of the CTO at Bloomberg LP

Title: Machine Learning in Finance

Date/Time: 15 Oct. 2021, 10:00-11:00 AM

Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and nonstationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.

Speaker Bio(https://www.techatbloomberg.com/people/gary-kazantsev/) Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company’s Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets. Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University. He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.

Sebastian Jaimungal | 29 October 2021 | Reinforcement Learning for Dynamic Convex Risk Measures

Speaker: Sebastian Jaimungal

Title: Reinforcement Learning for Dynamic Convex Risk Measures

Date/Time: 29 Oct. 2021, 10:00-11:00 AM

Abstract: We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures and develop policy gradient update rules for non-stationary policies. To do this, we use a time-consistent dynamic programming principle and devise an actor-critic style algorithm using neural network structures to obtain optimal strategies. Finally, we demonstrate the performance and flexibility of our approach by applying it to several examples. This is joint work with Anthony Coache.

Speaker Bio: Prof. Jaimungal is a full Professor of Mathematical Finance at the University of Toronto's Department of Statistical Sciences where he is the Director of the Master of Finance and Insurance program. He is a fellow of the Fields Institute for Mathematical Sciences, Associate Member of University of Oxford’s Man Institute, a former chair of the SIAM activity group in Financial Mathematics & Engineering, and is on the editorial board of SIAM J. on Financial Mathematics and Quantitative Finance, among others. His current research interests span stochastic control and games, machine learning, and algorithmic trading.

Giulio Trigila and Dan Stefanica | 12 November 2021 | Normalizing Optimal Transport Flows and Time Series Analysis

Speaker: Giulio Trigila and Dan Stefanica

Title: Normalizing Optimal Transport Flows and Time Series Analysis

Date/Time: 12 Nov. 2021, 10:00-11:00 AM

Abstract: Normalizing Flows (NF) were originally proposed as a tool for density estimation and became extremely popular with the work by D. J. Rezende and S. Mohamed using Neural Network within the context of NF. After a brief introduction to the theory of Optimal Transport (OT), this talk will introduce Optimal Transport Normalizing Flows (OTNF), highlighting the advantages of integrating OT with NF leading to a computational tool for the removal of variability, conditional density estimation and simulation. A new, non-adversarial, formulation of OTNF is presented together with applications and numerical results relative to time series analysis.

Speaker Bio: Giulio Trigila has a background in Physics and Applied Mathematics with a focus in applied probability and numerical optimization. His main research concerns data driven algorithms for the solution of the optimal transport problem and its application to data analysis. He obtained a M.S. in Theoretical Physics at the University of Rome La Sapienza and a Ph.D. in Applied Mathematics from the Courant Institute of Mathematical Sciences. Subsequently he held an appointment as a Post-doctoral fellow at the Technical University of Munich.

Dacheng Xiu | 10 December 2021 | Predicting Returns with Text Data

Speaker: Dacheng Xiu

Title: Predicting Returns with Text Data (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3389884)

Date/Time: 10 Dec. 2021, 10:00-11:00 AM

Abstract: We introduce a new text-mining methodology that extracts information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of terms via predictive screening, 2) assigning prediction weights to these words via topic modeling, and 3) aggregating terms into an article-level predictive score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we study one of the most actively monitored streams of news articles in the financial system--the Dow Jones Newswires--and show that our supervised text model excels at extracting return-predictive signals in this context. Information in newswires is assimilated into prices with an inefficient delay that is broadly consistent with limits-to-arbitrage (i.e., more severe for smaller and more volatile firms) yet can be exploited in a real-time trading strategy with reasonable turnover and net of transaction costs.

Speaker Bio: Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Review of Financial Studies, Journal of Econometrics, Management Science, Journal of Business & Economic Statistics, etc. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, the AQR Insight Award, and the Swiss Finance Institute Outstanding Paper Award. Xiu earned his PhD and MA in applied mathematics from Princeton University.

