Black-Scholes and Beyond: Exploring Machine Learning and Hedging Strategies

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Black-Scholes and Beyond: Exploring Machine Learning and Hedging Strategies

Dr. Jörg Kienitz explores machine learning, hedging strategies, and the role of open-source software.

Monday 12 June 2023

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Podcast Description

In this episode of the QuantSpeak podcast, Dan Tudball is joined by Dr. Jörg Kienitz. Jörg discusses the topic of his upcoming talk at the Black-Scholes 50th Anniversary Conference, the role that Open Source Software played in his recent paper, the importance of C++, and more.

Topics & Timestamps

[00:00 - 01:12] Opening, Introductions, Guest Bio, Talk Preview

[01:12 - 03:34] Career Background, Black-Scholes-Merton Legacy, Presentation Theme

[03:34 - 10:10] Hedging in the Age of Statistical Learning, Gaussian Mixture Models, Delta Hedging

[10:10 - 14:08] Analytical Approaches, Model Applications (Heston, Bergomi), Research Evolution

[14:08 - 19:35] Era of Machine Learning, Statistical vs. Numerical Methods, Bayesian Approaches

[19:35 - 27:58] Regulatory vs. Trading Use, Explainable AI, Outliers, Conditional Data Generation

[27:58 - 32:01] Open Source Software, Collaborative Research, Code Sharing, QuantLib and Libraries

[32:01 - 37:05] Open Access Research, Peer Review, Publication Quality, Community Value

[37:05 - 44:19] Team Building, Collaboration, Managing Skills, Fitting Team Members

[44:19 - 48:13] Leadership Skills, Hiring Mindset, Team Integration, Handling Challenges

[48:13 - 56:04] Quant Developer Role, AI Impact, Differentiating Skills, Industry Trends

[56:04 - 60:00] Programming Languages, Best Practices, C++, Python and More

[60:00 - 61:51] Key Takeaways, Adapting Black-Scholes Concepts, Research Collaboration

[61:51 - 63:56] Closing Remarks.

Disclaimer

Podcasts are for informational purposes only and provided “as is” without any representation or warranty from Fitch Learning of any kind. Comments or statements expressed by speakers may not be those of the Fitch Learning. Fitch Learning is not providing advice or recommendations. Fitch Learning, its directors, officers, or employees do not accept any liability for any loss arising from the use of information.