Generative AI in Risk Management Beyond Traditional Models

🎥 In this clip, Dr. Ernest Chan breaks down how Generative AI transforms modern risk management in trading and finance. Traditional models like VAR, CFAR, and copulas rely on parametric and often unconditional assumptions.

But what happens when real-world complexities—like tariffs, bond yields, or multiple macroeconomic shocks—enter the picture?

🤖 Enter Generative AI. Unlike conventional tools, Gen AI can model complex conditional probability distributions, enabling traders and risk managers to simulate realistic scenarios based on real-time prompts. Whether it's tail dependencies or rare market events, Gen AI adapts where older models fail.
📊 Perfect for anyone serious about quantitative finance, risk management, and AI-powered trading strategies.

🔍 Key Takeaways:
– Limitations of VAR, CFAR, and copula-based models
– Why traditional models ignore complex conditions
– Power of Gen AI in modelling regime-specific risks
– Real-world applications of conditional distributions in trading

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About the Speaker:
Dr. Ernest P. Chan is a recognized expert in applying statistical models and machine learning to finance. He is the Founder and Chief Scientist at PredictNow.ai, where he helps investors make informed decisions using advanced data-driven insights. Additionally, he is the Founder and Non-executive Chairman of QTS Capital Management, LLC, which focuses on systematic trading strategies. Dr. Chan has worked at notable organizations like IBM Research, Morgan Stanley, and Credit Suisse, gaining experience in pattern recognition, data mining, and quantitative trading.
Dr. Chan obtained his PhD in Physics from Cornell University and his B.Sc. in Physics from the University of Toronto. He has also authored several influential books, including Quantitative Trading and Algorithmic Trading.

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