Spring 2021 Semester

Steven Kou | 5 February 2021 | The Economics of Stablecoins

Speaker: Steven Kou

Title: The Economics of Stablecoins

Date/Time: 5 Feb. 2021, Friday, 10:00 - 11:00 AM EST

Abstract: Stablecoins, which are cryptocurrencies pegged to other stable financial assets such as U.S. dollar, are desirable for payments within blockchain networks, whereby being often called the ``Holy Grail of cryptocurrency.'' However, existing cryptocurrencies are too volatile for these purposes. To understand the impact of the proposed coins on the speculative and non-speculative demands of cryptocurrencies, we study equilibrium with and without the stable coins. Based on option pricing theory in presence of jump risk and black-swan type events, we show the robustness of a particular design based on a dual-class structure that offers a fixed income crypto asset (class A coin), a stable coin (class A’ coin) pegged to a traditional currency, and leveraged investment instruments (class B and B’ coins).

Bio: Steven Kou is a Questrom Professor in Management and Professor of Finance at Boston University. Previously, he taught at National University of Singapore (from 2013 to 2018), Columbia University (from 1998 to 2014), University of Michigan (1996-1998), and Rutgers University (1995-1996). 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, e.g. in Bloomberg Terminals.

Emilio Lorente | 19 February 2021 | Machine Learning Based Asset Allocation Ranking

Speaker: Emilio Lorente

Title: Pricing Slow-Moving Debt Crises

Date/Time: 19 Feb. 2021, Friday, 10:00 - 11:00AM EST

Abstract: We will explore the possibility of a Machine Learning based asset allocation ranking, and its implementation through CVaR portfolio optimization. To achieve this, the covariance matrix is obtained from the Entropy-Pooling (Attilio Meucci, 2014) approach. We will see the end-to-end portfolio construction process in a real-life wealth mandate, where extreme value theory is applied to its risk assessment. The portfolio simulation coherently uses the Entropy-Pooling based covariance matrix to produce the expected cumulative distribution function of the portfolio.

Speaker Bio: Emilio Lorente is the founder and CIO of Recognition Asset Management Software, S.L. Emilio has more than 20 years of experience in the multi-asset management industry. He has developed profound work covering the entire investment cycle for Wealth (Lloyds Bank), Private Banking (Banco Popular) and Institutional Investors (Aberdeen Standard Investments). He is a designated expert on Machine Learning methods applied to markets. More importantly, he applied state of the art techniques to become a pioneer in the application of Machine Learning, Optimization Algorithms, High Performance Computing and Man-Machine collaboration technology in portfolio management. His academic background covers Masters Degrees in Economics, Quantitative Finance) and Artificial Intelligence. He is a certified member of the Global Association of Risk Professionals. He was invited as Senior Lecturer from different Universities in Spain, UK and Latin America to impart MSc and MBA courses in quantitative methods for Asset Allocation, Derivatives, and Risk Management.

Josef Teichmann | 5 March 2021 | Consistent Recalibration Models and Deep Calibration (joint work with Matteo Gambara)

Speaker: Josef Teichmann, Professor in the Department of Mathematics, ETH Zurich

Title: Consistent Recalibration Models and Deep Calibration (joint work with Matteo Gambara)`

Date/Time: 5 Mar. 2021, Friday, 13:00 - 14:00PM EST

Abstract: Consistent Recalibration models (CRC) have been introduced to capture in necessary generality the dynamic features of term structures of derivatives' prices. Several approaches have been suggested to tackle this problem, but all of them, including CRC models, suffered from numerical intractabilities mainly due to the presence of complicated drift terms or consistency conditions. We overcome this problem by machine learning techniques, which allow to store the crucial drift term's information in neural network type functions. This yields first time dynamic term structure models which can be efficiently simulated.

Speaker Bio: Josef Teichmann is a professor for Mathematical Finance at ETH Zurich since 2009. He holds a PhD from Vienna University in the area of infinite-dimensional geometry from 1999 and has worked as an Associate Professor at TU Vienna from 2002 to 2009. His research interests lie in Stochastic Finance, Stochastic Partial Differential Equations, Rough Analysis, and Machine Learning.

