Empirical Finance for Finance and Banking

Empirical Finance for Finance and Banking provides the student with a relatively non-technical guide to some of the key topics in finance where empirical methods play an important role  Written for students taking Master’s degrees in finance and banking, it is also suitable for students and researchers in other areas, including economics. The first three introductory chapters outline the structure of the book and review econometric and statistical techniques, while the remaining chapters discuss various topics, including: portfolio theory and asset allocation, asset pricing and factor models, market efficiency, modelling and forecasting exchange and interest rates and Value at Risk. Understanding these topics and the methods covered will be helpful for students interested in working as analysts and researchers in financial institutions.  Designed for students with limited previous experience of econometrics, statistics or advanced financial theory, the text is written in an “easy-to-read” style.  It features empirical examples at the end of each chapter to demonstrate the empirical methods and theory discussed and uses MATLAB® for all calculations. A guide to answering end of chapter questions and relevant computer programs can be found on the companion website: www.wiley.com/college/sollis

Table of Contents:
Preface xii Chapter 1 Introduction 1 1.1 Subject Matter and Structure 1 1.2 Computer Software 4 1.3 Data 5 1.4 References 6 Chapter 2 Random Variables and Random Processes 7 2.1 Introduction 7 2.2 Random Variables and Random Processes 8 2.2.1 Random Variables 8 2.2.2 Random Processes 15 2.3 Time Series Models 18 2.3.1 Autoregressive (AR) and Moving Average (MA) Models 18 2.3.2 Autoregressive Moving Average (ARMA) Models 21 2.3.3 Non-stationary Time Series 22 2.3.4 Autoregressive Integrated Moving Average (ARIMA) Models 24 2.3.5 Parameter Estimation and Inference 25 2.3.6 The Box–Jenkins Approach 30 2.3.7 Vector Autoregressive (VAR) Models 34 2.3.8 Forecasting with Time Series Models 35 2.3.9 Evaluating Forecasts 38 2.3.10 Non-linear Time Series Models 42 2.4 Summary 44 2.5 End of Chapter Questions 44 2.6 References 46 Chapter 3 Regression and Volatility 49 3.1 Introduction 49 3.2 Regression Models 50 3.2.1 Linear Regression 50 3.2.2 Spurious Regression 56 3.2.3 Unit Root Tests 57 3.2.4 Cointegration 59 3.2.5 Forecasting with Regression Models 65 3.3 Modelling and Forecasting Conditional Volatility 66 3.3.1 Univariate Conditional Volatility 66 3.3.2 Conditional Covariance Matrices 72 3.4 Summary 75 3.5 End of Chapter Questions 76 3.6 References 77 Chapter 4 Portfolio Theory and Asset Allocation 80 4.1 Introduction 80 4.2 Returns 81 4.3 Dividend Discount Model 88 4.4 Modern Portfolio Theory 90 4.4.1 Basic Theory 90 4.4.2 Generalisations 97 4.4.3 Strengths and Weaknesses 98 4.5 Empirical Examples 100 4.6 Summary 107 4.7 End of Chapter Questions 109 4.8 Appendix 110 4.8.1 Data 110 4.8.2 MATLAB® Programs and Toolboxes 111 4.9 References 112 Chapter 5 Asset Pricing Models and Factor Models 113 5.1 Introduction 113 5.2 CAPM 114 5.2.1 Main Results 114 5.2.2 CAPM Applications 117 5.2.3 Empirically Testing the CAPM 118 5.2.4 Strengths and Weaknesses 120 5.3 Factor Models 122 5.3.1 Single-Index Model and APT Model 122 5.3.2 Macroeconomic Factor Models 124 5.3.3 Fama and French Models 125 5.3.4 Covariance Matrix Estimation 126 5.3.5 Strengths and Weaknesses 128 5.4 Empirical Examples 130 5.5 Summary 136 5.6 End of Chapter Questions 137 5.7 Appendix 138 5.7.1 Data 138 5.7.2 MATLAB® Programs and Toolboxes 139 5.8 References 140 Chapter 6 