DataCamp Course-Quantitative Risk Management in R; Alexander J. McNeil

DataCamp Course-Quantitative Risk Management in R; Alexander J. McNeil

Quantitative Risk Management in R

Course Description

In Quantitative Risk Management (QRM), you will build models to understand the risks of financial portfolios. This is a vital task across the banking, insurance and asset management industries. The first step in the model building process is to collect data on the underlying risk factors that affect portfolio value and analyze their behavior. In this course, you will learn how to work with risk-factor return series, study the empirical properties or so-called “stylized facts” of these data – including their typical non-normality and volatility, and make estimates of value-at-risk for a portfolio.

CHAPTERS:

  1. Exploring market risk-factor data

  2. Real world returns are riskier than normal

  3. Real world returns are volatile and correlated

  4. Estimating portfolio value-at-risk (VaR):

  • Value-at-risk and expected shortfall
  • Computing VaR and ES for normal distribution
  • International equity portfolio
  • Examining risk factors for international equity portfolio
  • Historical simulation
  • Estimating VaR and ES
  • Option portfolio and Black Scholes
  • Compute Black-Scholes price of an option
  • Equity and implied volatility risk factors
  • Historical simulation for the option example
  • Historical simulation of losses for option portfolio
  • Estimating VaR and ES for option portfolio
  • Computing VaR for weekly losses
  • Wrap-up

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