What This Document Is
This document presents a challenging paper/project assignment for students enrolled in an Advanced Financial Mathematics course (MATH 324) at the University of Connecticut. It outlines a substantial research and analytical task centered around credit risk and credit risk derivatives, designed to encourage in-depth exploration of theoretical concepts and practical application through modeling and calculation. The assignment is designed to be pursued individually, though collaboration is permitted with the expectation of unique, individual write-ups demonstrating personal understanding.
Why This Document Matters
This assignment is ideal for students seeking to solidify their understanding of advanced financial modeling techniques, particularly those related to assessing and managing credit risk. It’s most valuable for students preparing for careers in quantitative finance, risk management, or related fields. Successfully completing this assignment demonstrates a strong grasp of binomial models, derivative pricing, and the complexities of incorporating real-world factors like default risk into financial instruments. It’s best utilized as a capstone project to synthesize knowledge gained throughout the course.
Topics Covered
* Binomial Option Pricing Models
* Credit Risk Assessment
* Credit Risk Derivatives
* Risk-Neutral Valuation
* Replicating Portfolios & Hedging Strategies
* Corporate Bond Valuation
* Defaultable Securities
* Derivative Pricing (Calls, Puts, Digital Options)
What This Document Provides
* A detailed problem statement focused on extending the standard one-stage binomial model to incorporate default risk.
* Two distinct approaches to address the challenges of modeling default events.
* Specific tasks requiring the development of equations, solution methodologies, and portfolio construction.
* Guidance on incorporating market observables (like bond yields and option prices) into the modeling process.
* A clear deadline for submission and expectations regarding individual contributions.
