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• 33 min
  1. Risk management: some lessons from the credit crisis

  • Mr. Edward Fishwick
• Archived Lectures *These may not cover the latest advances in the field
• 22 min
  2. The need for a new paradigm

  • Mr. Alan Brown
• 41 min
  3. Leading bank credit portfolio strategies

  • Mr. Brian Dvorak
• 26 min
  4. Personal asset/liability management: using the discretionary wealth hypothesis within an equilibrium term structure

  • Mr. Dan diBartolomeo
• 18 min
  5. Quantmare, August 2007

  • Mr. Eoin Murray
• 36 min
  6. Risk decomposition (and risk budgeting)

  • Mr. Jason MacQueen
• 43 min
  7. Structural and reduced form models

  • Dr. Theo Darsinos
• 43 min
  8. Validation techniques I: regulatory and statistical background

  • Dr. Dirk Tasche
• 40 min
  9. Validation techniques II: discriminatory power and calibration

  • Dr. Dirk Tasche
• 52 min
  10. Estimating risk models

  • Prof. Kevin Dowd
• 24 min
  11. Hedge fund risk assessment

  • Mr. David Martin
• 45 min
  12. Modeling business dependencies for credit portfolios

  • Dr. Markus A. Leippold
• 26 min
  13. VaR when volatility is changing

  • Dr. Elizabeth Sheedy
• 30 min
  14. Conditional Value at Risk (CoVAR)

  • Mr. KiHoon Jimmy Hong
• 40 min
  15. Dependence modeling with copulas

  • Prof. Paul Embrechts
  • Dr. Johanna Neslehova
• 41 min
  16. Extreme value theory and copulas

  • Prof. Paul Embrechts
  • Dr. Johanna Neslehova
• 42 min
  17. Measures of financial risk

  • Prof. Kevin Dowd
• 37 min
  18. Modelling UK Mortgage Default in Light of the Financial Crisis

  • Mr. Warapong Wongwachara
• 23 min
  19. Statistical models for risk management

  • Prof. John Knight
• 34 min
  20. The structure of equity risk models

  • Mr. Jason MacQueen
• 42 min
  21. Utility theory and mean variance

  • Dr. Norvald Instefjord
• 40 min
  22. Definitions of risk

  • Dr. Norvald Instefjord
• 29 min
  23. Practical use of portfolio risk management today

  • Mr. Daryl Roxburgh
• 44 min
  24. Volatility

  • Dr. George A. Christodoulakis

Printable Handouts

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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