Earlier I posted about F# Deep Dives and its Early Access Program for reading chapters of the book as they become available.
I’m particularly struck by the contents of Chapter 4 – Numerical computing in the financial domain by Chao-Jen Chen. This looks like a stunning guide to financial programming with F#. Here are the contents of the chapter (or, perhaps it will turn out to be multiple chapters!). An early draft of the chapter is already available under the Early Access Program.
If you’re into financial computing with F#, please get into the early access program, review the content, and share it with your colleagues!
1 Introducing financial derivatives
2 Using probability functions of Math.NET
2.1 Configuring F# Interactive (profiling and floating point formatting)
2.2 Setting up Math.NET Numerics
2.3 Introducing Random Variables, Expectation and Variance
2.4 Generating Normally Distributed Samples
3 Geometric Brownian Motion and Monte Carlo Estimate
3.1 Modeling stock prices using geometric Brownian motion
3.2 Payoff Function, Discounted Payoff, and Monte Carlo estimate
3.3 Analyzing variance of Monte Carlo estimates
3.4 Pricing Path-dependent options (Asian options and barrier options)
3.5 Reducing Variance by antithetic variates