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.

• Preface xii Chapter 1 Introduction 11.1 Subject Matter and Structure 11.2 Computer Software 41.3 Data 51.4 References 6Chapter 2 Random Variables and Random Processes 72.1 Introduction 72.2 Random Variables and Random Processes 82.2.1 Random Variables 82.2.2 Random Processes 152.3 Time Series Models 182.3.1 Autoregressive (AR) and Moving Average (MA) Models 182.3.2 Autoregressive Moving Average (ARMA) Models 212.3.3 Non-stationary Time Series 222.3.4 Autoregressive Integrated Moving Average (ARIMA) Models 242.3.5 Parameter Estimation and Inference 252.3.6 The Box–Jenkins Approach 302.3.7 Vector Autoregressive (VAR) Models 342.3.8 Forecasting with Time Series Models 352.3.9 Evaluating Forecasts 382.3.10 Non-linear Time Series Models 422.4 Summary 442.5 End of Chapter Questions 442.6 References 46Chapter 3 Regression and Volatility 493.1 Introduction 493.2 Regression Models 503.2.1 Linear Regression 503.2.2 Spurious Regression 563.2.3 Unit Root Tests 573.2.4 Cointegration 593.2.5 Forecasting with Regression Models 653.3 Modelling and Forecasting Conditional Volatility 663.3.1 Univariate Conditional Volatility 663.3.2 Conditional Covariance Matrices 723.4 Summary 753.5 End of Chapter Questions 763.6 References 77Chapter 4 Portfolio Theory and Asset Allocation 804.1 Introduction 804.2 Returns 814.3 Dividend Discount Model 884.4 Modern Portfolio Theory 904.4.1 Basic Theory 904.4.2 Generalisations 974.4.3 Strengths and Weaknesses 984.5 Empirical Examples 1004.6 Summary 1074.7 End of Chapter Questions 1094.8 Appendix 1104.8.1 Data 1104.8.2 MATLAB® Programs and Toolboxes 1114.9 References 112Chapter 5 Asset Pricing Models and Factor Models 1135.1 Introduction 1135.2 CAPM 1145.2.1 Main Results 1145.2.2 CAPM Applications 1175.2.3 Empirically Testing the CAPM 1185.2.4 Strengths and Weaknesses 1205.3 Factor Models 1225.3.1 Single-Index Model and APT Model 1225.3.2 Macroeconomic Factor Models 1245.3.3 Fama and French Models 1255.3.4 Covariance Matrix Estimation 1265.3.5 Strengths and Weaknesses 1285.4 Empirical Examples 1305.5 Summary 1365.6 End of Chapter Questions 1375.7 Appendix 1385.7.1 Data 1385.7.2 MATLAB® Programs and Toolboxes 1395.8 References 140Chapter 6 Market Efficiency 1436.1 Introduction 1436.2 Market Efficiency Tests 1456.2.1 The Efficient Market Hypothesis 1456.2.2 Random Walk Tests 1476.2.3 Other Tests 1496.3 Econometric Forecasting 1526.4 Technical Analysis 1546.4.1 Overview 1546.4.2 Testing the Profitability of Technical Trading Rules 1586.5 Data-Snooping 1606.6 Empirical Examples 1626.7 Summary 1746.8 End of Chapter Questions 1756.9 Appendix 1766.9.1 Data 1766.9.2 MATLAB® Programs and Toolboxes 1776.10 References 179Chapter 7 Modelling and Forecasting Exchange Rates 1837.1 Introduction 1837.2 Exchange Rates 1847.3 Market Efficiency and Exchange Rate Parity Conditions 1877.3.1 Uncovered Interest Rate Parity 1877.3.2 Covered Interest Rate Parity 1887.3.3 Forward Rate Unbiasedness 1897.4 Market Efficiency Tests 1897.4.1 Random Walk Tests 1897.4.2 Regression Model Tests 1917.5 Purchasing Power Parity 1937.5.1 The Law of One Price and the Purchasing Power Parity Hypothesis 1937.5.2 Testing the Purchasing Power Parity Hypothesis: Linear Tests 1947.5.3 Testing the Purchasing Power Parity Hypothesis: Non-linear Tests 1987.6 Forecasting Exchange Rates 2067.6.1 Econometric Models 2067.6.2 Technical Analysis 2107.7 Empirical Examples 2127.8 Summary 2207.9 End of Chapter Questions 