Hello World!
I intend to upload notes for students who are interested in the area of financial mathematics. It will also be available for public to add pages to the blog (I will be targeting PhD’s/postdocs). I am sure that the blog will be more useful for financial maths students/graduates than using Wikipedia or Google. I also aim to include other topics such as probability, risk management and data analysis. The idea is to publish recent topics that have already been studied and written by one of us. It is easier than you think to convert a LaTeX text into html. I will re-name the blog, “haidora”, and come up with a cool one once I get some participants. It is a non-profitable project and the copyright of the material that you add will be yours.
If you are viewing the page using a mobile phone, you can then view all pages by clicking on the arrow next to the "Home" page on top.
I would be grateful to get a feedback, question, comment or correction on my work. I will also be happy to hear from people who would like to contribute to the project. You can contact me at: H.Haidar (at] hotmail.co.uk .
Haidar Haidar
Quantitative Finance notes - Haidar Haidar
In this blog, I intend to post information related to my area of interest in Financial Mathematics, Option Pricing, Monte Carlo methods, Probability Theory, Computational Finance and Quantitative Analysis.
Pages
- Home
- Risk Measures
- What is An Option?
- Topics in Probability
- Stochastic Differential Equations (SDE)
- Ito's Lemma
- Transform a SDE Into a PDE and Vise Versa
- Generating Random Numbers and Statistical Distributions
- Monte Carlo Techniques
- Monte Carlo Methods in Option Pricing
- Pricing American Options Using Least Square Monte Carlo
- Binomial Method to Price Options
- Derivation of the Black-Scholes PDE
- Transform the Black-Scholes PDE to the Heat Equation
- Finite Difference methods
- Analytic Approximation of American Options, G. Barone-Adesi and R. E. Whaley (1987)
- The Method of Maximum Likelihood Estimation
- Rank Correlation: Kendall \(\tau\)
- Principal Component Analysis (PCA)
- Acknowledgement