Title of the talk:  EVT Based Rate-Preserving Distributional Robustness for Tail Risk
Abstract: Risk measures such as Conditional Value at Risk (CVaR) focus on extreme losses, where limited data makes model errors unavoidable. To hedge against model misspecification, practitioners evaluate worst-case tail risk under ambiguity sets.  Working with a fairly general class of distortion risk measures, we use Extreme Value Theory (EVT) to characterize the asymptotic scaling of worst-case tail risk under standard ambiguity sets, including Wasserstein balls and classical Φ-divergence neighborhoods, and show that robustification can inflate tail risk at a different first order EVT rate than the nominal model as the tail level 𝛃 → 0. Motivated by this diagnostic, we propose a tail-calibrated robustification - Rate-Preserving EVT-DRO (RPEV-DRO), designed to match the nominal model’s asymptotic rate for risk while still guarding against misspecification. Under standard domain of attraction assumptions, we prove that the worst-case risk under RPEV-DRO grows at the same first-order asymptotic rate as the baseline as 𝛃 → 0 , uniformly over key tuning parameters. We also establish analogous guarantees for a data-driven implementation based on consistent tail-index estimation. Synthetic and real data experiments, including network style systemic risk losses, show that RPEV-DRO avoids the severe risk inflation often induced by Wasserstein and Φ-divergence formulations. 

Bio: Anand Deo is an Assistant Professor at the Indian Institute of Management Bangalore. He holds a Ph.D. in Operations Research and Financial Engineering from the Tata Institute of Fundamental Research Mumbai. His research interests are centered around Decision Making Under Uncertainty, Monte-Carlo Simulations, Quantitative Risk Management, Operations Research and Statistics. His current work aims at understanding how to combat model risk when dealing with optimization problems involving distribution tails.  His work has appeared in leading journals in Operations Research and has received the I-SIM Best Publication Award, 2024.  

Title of the talk:  Random Neural Network Algorithm for Solving Nonlinear PDEs in High-Dimensional Option Pricing*
Abstract: We present a random neural network–based algorithm that efficiently solves high-dimensional nonlinear partial differential equations (PDEs) and apply it to the pricing of high-dimensional financial derivatives under default risk. Our empirical results demonstrate that the algorithm can approximately solve nonlinear PDEs in up to 10,000 dimensions within seconds.

This talk is based on joint works with Christian Beck, Sebastian Becker, Patrick Cheridito, and Arnulf Jentzen, as well as Philipp Schmocker and Sizhou Wu.

Bio: Ariel Neufeld is a tenured Associate Professor in mathematics at the Nanyang Technological University in Singapore. He received his PhD in mathematics in May 2015 at ETH Zurich, where he spent half of his PhD at the Columbia University in the City of New York. Prior to joining NTU he was a postdoctoral researcher at ETH Zurich. His research focuses on model uncertainty in financial markets and distributionally robust optimization, machine learning algorithms and their applications in finance and insurance, financial and insurance mathematics, as well as stochastic analysis and stochastic optimal control. 

For his research, he obtained in 2021 the SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize "for outstanding contributions to utility maximization and hedging under model uncertainty and to modern numerical methods for finance and insurance"

Online*

Title of the talk:  E-commerce sellers' ratings and FinTech innovations
Abstract:  Coming soon!

Bio: Prabir Kumar Das is a Professor of Statistics/Analytics at Indian Institute of Foreign Trade (IIFT) Kolkata. Having more than 25 years of experience in Research, Training, and Teaching. He has published over 50 peer-reviewed research papers in prestigious journals, conf. proceedings, and books (ABDC-A/B, Scopus Q1/Q2, WoS, and RePEc-listed outlets) in diverse areas such as e-commerce, financial analytics, marketing, telecom, media, and climate research using statistical models and machine learning applications.

Prof. Das is trained in advanced statistical modeling from globally reputed institutions, such as the University of Reading, University of East Anglia, and the Rothamsted Experimental Station (UK). He has also received training from the SAS Institute, New York, USA, and has taken part in international academic engagements such as "Doing Business in China" at the Fudan University, Shanghai, PRC. He was awarded a scholarship from Harvard Business School to attend its Executive Education program.

Prof. Das holds a Ph.D. in Nonlinear Statistical Models from the IASRI, New Delhi.

Title of the talk: Rollover Risk and Systematic Risk of Shadow Banks
Abstract: We investigate the relation between rollover risk and systemic risk exposure in the shadow banking sector. Our sample consists of handcollected data from annual reports of retail non-bank financial companies (Retail-NBFCs), which constitute a significant part of the shadow banking sector in India, and commercial paper holdings of liquid debt mutual funds (LDMFs), which are a key source of financing for Retail-NBFCs. In 2018, Retail NBFCs experienced a sharp increase in rollover risk following defaults by subsidiaries of Infrastructure Financing and Leasing Services (IL&FS). We use this empirical setting to construct a rollover risk measure and show that it is more informative about systemic risk exposure than existing proxies. We also show that systemic risk exposure of Retail-NBFCs is positively related to our measure of rollover risk and negatively related to the level of liquidity buffer in the LDMF sector; furthermore, we show that the impact of rollover risk on systemic risk exposure is less adverse when liquidity buffer in the LDMF sector is high.

