hfsl_course
 Fall On Campus
 Spring On Campus
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.
COURSE REQUIREMENTS
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 Inclass 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, ApressSpringer, 2014.
Risk Management and Financial Institutions, John Hull, John Wiley & Sons, 2012.
Monte Carlo Methods in Financial Engineering, Paul Glasserman, SpringerVerlag, 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
EXAM ROOM CONDITIONS
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.
Laptops
Cell Phones
Tablets
Smart Watches
Google Glass
Other
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
Textbooks
Readings
Other
Week Starting 
Readings 
Assignment 
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 3.13.2: Inverse Transform Method / Mixed Gaussians (method one and two) 
October 10th (TUESDAY) 
Chapter 3: Statistical Analysis of Financial Data 
Problems 3.33.4: Calibrate stock returns to a Mixed Gaussian and a Student tDist 
October 16th 
MIDTERM 

October 23rd 
Chapter 3: Statistical Analysis of Financial Data 
Problems 3.5: Create a Skew Normal DistProblems 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) 