Learn · Computational Finance 1
Lecture 58 of 75Week 7
Optimization Tutorial 1
Prof. Sourish Das and Prof. Bikramaditya Datta · Chennai Mathematical Institute
Credits and licence
- From
- Computational Finance 1, Chennai Mathematical Institute (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- 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
- Tweens profiles and older. How courses are placed
All the lectures
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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