Hello Daniel, I am getting your time. you are an eminent personality. Hope I will the best use of this opportunity.
1. our background (Accountancy) is not exactly quantitative.: not just 'exactly', it is not even remotely related to Maths. As a kid, I scored 35/100 in Mathematics in O level (around age 16). This means the education system has been kind enough to promote me to the next class. I have three professional qualifications in the field of accounting.
2. My book and courses are mainly for MFE MSc Stem students: the most precise reason why I am planning to join evening class in a UK university for MFE. But yes, I will raise to this challenge to complete this field of study successfully.
3. suggest enrolling at a local university and do an applied maths degree. : I will join MFE. But i will be happy if you can let me know which part of Applied Mathematics I am missing or I need to hone my skills on?
4. You are not yet ready for this book.: I am aware it will take a lot of effort but i will do my best to raise to the challenge and write the relevant (from job perspective so limited) notes on your book.
5. I have hired two Phd Students, one in Mathematics and the other in Statistics.
why?
Based on the curriculum in various US universities for MFE, I initially thought Stochastic calculus was the toughest of all of the subjects. I also remember seeing a few Chinese reading Stochastic calculus in London Underground tube during their commute. I then found the prerequisites for stochastic calculus. I understood it was Real Analysis, Measure Theory and Probability. But anyway I studied most of the topics in Graduation level and a few topics from Post graduation level. I did not mention earlier but I have covered Numerical Integration ( Newton Quadrature formula, Trapezoidal rule, Simpson's 1/8, 3/8 and Weddles rule) , also Numerical solutions of ODE (Taylor series, Picards methods, Euler's method, Euler's modified method, Runga Kutta, first order second order, third order and fourth order method)
I also covered Finite Difference methods (forward and backward), Gauss central difference, Sterling difference, interpolation with equal intervals, Newton's formula for interpolation, interpolation with unequal intervals, Lagrange's interpolation formula'
I covered some portion/relevant portion from Probability theory book by Ross Sheldon (quite useful for stochastic calculus-continuous time models) Hence required a Phd Statistics Student for Probability theory.
Statistics is important, but useless in PDE and stuff like that.: i humbly agree and disagree with you.
Agree: from the very very very very limited level of knowledge I have yes it is not relevant for ODE/PDe and numerical methods
Disagree: (this is purely from my experience) If I did not read Stochastic calculus, then I would not have known PDE methods along with numerical methods are used to solve the interest rate curve problems (I am writing here from my understanding in Steven Shreve's book)
But my basic question is or I am still wondering is how will your online course help me understand your book well. I mean will it explain concepts or work problems, provides reference material?
I am sure it will be robust, but I am curious to know before I enrol.
Thank you for your time. I am happy to interact with you. Hope I have not been rude anywhere. I also hope I am able to establish, though I do not have a formal degree in MAthematics I am making the best of the resources available at my disposal to learn this fantastic subject.
NB: Between Monte carlo and PDE, I chose PDE as I like the subject. There are several prerequisites for Monte Carlo, for which I do not have time (now) and also the financial resources.
