Financial Econometric Modeling

Financial econometrics brings financial theory and econometric methods together with the power of data to advance understanding of the global financial universe upon which all modern economies depend. Financial Econometric Modeling is an introductory text that meets the learning challenge of integrating theory, measurement, data, and software to understand the modern world of finance. Empirical applications with financial data play a central position in this book's exposition. Each chapter is a how-to guide that takes readers from ideas and theories through to the practical realities of modeling, interpreting, and forecasting financial data. The book reaches out to a wide audience of students, applied researchers, and industry practitioners, guiding readers of diverse backgrounds on the models, methods, and empirical practice of modern financial econometrics.

Financial Econometric Modeling delivers a self-contained first course in financial econometrics, providing foundational ideas from financial theory and relevant econometric technique. From this foundation, the book covers a vast arena of modern financial econometrics that opens up empirical applications with data of the many different types that are now generated in financial markets. Every chapter follows the same principle, ensuring that all results reported in the book may be reproduced using standard econometric software packages like Stata or EViews, with a full set of data and programs provided to ensure easy implementation.

Stan Hurn is Professor of Econometrics at Queensland University of Technology. He held previous positions at the University of Glasgow and Brasenose College, Oxford. He is a Fellow of the Society of Financial Econometrics and Founding Member and Director of the National Centre for Econometric Research in Australia.

Vance L. Martin is Professor of Econometrics at the University of Melbourne. He has published widely in the area of financial econometrics and is coauthor, with Stan Hurn, of the highly successful introductory text Econometric Modeling with Time Series Specification, Estimation, and Testing (2013).

Peter C.B. Phillips is Sterling Professor of Economics at Yale University, Distinguished Professor at the University of Auckland, and Distinguished Term Professor at Singapore Management University. He is Founding Editor of the journal Econometric Theory and an elected fellow of many learned societies including the British Academy, the American Academy of Arts and Sciences, and the Royal Society of New Zealand. His work has advanced diverse areas of econometrics, introduced new methods of research in financial economics, and influenced applied work throughout the social and business sciences.

Jun Yu is Lee Kong Chian Professor of Economics and Finance at Singapore Management University and Lead Principal Investigator at the Centre for Research on the Economics of Aging (CREA). He is a Fellow of the Journal of Econometrics and the Society of Financial Econometrics, and an Associate Editor of the Journal of Econometrics, Econometric Theory, and Journal of Financial Econometrics.

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