Quantitative Risk Management in R

You will learn to estimate value-at-risk (VaR) for a portfolio using historical simulation, which is a core risk measure in banking, insurance, and asset management.

You will study non-normality, heavy tails, volatility clustering, and serial correlation in real financial return series, comparing them against the normal and Student t distributions.

You will use the qrmdata package for financial return datasets and apply graphical normality tests, Ljung-Box tests for serial correlation, and volatility analysis tools available in R.

A finance background is helpful but not required. You do need Intermediate R and experience with time series data manipulation in R to handle the coursework.

Banking, insurance, and asset management all require professionals who can model portfolio risk, assess return distributions, and estimate value-at-risk.