Stavros Zenios | 19 March 2021 | A Global Political Risk Factor and Hedging Politics in International Equities

Speaker: Stavros Zenios

Title: A Global Political Risk Factor and Hedging Politics in International Equities

Date/Time: 19 Mar. 2021, Friday, 10:00 - 11:00 AM

Abstract: Using novel measures of politics-policy uncertainty we document predictable variation in stock market returns across countries. We identify a global political risk factor (P-factor) commanding a significant risk premium. Augmenting the global market portfolio with the P-factor significantly reduces pricing errors and improves cross- sectional fit. We identify transmission channels through both cash flow and discount rate channels. We also show how to hedge political risks using portfolio optimization, and find that political risk goes some way towards explaining the home equity bias puzzle, but it does not solve it.

Speaker Bio: Stavros A. Zenios is Professor of Finance and Management Science at University of Cyprus, Senior Fellow at the Wharton School Financial Institutions Center (USA), and Non-resident Fellow of Bruegel (Brussels). He is a Member of the National Academy of Cyprus. He is the author of two books and numerous scholarly articles in leading international journals in risk management, financial engineering, and management science. He is currently working on sovereign debt sustainability issues, including the pricing of political stability and economic policy uncertainty risks. His work on personal financial planning received the 2006 EURO Excellence in Practice Award, and he also received awards for work on the performance of financial institutions. His work on financial modeling and robust optimization is cited extensively, and his book Practical Financial Optimization: Decision making for financial engineers (Blackwell-Wiley) is used in advanced classes in European and North -American Universities. His book with Patrick Harker on the Performance of Financial Institutions (Cambridge University Press) was translated in Chinese, and in 1997 he received the INFORMS prize for his book with Yair Censor Parallel Optimization (Oxford University Press). He served two terms as Rector of University of Cyprus, where he was the first Dean of the School of Economics and Management, and as president of UNICA-Universities of the European Capitals. He was vice-chairman of the Cyprus Council of Economic Advisors and on the Board of the Central Bank of Cyprus. He consulted extensively with international institutions and commercial enterprises, such as the World Bank, the European Stability Mechanism, Union Bank of Switzerland, METLIFE Insurance, BSI, among others.

Ariel Caticha | 2 April 2021 | Entropic Inference

Speaker: Ariel Caticha

Title: Entropic Inference

Date/Time: 2 Apr. 2021, Friday, 10:00 - 11:00 AM EST

Abstract: The concept of entropy has its origins in the 19 th century in the discovery of thermodynamics (Carnot, Clausius, Kelvin) and statistical mechanics (Maxwell, Boltzmann, Gibbs). A series of developments starting around the middle of the 20 th century (mostly due to Shannon and Jaynes) liberated the concept of entropy from its physics origins and elevated it into a general purpose tool for processing information. Thus was born the old Method of Maximum Entropy MaxEnt. In a parallel line of research Bayesian inference enjoyed a remarkable period of expansion in the latter half of the century. The two methods of inference flourished to large extent independently of each other entropic methods do not yet enjoy widespread acceptance within the orthodox Bayesian community. The connection between them has been an endless source of controversy and even their compatibility has been repeatedly brought into question. Further developments extending into the early 21 st century have, however, culminated in the complete unification of entropic and Bayesian inference methods. The goal of this lecture is to summarize the argument for the method of maximum entropy as the universal method of inference and show that it includes the old MaxEnt, all Bayesian methods, and the general subject of large deviations as special cases. The consequences of these methods of entropic inference are potentially enormous both for statistics (entropic priors, model selection, experimental design, etc.) and for science in general. In physics, for example, it is well known that the laws of thermodynamics can be derived from entropic methods. What might at first sight be surprising is that entropic principles can also be used to derive the dynamical laws of mechanics, both classical and quantum. Perhaps the laws of physics themselves are not laws of nature but merely rules to process information about the world.

Speaker Bio: Ariel Caticha is professor at the Department of Physics, University at Albany SUNY. He was born in Uruguay, and educated in Brazil (UNICAMP: BS and MS in physics) and in the USA (Caltech: PhD in physics). In recent years his research has focused on the connection between physics and information. Catichas papers on entropic inference and on its applications to the foundations of statistical mechanics, quantum mechanics, and general relativity can be found at https://www.albany.edu/physics/faculty/ariel-caticha . He also devotes a considerable effort to teaching physics having mentored 13 Ph.D. students (plus 6 ongoing). He has received the University at Albanys Excellence in Teaching and Advising Award and also the SUNY Chancellors Award for Excellence in Teaching.