Market Efficiency 143 6.1 Introduction 143 6.2 Market Efficiency Tests 145 6.2.1 The Efficient Market Hypothesis 145 6.2.2 Random Walk Tests 147 6.2.3 Other Tests 149 6.3 Econometric Forecasting 152 6.4 Technical Analysis 154 6.4.1 Overview 154 6.4.2 Testing the Profitability of Technical Trading Rules 158 6.5 Data-Snooping 160 6.6 Empirical Examples 162 6.7 Summary 174 6.8 End of Chapter Questions 175 6.9 Appendix 176 6.9.1 Data 176 6.9.2 MATLAB® Programs and Toolboxes 177 6.10 References 179 Chapter 7 Modelling and Forecasting Exchange Rates 183 7.1 Introduction 183 7.2 Exchange Rates 184 7.3 Market Efficiency and Exchange Rate Parity Conditions 187 7.3.1 Uncovered Interest Rate Parity 187 7.3.2 Covered Interest Rate Parity 188 7.3.3 Forward Rate Unbiasedness 189 7.4 Market Efficiency Tests 189 7.4.1 Random Walk Tests 189 7.4.2 Regression Model Tests 191 7.5 Purchasing Power Parity 193 7.5.1 The Law of One Price and the Purchasing Power Parity Hypothesis 193 7.5.2 Testing the Purchasing Power Parity Hypothesis: Linear Tests 194 7.5.3 Testing the Purchasing Power Parity Hypothesis: Non-linear Tests 198 7.6 Forecasting Exchange Rates 206 7.6.1 Econometric Models 206 7.6.2 Technical Analysis 210 7.7 Empirical Examples 212 7.8 Summary 220 7.9 End of Chapter Questions 220 7.10 Appendix 221 7.10.1 Data 221 7.10.2 MATLAB® Programs and Toolboxes 224 7.11 References 225 Chapter 8 Modelling and Forecasting Interest Rates 231 8.1 Introduction 231 8.2 Bonds 232 8.2.1 Yields and Prices 232 8.2.2 The Term Structure of Interest Rates 235 8.2.3 Duration and Convexity 238 8.3 Interest Rate Models 242 8.3.1 Vasicek Model 242 8.3.2 CIR Model 249 8.3.3 CKLS Model 250 8.3.4 Forecasting Interest Rates 253 8.4 Empirically Testing the Expectations Hypothesis 254 8.4.1 Introduction 254 8.4.2 Testing the Expectations Hypothesis 255 8.4.3 Results and the Expectations Hypothesis Paradox 258 8.5 Empirical Examples 261 8.6 Summary 267 8.7 End of Chapter Questions 268 8.8 Appendix 268 8.8.1 Data 268 8.8.2 MATLAB® Programs and Toolboxes 270 8.9 References 271 Chapter 9 Market Risk Management 274 9.1 Introduction 274 9.2 VaR by the Delta-Normal Approach 275 9.2.1 VaR for a Single Asset 275 9.2.2 VaR for a Portfolio 278 9.2.3 RiskMetrics and the Delta-Normal Approach 280 9.3 VaR by Historical Simulation 282 9.4 VaR by Monte Carlo Simulation 283 9.5 VaR for Bonds 285 9.6 VaR for Derivatives 287 9.6.1 VaR by Delta-Gamma 287 9.6.2 VaR by Monte Carlo Simulation 292 9.7 Backtesting 295 9.8 Financial Regulation and VaR 299 9.9 Empirical Examples 306 9.10 Summary 319 9.11 End of Chapter Questions 320 9.12 Appendix 321 9.12.1 Data 321 9.12.2 MATLAB® Programs and Toolboxes 322 9.13 References 324 Appendix Statistical Tables 326 A.1 Areas Under the Standard Normal Distribution 327 A.2 Critical Values for Student’s t-distribution 328 A.3 Critical Values for the F-distribution 329 A.4 Critical Values for the Chi-square Distribution 332 A.5 Cumulative Distribution Function for the Dickey–Fuller Test 334 A.6 Response Surfaces for Critical Values of Cointegration Tests 335 Index 336

About the Author :
Robert Sollis is Professor of Financial Economics at Newcastle University Business School. His main teaching and research interests lie in the area of applied econometrics, with a particular focus on macroeconomic and financial time series analysis. He has published in internationally recognized academic journals (e.g. Journal of Money, Credit and Banking, Journal of Applied Econometrics, Journal of Time Series Analysis), and in 2002 co-authored the textbook Applied Time Series Modelling and Forecasting with Richard Harris.