2207.10 Appendix 2217.10.1 Data 2217.10.2 MATLAB® Programs and Toolboxes 2247.11 References 225Chapter 8 Modelling and Forecasting Interest Rates 2318.1 Introduction 2318.2 Bonds 2328.2.1 Yields and Prices 2328.2.2 The Term Structure of Interest Rates 2358.2.3 Duration and Convexity 2388.3 Interest Rate Models 2428.3.1 Vasicek Model 2428.3.2 CIR Model 2498.3.3 CKLS Model 2508.3.4 Forecasting Interest Rates 2538.4 Empirically Testing the Expectations Hypothesis 2548.4.1 Introduction 2548.4.2 Testing the Expectations Hypothesis 2558.4.3 Results and the Expectations HypothesisParadox 2588.5 Empirical Examples 2618.6 Summary 2678.7 End of Chapter Questions 2688.8 Appendix 2688.8.1 Data 2688.8.2 MATLAB® Programs and Toolboxes 2708.9 References 271Chapter 9 Market Risk Management 2749.1 Introduction 2749.2 VaR by the Delta-Normal Approach 2759.2.1 VaR for a Single Asset 2759.2.2 VaR for a Portfolio 2789.2.3 RiskMetrics and the Delta-Normal Approach 2809.3 VaR by Historical Simulation 2829.4 VaR by Monte Carlo Simulation 2839.5 VaR for Bonds 2859.6 VaR for Derivatives 2879.6.1 VaR by Delta-Gamma 2879.6.2 VaR by Monte Carlo Simulation 2929.7 Backtesting 2959.8 Financial Regulation and VaR 2999.9 Empirical Examples 3069.10 Summary 3199.11 End of Chapter Questions 3209.12 Appendix 3219.12.1 Data 3219.12.2 MATLAB® Programs and Toolboxes 3229.13 References 324Appendix Statistical Tables 326A.1 Areas Under the Standard Normal Distribution 327A.2 Critical Values for Student’s t-distribution 328A.3 Critical Values for the F-distribution 329A.4 Critical Values for the Chi-square Distribution 332A.5 Cumulative Distribution Function for the Dickey–Fuller Test 334A.6 Response Surfaces for Critical Values of Cointegration Tests 335Index 336

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.

• Preface xii Chapter 1 Introduction 11.1 Subject Matter and Structure 11.2 Computer Software 41.3 Data 51.4 References 6Chapter 2 Random Variables and Random Processes 72.1 Introduction 72.2 Random Variables and Random Processes 82.2.1 Random Variables 82.2.2 Random Processes 152.3 Time Series Models 182.3.1 Autoregressive (AR) and Moving Average (MA) Models 182.3.2 Autoregressive Moving Average (ARMA) Models 212.3.3 Non-stationary Time Series 222.3.4 Autoregressive Integrated Moving Average (ARIMA) Models 242.3.5 Parameter Estimation and Inference 252.3.6 The Box–Jenkins Approach 302.3.7 Vector Autoregressive (VAR) Models 342.3.8 Forecasting with Time Series Models 352.3.9 Evaluating Forecasts 382.3.10 Non-linear Time Series Models 422.4 Summary 442.5 End of Chapter Questions 442.6 References 46Chapter 3 Regression and Volatility 493.1 Introduction 493.2 Regression Models 503.2.1 Linear Regression 503.2.2 Spurious Regression 563.2.3 Unit Root Tests 573.2.4 Cointegration 593.2.5 Forecasting with Regression Models 653.3 Modelling and Forecasting Conditional Volatility 663.3.1 Univariate Conditional Volatility 663.3.2 Conditional Covariance Matrices 723.4 Summary 753.5 End of Chapter Questions 763.6 References 77Chapter 4 Portfolio Theory and Asset Allocation 804.1 Introduction 804.2 Returns 814.3 Dividend Discount Model 884.4 Modern Portfolio Theory 904.4.1 Basic Theory 904.4.2 Generalisations 974.4.3 Strengths and Weaknesses 984.5 Empirical Examples 1004.6 Summary 1074.7 End of Chapter Questions 1094.8 Appendix 1104.8.1 Data 1104.8.2 MATLAB® Programs and Toolboxes 1114.9 References 112Chapter 5 Asset Pricing Models and Factor Models 1135.1 Introduction 1135.2 CAPM 