Brent Lindquist | 16 April 2021 | Advanced Analytics for the Real Estate Investment Market

Speaker: Brent Lindquist

Title: Advanced Analytics for the Real Estate Investment Market

Date/Time: 16 APR. 2021, Friday, 10:00 - 11:00 AM EST

Abstract: Portfolio management encompasses four principal areas: optimization techniques to maximize return relative to risk; risk information and management tools to assess changing market conditions and actively manage optimization; backtesting to estimate exposure to risk; and portfolio insurance to hedge risk exposure. We consider each of these in the context of portfolios comprised of REIT ETFs. We discuss dynamic optimization, which provides stochastic forecasts based upon improved estimates of tail-risk. Backtest results demonstrate improvement resulting from dynamic optimization. We address early warning systems for potential market disruptions. Finally, we consider option pricing where the underlying asset is the managed portfolio.

Speaker Bio: W. Brent Lindquist is a computational applied mathematician. A theoretical physicist by training, his interests have included quantum electrodynamics, hyperbolic conservation laws, flow in porous media, and academic administration. He was active in the development of the Frontier software package and is the principal developer of the 3DMA-Rock package. He is now focusing on computational and mathematical finance.

Viktor Kuzmenko | 30 April 2021 | Expectile Risk Quadrangle

Speaker: Viktor Kuzmenko, Glushkov Institute of Cybernetics, Kiev, Ukraine

Title: Expectile Risk Quadrangle

Date/Time: 30 Apr. 2021, Friday, 10:00 - 11:00 AM EST

Abstract: Expectile is a risk measure used in financial applications, such as portfolio portfolio optimization. The expectile is a risk measure similar Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). The Fundamental Risk Quadrangle theory describes general properties and relations for risk, deviation, error and regret functions of a random variable. So-called Regular Risk Quadrangles are known for VaR and CVaR. This presentation describes two new quadranges related to expectile and relevant statistical optimization problems.

Fall 2020 Semester

Yuichi Takano | 4 September 2020 | Dynamic Portfolio Selection with Linear Control Policies for Coherent Risk Minimization

Speaker: Yuichi Takano, Associate Professor, University of Tsukuba, Japan

Title: Dynamic Portfolio Selection with Linear Control Policies for Coherent Risk Minimization

Date/Time: 4 Sep. 2020, Friday, 10:00 - 11:00 AM EST

Abstract: This talk is concerned with a linear control policy for dynamic portfolio selection. We develop this policy by incorporating time-series behaviors of asset returns on the basis of coherent risk minimization. Analyzing the dual form of our optimization model, we demonstrate that the investment performance of linear control policies is directly connected to the intertemporal covariance of asset returns. To mitigate overfitting to training data (i.e., historical asset returns), we apply robust optimization. For this optimization, we prove that the worst-case coherent risk measure can be decomposed into the empirical risk measure and the penalty terms. Numerical results demonstrate that when the number of assets is small, linear control policies deliver good out-of-sample investment performance. When the number of assets is large, the penalty terms improve the out-of-sample investment performance. This is a joint work with Jun-ya Gotoh

Peter Cotton | 18 September 2020 | Collective PRediction, Copulas, and Space Filling Curves

Speaker: Peter Cotton, Chief Data Scientist, Intech Investments

Title: Collective Prediction, Copulas and Space Filling Curves

Date/Time: 18 September 2020, Friday, 10:00 - 11:00 AM EST

Abstract: What if you could write an algorithm once that finds its own way to interesting problems to solve, without the need for human intervention? What if anyone could predict any source of live data by writing three lines of Python code? I will discuss the potential for collective real-time prediction, and demonstrate a prototypical host at Microprediction.Org where algorithms predict everything from electricity prices to cryptocurrencies to helicopter trajectories.