Bio: V. Ravi Anshuman is a Professor of Finance in the Finance & Accounting area of the Indian Institute of Management Bangalore. Prof. Anshuman’s research interests cover the areas of market microstructure, capital markets, and corporate financial management issues in emerging markets. His publications have appeared in the Journal of Political Economics: Macroeconomics, Review of Financial Studies, Journal of Financial Economics, Journal of Financial Markets, Journal of Applied Corporate Finance, etc. He has co-authored the international edition of the book titled Valuation: Analyzing Global Business Opportunities, with Sheridan Titman and John Martin. He has held a tenure-track academic position at Boston College and visiting academic positions at Hong Kong University of Science and Technology, Indian School of Business and The University of Texas at Austin. He has served on several committees at the National Stock Exchange (NSE), the Reserve Bank of India (RBI), the Securities Exchange Board of India (SEBI), and the Ministry of Finance (India).

From 2019 to 2024, Prof. Anshuman served as a Part-time Board Member in the Board of the Securities Exchange Board of India (SEBI).

Title of the talk:  Sustainable Investment: Maintaining portfolio performance while reducing the carbon footprint
Abstract: Amid the global crisis of climate change, urgent action is imperative. In this study, we develop two types of decarbonized indices, which render a dynamic hedging approach for passive investors. Focusing on long-term returns with minimal active trading and risk exposure, we create the decarbonized indices for NIFTY-50, a benchmark index for the Indian market. Proposed methodology relies on suitable optimization techniques to choose the portfolio weights that minimize the tracking error while significantly reducing carbon footprints. These indices are shown to perform better than existing benchmarks, especially during major climate events. They are likely to offer investors a buffer to adapt to climate policies and carbon pricing. In ongoing work, we use tail risk measures instead of variance to control the risk of the portfolio.

Bio: Rituparna Sen is an Associate Professor at the Applied Statistics Division, Indian Statistical Institute, Bangalore. She worked as Assistant Professor at UCDavis after obtaining a PhD in statistics from the University of Chicago. She has authored over thirty papers and a book titled Computational Finance with R. She is the editor of the journal Applied Stochastic Models in Business and Industry and associate editor of several other journals. She has guided five PhD students, seven masters theses and numerous undergraduate interns. Rituparna is an elected member of the International Statistical Institute. She has been awarded the Young Statistical Scientist Award by the International Indian Statistical Association, the Best Student Paper Award by the American Statistical Association and Women in Mathematical Sciences award by Technical University of Munich, Germany.

Title of the talk: A Primer on Blended Finance and its Framework
Abstract:  In this expository lecture, we present a structured overview of Structured Blended Finance (SBF) design, with an emphasis on the quantitative drivers of risk management and cash flow distribution. We begin with the general architecture of blended finance, highlighting the role of concessional capital within the paradigm of asymmetric payoff profiles for heterogeneous investor objectives. Traditional credit risk modelling approaches, including intensity models, default probabilities and dependency structures, are used in analyzing the portfolio loss distributions. These loss distributions are pertinent in determining the characteristics of the tranches. We then move on to examining the cash flow framework of pay-through structures, highlighting how contractual waterfalls translate portfolio-level outcomes into tranche-specific returns over the life of the fund. Finally, an algorithm on the operational aspects of SBFs is discussed to demonstrate how credit risk modelling and simulated cash flows are combined to evaluate tranche risk-return profiles and to calibrate fund structures, in a manner that is consistent with senior investor constraints in conjunction with the objectives of the originator.

Bio: Professor Chakrabarty earned his Ph.D. from the University of Illinois at Chicago, USA and was a Visiting Fellow at the Tata Institute of Fundamental Research, Centre for Applicable Mathematics, before joining Indian Institute of Technology Guwahati as a faculty member. He has eighteen years of teaching and research experience in the areas of financial engineering, computational finance, portfolio theory, and financial risk management, both at undergraduate and postgraduate levels. He has also developed and delivered two NPTEL MOOC courses and has supervised seven Ph.D. theses. He has several papers in the areas of sustainable finance, computational finance, portfolio analysis and financial risk management in journals of repute, such as Energy Economics, SN Business and Economics, Asia Pacific Financial Markets, Journal of Computational and Applied Mathematics and Journal of Quantitative Economics. Professor Chakrabarty serves on the Editorial Board of three international scientific journals, is a recipient of the Reserve Bank of India Faculty Scholarship and is currently a Fellow of the Institute of Mathematics and Its Applications (UK).