• This course introduces time series methodology emphasizing the data analytic aspects related to financial applications. Topics that will be discussed are as follows: Univariate linear stochastic models: ARMA and ARIMA models building and forecasting using these models. Univariate non-linear stochastic models: Stochastic variance models, ARCH processes and other non-linear univariate models. Topics in the multivariate modeling of financial time series. Applications of these techniques to finance such as time series modeling of equity returns, trading day effects and volatility estimations will be discussed.

    For further information see the academic catalog: IAM526

  • Unconstrained optimization: line search methods, steepest descent, Newton and quasi Newton methods, the conjugate gradient method constrained optimization: equality and inequality constraints, linear constraints and duality, linear programming, the simplex method, Lagrange multiplier algorithms, interior point methods, penalty methods, large scale optimization.

    For further information see the academic catalog: IAM566

  • This course is intended to all students at the Institute. After a short introduction to Matlab various algorithms, their complexity will be introduced and symbolic, numerical and stochastic algorithms will be followed. Students will be encouraged to carry out several projects in groups. Moreover, students in groups will complete a term project at the end of the semester.

    For further information see the academic catalog: IAM565

  • Basics of programming, introducing MATLAB, programming with MATLAB, basic algorithms and problem solving in Linear Algebra, Differential Equations, Optimization, and so an. Reporting and presenting problems and their solutions, introducing \(\LaTeX\) and/or Scientific Workplace, Typesetting text and mathematical formulae, graphing, making bibliography and index, packages and defining your own styles.

    For further information see the academic catalog: IAM564