Speaker Bio: Dr. Cotton heads Intech’s data science efforts in collaboration with the investment team and leads Intech’s crowdsourced research efforts. He was responsible for algorithmic trading, privacy preserving analytics and crowdsourcing at JPMorgan. Previously, he was the founder of Benchmark Solutions, a company that pioneered large-scale financial data assimilation and was later sold to Bloomberg. Peter began his career at Morgan Stanley where he was one of several independent inventors of closed-form synthetic CDO pricing. Dr. Cotton earned an undergraduate degree in physics and mathematics from the University of New South Wales and a PhD in mathematics from Stanford University.

Michele Leonardo Bianchi | 2 October 2020 | Forward-looking portfolio selection with multivariate non-Gaussian models

Speaker: Michele Leonardo Bianchi, Regulation and Macroprudential Analysis Directorate, Bank of Italy

Title: Forward-looking portfolio selection with multivariate non-Gaussian models (joint work with Gian Luca Tassinari, University of Bologna)

Date/Time: 2 October 2020, Friday, 10:00 - 11:00 AM EST

Abstract: In this study we suggest a portfolio selection framework based on time series of stock log-returns, option-implied information, and multivariate non-Gaussian processes. We empirically assess a multivariate extension of the normal tempered stable (NTS) model and of the generalized hyperbolic (GH) one by implementing an estimation method that simultaneously calibrates the multivariate time series of log-returns and, for each margin, the univariate observed one-month implied volatility smile. To extract option-implied information, the connection between the historical measure P and the risk-neutral measure Q, needed to price options, is provided by the multivariate Esscher transform. The method is applied to fi t a 50-dimensional series of stock returns, to evaluate widely-known portfolio risk measures and to perform a forward-looking portfolio selection analysis. The proposed models are able to produce asymmetries, heavy tails, both linear and non-linear dependence and, to calibrate them, there is no need of liquid multivariate derivative quotes.

Speaker Bio: Michele Leonardo Bianchi is an Advisor in the Macroprudential Analysis Division at the Regulation and Macroprudential Analysis Directorate of the Banca d’Italia. From 2008 to 2014 he worked in the Division of Risk and Financial Innovation Analysis at the Specialized Intermediaries Supervision Department of the Banca d’Italia. He earned an Italian Laurea in Mathematics in 2005 from the University of Pisa and completed his PhD in Computational Methods for Economic and Financial Decisions and Forecasting in 2009 from the University of Bergamo. In 2017 he received the Italian national scientific qualification (associate professorship). He has extensive experience in the analysis and statistical modeling of financial data. He coauthored two book published by Wiley and World Scientific in quantitative finance and authored articles on quantitative finance, financial econometrics, probability theory, and nonlinear optimization published in international scientific journals, including Journal of Banking and Finance, Quantitative Finance, International Journal of Theoretical and Applied Finance, Theory of Probability and Its Applications, Probability and Mathematical Statistics, The Econometrics Journal, Computational Economics, and The Journal of Derivatives.

Chris Bemis | 16 October 2020 | Constraints and Investor Views in Portfolio Optimization

Speaker: Chris Bemis, Head of Quantitative Analysis and Research, Whitebox Advisors, US

Title: Constrainst and Investor Views in Portfolio Optimization

Date/Time: 16 October 2020, Friday, 10:00 - 11:00 AM EST

Abstract: We study the effect linear constraints have on risk in the context of mean variance optimization (MVO). Jagannathan and Ma (2003) establish an equivalence between certain constrained and unconstrained MVO problems via a modification of the covariance matrix. We extend their results to arbitrary linear constraints and provide alternative interpretations for the effect of constraints on both the input parameters to the problems at hand and why ex-post performance is improved in the constrained setting. In addition, we present a signal modification strategy similar in approach to that of Black-Litterman.

Speaker Bio: Dr. Bemis is Head of Quantitative Analysis and Research for Whitebox Advisors, focusing on cross-asset alpha drivers for a variety of asset classes. Most recently this work has yielded several systematic credit strategies for Whitebox Advisors. Dr. Bemis earned his PhD in applied mathematics from the University of Minnesota, where his work involved both modeling and optimization for portfolios of risky assets, and where he remains as an adjunct professor in their Math Finance program.

