The seminar takes place in the Spring of 2025, Tuesdays and Thursdays, 7:40 pm — 8:55 pm.
Location: 312 Mathematics Building
For directions, please see Directions to Campus and Morningside Campus Map.
Organizer: Jaehyuk Choi
Schedule of Presentations
Scroll down for the Schedule of Past Presentations.
Thursday, April 3, 2025
Title: Strats/Quants – roles, trend and career
Speaker: Jeong Song, Morgan Stanley
Jeong Song is a Managing Director at Morgan Stanley with over 16 years of experience in the Fixed Income Division. As a strat and quant, he leads the Flow Credit, Structured Credit, Financing, and Lending businesses. Jeong holds a Ph.D. in Finance from Columbia Business School and a B.S. in Electrical Engineering from Seoul National University.
Abstract:
Tuesday, April 8, 2025
Title: Model Risk Management Tour
Speaker: Jonathan Schachter, CEO, Delta Vega Inc.
Abstract:
A preview of forthcoming Schachter et. al, “A First Textbook on Model Risk Management” (Elsevier/Academic Press, in press, Jan. 2026) will be given, a comprehensive look at how MRM has developed in a fertile way since the GFC and Fed SR 11-7 escalated its importance. The seventeen chapters cover a range of topics, including pricing, market risk, credit risk, portfolio risk, operational risk, AI risk, and miscellaneous. Appendices give a global view of MRM, and a statistics refresher. Key textbook insights include a deep dive into correlation, tonsuring statistics, cointegration, Monte Carlo seed variance, and sampling distributions of order statistics/percentiles (e.g., VaR, PFE).
Thursday, April 10, 2025
Title: An overview of the current market and trading landscape of digital assets
Speaker: Shiliang Tang, MNNC Group
Shiliang is currently the General Partner at MNNC Group, a multistrategy digital asset hedge fund, and advisor to FalconX. Previously, Shiliang was Co-founder and President of Arbelos Markets, a next generation principal trading firm focused on the bilateral derivatives markets. Arbelos Markets was recently acquired by FalconX. Before Arbelos Markets, Shiliang served as the Chief Investment Officer for LedgerPrime, a digital asset investment firm. Shiliang started his career as a quantitative trader within the proprietary trading groups of UBS and Merrill Lynch.
Shiliang received his B.S. in Chemical Engineering from the Massachusetts Institute of Technology, and has completed both the CFA and CAIA programs.
Abstract:
A history and overview of the evolution of cryptocurrency markets. We’ll delve into spot and derivative markets, the creation and evolution of the perpetual instrument, options and how they are usually used in cryptocurrency markets, DeFi markets and the invention of the AMM, and various other market dynamics currently in the crypto markets.
Tuesday, April 15, 2025
Title:
Speaker:
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Thursday, April 17, 2025
Title: On Generation of Latent Diffusion in the FX Market.
Speaker: Wei Deng, Machine Learning Researcher, Morgan Stanley
Wei Deng is a machine learning researcher at Morgan Stanley in New York. He leverages advancements in generative AI to address pricing and hedging challenges in fixed-income assets. He earned his Ph.D. in Applied Mathematics in 2021, with a thesis on Monte Carlo sampling in the era of deep learning. Wei’s current research focuses on scalable sequential Monte Carlo sampling, with applications ranging from inference-time steering and reasoning in diffusion models and language models to stochastic volatility modeling in finance.
Abstract:
The simulation and generation of FX data are essential for developing more robust hedging strategies and enhancing risk management. A key objective is to ensure that the simulator accurately captures various stylized facts necessary for modeling real-world market behavior. Traditionally, simulations have relied on vanilla stochastic volatility models, which are limited in their ability to represent complex distributional dynamics. While generative adversarial networks (GANs) have been explored as generative models, they often suffer from instability. To overcome these challenges, we propose leveraging latent diffusion models for FX data generation. Empirical results indicate that transformer-based architectures effectively capture the nonlinear and long-term dynamics of latent volatility, making them not only more representative but also more stable than both traditional parametric stochastic volatility models and GANs.
Tuesday, April 22, 2025
Title:
Speaker: Sharon Asaf, Citi
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Thursday, April 24, 2025
Title: Vanilla options pricing/fitting (Tentative)
Speaker: Srivatsan Balakrishnan, Vola Dynamics LLC
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Tuesday, April 29, 2025
Title: Harnessing Market Psychology: Integrating Behavioral Finance, NLP, and Deep Learning in Market Sentiment Analysis
Speaker: Claire Fu
Claire Fu is a graduate student in the Mathematics of Finance (MAFN) program at Columbia University, with a strong background in quantitative finance, AI-driven trading strategies, and market structure analysis. She has worked extensively in fixed income, foreign exchange, and derivatives markets, applying machine learning and automation to enhance market research and trading execution.
