FE535 Introduction to Financial Risk Management
Financial Engineering
M 06:15-08:45PM
  • Fall On Campus
  • Spring On Campus
Market Quotes of Major Asset Classes
Risk Types of Major Asset Classes
Statistical Analysis of Financial Data for Risk Management
Stochastic Processes and Risk Measures (VAR, CVAR)

Risk Control and derivative pricing are major concerns for financial institutions. Yet, as recent events have shown us there is a real need for adequate statistical tools to measure and anticipate the amplitude of the potential moves of the financial market. Many of the standard models seen on Wall Street however are based on simplified assumptions and can lead to systematic (and sometimes dramatic) underestimation of real risks. Starting from a detailed analysis of market data, one can take into account more faithfully the real behavior of financial markets (in particular the ‘rare events’) for asset allocation, derivative pricing and hedging, and risk control.


Various financial instruments will be presented in a form familiar to Wall Street traders (i.e. Bloomberg screens). The purpose of Risk Management is to provide a valuation of these financial contracts ("pricing") and to provide various measures of risk and methods to hedge these risks as best as possible ("hedging"). These tasks are not just performed by "Risk Managers" but by "Traders" who price and hedge their respective trading books on a daily basis. Successful trading (over extended periods of time) comes down to successful risk management. Successful risk management comes down to robust valuation which is the main prerogative of Financial Engineering. Valuation of financial instruments begins with an analysis of possible future events (i.e. stock price moves, interest rate moves, defaults, etc.). Dealing with the future involves the mathematics of statistics and probability. The first step is to find a probability distribution that is suitable for the financial instrument at hand. The next step is to calibrate this distribution. The third step is to generate future events using this calibrated distribution and based on this, provide the necessary valuation and risk measures for the financial contract at hand. The failure of any of these steps can lead to incorrect valuation and therefore an incorrect assessment of the risks of the financial instrument under consideration.


All the homework assignments require the use of Excel with the following properties:

1) Functions:

    a. Offset()

    b. Rand()

    c. Norminv()

    d. Skew(), Kurt(), Average(), Stdev(), Frequency()

    e. Gammaln()

2) Data Analysis Function: Histogram

Attention Apple Users: Even though you may have Excel, the above functionality does not come with all Apple versions of Excel so you better check to see what your Excel provides.

    Attendance Required

    Participation Required

    Homework Mostly in Excel.

    Exams In-class and closed book


Homework assignments must be uploaded to the Canvas shell of the course.


Practical Methods of Financial Engineering and Risk Management, Rupak Chatterjee, Apress-Springer, 2014.

Risk Management and Financial Institutions, John Hull, John Wiley & Sons, 2012.

Monte Carlo Methods in Financial Engineering, Paul Glasserman, Springer-Verlag, 2004.

Fixed Income Securities, 3rd Edition, Bruce Tuckman & Angel Serrat, Wiley Finance, 2012.

Gareds will be based on:

20% Homeworks

30% Midterm

50% Final Exam


The following procedures apply to exams for this course. As the instructor, I reserve the right to modify any conditions set forth below by printing revised Exam Room Conditions on the exam.

1. Students may not use the following devices during exams. Any electronic devices that are not mentioned in the list below are also not permitted.


    Cell Phones


    Smart Watches

    Google Glass


2. Students may not use the following materials during exams. Any materials that are not mentioned in the list below are also not permitted.

    Handwritten Notes

    Typed Notes 




Week Starting



August 28th

Chapter 1: Financial Instruments


September 11th

Chapter 1 & 2: Financial Instruments and Building a Yield Curve

Problem 2.1:Building a LIBOR Yield Curve

September 18th

Chapter 1: Financial Instruments


September 25th

Chapter 1: Financial Instruments

Problem 1.1: Black Formula Calculator and Implied Volatility

October 2nd

Chapter 3: Statistical Analysis of Financial Data 

Problems 3.1-3.2: Inverse Transform Method / Mixed Gaussians (method one and two)

October 10th (TUESDAY)

Chapter 3: Statistical Analysis of Financial Data 

Problems 3.3-3.4:  Calibrate stock returns to a Mixed Gaussian and a Student t-Dist

October 16th



October 23rd

Chapter 3: Statistical Analysis of Financial Data 

Problems 3.5: Create a Skew Normal Dist

Problems 3.6: VAR/CVAR 

October 30th

Chapter 3: Statistical Analysis of Financial Data

Problems 3.7: Term Structure of Skew, Kurt, Up & Down Volatility 

 November 6th

 Chapter 4: Stochastic Processes


 November 13th

 Chapter 4: Stochastic Processes

Problems 4.1: Brownian Motion MC Simulator

 November 20th

 Chapter 4: Stochastic Processes 

 Problem 4.2: Ito’s Lemma

 November 27th

 Chapter 4: Statistical Modeling of Trading Strategies

 Problem 4.3: GARCH(1,1)

 Dec 4th

 Chapter 4: Statistical Modeling of Trading Strategies

 Problem 4.5: Pairs Trading

 Dec 11th

 FINAL EXAM (not yet confirmed)