Lecture 1, Part I: Introduction of the Class

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.