Claire recently spoke at the Bloomberg Quant Seminar, discussing the intersection of behavioral finance, NLP, and deep learning in market sentiment analysis. Her research focuses on leveraging large language models (LLMs) such as FinBERT, BloombergGPT, and FinGPT to quantify market psychology and integrate sentiment-driven insights into trading strategies.
Her areas of interest include quantitative research, systematic trading, and AI-powered portfolio optimization.
Abstract:
Financial markets are shaped not only by fundamentals but also by investor sentiment, emotions, and market narratives. Traditional trading models rely on price and volume data, yet market psychology—driven by behavioral biases such as loss aversion and herding behavior—often creates inefficiencies that can be quantified and exploited using AI-driven techniques.
In this talk, we explore how Natural Language Processing (NLP) and deep learning can enhance sentiment-based market predictions and improve trading strategies. Key discussion points include:
• How FinBERT, LSTMs, and transformer-based models extract sentiment from financial news, earnings reports, and FOMC statements.
• Empirical backtesting results showing the impact of sentiment signals on market prediction accuracy.
• The role of social media sentiment in driving asset price volatility and its implications for trading strategies.
By combining behavioral finance, NLP, and deep learning, this session will demonstrate how sentiment-driven models can detect market shifts ahead of price movements, offering traders and portfolio managers a quantitative edge in volatile markets.
Thursday, May 1, 2025
Title: Identifying Patterns in Financial Markets: Extending the Statistical Jump Model for Regime Identification
Speaker: Petter Kolm
Professor and Director of the M.S. in Mathematics in Finance Program, Courant Institute of Mathematical Sciences, New York University
https://www.linkedin.com/in/petterkolm/
Petter is the Director of the Mathematics in Finance Master’s program and a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. In this role he interacts with major financial institutions such as investment banks, financial service providers, insurance companies and hedge funds. Petter worked in the Quantitative Strategies group at Goldman Sachs Asset Management developing proprietary investment strategies, portfolio and risk analytics in equities, fixed income and commodities.
Petter was awarded “Quant of the Year” in 2021 by Portfolio Management Research (PMR) and Journal of Portfolio Management (JPM) for his contributions to the field of quantitative portfolio theory. Petter is a frequent speaker, panelist and moderator at academic and industry conferences and events. He is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Financial Data Science (JFDS), Journal of Investment Strategies (JoIS), and Journal of Portfolio Management (JPM). Petter is an Advisory Board Member of Aisot, Axyon, GoQuant, and Volatility and Risk Institute at NYU Stern. He is also on the Board of Directors of the International Association for Quantitative Finance (IAQF) and Society of Quantitative Analysts (SQA), and Scientific Advisory Board Member of the Artificial Intelligence Finance Institute (AIFI).
Petter is the co-author of several well-known finance books including, Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006); Trends in Quantitative Finance (CFA Research Institute, 2006); Robust Portfolio Management and Optimization (Wiley, 2007); and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). Financial Modeling of the Equity Markets was among the “Top 10 Technical Books” selected by Financial Engineering News in 2006.
As a consultant and expert witness, Petter provides services in areas including alternative data, data science, econometrics, forecasting models, high frequency trading, machine learning, portfolio optimization with transaction costs, quantitative and systematic trading, risk management, robo-advisory, smart beta strategies, trading strategies, transaction costs, and tax-aware investing.
He holds a Ph.D. in Mathematics from Yale University; an M.Phil. in Applied Mathematics from the Royal Institute of Technology, Stockholm, Sweden; and an M.S. in Mathematics from ETH Zurich, Switzerland.
Abstract:
Regime-driven models are popular for addressing temporal patterns in both financial market performance and underlying stylized factors, wherein a regime describes periods with relatively homogeneous behavior. Recently, statistical jump models have been proposed to learn regimes with high persistence, based on clustering temporal features while explicitly penalizing jumps across regimes. In this article, we extend the jump model by generalizing the discrete hidden state variable into a probability vector over all regimes. This allows us to estimate the probability of being in each regime, providing valuable information for downstream tasks such as regime-aware portfolio models and risk management. Our model’s smooth transition from one regime to another enhances robustness over the original discrete model. We provide a probabilistic interpretation of our continuous model and demonstrate its advantages through simulations and real-world data experiments. The interpretation motivates a novel penalty term, called mode loss, which pushes the probability estimates to the vertices of the probability simplex thereby improving the model’s ability to identify regimes. We demonstrate through a series of empirical and real world tests that the approach outperforms traditional regime-switching models. This outperformance is pronounced when the regimes are imbalanced and historical data is limited, both common in financial markets.