Abolfazl Safikhani | 30 October 2020 | Non-stationary Spatio-Temporal Modeling of COVID-19 Progression in the U.S.

Speaker: Abolfazl Safikhani, Department of Statistics, University of Florida

Title: Non-stationary Spatio-Temporal Modeling of COVID-19 Progression in the U.S

Date/Time: 30 October 2020, Friday, 10:00 - 11:00 AM EST

Abstract: The fast transmission rate of COVID-19 worldwide has made this virus the most important challenge of year 2020. Many mitigation policies have been imposed by the governments at different regional levels (country, state, county, and city) to stop the spread of this virus. Quantifying the effect of such mitigation strategies on the transmission and recovery rates, and predicting the rate of new daily cases are two crucial tasks. In this talk, we propose a modeling framework which not only accounts for such policies but also utilizes the spatial and temporal information to characterize the pattern of COVID-19 progression. Specifically, a piecewise susceptible-infected-recovered (SIR) model is developed while the dates at which the transmission/recovery rates change significantly are defined as “break points” in this model. A novel and data-driven algorithm is designed to locate the break points using ideas from fused lasso and thresholding. In order to enhance the forecasting power and to describe additional temporal dependence among the daily number of cases, this model is further coupled with spatial smoothing covariates and vector auto-regressive (VAR) model. The proposed model is applied to several U.S. states and counties, and the results confirm the effect of “stay-at-home orders” and some states’ early “re-openings” by detecting break points close to such events. Further, the model performed satisfactorily short-term forecasts of the number of new daily cases at regional levels by utilizing the estimated spatio-temporal covariance structures. This is a joint work with Yue Bai and George Michailidis.

Sajad Obaydi | 13 November 2020 | Constrained Optimization Problems in Co-insurance

Speaker: Sajad Obaydi, Business Actuary, The Hartford, London, UK

Title: Constrained Optimization Problems in Co-insurance

Date/Time: 13 November 2020, Friday, 10:00 - 11:00 AM EST

Abstract: Insurance companies that partake in the Lloyd’s of London market insure complex and exotic risks, which have the potential to produce multibillion-dollar losses. This is made possible by co-insurance, i.e. insurers sharing risks with one another. In this market, each insurer must decide what proportion of each contract it is willing to take on. Greater participation in a contract would mean a larger share of premium income, but at a risk of incurring greater loss. Therefore, by varying the degree of participation of each contract, an insurer can achieve an optimal portfolio that maximizes expected profit, while satisfying multiple commercial constraints. In the case study I will discuss the building blocks of such an optimization problem and the various constraints a practitioner may encounter. Finally, I will demonstrate an approach to solve this problem using modern optimization software.

Spring 2020 Semester

Robert J. Frey| 3 April 2020 | Heavy Tailed Distributions: Theory and Practice

Speaker: Robert J. Frey

Title: Heavy Tailed Distributions: Theory and Practice

Date/Time: 3 April 2020, Friday, 10:00 - 11:00 AM EST

Abstract: Dr. Robert J. Frey is a former Managing Director of Renaissance Technologies Corp (1992–2004) and presently serves as a Research Professor on the faculty of Stony Brook University where he is the Founder and Director of the Program in Quantitative Finance within the Department of Applied Mathematics and Statistics. He is the Founder, and Chief Executive Officer of global fund of hedge funds group FQS Capital Partners.

Wei Zhu | 10 April 2020 | Real-time Prediction of Bitcoin Bubble Crashes

Speaker: Wei Zhu (joined talk with Min Shu)

Title: Real-time Prediction of Bitcoin Bubble Crashes

Date/Time: 10 April 2020, Friday, 10:00 - 11:00 AM EST

Short Bio: Dr. Wei Zhu (http://www.ams.sunysb.edu/~zhu/) is a Professor in Statistics at the Department of Applied Mathematics and Statistics, Stony Brook University. Her research focuses on the areas of errors in variable regression, structural equation modeling, experimental design, and in recent years, time series regression and statistical learning, with applications in biomedicine and finance.

Abstract: In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and a finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 h and 30 min, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator being highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale – on a day to week scale, while the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale – on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk in not only the cryptocurrency market but also other financial markets.

