# Lecture 1, Part I: Introduction of the Class Source: https://www.youtube.com/watch?v=b8u2CQLQBVU Recap page: https://rapidrecap.app/video/b8u2CQLQBVU Generated: 2025-12-03T16:12:07.408+00:00 --- ## Quick Overview The introductory class outlines a curriculum split between mathematical foundations taught by Peter and practical application segments featuring industry experts, covering topics from quantitative equity investing and PCA to Black-Scholes and machine learning, while emphasizing the use of RStudio Cloud for data analysis exercises. **Key Points:** - Vasily Strela, who has a PhD in math from MIT (advised by Gil Strang) and currently runs fixed-income quants at RBC, organizes the class to bridge mathematical theory and real-world finance applications. - The course structure intertwines a mathematical part taught by Peter with an application part taught by industry professionals, with prerequisites including linear algebra, statistics, and calculus, but no prior finance knowledge required. - Guest speakers include Jeff Shen from BlackRock on quantitative equity investing, Stefan Andreev from Two Sigma on PCA in finance, and John Hull on machine learning, highlighting the high caliber of industry involvement. - Peter, who holds a PhD in statistics from UC Berkeley and has experience working with hedge funds like IKOS, stresses that his math lectures focus on practical and useful financial modeling tools. - The class will use the language R and RStudio Cloud for illustrations, facilitating data collection from sources like Yahoo Finance and the Federal Reserve Economic Database, as students use an introductory R notebook called 'FM Intro1'. - Notable examples discussed include the VIX index as a 'fear gauge' and the striking event where the crude oil futures contract price went negative in 2020, an event brokers did not program for. - Students must complete Assignment 0 by submitting a survey detailing their background and interests to help the instructors get to know them. **Context:** This transcript captures the opening moments of a class, Lecture 1, Part I, led by Vasily Strela and Peter, designed to introduce students to the application of mathematics in the real world, specifically within finance. Vasily details his background transitioning from academic math research (wavelets/signal processing) to working as a quant in mathematical finance at places like Morgan Stanley and currently RBC. Peter introduces himself as a statistician from UC Berkeley and Harvard who transitioned into quantitative finance consulting and hedge fund work, emphasizing his passion for financial applications. ## Detailed Analysis The introductory lecture lays out the structure and purpose of the course: integrating mathematical concepts taught by Peter with practical applications presented by numerous industry practitioners. Vasily Strela, a quant at RBC with an MIT math PhD, emphasizes the value of combining academic rigor with industry experience. Prerequisites for the course are standard undergraduate math (linear algebra, statistics, calculus), but no finance background is needed. The schedule features high-profile guests like Tarek Mansoor and Luna Lopez (who founded the Kalshi exchange), Andrew Lo, and the legendary John Hull, who will cover topics ranging from quantitative equity investing and principal component analysis to Black-Scholes and machine learning. Peter confirms that his mathematical lectures focus on practical tools for financial modeling. Furthermore, the class will utilize R and RStudio Cloud for data analysis, demonstrated by an initial notebook, 'FM Intro1,' which shows how to collect and visualize financial data like the S&P 500, VIX (the 'fear gauge'), Bitcoin, and the unprecedented negative pricing of crude oil futures in 2020. The session concludes with the distribution of Assignment 0, a simple survey for student introduction. ### Class Structure and Goals - Mathematical part taught by Peter intertwined with application part by industry experts - Aims to introduce real-world applications of math in finance - Prerequisites include linear algebra, statistics, and calculus. ### Instructor Backgrounds - Vasily Strela transitioned from MIT math PhD (wavelets) to running fixed-income quants at RBC - Peter holds a statistics PhD from UC Berkeley and worked with hedge funds, emphasizing practical math for modeling. ### Guest Speakers and Topics - Jeff Shen on quantitative equity investing - Stefan Andreev on PCA - Andrew Gustavsson on swaps and curve construction - John Hull on machine learning - Tarek Mansoor and Luna Lopez discussing their MIT-alum founded exchange, Kalshi. ### Software and Data Use - The course uses the language R via RStudio Cloud for illustrations - Facilitates access to state-of-the-art statistical methods and data collection from sources like Yahoo Finance and the Federal Reserve Economic Database. ### Key Financial Illustrations - Discussion included the VIX index as a 'fear gauge' - The striking event of crude oil futures going negative in 2020, which surprised broker systems - The potential and skepticism surrounding Bitcoin's market performance.