Past Presentations
Tuesday, January 21, 2025
Title: Navigating the Currents: Key Aspects of Oil and Gas Trading
Speaker: Samvel Gevorkyan, Freepoint, Energy and Commodity Markets
Samvel Gevorkyan is a quantitative researcher at Freepoint Commodities. He is managing a systematic book, trading crude oil and natural gas and has been building algorithmic strategies and analytics for the past 6 years.
Abstract:
We are going to give a brief introduction to commodities markets, walk through the main components of Supply and Demand, and consider some aspects of systematic strategy design in order to extract risk premia based on physical as well as financial drivers. We will attempt to look at energy futures markets through the eyes of discretionary macro portfolio managers, CTA funds, and relative value strategists to gain insights into the movement of capital.
Thursday, January 23, 2025
Title: The Business of Trading
Speaker: Albert An, CEO Tower Research
Albert An joined Tower in 2016 as Head of Trading Supervision and subsequently assumed the role of Chief Technology Officer before becoming Chief Executive Officer in 2019. Prior to joining Tower, he was Co-Head of Electronic Volatility Trading and Retail Market Making at UBS. Before UBS, he held various senior electronic trading roles, including Head of Automated Market Making at Credit Suisse and Head of Automated Market Making at Merrill Lynch. He started his career at Wolverine Trading where he was a Principal and co-founder of their European trading business. He graduated from The University of Chicago with a BA in Economics and earned a MSc in Mathematical Trading and Finance from the University of London.
Abstract:
The talk discusses various aspects of trading business followed by questions and answers.
Tuesday, January 28, 2025
Title: Overview of MBS Market and Trading
Speaker: Margaret Lu, Portfolio Manager, Wells Fargo
Ms. Lu is currently senior portfolio manager at Wells Fargo bank, managing and trading for its investment portfolio with a focus on MBS and Treasuries. She also executes MBS related trades for the mortgage servicing portfolio of the bank. U.S. mortgage market and Treasury market currently have $12trillion and $28trillion outstanding securities respectively. Being one of the largest banks by asset size, Wells Fargo’s investment portfolio is also one of the largest in the nation, which consists of primarily MBS and Treasuries. Ms. Lu’s responsiblity includes relative value analysis, security selection to optimize income and regulatory measures, trade execution and market monitoring. Prior to Wells, Ms. Lu was head of securitized products risk management at the Chief Investment Office at JPMorgan where she managed the market risk of MBS, and non-agency RMBS, CMBS and CLOs for the investment portfolio at JPM. Ms. Lu started her career in securitization of CLOs backed by middle market loans and broadly syndicated loans at Merrill Lynch and Natixis. Ms. Lu has a master’s degree in Physics from City University of New York and received training in Financial Engineering from the FAME (Financial Asset Management and Engineering) program offered by Swiss Finance Institute at University of Lausanne. She is a graduate of the MAFN program.
Abstract:
An overview of U.S. mortgage market, the historical evolution of the market. The building bricks of MBS trading – the TBA security, including how it’s created, what are the key analytics measures used to gauge the relative value, and how it’s traded, in particular, how the TBA is rolled as dollar roll. Dollar rolls provide fundamental liquidity to MBS market and are crucial measures of market condition. We will look into how the dollar rolls are valued and traded. In addition, the session will explain how the market differentiates the value of MBS pools based on the variation of the mortgage loans’ characteristics, that is, how specified pools are valued and traded, as well as how to determine the relative value of such pools.
Thursday, January 30, 2025
Title: Investment Banking case example. Cruise line sector.
Speaker: Filo Fiorani, Managing Director, Bank of America
Filo Fiorani is a Managing Director in Real Estate, Gaming & Lodging Investment Banking at Bank of America.
Abstract:
The Cruise line sector was one of the most impacted during covid. We present the investment banking perspective to help it survive, and describe the situation of the sector today.
