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Academic resources for my colleagues and students

Stat 449-649, Fall 2015

Dear students,

In the Fall 2015 I will (once again) instruct STAT 449 /649 (9:25AM-10:40AM, Tu/Th) Quantitative Financial Risk Management,  an essential element of

  • Center for Computational Finance and Economics Systems (CoFES) program, lead by Dr. Katherine Ensor, Department of Statistics, Rice University
  • Financial Computation and Modeling (FCAM) program

This is an rewarding and challenging course on theory and applications of financial derivatives, based on a popular textbook by John C. Hull, Options, Futures, and Other Derivatives, 9th ed and Python programming language.

Prerequisites extend to a reasonable familiarity with programming (not necessarily Python or R), and reasonable Fall course load. Most students realize their level of preparedness after the 1st HW (within first 2 weeks of the course). You do not need to know Python, but you will learn plenty in this course. I have not yet decided if I will allow R for HW or not.

STAT 449 is equivalent to ECON 449, so students can only take one of the two courses.

Summer preparation (inquired by some):

  • For starters, try coding Black Scholes model and lattice (binomial/trinomial trees) in Python.
    • To spice it up, store lattice values in a matrix, in a vector or in a function (recursive). I may add more examples a bit later.
  • You can also try coding plots of options (and combinations of options: buy/sell of calls/puts) payouts from chapter 1 of the assigned text.
  • Try (play with 🙂 ) code version control (see my Tech Resources)
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  • We will cover numerous risk measurements/management techniques, as well as pricing of derivative securities via
    • Black-Scholes model
    • lattice (tree) pricing models (binomial, trinomial, multinomial; recombining, non-recombining; symmetric, skewed,…)
    • Monte Carlo simulations
    • finite differences methods
  • The course stresses computational applications, introduces Bloomberg analytics, involves techniques from high performance computing (HPC):
    • vectorization, recursion, parallelization, matrix algebra solutions, memoisation, SQL and databases (if time permits), etc.
  • The curriculum also covers
    • pricing and managing risk of fixed income instruments (eg. bonds, swaps, interest rate derivatives, etc.)
    • modeling of volatility (via ARCH, GARCH, EWMA, …)
    • advanced numerical algorithms
    • stochastic modeling of market variables (observed, such as stock price, and unobserved, such as volatility)
    • Ito’s lemma and integral
    • integral Stochastic PDE
    • popular stochastic models for interest rates
    • non/linear algorithms to impute (interpolate and extrapolate) missing data
    • 2D & 3D visualization of multidimensional financial data
    • and more
  • The course will help students to
    • boost CVs and résumés
    • target jobs in the quantitative finance
    • prepare for CFA, CAIA, FRM, BAP and other industry certifications
    • prepare for job interviews
    • network with other students and faculty sharing interest in QFRM

Additionally, students will earn 5 Bloomberg certificates and a set of valuable skills. There is a plan to develop a class-based package project (instead of a final exam) that students will contribute to. We may utilize Git (code version control and project management). These are invaluable skills and credentials to add to your CV in search for a job.

  • Other languages may be admitted for HW/project submissions, given sufficient demand (3-4 students). Still, I will teach in Python, but can assist in either language below. TBD.
    • Note: Quant finance in SQL is rather interesting (or cumbersome, but fast), due to limited (built-in) visualization or stat functionality. Still it’s possible for the daring.


If you plan to take this course, please don’t overload yourself with the semester credit hours and spend the time to study Python programming. We will be using Python (programming language) extensively, since it is a highly demanded language in the financial (and other) industries. It is important that students are well prepared for this course (see prerequisites), know basic Python programming and have reasonable general coding skills and are familiar with statistical methods (regression, probability, distribution (mainly Gaussian), parameter estimation, etc.)

Best wishes,