The financial engineering of forwards, futures, swaps, and options, with Python tools for fixed income and options
InstructorCameron Connell
The curriculum covers essential stochastic processes including Brownian motion and log-normal models to support asset price modeling.
One sampled review suggests that learners have noted the presence of detailed notes provided alongside explanations, aiding in the comprehension of complex concepts.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
An instructor explains the bond process using a whiteboard diagram that maps financial exchanges like interest and par value between a bond holder and issuer.
The instructor points to a whiteboard displaying the condition for net positive cash receipt, represented by the inequality where the difference between two values is greater than zero.
This slide displays a graph of a scaled random walk with m=4000 steps, illustrating the stochastic path over time as part of a lesson on Brownian Motion.
Instruction integrates Python-based tools for practical applications in bond analysis, yield curves, and options pricing.
One signal suggests potential difficulty running provided Python code in certain local environments, such as Anaconda with Jupyter notebooks.
The curriculum aligns well with the requirements of calculus and probability for technical professionals.