- Joined
- 10/16/12
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Hi,
I've spent the past years working academically on a career change from a statistician in the pharmaceutical industry to finance.
I'm inquiring on best resources for industrial strength financial modeling in C++ and/or Python (pricing and portfolio choice).
My background includes:
PhD in mathematics including PDE, functional analysis, measure theory, computational modeling and numerical analysis.
PhD equivalent in statistics including measure theoretic probability (Billingsley, Shiryaev) and stochastic processes (Revuz and Yor, Le Gall, Oksendal, Rogers and Williams), theoretical statistics (Lehmann and Casella, Van der Vaart), statistical modeling, time series analysis (Hamilton), categorical data analysis, longitudinal data analysis and Bayesian statistics.
The phd level statistics and probability courses assumes upper division probability and statistics (Ross, Grimmett and Stirzaker, Casella and Berger).
Minor in computer science (C++, structure and interpretation of computer programs (LISP), data structures, algorithms, network computing, object oriented design)
Economics/Finance coursework included, following most of the standard phd course sequence in finance:
Microeconomic theory (Mas-Colell, Whinston and Green; and Kreps)
Game Theory (Fudenberg and Tirole, Maschler, Solan, and Zamir)
Econometric Analysis (Greene)
Introduction to financial economics (Huang and Litzenberger)
Continuous time financial economics (Darrell Duffie)
Dynamic optimization with economic applications (Stokey and Lucus)
Mathematical economics (course notes by Kim Border)
MBA readings Hull supplemented with Courant's course notes on derivatives
Assuming programming background in C++, Python, R and SAS what are some industry standard resources for implementing financial models (option pricing and portfolio choice) that include coded examples?
For example, I'm aware or Monte Carlo Methods for Financial Engineering and Numerical Recipes in C++ and more advanced texts such as "stochastic simulation", covering aspects of nonlinear stochastic partial differential equation (for nonlinear options pricing and term structure models) but figuring out what sections are relevant for industrial applications and the job hunt is challenging. Most financial engineering programs seem to have survey courses covering a wide variety of techniques using Python (scientific computing in finance - NYU) or spread the material out over many courses (financial computing, data science and machine learning sequences using C++ and Python - CMU). My intention is to move in the direction of trading more than risk management.
Thank you
Dean
I've spent the past years working academically on a career change from a statistician in the pharmaceutical industry to finance.
I'm inquiring on best resources for industrial strength financial modeling in C++ and/or Python (pricing and portfolio choice).
My background includes:
PhD in mathematics including PDE, functional analysis, measure theory, computational modeling and numerical analysis.
PhD equivalent in statistics including measure theoretic probability (Billingsley, Shiryaev) and stochastic processes (Revuz and Yor, Le Gall, Oksendal, Rogers and Williams), theoretical statistics (Lehmann and Casella, Van der Vaart), statistical modeling, time series analysis (Hamilton), categorical data analysis, longitudinal data analysis and Bayesian statistics.
The phd level statistics and probability courses assumes upper division probability and statistics (Ross, Grimmett and Stirzaker, Casella and Berger).
Minor in computer science (C++, structure and interpretation of computer programs (LISP), data structures, algorithms, network computing, object oriented design)
Economics/Finance coursework included, following most of the standard phd course sequence in finance:
Microeconomic theory (Mas-Colell, Whinston and Green; and Kreps)
Game Theory (Fudenberg and Tirole, Maschler, Solan, and Zamir)
Econometric Analysis (Greene)
Introduction to financial economics (Huang and Litzenberger)
Continuous time financial economics (Darrell Duffie)
Dynamic optimization with economic applications (Stokey and Lucus)
Mathematical economics (course notes by Kim Border)
MBA readings Hull supplemented with Courant's course notes on derivatives
Assuming programming background in C++, Python, R and SAS what are some industry standard resources for implementing financial models (option pricing and portfolio choice) that include coded examples?
For example, I'm aware or Monte Carlo Methods for Financial Engineering and Numerical Recipes in C++ and more advanced texts such as "stochastic simulation", covering aspects of nonlinear stochastic partial differential equation (for nonlinear options pricing and term structure models) but figuring out what sections are relevant for industrial applications and the job hunt is challenging. Most financial engineering programs seem to have survey courses covering a wide variety of techniques using Python (scientific computing in finance - NYU) or spread the material out over many courses (financial computing, data science and machine learning sequences using C++ and Python - CMU). My intention is to move in the direction of trading more than risk management.
Thank you
Dean