Lecture Notes in Empirical Finance (MSc, PhD)

Apr 19, 2013 - Figures 3.40–3.41 give an illustration of how the movements in the ...... Granger, C. W. J., 1992, “Forecasting stock market prices: lessons for ...
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Lecture Notes in Empirical Finance (MSc, PhD) Paul Söderlind1 19 April 2013

1 University

of St. Gallen. Address: s/bf-HSG, Rosenbergstrasse 52, CH-9000 St. Gallen, Switzerland. E-mail: [email protected] Document name: EmpFinPhDAll.TeX.

Contents

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Econometrics Cheat Sheet 1.1 GMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 MLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 The Variance of a Sample Mean: The Newey-West Estimator 1.4 Testing (Linear) Joint Hypotheses . . . . . . . . . . . . . . 1.5 Testing (Nonlinear) Joint Hypotheses: The Delta Method . .

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A Statistical Tables

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B Matlab Code B.1 Autocovariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B.2 Numerical Derivatives . . . . . . . . . . . . . . . . . . . . . . . . .

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Simulating the Finite Sample Properties 2.1 Monte Carlo Simulations . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24 24 30

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Return Distributions 3.1 Estimating and Testing Distributions . . . . . . . . 3.2 Estimating Risk-neutral Distributions from Options 3.3 Threshold Exceedance and Tail Distribution . . . 3.4 Exceedance Correlations . . . . . . . . . . . . . . 3.5 Beyond (Linear) Correlations . . . . . . . . . . . 3.6 Copulas . . . . . . . . . . . . . . . . . . . . . . 3.7 Joint Tail Distribution . . . . . . . . . . . . . . .

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Predicting Asset Returns 4.1 A Little Financial Theory and Predictability . . 4.2 Autocorrelations . . . . . . . . . . . . . . . . 4.3 Multivariate (Auto-)correlations . . . . . . . . 4.4 Other Predictors . . . . . . . . . . . . . . . . . 4.5 Maximally Predictable Portfolio . . . . . . . . 4.6 Evaluating Forecast Performance . . . . . . . . 4.7 Spurious Regressions and In-Sample Overfitting 4.8 Out-of-Sample Forecasting Performance . . . . 4.9 Security Analysts . . . . . . . . . . . . . . . .

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Predicting and Modelling Volatility 5.1 Heteroskedasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 ARCH Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 GARCH Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Non-Linear Extensions . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 GARCH Models with Exogenous Variables . . . . . . . . . . . . . . 5.6 Stochastic Volatility Models . . . . . . . . . . . . . . . . . . . . . . 5.7 (G)ARCH-M . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Multivariate (G)ARCH . . . . . . . . . . . . . . . . . . . . . . . . . 5.9 “A Closed-Form GARCH Option Valuation Model” by Heston and Nandi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 “Fundamental Values and Asset Returns in Global Equity Markets,” by Bansal and Lundblad . . . . . . . . . . . . . . . . . . . . . . . .

85 85 87 103 108 113 114 118 120 130 136 136 148 153 157 159 160 161 163 169 176

A Using an FFT to Calculate the PDF from the Characteristic Function 180 A.1