1145.2.1 Main Results 1145.2.2 CAPM Applications 1175.2.3 Empirically Testing the CAPM 1185.2.4 Strengths and Weaknesses 1205.3 Factor Models 1225.3.1 Single-Index Model and APT Model 1225.3.2 Macroeconomic Factor Models 1245.3.3 Fama and French Models 1255.3.4 Covariance Matrix Estimation 1265.3.5 Strengths and Weaknesses 1285.4 Empirical Examples 1305.5 Summary 1365.6 End of Chapter Questions 1375.7 Appendix 1385.7.1 Data 1385.7.2 MATLAB® Programs and Toolboxes 1395.8 References 140Chapter 6 Market Efficiency 1436.1 Introduction 1436.2 Market Efficiency Tests 1456.2.1 The Efficient Market Hypothesis 1456.2.2 Random Walk Tests 1476.2.3 Other Tests 1496.3 Econometric Forecasting 1526.4 Technical Analysis 1546.4.1 Overview 1546.4.2 Testing the Profitability of Technical Trading Rules 1586.5 Data-Snooping 1606.6 Empirical Examples 1626.7 Summary 1746.8 End of Chapter Questions 1756.9 Appendix 1766.9.1 Data 1766.9.2 MATLAB® Programs and Toolboxes 1776.10 References 179Chapter 7 Modelling and Forecasting Exchange Rates 1837.1 Introduction 1837.2 Exchange Rates 1847.3 Market Efficiency and Exchange Rate Parity Conditions 1877.3.1 Uncovered Interest Rate Parity 1877.3.2 Covered Interest Rate Parity 1887.3.3 Forward Rate Unbiasedness 1897.4 Market Efficiency Tests 1897.4.1 Random Walk Tests 1897.4.2 Regression Model Tests 1917.5 Purchasing Power Parity 1937.5.1 The Law of One Price and the Purchasing Power Parity Hypothesis 1937.5.2 Testing the Purchasing Power Parity Hypothesis: Linear Tests 1947.5.3 Testing the Purchasing Power Parity Hypothesis: Non-linear Tests 1987.6 Forecasting Exchange Rates 2067.6.1 Econometric Models 2067.6.2 Technical Analysis 2107.7 Empirical Examples 2127.8 Summary 2207.9 End of Chapter Questions 2207.10 Appendix 2217.10.1 Data 2217.10.2 MATLAB® Programs and Toolboxes 2247.11 References 225Chapter 8 Modelling and Forecasting Interest Rates 2318.1 Introduction 2318.2 Bonds 2328.2.1 Yields and Prices 2328.2.2 The Term Structure of Interest Rates 2358.2.3 Duration and Convexity 2388.3 Interest Rate Models 2428.3.1 Vasicek Model 2428.3.2 CIR Model 2498.3.3 CKLS Model 2508.3.4 Forecasting Interest Rates 2538.4 Empirically Testing the Expectations Hypothesis 2548.4.1 Introduction 2548.4.2 Testing the Expectations Hypothesis 2558.4.3 Results and the Expectations HypothesisParadox 2588.5 Empirical Examples 2618.6 Summary 2678.7 End of Chapter Questions 2688.8 Appendix 2688.8.1 Data 2688.8.2 MATLAB® Programs and Toolboxes 2708.9 References 271Chapter 9 Market Risk Management 2749.1 Introduction 2749.2 VaR by the Delta-Normal Approach 2759.2.1 VaR for a Single Asset 2759.2.2 VaR for a Portfolio 2789.2.3 RiskMetrics and the Delta-Normal Approach 2809.3 VaR by Historical Simulation 2829.4 VaR by Monte Carlo Simulation 2839.5 VaR for Bonds 2859.6 VaR for Derivatives 2879.6.1 VaR by Delta-Gamma 2879.6.2 VaR by Monte Carlo Simulation 2929.7 Backtesting 2959.8 Financial Regulation and VaR 2999.9 Empirical Examples 3069.10 Summary 3199.11 End of Chapter Questions 3209.12 Appendix 3219.12.1 Data 3219.12.2 MATLAB® Programs and Toolboxes 3229.13 References 324Appendix Statistical Tables 326A.1 Areas Under the Standard Normal Distribution 327A.2 Critical Values for Student’s t-distribution 328A.3 Critical Values for the F-distribution 329A.4 Critical Values for the Chi-square Distribution 332A.5 Cumulative Distribution Function for the Dickey–Fuller Test 334A.6 Response Surfaces for Critical Values of Cointegration Tests 335Index 336