In this chapter, we have covered the types of risks associated with financial institutions, such as market risk, portfolio risk, VaR, Monte Carlo simulation, hedging, Basel regulations, credit risk, and fraud detection. Also, we have discussed how the strengths of R can be leveraged for measuring different types of risk. In this chapter, we have demonstrated examples of measuring risks such as market, portfolio, and credit using R, and also how to use techniques such as random forest classification for fraud detection.
In the next chapter, we will be covering various optimization techniques used in trading algorithms and parameter estimation. Optimization techniques such as dynamic rebalancing, walk forward testing, grid testing, and genetic algorithm will be covered.
