Synthetic VIX Data Generation Using ML Techniques

Synthetic VIX Data Generation Using ML Techniques
Thought Leadership Webinar:  Complimentary to the PRMIA network! Join us for this high-level overview of ML and AI for Financial Professionals. This webinar will provide a look inside our newest, 8-week interactive online series, "Foundations of ML & AI for Financial Professionals," starting in May 12, 2020 -  a partnership with QuantUniversity.

Presented By:
Sri Krishnamurthy, CFA, CAP 

April 15, 2020

10:00 a.m. - 11:00 a.m. EDT
3:00 p.m. - 4:00 p.m. GMT

Session Length:
60 minutes


About This Webinar
This webinar will provide a look inside our newest, 8-week interactive online series, "Foundations of ML & AI for Financial Professionals," starting in May 12, 2020 - a partnership with QuantUniversity.  

In this webinar, we aim to bring clarity to how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques, and AI in the investment industry. At the end of this webinar, participants will see a concrete picture of how machine learning and AI techniques are fueling the Fintech wave!


Part 1: We will discuss key trends in AI and machine learning in the financial services industry. We will discuss the key use cases, challenges, and best practices of using AI and ML techniques in financial services. We will also discuss key players and drivers for the AI and Machine learning revolution.

Part 2: We will illustrate a case study where AI and machine learning techniques are applied in financial services.

Case study: Synthetic VIX data generation using Machine learning techniques
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic data generators could be used for scenario generation when modeling future scenarios when trained on real and synthetic scenarios. The advent of novel techniques in Machine Learning has rekindled interest in using deep learning techniques like Generative Adversarial Networks (GANs) and Encoder-Decoder architectures in financial synthetic data generation.

In this case study, we discuss a recent study we did to see the efficacy of synthetic data generation when there are significant VIX changes in the market during short time horizons. We used QuSynthesize, a synthetic data generator for time-series based datasets and used historical VIX datasets and synthetic VIX scenarios to generate futuristic scenarios. 


About Our Expert  

    Sri Krishnamurthy, CFA, CAP is the founder of, a data and Quantitative Analysis Company, and the creator of the Analytics Certificate program and Fintech Certificate program. Sri has more than 15 years of experience in analytics, quantitative analysis, statistical modeling and designing large-scale applications. Prior to starting QuantUniversity, Sri worked at Citigroup, Endeca,  MathWorks, and with more than 25 customers in the financial services and energy industries. He has trained more than 1000 students in quantitative methods, analytics, and big data in the industry and at Babson College, Northeastern University, and Hult International Business School.

Sri earned an MS in Computer Systems Engineering and another MS in Computer Science, both from Northeastern University and an MBA with a focus on Investments from Babson College.

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4/15/2020 10:00 AM - 11:00 AM
Thought Leadership Webinar

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