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- 5/22/12
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Hi, I'm a portuguese student and I've applied to a Master in Finance. I'm considering some optional courses at the moment and it would be great if you could give me some advice.
I have a bachelor in economics, in which, among others I've completed:
Math 1:
Real functions of n real variables:
1 - Graph of a function;
2 - Limits and continuity;
3 - Differentiation: partial differentiation, differentiability, approximation of functions, derivative of composite functions;
4 - Homogeneous functions;
5 - Implicit functions;
6 - Free and constrained optimization.
Math 2: Algebra and Integrals
Statistics 1 and 2
Econometrics:
0. INTRODUCTION: subject and methodology of Econometrics
1. THE LINEAR REGRESSION MODEL: elementary concepts and notation; least squares (OLS) estimators of the regression coefficients; coefficient of determination; assumptions of the classical model of linear regression; properties of the OLS estimators; the estimator of the variance of the disturbances
2. EXTENSIONS OF THE LINEAR REGRESSION MODEL: choosing a functional form; dummy variables; models for the trend and seasonal components
3. INFERENCE IN THE LINEAR REGRESSION MODEL: the normality assumption; maximum likelihood (ML) estimators; testing hypothesis about a single coefficient; testing hypothesis about linear restrictions on coefficients; testing the overall significance of the regression; forecasting; testing the equality between sets of coefficients in two regressions
2 Finance courses (corporate finance and financial markets and investments)
The courses that I'm considering are:
- Stochastic Processes (Markov Chain, Brownian Motion, Martingales, etc)
- Numerical methods and Computational Simulation
The syllabus from the second one is:
Numerical solution of systems of linear equations (direct and iterative methods);
Numerical solution of systems of ordinary differential equations (Euler, Runge-Kutta, finite differences);
Numerical solution of systems of partial differential equations (finite differences, Galerkin methods, FFTs). Advection-diffusion equations (Crank-Nicholson and leap-frog schemes).
Stability analysis, consistency and convergence of numerical schemes.
Simulation: fundamental concepts and issues. Random number generation.
Monte Carlo Methods. Monte Carlo Integration. Convergence diagnosis and variance reduction. Monte Carlo statistical inference. Markov Chain Monte Carlo (MCMC).
Do I learned the basics for these courses?
Would these two courses give me an edge on quant or finance related job?
Thank you in advance.
I have a bachelor in economics, in which, among others I've completed:
Math 1:
Real functions of n real variables:
1 - Graph of a function;
2 - Limits and continuity;
3 - Differentiation: partial differentiation, differentiability, approximation of functions, derivative of composite functions;
4 - Homogeneous functions;
5 - Implicit functions;
6 - Free and constrained optimization.
Math 2: Algebra and Integrals
Statistics 1 and 2
Econometrics:
0. INTRODUCTION: subject and methodology of Econometrics
1. THE LINEAR REGRESSION MODEL: elementary concepts and notation; least squares (OLS) estimators of the regression coefficients; coefficient of determination; assumptions of the classical model of linear regression; properties of the OLS estimators; the estimator of the variance of the disturbances
2. EXTENSIONS OF THE LINEAR REGRESSION MODEL: choosing a functional form; dummy variables; models for the trend and seasonal components
3. INFERENCE IN THE LINEAR REGRESSION MODEL: the normality assumption; maximum likelihood (ML) estimators; testing hypothesis about a single coefficient; testing hypothesis about linear restrictions on coefficients; testing the overall significance of the regression; forecasting; testing the equality between sets of coefficients in two regressions
2 Finance courses (corporate finance and financial markets and investments)
The courses that I'm considering are:
- Stochastic Processes (Markov Chain, Brownian Motion, Martingales, etc)
- Numerical methods and Computational Simulation
The syllabus from the second one is:
Numerical solution of systems of linear equations (direct and iterative methods);
Numerical solution of systems of ordinary differential equations (Euler, Runge-Kutta, finite differences);
Numerical solution of systems of partial differential equations (finite differences, Galerkin methods, FFTs). Advection-diffusion equations (Crank-Nicholson and leap-frog schemes).
Stability analysis, consistency and convergence of numerical schemes.
Simulation: fundamental concepts and issues. Random number generation.
Monte Carlo Methods. Monte Carlo Integration. Convergence diagnosis and variance reduction. Monte Carlo statistical inference. Markov Chain Monte Carlo (MCMC).
Do I learned the basics for these courses?
Would these two courses give me an edge on quant or finance related job?
Thank you in advance.
