Course 3: Data Science for Finance
The course “Data Science for Finance” covers multivariate econometrics and advanced inference for stress-testing and portfolio construction.
In particular, this course covers the following topics:
- Dependence and copulas
- Multivariate discrete time processes: random walk, VARMA, linear state space models, GARCH
- Continuous time processes: Brownian motion, Poisson, Levy, Ornstein-Uhlenbeck, stochastic volatility
- Black-Litterman and minimum relative entropy
After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.
Dependence: correlation and copula
Stochastic processes and prediction
Discrete time mean-covariance processes
Discrete time probabilistic stochastic models
Continuous time processes
Inference
