Lectures for Part II: Time Series Models in Finance

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4. ARCH and GARCH <strong>Models</strong> (cont) MaPhySto Workshop 9/04 28
4. ARCH and GARCH <strong>Models</strong> (cont) MaPhySto Workshop 9/04 29
Page 1 and 2: Nonlinear Time Series Modeling Part
Page 3 and 4: 1. Classification of White Noise As
Page 5 and 6: 2. Examples (1) All-pass processs.
Page 7 and 8: 2. Examples (cont) MaPhySto Worksho
Page 13 and 14: 2. Examples (cont) Likelihood funct
Page 15 and 16: 2. Examples (cont) The sample ACF.
Page 17 and 18: MaPhySto Workshop 9/04 17
Page 19 and 20: Log returns for IBM 1/3/62-11/3/00
Page 21 and 22: ACF of squares for IBM (a) 1961-198
Page 23 and 24: Hill’s estimator of tail index Th
Page 25 and 26: Hill’s plot of tail index for IBM
Page 27: 4. ARCH and GARCH Models (cont) MaP
Page 31 and 32: 4. ARCH and GARCH Models (cont) MaP
Page 37 and 38: 4. ARCH and GARCH Models (cont) i M
Page 41 and 42: 4. ARCH and GARCH Models (cont) SS
Page 49 and 50: Parameter Estimation for Finite-Var
Page 67 and 68: 5. Forecasting with GARCH (cont) Ma
Page 69 and 70: 7. Stochastic Volatility Models MaP
Page 71 and 72: 7. Stochastic Volatility Models (co
Page 79 and 80:
7. Stochastic Volatility Models (co
Page 81 and 82:
MaPhySto Workshop 9/04 81
Page 83 and 84:
8. Regular variation and applicatio
Page 85 and 86:
8.2 Regular variation — multivari
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2. If X 1 = X 2 > 0, then X= (X 1 ,
Page 89 and 90:
8.3 Applications of multivariate re
Page 91 and 92:
8.4 Applications of theorem 1. Kest
Page 93 and 94:
8.4 Examples (cont) Example of ARCH
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8.6 Extremes for GARCH and SV proce
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8.6 Extremes for GARCH and SV proce
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8.7 Summary of results for ACF of G
Page 101 and 102:
Sample ACF for Squares of GARCH (10
Page 103 and 104:
Example: Amazon returns May 16, 199
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Lectures for Part II: Time Series Models in Finance

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