Tuesday, February 4, 2025
Title: MAFN Town Hall Meeting
Speaker: Jaehyuk Choi, Columbia University
Thursday, February 6, 2025
Title: Drawdown Betas and Portfolio Optimization
Speaker: Stan Uryasev, Stony Brook University
Stan Uryasev is Professor and Frey Family Endowed Chair of Quantitative Finance at the Stony Brook University. He received his M.S. in Applied Mathematics from the Moscow Institute of Physics and Technology (MIPT), Russia, in 1979 and Ph.D. in Applied Mathematics from the Glushkov Institute of Cybernetics, Kiev, Ukraine in 1983. From 1979 to 1987 he held a research position at the Glushkov Institute. From 1988 to 1992 he was a Research Scholar at the International Institute for Applied System Analysis, Luxenburg, Austria. From 1992 to 1998 he held the Scientist position at the Risk and Reliability Group, Brookhaven National Laboratory, Upton, NY. From 1998 to 2019 he was the George and Rolande Willis Endowed Professor at the University of Florida, and the director of the Risk Management and Financial Engineering Lab. His research is focused on efficient computer modeling and optimization techniques and their applications in finance and DOD projects. He published four books (two monographs and two edited volumes) and more than 130 research papers. He is a co-inventor of the Conditional Value-at-Risk and the Conditional Drawdown-at-Risk optimization methodologies. He developed optimization software in risk management area, including Drawdown and Credit Risk minimization. His joint paper with Prof. Rockafellar on Optimization of Conditional Value-At-Risk in The Journal of Risk, Vol. 2, No. 3, 2000 is among the 100 most cited papers in Finance. Many risk management/optimization packages implemented the approach suggested in this paper (MATLAB implemented a toolbox). Stan Uryasev is a frequent speaker at academic and professional conferences. He has delivered seminars on the topics of risk management and stochastic optimization. He is on the editorial board of a number of research journals and is Editor Emeritus and Chairman of the Editorial Board of the Journal of Risk.
Abstract:
The talk discusses a new dynamic portfolio performance risk measure called Expected Regret of Drawdown (ERoD), which is an average of the drawdowns exceeding a specified threshold (e.g., 20%). ERoD is similar to Conditional Drawdown-at-Risk (CDaR) which is the average of some percentage of the largest drawdowns. CDaR and ERoD portfolio optimization problems are equivalent and result in the same set of optimal portfolios. Necessary optimality conditions for ERoD portfolio optimization lead to Capital Asset Pricing Model (CAPM) equations. ERoD Beta, similar to the Standard Beta, relates returns of the securities and those of a market. ERoD Beta is equal to [average losses of a security over time intervals when market is in drawdown exceeding the threshold] divided by [average losses of the market in drawdowns exceeding the threshold]. Therefore, a negative ERoD Beta identifies a security which has positive returns when the market has drawdowns exceeding the threshold. ERoD Beta accounts only for time intervals when the market is in drawdown and conceptually differs from Standard Beta which does not distinguish up and down movements of the market. Moreover, ERoD Beta provides quite different results compared to the Downside Beta based on Lower Semi-deviation. ERoD Beta is conceptually close to CDaR Beta which is based on a percentage of worst case market drawdowns. However, ERoD Beta has some advantage compared to CDaR Beta because the magnitude of the drawdowns is known (e.g., exceeding a 20% threshold), while CDaR Beta is based on a percentage of the largest drawdowns with unknown magnitude. We have built a website reporting CDaR and ERoD Betas for stocks and the SP500 index as an optimal market portfolio. The case study showed that CDaR and ERoD Betas exhibit persistence over time and can be used in risk management and portfolio construction.
Tuesday, February 11, 2025
Title:
Speaker: Alexey Surkov, Deloitte
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Thursday, February 13, 2025
Title: Dimensional Analysis in Financial Engineering
Speaker: Jaehyuk Choi (MAFN Program Director), Columbia University
Abstract:
Tuesday, February 18, 2025
Title: Archegos and Credit Suisse, KeyBank
Speaker: Peter Cai
Peter Cai is a seasoned executive and recognized leader in risk management within major financial institutions. Currently, he serves as the Chief Market and Treasury Risk Officer at KeyBank, where he oversees asset liability management risk, interest rate risk, liquidity risk, traded market risk, and counterparty risk. Previously, Peter was the Global Head of Risk Data, Analytics, Reporting, and Technology (DART) at Citi, where he spearheaded critical transformation initiatives and managed teams of risk managers, analysts, quants, engineers, and consultants across all risk disciplines. His extensive experience also includes serving as the Global Head of Asset Liability and Investment Risk at Barclays, Chief Risk Officer at Global Atlantic (formerly Goldman Sachs Reinsurance), and roles in Enterprise and Portfolio Risk Management at Morgan Stanley, as well as Fixed Income Risk Strategist at Lehman Brothers. Peter holds a Ph.D. in Materials Science from Pennsylvania State University and a B.S. in Mathematics and Applied Mechanics from Fudan University in China.
