Course 1: Mathematical Statistics for Finance

Mathematical Statistics for Finance provides an in-depth discussion about relevant financial tools across probabilistic and mean-covariance frameworks, decision theory and estimation theory

• The “probabilistic” ecosystem: pdf, cdf, cf, marginalization, push-forward, grade, elicitability, copula, conditioning
  
• The “mean-covariance/linear” ecosystem: mean vector, covariance matrix, ellipsoid, affine equivariance, correlation, linear prediction, whitening
  
• Decision theory: state, action, loss, risk, Bayes, frequentist, intervention, observation
  
• Estimation theory and estimation risk assessment: data, estimator, estimative decision problem, predictive decision problem, excess risk, bias-variance, approximation-estimation, model selection
  
• Inference: views, Black-Litterman, 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.