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MS and E347 - Credit Risk: Modeling and Management

Expand your knowledge of quantitative credit risk modeling and credit derivatives to analyze powerful default market data trends

Course at a Glance

Mode of learning : Online - Self Paced

Domain / Subject : Engineering & Technology

Function : Information Technology(IT)

Trainer name : Kay Giesecke

Starts on : 6th Jan 2015

Difficulty : Advanced

MS and E347 - Credit Risk: Modeling and Management

Available Online

Overview
Expand your knowledge of quantitative credit risk modeling and credit derivatives to analyze powerful default market data trends. Evaluate the risk present in credit markets and learn how to successfully use important market-implied default probabilities. Practical examples will use market data to help break down complex topics such as model calibration and default timing.

Instructors

Kay Giesecke Assistant Professor, Management Science and Engineering

Units
3.0

Grading
Grading is based on a project, in which students work in teams on a real-world problem. Most projects require formulating a quantitative model, implementing it and estimating the model parameters from data. At the end of the quarter, the teams will present the projects to the class and receive feedback. We will suggest some problems but students are welcome to propose their own projects.

Course eligibility

Prerequisites
Knowledge of probability and stochastic processes at the level of MS&E 321 or 322 or similar (filtration, general conditional expectation, Brownian motion, stochastic integral, martingale, Ito formula, Stieltjes integral, change of measure).

1 year of college level calculus (through calculus of several variables)
Background in statistics
An undergraduate degree with a GPA of 3.0 or equivalent
Recommended
Knowledge of financial engineering at the level of  Stanford Courses MATSCI 342, MATH 180, MATH 240 or F 622 is highly desirable (fundamental theorems of asset pricing in continuous time). Knowledge of some programming language or mathematical/statistical software package such as MATLAB, Mathematica or R is beneficial.

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Topics Include

  • Credit default swaps(CDs)
  • Cash collateralized debt obligations
  • Estimation from historical default data
  • Portfolio credit risk

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