The RTQF 2026: Unpacking the Latest Trends in Quantitative Finance

The Recent Trends in Quantitative Finance (RTQF) 2026 symposium, held at the Indian Institute of Science Bangalore on February 20th and 21st, brought together experts from academia and industry to discuss cutting-edge developments in quantitative finance. The event featured talks on various topics, including EVT-based rate-preserving distributional robustness for tail risk, random neural network algorithms for solving nonlinear PDEs, and the application of systems thinking in finance.

A New Era of Risk Management: EVT-Based Rate-Preserving Distributional Robustness

One of the key highlights of the symposium was Prof. Anand Deo's talk on EVT-based rate-preserving distributional robustness for tail risk. Deo presented a new framework for characterizing the asymptotic scaling of worst-case tail risk under standard ambiguity sets, including Wasserstein balls and classical Φ-divergence neighborhoods. The proposed approach, Rate-Preserving EVT-DRO (RPEV-DRO), aims to match the nominal model's asymptotic rate for risk while guarding against misspecification.

The RPEV-DRO framework has significant implications for risk management in finance. By providing a more accurate characterization of tail risk, financial institutions can better manage their exposure to extreme events and make more informed investment decisions. Deo's work also highlights the importance of robustifying models to handle uncertainty and ambiguity in quantitative finance.

The Rise of Machine Learning in Finance: Random Neural Network Algorithms

Prof. Ariel Neufeld's talk on random neural network algorithms for solving nonlinear PDEs was another notable presentation at the symposium. Neufeld presented a novel algorithm that efficiently solves high-dimensional nonlinear PDEs, with applications to option pricing under default risk. The empirical results demonstrated that the algorithm can approximately solve nonlinear PDEs in up to 10,000 dimensions within seconds.

The use of machine learning techniques in finance is becoming increasingly prevalent, and Neufeld's work represents a significant step forward in this area. By leveraging random neural networks, financial institutions can develop more accurate models for pricing complex derivatives and better manage risk in high-dimensional systems.

A Systems Thinking Approach to Finance: Unlocking New Value Across Emerging Areas

Biju Mathew's talk on applying a systems thinking lens to emerging areas in finance was another highlight of the symposium. Mathew argued that traditional model-centric thinking may no longer be sufficient, as financial systems become increasingly complex and interconnected. By adopting a systems thinking approach, financial institutions can unlock new value across emerging areas in finance.

Mathew's talk highlighted several challenges associated with applying systems thinking in finance, including the need to reimagine supply chains through digital tokenization and model autonomous AI agents that transact on behalf of humans. However, he also emphasized the potential benefits of this approach, including improved risk management and more efficient capital allocation.

What the Data Actually Shows: A 10-Year Backtest Reveals...

The symposium featured several talks on practical implementation and portfolio implications of quantitative finance. One notable presentation was by Prof. Rituparna Sen, who discussed sustainable investment strategies that maintain portfolio performance while reducing carbon footprints. Sen presented a dynamic hedging approach for passive investors, which creates decarbonized indices for NIFTY-50.

The proposed methodology relies on suitable optimization techniques to choose portfolio weights that minimize tracking error while significantly reducing carbon footprints. The results show that the decarbonized indices perform better than existing benchmarks during major climate events.

A New Approach for Pricing Share Buyback Contracts

Himalaya Senapati's talk on a new approach for pricing share buyback contracts was another highlight of the symposium. Senapati presented a recent methodology that replaces traditional control-based approaches with optimized heuristic strategies designed to maximize contract value. The valuation framework builds on classical techniques used for pricing path-dependent Bermudan options, enabling efficient numerical implementation.

The proposed approach has significant implications for companies considering share buyback contracts. By providing a more accurate characterization of contract value, companies can make more informed decisions about capital allocation and risk management.

The Hidden Cost of Volatility Drag: What the Data Actually Shows

Bhashkar Balan's talk on what needs to change in DeFi for wider adoption highlighted several challenges associated with broad-based adoption. Balan argued that overcoming these challenges requires a shift from protocol-centric design toward user- and institution-aware architectures. Privacy-preserving techniques such as zero-knowledge proofs, selective disclosure, and confidential execution can enable secure yet compliant interactions.

Balan's talk emphasized the need for improved abstraction layers and execution environments to simplify user interaction with smart contracts. By addressing these challenges, DeFi protocols can become more accessible to retail and institutional participants.

A Primer on Blended Finance: Unlocking New Value Across Emerging Areas

Prof. Siddhartha P. Chakrabarty's talk on a primer on blended finance represented another highlight of the symposium. Chakrabarty presented a structured overview of Structured Blended Finance (SBF) design, with an emphasis on quantitative drivers of risk management and cash flow distribution.

Chakrabarty's work highlights the potential benefits of blended finance in unlocking new value across emerging areas in finance. By leveraging concessional capital within asymmetric payoff profiles for heterogeneous investor objectives, financial institutions can develop more efficient and effective funding models.

A New Era of Risk Management: What the Data Actually Shows

The RTQF 2026 symposium represented a significant step forward in the field of quantitative finance. The talks highlighted several key developments, including EVT-based rate-preserving distributional robustness for tail risk, random neural network algorithms for solving nonlinear PDEs, and the application of systems thinking in finance.

The proposed approaches have significant implications for risk management in finance, with potential benefits including improved accuracy, reduced risk, and more efficient capital allocation. As financial institutions continue to grapple with complex risks and challenges, these developments offer a promising new direction for quantitative finance.