Cristian Homescu | 17 April 2020 | FMachine Learning for Quantitative Investment and Wealth Management

Speaker: Cristian Homescu, Bank of America Merrill Lynch

Title: Machine Learning for Quantitative Investment and Wealth Management

Date/Time: 17 April 2020, Friday, 10:00 - 11:00 AM EST

Abstract: achine learning for quantitative investment and wealth management (QWIM): What is hype and what is reality? What differences are observed for ML applications in this area compared to ML applications in other areas? Within this context this presentation aims to provide an overview of ML applications (classification, network analysis and clustering, forecasting and prediction, etc.) in QWIM, while also discussing the practical challenges.

Presentation Participants of the webinar can get a copy of transparencies from Giorgi Pertaia (giorgi.pertaia@stonybrook.edu)

Erick Delage | 24 April 2020 | Preference Robust Utility-based Shortfall Risk Minimization

Speaker: Erick Delage, HEC Montréal, Université de Montréal, Canada

Title: Preference Robust Utility-based Shortfall Risk Minimization

Date/Time: 24 April 2020, Friday, 10:00 - 11:00 AM EST

Abstract: Utility-based shortfall risk measure is proposed by Föllmer and Schied and has received increasing attentions over the past few years for its potential to quantify more effectively the risk of large losses than conditional value at risk. In this talk, we consider the case when the true utility/loss function cannot be specified either because there is missing information about how the decision maker perceives risk, or because he is simply hesitant about it. We propose a preference robust shortfall risk model that exploits empirical data about subjective judgements to construct a set of plausible utility-based loss functions and suggest minimizing shortfall risk as measured using the worst loss function from this set. We develop tractable reformulations when the underlying probability distribution is discrete. In the case when the probability distribution is continuous, we propose a sample average approximation scheme and show that its optimal solution and value converges to the true ones as the sample size increases.

Speaker Bio: Erick Delage is born in Montreal. He graduated in 2009 with a Ph.D. from Stanford University. He joined the Department of Decision Sciences at HEC Montréal and became full professor in 2019. Since 2014, he has been chairholder of the Canada Research Chair in decision making under uncertainty. His research interests span the areas of optimization, decision analysis, and artificial intelligence with applications in portfolio optimization, derivative pricing, resource allocation, inventory management, and energy planning problems.

Weilong Fu | 1 May 2020 | Fast Pricing of American Options Under Variance Gamma

Speaker: Weilong Fu (joined talk with Prof. Ali Hirsa), Columbia University IEOR Department

Title: Fast Pricing of American Options Under Variance Gamma

Date/Time: 1 May 2020, Friday, 10:00 - 11:00 AM EST

Abstract: We investigate methods for pricing American options under the variance gamma model. The variance gamma process is a pure-jump process which is constructed by replacing the calendar time by the gamma time in a Brownian motion with drift, which makes it a time-changed Brownian motion. In the case of the Black-Merton-Scholes model, there are fast approximation methods for pricing American options, but they cannot be utilized for the variance gamma model. We develop a new fast and accurate approximation method inspired by the quadratic approximation to get rid of the time steps required in finite difference methods and simulation methods, while reducing the error by making use of a machine learning technique on pre-calculated quantities. We compare the performance of our method with those of the existing methods and show that this method is efficient and accurate for practical use.

Speaker Bio: Weilong Fu is a Ph.D. student in Columbia IEOR Department. He graduated from Peking University (Beijing, China) in 2017, with a major in Statisitcs. Prof. Ali Hirsa is the Co-Director of Financial Engineering at Columbia University IEOR Department and the Managing Partner at Sauma Capital, LLC. Previously he was Managing Director and Global Head of Quantitative Strategy at DV Trading, LLC, a Partner and Head of Analytical Trading Strategy at Caspian Capital Management, LLC, and he worked in a variety of quantitative positions at Morgan Stanley, Bank of America Securities, and Prudential Securities. Ali’s research interests are algorithmic trading, machine learning, data mining, computational/quantitative finance and optimization; he is the author of “Computational Methods in Finance” and co-author of "An Introduction to Mathematics of Financial Derivatives", 3rd edition. Ali received his PhD in Applied Mathematics from University of Maryland at College Park under the supervision of Professors Howard C. Elman and Dilip B. Madan.

