Peter Carr Seminar Series: Bruno Dupire & Bruno Kamdem
4 PM
Bruno Dupire
Title
A Generic Framework for Statistical Arbitrage
Abstract
What determines the potential of a trade? What are the main ingredients? We propose a fairly general setting. After having defined the pricing state variables relevant to the situation, one has to develop market scenarios for them, together with weights and identify the tradable instruments. Then any portfolio of them leads to a value for each scenario; the collection of them forms the histogram of the PnL and a risk measure gives a score to the histogram, hence to the portfolio. The score is then optimized over the portfolios. We address the issues of scenario generation, exploring the space of tradable instruments as including dynamic trading of the underlying makes it very vast and debate the choice of the risk measure. We retrieve classical arbitrage results and provide numerous examples, ranging from super-replication to optimal hedge to optimized portfolios.
Bio
Bruno Dupire is heading the Quantitative Research at Bloomberg L.P. after having led research teams at several banks. 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. He is running the Bloomberg Quant (BBQ) seminar, the largest monthly quant seminar.
5 PM
Bruno Kamdem
Title
Asset Return Prediction: Reimagining Generative ESG Indexes and Market Interconnectedness
Abstract
Financial economists have long studied risk premiums, pricing biases, and diversification impediments. This study asks: what is the relationship between a firm's ESG commitment and its market returns? To answer this, we developed an algorithmic protocol. It identified three new, non-observable time-series factors for E, S, and G. We then tested these factors for information content. This was done by building a six-factor Fama and French model. We recognized that these models often have complex, nonlinear relationships. Therefore, we estimated the model using an enhanced shallow-learning neural network. We also used explainable AI (XAI) to simplify the machine learning outputs. Our work extends the literature in two key ways. First, we identify novel time-series-based E, S, and G factors. Second, we demonstrate how machine learning can model asset returns amidst the complexity of sustainability factors. To validate our approach, we compared our neural-network-estimated ESG weights with London Stock Exchange ESG ratings. The results provide strong support for our methodology.
Bio
Bruno G. Kamdem is an Assistant Professor in the Department of Business Management at the School of Business, SUNY Farmingdale. He teaches Business Statistics, Business Data Management, Advanced Business Analytics, and Advanced Business Statistics, equipping students with the analytical and strategic decision-making skills needed to thrive in today’s dynamic business environment. Before entering academia, Bruno served in the U.S. Government and co-founded Lepton Actuarial and Consulting, LLC, a minority-owned startup specializing in data-driven consulting solutions. His research explores cutting-edge topics including explainable AI (XAI), advanced neural networks, and stochastic differential game theory for sustainable and optimal supply chain management. He also investigates reinforcement learning, sustainable finance, carbon derivatives, and ESG risk analysis.
Bruno brings extensive teaching and consulting experience in Sustainable Finance, Corporate Finance and Financial Markets, Financial Software Development with Python, Commodity Markets and Green Energy Finance, and Decision-Making under Uncertainty and Risk Management. In 2021 and 2022, he designed and taught the inaugural “Sustainable Finance” course for the Master’s program in Finance and Risk Engineering at NYU Tandon School of Engineering. He holds a Ph.D. in Operations Research from The George Washington University’s School of Engineering and Applied Science, an M.S. in Applied Mathematics, and a B.S. in Mathematics (PiMu Epsilon) and Economics from the University of Maryland, Baltimore County. His work has been published in peer-reviewed journals and presented at leading international conferences. Highlights are available on his faculty webpage: https://www.farmingdale.edu/faculty/?fid=111016
