• I: Fundamentals
• 1. Prices and Returns
• 1.1 What is Financial Econometrics?
• 1.2 Financial Assets
• 1.3 Equity Prices and Returns
• 1.4 Stock Market Indices
• 1.5 Bond Yields
• 1.6 Exercises
• 2. Financial Data
• 2.1irst Look at the Data
• 2.2 Summary Statistics
• 2.3 Percentiles and Value at Risk
• 2.4 The Efficient Market Hypothesis
• 2.5 Exercises
• 3. Linear Regression
• 3.1 The Capital Asset Pricing Model
• 3.2 Multi-factor CAPM
• 3.3 Properties of Ordinary Least Squares
• 3.4 Diagnostics
• 3.5 Measuring Portfolio Performance
• 3.6 Minimum Variance Portfolios
• 3.7 Event Analysis
• 3.8 Exercises
• 4. Stationary Dynamics
• 4.1 Stationarity
• 4.2 Univariate Time Series Models
• 4.3 Autocorrelation and Partial Autocorrelations
• 4.4 Mean Aversion and Reversion in Returns
• 4.5 Vector Autoregressive Models
• 4.6 Analysing VARs
• 4.7 Diebold-Yilmaz Spillover Index
• 4.8 Exercises
• 5. Nonstationarity
• 5.1 The RandomWalk with Drift
• 5.2 Characteristics of Financial Data
• 5.3 Dickey-Fuller Methods and Unit Root Testing
• 5.4 Beyond the Simple Unit Root Framework
• 5.5 Asset Price Bubbles
• 5.6 Exercises
• 6. Cointegration
• 6.1 The Present Value Model and Cointegration
• 6.2 Vector Error Correction Models
• 6.3 Estimation
• 6.4 Cointegration Testing
• 6.5 Parameter Testing
• 6.6 Cointegration and the Gordon Model
• 6.7 Cointegration and the Yield Curve
• 6.8 Exercises
• 7. Forecasting
• 7.1 Types of Forecasts
• 7.2 Forecasting Univariate Time Series Models
• 7.3 Forecasting Multivariate Time Series Models
• 7.4 Combining Forecasts.
• 7.5 Forecast Evaluation Statistics
• 7.6 Evaluating the Density of Forecast Errors
• 7.7 Regression Model Forecasts
• 7.8 Predicting the Equity Premium
• 7.9 Stochastic Simulation of Value at Risk
• 7.10 Exercises
• II. Methods
• 8. Instrumental Variables
• 8.1 The Exogeneity Assumption
• 8.2 Estimating the Risk-Return Tradeoff
• 8.3 The General Instrumental Variables Estimator
• 8.4 Testing for Endogeneity
• 8.5 Weak Instruments
• 8.6 Consumption CAPM
• 8.7 Endogeneity and Corporate Finance
• 8.8 Exercises
• 9. Generalised Method of Moments
• 9.1 Single Parameter Models
• 9.2 Multiple Parameter Models
• 9.3 Over-Identified Models
• 9.4 Estimation
• 9.5 Properties of the GMM Estimator
• 9.6 Testing
• 9.7 Consumption CAPM Revisited
• 9.8 The CKLS Model of Interest Rates
• 9.9 Exercises
• 10. Maximum Likelihood
• 10.1 Distributions in Finance
• 10.2 Estimation by Maximum Likelihood
• 10.3 Applications
• 10.4 Numerical Methods
• 10.5 Properties
• 10.6 Quasi Maximum Likelihood Estimation
• 10.7 Testing
• 10.8 Exercises
• 11. Panel Data Models
• 11.1 Types of Panel Data
• 11.2 Reasons for Using Panel Data
• 11.3 Two Introductory Panel Models
• 11.4 Fixed and Random Effects Panel Models
• 11.5 Dynamic Panel Models
• 11.6 Nonstationary Panel Models
• 11.7 Exercises
• 12. Latent Factor Models
• 12.1 Motivation
• 12.2 Principal Components
• 12.3atent Factor CAPM
• 12.4 Dynamic Factor Models: the Kalman Filter
• 12.5arametric Approach to Factors
• 12.6 Stochastic Volatility
• 12.7 Exercises
• III: Topics
• 13. Univariate GARCH Models
• 13.1 Volatility Clustering.
• 13.2 The GARCH Model
• 13.3 Asymmetric Volatility Effects
• 13.4 Forecasting
• 13.5 The Risk-Return Tradeoff.
• 13.6 Heatwaves and Meteor Showers
• 13.7 Exercises
• 14. Multivariate GARCH Models
• 14.1 Motivation
• 14.2 Early Covariance Estimators
• 14.3 The BEKK Model
• 14.4 The DCC Model
• 14.5 Optimal Hedge Ratios
• 14.6 Capital Ratios and Financial Crises
• 14.7 Exercises
• 15. Realised Variance and Covariance
• 15.1 High Frequency Data
• 15.2 Realised Variance
• 15.3 Integrated Variance
• 15.4 Microstructure Noise
• 15.5 Bipower Variation and Jumps
• 15.6 Forecasting
• 15.7 The Realised GARCH Model
• 15.8 Realised Covariance
• 15.9 Exercises
• 16. Microstructure Models
• 16.1 Characteristics of High Frequency Data
• 16.2 Limit Order Book
• 16.3 Bid Ask Bounce
• 16.4 Information Content of Trades
• 16.5 Modelling Price Movements in Trades
• 16.6 Modelling Durations
• 16.7 Modelling Volatility in Transactions Time
• 16.8 Exercises
• 17. Options
• 17.1 Option Pricing Basics.
• 17.2 The Black-Scholes Option Price Model
• 17.3irst Look at Options Data
• 17.4 Estimating the Black-Scholes Model
• 17.5 Testing the Black-Scholes Model
• 17.6 Option Pricing and GARCH Volatility
• 17.7 The Melick-Thomas Option Price Model
• 17.8 Nonlinear Option Pricing.
• 17.9 Using Options to Estimate GARCH Models
• 17.10 Exercises
• 18. Extreme Values and Copulas
• 18.1 Motivation.
• 18.2 Evidence of Heavy Tails
• 18.3 Extreme Value Theory
• 18.4 Modelling Dependence using Copulas
• 18.5 Properties of Copulas
• 18.6 Estimating Copula Models
• 18.7 MGARCH Model Using Copulas
• 18.8 Exercises
• 19. Concluding Remarks
• A. Mathematical Preliminaries
• A.1 Summation Notation
• A.2 Expectations Operator
• A.3 Differentiation
• A.4 Taylor Series Expansions
• A.5 Matrix Algebra
• A.6 Transposition ofatrix
• A.7 Symmetric Matrix
• B. Properties of Estimators
• B.1 Finite Sample Properties
• B.2 Asymptotic Properties
• C. Linear Regression Model in Matrix Notation
• D. Numerical Optimisation
• E. Simulating Copulas
• Author index
• Subject index