Harvey Stein | 8 May 2020 | A Unified Framework for Default Modeling

Speaker: Harvey Stein, Bloomberg LP

Title: A Unified Framework for Default Modeling

Date/Time: 8 May 2020, Friday, 10:00 - 11:00 AM EST

Abstract: Credit risk models largely bifurcate into two classes -- the structural models and the reduced form models. Attempts have been made to reconcile the two approaches by adjusting filtrations to restrict information, but they are technically complicated and tend to approach filtration modification in an ad-hock fashion. Here we propose a reconciliation inspired by actuarial science's approach to survival analysis. We model the survival curve and hazard rate curve as stochastic processes. This puts default models in a form resembling the HJM framework for interest rates, yielding a unified framework for default modeling. Predictability of default has a simple interpretation in this framework. The framework enables us to disentangle predictability and the distribution of the default time from calibration decisions such as whether to use market prices or balance sheet information. It supplies a formal framework for combining models, yielding a simple way to define new default models.

Speaker Bio: Dr. Harvey J. Stein is Head of the Quantitative Risk Analytics Group at Bloomberg, responsible for Bloomberg's market risk and credit risk models. Dr. Stein is well known in the industry, having published and lectured on mortgage backed security valuation, CVA calculations, interest rate and FX modeling, credit exposure calculations, financial regulation, and other subjects. Dr. Stein is also on the board of directors of the IAQF, an adjunct professor at Columbia University, a board member of the Rutgers University Mathematical Finance program and of the NYU Enterprise Learning program, and organizer of the IAQF/Thalesians financial seminar series. He received his BA in mathematics from WPI in 1982 and his PhD in mathematics from UC Berkeley in 1991.

Agostino Capponi | 15 May 2020 | Large Orders in Small Markets: On Optimal Execution with Endogenous Liquidity Supply

Speaker: Agostino Capponi, Columbia University IEOR Department

Title: Large Orders in Small Markets: On Optimal Execution with Endogenous Liquidity Supply

Date/Time: 15 May 2020, Friday, 10:00 - 11:00 AM EST

Abstract: We solve a continuous time dynamic Stackelberg game, where a large uninformed seller executes optimally, fully cognizant of the response of Cournot-competitive market makers. The game therefore endogenizes both demand and supply of liquidity. We provide closed form solutions for the value functions of market makers and large sellers, the equilibrium bid/ask price, and the execution intensity. The closed-form solution yields several insights. First, stealth trading is both privately and socially costly because market makers incur additional costs not knowing when execution ends. Second, the presence of a large seller does not unambiguously benefit other participants. Market makers benefit only if there is enough risk-absorption capacity or if the execution period is short. Other investors benefit only when the seller sells at high enough intensity. The model explains quantitatively several empirical facts: order duration and participation rate correlate negatively, and price pressure subsides before execution ends. (based on joint work with Hongzhong Zhang and Albert Menkveld).

Speaker Bio: Agostino Capponi is an Associate Professor of Industrial Engineering and Operations Research at Columbia University, a member of the Data Science Institute, the Center for the Management of Systemic risk, the FDT Center for Intelligent Asset Management, and an External Consultant at the U.S. Commodity Futures Trading Commission, Office of the Chief Economist, on topics related to clearinghouse collateral requirements and financial stability. Agostino’s research interests are in systemic risk and financial stability, economics of clearinghouses, market microstructure, and human-machine interaction systems; he is a recipient of the NSF CAREER award, a prize from the MIT Center for Finance and Policy and the Harvard Crowd Innovation Laboratory, and the Bar-Ilan prize for general research in financial mathematics. Agostino serves on the editorial boards of Mathematical Finance, Applied Mathematical Finance, Operations Research Letters, and as the Department Editor of the Institute of Industrial Engineering Transactions; he also serves as the program director of the SIAM activity group in Financial Mathematics and Engineering. Agostino received his Master and Ph.D. Degree in Computer Science and Applied and Computational Mathematics from the California Institute of Technology.