Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Recitations: 1 session / week, 1.5 hours / session
This course covers the main quantitative methods of finance. The course covers three broad sets of topics: derivative pricing using stochastic calculus, dynamic optimization, and financial econometrics. The emphasis is on rigorous and in-depth development of the key techniques and their application to practical problems.
15.401 Finance Theory I is a prerequisite for this course. 15.437 Options and Futures Markets is a recommended co-requisite.
Rudimentary programming skills are necessary. Homework assignments involve computer implementation of quantitative methods in MATLAB®. Prior knowledge of MATLAB® is not required. In addition to formal prerequisites, the course assumes solid undergraduate-level background in calculus, probability, and statistics.
|Group problem sets (6 total)||50%|
The lectures will include suggestions for additional readings for each topic. Since there is no single textbook covering all the relevant topics, several books will be used. [Back] covers topics in stochastic calculus and derivative pricing. [Tsay] covers time-series methods in financial econometrics, and is the most frequently used textbook. [Cochrane] and [CL&M] cover advanced topics in financial econometrics. [D&S] covers basic background in probability and statistics and can be used for review as necessary.
List of Topics
The class will cover the following core topics:
- Absence of arbitrage and risk-neutral pricing;
- Itô stochastic calculus, Black-Scholes model and extensions, interest rate models;
- Dynamic programming, asset allocation, Merton's solution, numerical methods for dynamic portfolio choice;
- Monte Carlo simulation for derivative pricing;
- Maximum likelihood and quasi-maximum likelihood estimation;
- Generalized method of moments (GMM) basics, regression as GMM, standard errors, delta-method;
- Small-sample inference, bootstrap;
- Volatility models, GARCH.
Advanced topics include:
- Derivative pricing and dynamic portfolio choice;
- Extensions of GARCH, MIDAS models, multivariate volatility models;
- Exploiting return predictability.
|WEEK #||KEY DATES|
|3||Problem set 1 due|
|5||Problem set 2 due|
|6||Problem set 3 due|
|9||Problem set 4 due|
|11||Problem set 5 due|
|13||Problem set 6 due|