Course13010600375 lectures
Computational Finance 1
Prof. Sourish Das and Prof. Bikramaditya Datta · Chennai Mathematical Institute
About
This course introduces the fundamental concepts of Computational Finance through an integrated approach involving finance theory, mathematical techniques, and programming. Students will explore topics such as pricing bonds and options, portfolio optimization, and the Black-Scholes model. Each week connects financial applications with core mathematical tools like linear algebra, numerical methods, and probability. Programming concepts and algorithms support practical understanding and help develop computational skills for solving real-world finance problems.
Intended audience: Advanced Undergraduate / Postgraduate of Statistics, Finance, Economics, Engineering
Prerequisites: One course each in (1) probability and statistics, and (2) basic programming.
Lectures
Week 1
- A Tour of Computational Finance
- Algorithms and Computations
- Rates, Compounding & Interpolation
- Taylor Expansions
- Numerical Errors and Ill-Conditioning
- Portfolio Choice
- Workout problems 1
- Workout problems 2
- Hands-on with R week 1
- Hands-on with Python week 1
Week 2
- Linear Algebra Computationally
- Variance, Covariance, and the Matrix
- Choosing Weights to Hit a Target and Ill Conditioning
- The Shape of Risk: Eigenvalues
- CAPM and Factor Models as Showcases
- worked out problems
- Hands-on with R week 2
- Hands-on with Python week 2
Week 3
- Options, and Functions
- Payoffs at Expiration
- Functions in Three Senses
- A First Option Price
- The Lattice Extends
- A Note on Exotic Options
- Worked Out Problems
- Hands-on with R week 3
- Hands-on with Python week 3
Week 4
- Non-Linear Equations in Finance
- The Implied-Volatility Surface
- Newton's Method: Faster When It Works
- The Secant Method
- Building the Curve and the Surface
- Worked Out Problems
- Hands-on with R week 4
- Hands-on with Python week 4
Week 5
- Computational Calculus of Bond and Portfolio | Numerical Differentiation
- Duration and Convexity | Clean and Dirty Price
- Portfolio's Objective Function
- Trapezoidal and Simpson's Rule
- Choice under Uncertainty | Expected Utility and more
- Worked Out Problems 1
- Worked Out Problems 2
- Hands-on with R week 5
- Hands-on with Python week 5
Week 6
- Computational Aspects of Black Scholes
- Geometrix Brownian Motion
- PDE with American Options
- American Options
- Worked Out Problems 1
- Worked Out Problems 2
- Pricing Options with R; Binomial, GBM, PDE
- Pricing Options with Python; Binomial, GBM, PDE
Week 7
- Constrained Optiomisation and Arbitrage Detection
- Equality Constrained Markowitz
- The Efficient Frontier | Capital Market Line
- Inequality Constraint; KKT Conditions
- Arbitrage Detection by Linear Programming
- Optimization Tutorial 1
- Optimization Tutorial 2
- Optimization Tutorial 3
- Worked Out Problems 1
- Worked Out Problems 2
- Markowitz Portfolio Optimization and Arbitrage Detection using R
- Markowitz Portfolio Optimization and Arbitrage Detection using Python
Week 8
- Pricing Options and Portfolio using Monte Carlo
- From Random Walk to Geometric Brownian Motion
- Sampling from Distribution; Monte Carlo Simulator
- European Call by Monte Carlo
- Expected Utility under parameter Uncertainty
- Beginning from the END
- Worked Out Problems 1
- Worked Out Problems 2
- Worked Out Problems 3
- Monte Carlo Simulation, GBM, Pricing Asian Call using R and Colab
- Monte Carlo Simulation, GBM, Pricing Asian Call using Python and Colab
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- Chennai Mathematical Institute
- Department
- Economics
- Taught by
- Prof. Sourish Das, Prof. Bikramaditya Datta
- Level
- Undergraduate
- Language
- English
- Subjects
- Economics
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Madras.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed
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