|Course Dates||Length||Meeting Times||Status||Format||Instructor(s)||CRN|
|June 21, 2021 - July 21, 20216/21 - 7/21||4 Weeks||Online||Waitlisted||Online||Mehdi Khorami||11803|
|July 19, 2021 - August 18, 20217/19 - 8/18||4 Weeks||Online||Waitlisted||Online||Mehdi Khorami||11804|
Quantitative Analysts (Quants for short) are considered the rocket scientists of Wall Street. They design and implement complex mathematical models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds, but also by insurance companies and management consultancies and others.
This course has two parts: Interest Rates and Probability Theory for Finance. The topics covered in the first part include Interest Rates, Compound Interest, Annuities, and Loan Amortization. The second part of the course will focus on Probability Theory for Finance, covering topics such as Probability models, Probability Distributions, Discrete Expectations, and Conditional Probability. The course explores applications of these concepts in the financial industry.
This course provides students with the basic mathematical concepts and techniques used in finance and business, highlighting the inter-relationships of the mathematics and developing problem-solving skills with a particular emphasis on financial and business applications. The course will concentrate not only on the theory but also on practical applications.
Upon successful completion of the course students will be able to:
• Demonstrate understanding of basic concepts such as cash flow models, simple and compound rates of interest
• Have a knowledge of the various types of annuities and use them to solve financial transaction problems
• Employ methods related to these concepts in a variety of financial applications
• Calculate probabilities for various outcomes in finance
• Define and identify some basic probability distributions and random variables
• Apply logical thinking to problem-solving in the context of financial instruments.
Prerequisites: Strong Basic Algebra