Event Description
Crypto assets represent a new financial primitive whose behavior cannot be fully explained by classical asset pricing theory. Unlike traditional securities, they operate at the intersection of stochastic processes, network theory, game theory, cryptography, and mechanism design, within an open, continuously evolving market microstructure.
This presentation explores the mathematical foundations of crypto assets, focusing on how value, risk, and dynamics emerge from protocol-level rules rather than centralized issuers.
Event Description
For the special, but important, case of American option pricing, practitioners often find that otherwise well-functioning finite difference methods yield results that are slower, less accurate, and less stable than expected. Perhaps as a consequence, simpler special-purpose methods (e.g., the binomial tree) are sometimes preferred for American options, despite various theoretical and practical drawbacks. In this talk we show how to remedy this situation, through a range of minimally invasive “tips and tricks” that significantly improve the speed and stability of the popular theta-method finite difference scheme when applied to American option values and greeks.
Event Description
False findings in financial research can lead to costly misallocations, flawed regulation, and misguided strategies. This event explores why these errors occur, focusing on the widespread assumption that financial time series are stationary and a-Holder continuous- an assumption at odds with markets shaped by regime shifts, stochastic volatility, and structural breaks.
Through case studies spanning AI-driven models and advanced quantitative techniques, Bloch argues for a fundamental shift toward methodologies that embrace non-stationary dynamics and recognize the limits of prediction in complex systems.
What You’ll Gain
- Critical insights into why false findings persist in financial research and their real-world impact
- Understanding of flawed assumptions in financial modeling, particularly around stationarity and continuity
- Exposure to case studies demonstrating failures in AI-driven models and quantitative techniques
- Alternative frameworks that embrace non-stationary dynamics and market complexity
- Practical perspective on the limits of prediction in complex financial systems
Who Should Attend
This session is ideal for:
- Quantitative analysts and financial researchers
- Risk managers and model validators
- Data scientists working in finance
- Anyone interested in the intersection of AI, finance, and model reliability
- Students and professionals concerned with research integrity and methodology
Event description
Breaking into quant finance is highly competitive, and interview success requires more than just technical expertise. This session will demystify the quant interview process, giving you practical tools and confidence to navigate even the most challenging questions.
Brian will walk you through real-world scenarios, share common pitfalls to avoid, and reveal what separates good candidates from exceptional ones. Whether you’re preparing for your first quant role or looking to advance your career, this session will equip you with actionable strategies to make a lasting impression.
What You’ll Gain
- Insider perspective on what top quant finance employers are really looking for
- Proven frameworks for tackling technical and analytical interview questions
- Practical techniques to demonstrate your quantitative skills and problem-solving prowess
- Expert preparation strategies and curated resources to give you a competitive edge
- Understanding of interviewer psychology and how to craft compelling, aligned responses
Who Should Attend
This session is ideal for:
- Students and graduates exploring careers in quantitative finance
- Early-career professionals preparing for quant interviews
- Anyone looking to transition into quantitative roles in finance
- Individuals seeking to sharpen their interview technique and market understanding
Event Description
This study investigates the market impact of the US-Mexico-Canada Trade Agreement (USMCA) on the stock market returns and volatility spillovers across the respective countries. We show that investors’ responses were significantly negative for each of the countries and most sectors around the critical event dates. The negative return impacts observed are consistent with multilateral wealth destruction for each party to the agreement and to the bloc as a whole. Volatility spillover effects are observed, with the US having largest impact. No significant evidence of a change in the volatility spillover effect is the period subsequent to USMCA official ratification. Since its inception, ongoing trade disputes as a consequence of USMCA led to speculation that the agreement’s future is dubious in the upcoming review in July 2026. This speculation has turned to almost a certainty with the inception of a Strategic Trade War between the parties on February 5, 2025.
Learning Outcomes
- Understand the impact of trade agreements on equity markets
Explain how the USMCA influenced stock returns and investor sentiment across the US, Mexico, and Canada.
- Analyze volatility spillover effects during geopolitical events
Identify how volatility transmission occurs between markets and why the US had the largest influence.
- Evaluate market risks arising from ongoing trade disputes
Discuss the implications of strategic trade wars and the uncertainty surrounding USMCA’s future.
Event Description
“It’s a difference of opinion that makes a horse race.” Generally attributed to Mark Twain, this quote encapsulates the debate over the usefulness of research amid growing concerns in the scientific community about the replicability, reproducibility and reliability of research findings.
By attending this session, participants will:
- Understand the limitations of traditional statistical methods – including p-values and NHST—and explore alternative approaches such as Bayesian inference and effect size estimation.
- Evaluate methodological reforms like open science practices and preregistration, and their impact on improving research transparency and credibility.
- Gain practical insights into computational reproducibility, including how programming tools can support robust and replicable quantitative research.
Event description
Discover what a typical day entails for a Risk Manager, including daily tasks, challenges, and collaborative efforts with various teams. Dr. Girish Mamtani will discuss his career journey, offering insights into the experiences and skills that paved his way to becoming a successful Risk Manager. Learn about the essential skills required for the role, such as critical thinking and attention to detail, and how the Certificate in Quantitative Finance (CQF) can enhance these abilities.
Event Description
In this exclusive session, you’ll witness firsthand how artificial intelligence is eliminating the traditional barriers between complex analytical thinking and execution. See live demonstrations of breakthrough technology that lets you query sophisticated trading analytics using natural language—no more wrestling with syntax or hunting through documentation.
What you’ll gain:
• Immediate productivity boost: Learn techniques to accelerate analytical workflows
• Competitive insights: Understand how AI-assisted analytics are creating new opportunities in market analysis
• Practical implementation: Get actionable strategies you can deploy immediately in your own quantitative work
• Future-ready skills: Position yourself at the forefront of the AI-driven transformation in quantitative finance
Live demonstrations will show real-world applications using cutting-edge tools, followed by an interactive Q&A where you can explore specific use cases relevant to your work.
Perfect for: Quantitative analysts, traders, risk managers, and technology professionals looking to leverage AI for competitive advantage.
Event Description
Financial regulatory agencies conduct periodic stress testing of systemically significant financial institutions to ensure they have the requisite capital to continue functioning as viable businesses during times of economic stress without jeopardizing the stability of the financial system. Manual design of these scenarios using historical data, exclusively or primarily, is hamstrung by the inherent limitations of historical experience, which may be inadequate to model unforeseen economic scenarios. To further compound the problem, correlations between macroeconomic variables may change and evolve in markedly different manner during those periods of economic malaise and a manual design of testing scenarios is likely to overlook those aspects of macroeconomic variable evolution. This talk will showcase the ability of generative AI methods to handle the twin challenges of economic scenario generation – generating realistically evolving scenarios that can capture the breadth of potential but unforeseen periods of stress.
