Printable Handouts

PDF

Navigable Slide Index

1. Introduction
2. Definition of volatility
3. Risk
4. Risk measures
5. Risk measures - example 1
6. Risk measures - example 2
7. Risk measures - summary
8. Empirical characteristics of S and P 500
9. Empirical characteristics of S and P 500 returns
10. Correlogram of SP500
11. S and P 500 squared returns
12. S and P 500 absolute returns
13. Empirical characteristics of high-frequency returns
14. Rolling window volatility
15. Volatility modelling (1)
16. Volatility modelling (2)
17. The ARCH family of models (1)
18. The ARCH family of models - a long lag structure
19. The ARCH family of models (2)
20. GARCH (1,1) news-impact curve (1)
21. GARCH (1,1) news-impact curve (2)
22. Asymmetric GARCH processes
23. EGARCH
24. EGARCH (1,1) news-impact
25. Estimation
26. Maximum likelihood
27. Empirical example - ARCH(p)
28. Empirical example - GARCH(1,1), EGARCH(1,1)
29. Model assessment
30. Robust standard errors
31. Model selection
32. Likelihood ratio test
33. Bayesian criteria: Akaike, Schwarz
34. Stochastic volatility
35. Risk-metrics
36. Forecasting
37. Forecasting asset returns under ARCH errors
38. Confidence intervals for S and P 500 returns
39. Forecasting volatility with ARCH (1)
40. Forecasting volatility with GARCH (1,1)
41. Forecasting with stochastic volatility
42. Forecast error statistics
43. Pathology of error statistics
44. Volatility forecast evaluation
45. Volatility forecast evaluation - different approches
46. Implied volatility - exchange-traded options
47. Implied volatility (1)
48. Implied volatility (2)
49. Black-scholes implied volatility (1)
50. Black-scholes implied volatility (2)
51. Implied volatility surface
52. References

Topics Covered

• Volatility is the most heavily used measure of risk in financial decision making
• Discussion of validity of various measures of risk
• Statement of conditions under which volatility is a good measure
• Explanation of the empirical properties of data and their dynamics
• Why models need to capture these characteristics
• Analysis of various approaches of volatility estimation with particular emphasis on dynamic models in both univariate and multivariate contexts
• Techniques for volatility model validation
• Explanation of possible pitfalls
• Out-of-sample volatility forecasting using dynamic models and various methods for volatility forecast evaluation

Links

Series:

• Quantitative Financial Risk Management

Categories:

• Finance, Accounting & Economics

Talk Citation

Publication History

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