Printable Handouts

PDF

Navigable Slide Index

1. Introduction
2. Key points
3. Main tasks
4. Assemble data set
5. Choose estimation methodology: 'VaR trinity'
6. Non-parametric methods
7. Example: estimating HS VaR (1)
8. Example: estimating HS VaR (2)
9. HS VaR from cumulative histogram
10. Advantages of NP approaches
11. Disadvantages of NP approaches
12. Parametric approaches
13. Implementing parametric approaches
14. Example: normal VaR (1)
15. Example: normal VaR (2)
16. Pros/cons of normality
17. Fat-tailed distribution
18. NP vs. parametric approaches
19. Monte Carlo (MC) methods
20. Usefulness of MC methods
21. MCS with single risk factor (1)
22. MCS with single risk factor (2)
23. Euler method
24. Alternative to Euler method (1)
25. Alternative to Euler method (2)
26. Using MCS to estimate RMs
27. Example: Black-Scholes call VaR
28. Generating 'random' numbers
29. Advantages of MCS methods
30. MCS: conclusions
31. Estimating expected shortfall
32. How to estimate ES?
33. Example: N(0,1), n=10
34. Average tail VaR method
35. Outstanding issues (1)
36. Outstanding issues (2)
37. References

Topics Covered

• Defining a risk model
• Assembling data
• Non-parametric estimation methods
• Parametric estimation methods
• Monte Carlo simulation methods

Links

Series:

• Quantitative Financial Risk Management

Categories:

• Finance, Accounting & Economics

Talk Citation

Publication History

• Published on October 1, 2007
• Archived on April 4, 2022