Abstract:
This talk will shine a light on the Archegos failure that ultimately contributed to the downfall of Credit Suisse. First, we will introduce the business, the client, and the products that tie these two together. Then, covering a multiyear period leading up to the disastrous losses in early 2021, the focus will be on market dynamics, risk methodology, risk governance, and ultimately, risk management failure. We will collectively learn a few important lessons from this incident, as well as from other historically prominent headline losses, such as JP Morgan’s London Whale and other situations dating back to the 2008 financial crisis.
Thursday, February 20, 2025
Title: High Frequency Properties of SP500 Index
Speaker: Mikhail Smirnov, Columbia University
Abstract:
Tuesday, February 25, 2025
Title: Recent advances in the SABR model
Speaker: Jaehyuk Choi (MAFN Program Director), Columbia University
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Thursday, February 27, 2025
Title: Intro to Interest Rate Derivatives
Speaker: Soumya Mishra, Trader, Bank of New York Mellon
Soumya Mishra is a trader on the Derivatives Desk at BNY within Fixed Income and Markets. She primarily works with interest rate derivatives: swaps, caps, and bermudan options.
Abstract:
A brief history of interest rates and their evolution since 2008 along with current interest rate derivatives and options strategies used to manage risk.
The session will involve learning the basics of trading swaps, caps, floors, listed options, and futures. We’ll touch on the effect of the implied vol surface, convexity, and duration in pricing along with the similarites and differences between these instruments. The lecture will go over how to analyze the risk associated with each of these types of trades and high level components that affect the portfolio as a whole.
Tuesday, March 4, 2025
Title: Large Language Models (LLMs) in Finance
Speaker: Miquel Noguer i Alonso, PhD
Miquel Noguer i Alonso is a seasoned financial professional and academic with over 30 years of experience in the industry. He is the Founder of the Artificial Intelligence Finance Institute and Head of Development at Global AI. His career includes roles such as Executive Director at UBS AG and CIO for Andbank. He has served on the European Investment Committee UBS for a decade and is on the advisory board of FDP Institute and CFA NY.
As an academic, he has been teaching AI, Big Data, and Fintech at NYU Courant Institute, NYU Tandon, Columbia University, and ESADE. He pioneered the first Fintech and Big Data course at the London Business School in 2017. He has authored 100 papers on Artificial Intelligence an Finance and recently published the book Artificial Intelligence in Finance with Risk Books, Quantitative Portfolio Optimization for Wiley Finance and Large Language Models for Springer.
He holds an MBA and a Degree in Business Administration from ESADE, a PhD in Quantitative Finance from UNED, and other prestigious certifications. His research interests include asset allocation, machine learning, algorithmic trading, and Fintech.
Abstract:
Large Language Models (LLMs) are transforming the financial industry by enhancing decision-making, automating analysis, and optimizing workflows. These AI-driven models, such as GPT-4, BloombergGPT, and FinBERT, offer significant advantages in risk management, quantitative finance, asset allocation, and regulatory compliance. Below are some key areas where LLMs are making an impact in finance.
Mr Noguer will be covering 5 papers written about LLMs in Finance.
Thursday, March 6, 2025
Title: A Practitioner’s View on Model Risk Management (Quantitative Concepts to Real-World Governance)
Speaker: Max Samadov, SAS Institute
Max is a seasoned risk analytics leader with over 15 years of experience guiding global financial institutions through transformative regulatory, AI/ML, and data-driven initiatives. As a North American Head of Risk at SAS Customer Advisory, Max oversees a high-performing team responsible for delivering enterprise-wide risk solutions. A former product manager at IBM Algorithmics, Max led the development of Basel II/III-compliant offerings adopted by major banks worldwide. Max combines deep industry expertise with a customer-centric approach, his passion for translating quantitative concepts into real-world governance makes him a sought-after speaker at conferences and executive forums alike. Max holds MEng degrees in Data Analytics from Cornell University, MA in Public Relations from Ball State University and BSc in Economics from Donetsk National University and holds FRM certification from GARP.
