- Page 1 and 2: Nonlinear Time Series Modeling Part
- Page 3: 1. Classification of White Noise As
- Page 7 and 8: 2. Examples (cont) MaPhySto Worksho
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- Page 13 and 14: 2. Examples (cont) Likelihood funct
- Page 15 and 16: 2. Examples (cont) The sample ACF.
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- 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 and 28: 4. ARCH and GARCH Models (cont) MaP
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- Page 49 and 50: Parameter Estimation for Finite-Var
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4. ARCH and GARCH Models (cont) MaP
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4. ARCH and GARCH Models (cont) MaP
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4. ARCH and GARCH Models (cont) MaP
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4. ARCH and GARCH Models (cont) MaP
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MaPhySto Workshop 9/04 63
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4. ARCH and GARCH Models (cont) MaP
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5. Forecasting with GARCH (cont) Ma
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7. Stochastic Volatility Models MaP
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7. Stochastic Volatility Models (co
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7. Stochastic Volatility Models (co
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7. Stochastic Volatility Models (co
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7. Stochastic Volatility Models (co
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7. Stochastic Volatility Models (co
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MaPhySto Workshop 9/04 81
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8. Regular variation and applicatio
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8.2 Regular variation — multivari
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2. If X 1 = X 2 > 0, then X= (X 1 ,
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8.3 Applications of multivariate re
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8.4 Applications of theorem 1. Kest
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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
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Sample ACF for Squares of GARCH (10
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Example: Amazon returns May 16, 199