Abstract:
This session introduces the fundamental principles and practical realities of Model Risk Management (MRM), highlighting how inadequate model governance can lead to catastrophic outcomes in financial markets. We will explore significant historical failures, review key regulatory frameworks (including SR 11-7 and E-23), and discuss best-practice structures such as the “three lines of defense.” Roles and responsibilities within MRM—encompassing model owners, validators, model risk managers, and internal auditors—will be examined, emphasizing the critical interplay of quantitative rigor and corporate governance. We will also address the emerging importance of AI governance, given the rise of machine learning models. Finally, the session concludes with a demonstration of how software solutions operationalize model governance, showcasing features like model inventories, model cards, performance monitoring, assessment workflows, and reporting capabilities.
Tuesday, March 11, 2025
Title: Investor Composition and the Liquidity Component in the U.S. Corporate Bond Market
Speaker: Haiyue Yu, Princeton University Bendheim Center for Finance
Abstract:
Thursday, March 13, 2025
Title: Virtual Barrels: Quantitative Trading in the Oil Market
Speaker: Ilia Bouchouev Ph.D., Pentathlon Investments, LLC
Dr. Ilia Bouchouev is the former President of Koch Global Partners where he launched and managed global derivatives trading business for over 20 years and was recognized as one of the pioneers in energy options trading. He is currently a managing partner at Pentathlon Investments and an adjunct professor at New York University, where he teaches energy trading at The Courant Institute of Mathematical Sciences. He is also a senior research fellow with The Oxford Institute for Energy Studies.
Ilia Bouchouev published in top academic journals on energy markets and derivatives modelling. He is frequently quoted by Wall Street Journal, Financial Times, Bloomberg, many other news providers, and on social media.
He is the author of the recent book “Virtual Barrels” on quantitative oil trading:
Abstract:
We start by explaining the crucial role of storage in trading energy commodities, such as oil. The price of oil is stylistically modelled as a financial derivative of stochastic inventories with boundary conditions given by two extreme cases of inventory “stock-outs” and “tank-tops”. We illustrate this theory with practical examples, including the episode of negative oil prices. We then explain how the performance of many popular risk premia strategies, such as momentum and carry, relate to the theory of storage. Subsequently, we move to options and discuss gamma trading and volatility risk premium in the oil market. We conclude by presenting the novel quadratic normal model for options and describe how it can be effectively used for trading volatility smile.
Tuesday, March 18, 2025 – SPRING BREAK
Thursday, March 20, 2025 – SPRING BREAK
Tuesday, March 25, 2025
Title: From Algorithms to Autonomy: Systematic Design of Real-Time Pricing and Trading Engines for Municipal Bonds
Speaker: Roberto Strepparava, CME Group
Roberto Strepparava is currently quant researcher at CME Group where he develops and manages risk systems and valuation for fixed income and rate (linear, nonlinear) products. Prior to that he was a fixed income quant researcher at JPMorgan, at Guggenheim Partners under Marcos Lopez De Prado and at Bloomberg LP in BVAL. Roberto holds a M.Sc. in Theoretical Physics from Milan University and a Ph.D. in Mathematics from Padua University, both on dynamical systems theory.
Abstract:
We outline the comprehensive design and evolution of advanced pricing engines for municipal bonds. We first examine a semi-automated system that addresses two critical tasks: a) creating a real-time pricing engine for the vast and illiquid municipal bond market, and b) developing a methodology for estimating the fair value of new issuances. The system integrates multiple data sources, applies sophisticated bid-ask and lot size adjustments, employs neural networks for benchmark construction, and leverages ensemble machine learning for price blending.
We then explore the future vision to transform the system into a fully autonomous trading platform capable of bond selection, risk assessment, trade execution, and performance analytics without human intervention.
Thursday, March 27, 2025
Title: Kelly Criterion for one and several investments and its calculation
Speaker: Mikhail Smirnov
Abstract:
Kelly criterion, which is an optimal fixed-fraction betting strategy in favorable games was introduced by J.L.Kelly of Bell Labs in 1956. It was further applied by E.Thorpe who solved Blackjack and stock warrant arbitrage to gambling and investment situations. In financial markets Kelly criterion is known as a Merton optimal growth portfolio strategy. E.Thorp using 1926-1984 data found SP500 index optimal Kelly leverage to be 117%. We extend Thorp and find that more recently 1996-2024 SP500 Kelly leverage was significantly higher at 240%, discuss fractional Kelly investing, calculate multivariable US stocks/bonds/corporate bonds Kelly ratios and propose methodologies for practically calculating them.
Tuesday, April 1, 2025
Title: Model Risk Management for Algorithmic Trading
Speaker: Jesus Ruiz-Mata, Bank of America
Abstract: