{"product_id":"market-risk-analysis-practical-financial-econometrics-isbn-9780470998014","title":"Market Risk Analysis, Practical Financial Econometrics","description":"Written by leading market risk academic, Professor Carol Alexander, Practical Financial Econometrics forms part two of the Market Risk Analysis four volume set. It introduces the econometric techniques that are commonly applied to finance with a critical and selective exposition, emphasising the areas of econometrics, such as GARCH, cointegration and copulas that are required for resolving problems in market risk analysis. The book covers material for a one-semester graduate course in applied financial econometrics in a very pedagogical fashion as each time a concept is introduced an empirical example is given, and whenever possible this is illustrated with an Excel spreadsheet.\u003cbr\u003e \u003cp\u003eAll together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM. Empirical examples and case studies specific to this volume include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eFactor analysis with orthogonal regressions and using principal component factors;\u003c\/li\u003e \u003cli\u003eEstimation of symmetric and asymmetric, normal and Student \u003ci\u003et\u003c\/i\u003e GARCH and E-GARCH parameters;\u003c\/li\u003e \u003cli\u003eNormal, Student \u003ci\u003et\u003c\/i\u003e, Gumbel, Clayton, normal mixture copula densities, and simulations from these copulas with application to VaR and portfolio optimization;\u003c\/li\u003e \u003cli\u003ePrincipal component analysis of yield curves with applications to portfolio immunization and asset\/liability management;\u003c\/li\u003e \u003cli\u003eSimulation of normal mixture and Markov switching GARCH returns;\u003c\/li\u003e \u003cli\u003eCointegration based index tracking and pairs trading, with error correction and impulse response modelling;\u003c\/li\u003e \u003cli\u003eMarkov switching regression models (Eviews code);\u003c\/li\u003e \u003cli\u003eGARCH term structure forecasting with volatility targeting;\u003c\/li\u003e \u003cli\u003eNon-linear quantile regressions with applications to hedging.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eList of Figures xiii\u003c\/p\u003e \u003cp\u003eList of Tables xvii\u003c\/p\u003e \u003cp\u003eList of Examples xx\u003c\/p\u003e \u003cp\u003eForeword xxii\u003c\/p\u003e \u003cp\u003ePreface to Volume II xxvi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. 1 Factor Models 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.1. 1 Introduction 1\u003c\/p\u003e \u003cp\u003eII.1. 2 Single Factor Models 2\u003c\/p\u003e \u003cp\u003eII.1.2. 1 Single Index Model 2\u003c\/p\u003e \u003cp\u003eII.1.2. 2 Estimating Portfolio Characteristics using OLS 4\u003c\/p\u003e \u003cp\u003eII.1.2. 3 Estimating Portfolio Risk using EWMA 6\u003c\/p\u003e \u003cp\u003eII.1.2. 4 Relationship between Beta, Correlation and Relative Volatility 8\u003c\/p\u003e \u003cp\u003eII.1.2. 5 Risk Decomposition in a Single Factor Model 10\u003c\/p\u003e \u003cp\u003eII.1. 3 Multi-Factor Models 11\u003c\/p\u003e \u003cp\u003eII.1.3. 1 Multi-factor Models of Asset or Portfolio Returns 11\u003c\/p\u003e \u003cp\u003eII.1.3. 2 Style Attribution Analysis 13\u003c\/p\u003e \u003cp\u003eII.1.3. 3 General Formulation of Multi-factor Model 16\u003c\/p\u003e \u003cp\u003eII.1.3. 4 Multi-factor Models of International Portfolios 18\u003c\/p\u003e \u003cp\u003eII.1. 4 Case Study: Estimation of Fundamental Factor Models 21\u003c\/p\u003e \u003cp\u003eII.1.4. 1 Estimating Systematic Risk for a Portfolio of US Stocks 22\u003c\/p\u003e \u003cp\u003eII.1.4. 2 Multicollinearity: A Problem with Fundamental Factor Models 23\u003c\/p\u003e \u003cp\u003eII.1.4. 3 Estimating Fundamental Factor Models by Orthogonal Regression 25\u003c\/p\u003e \u003cp\u003eII.1. 5 Analysis of Barra Model 27\u003c\/p\u003e \u003cp\u003eII.1.5. 1 Risk Indices, Descriptors and Fundamental Betas 28\u003c\/p\u003e \u003cp\u003eII.1.5. 2 Model Specification and Risk Decomposition 30\u003c\/p\u003e \u003cp\u003eII.1. 6 Tracking Error and Active Risk 31\u003c\/p\u003e \u003cp\u003eII.1.6. 1 Ex Post versus Ex Ante Measurement of Risk and Return 32\u003c\/p\u003e \u003cp\u003eII.1.6. 2 Definition of Active Returns 32\u003c\/p\u003e \u003cp\u003eII.1.6. 3 Definition of Active Weights 33\u003c\/p\u003e \u003cp\u003eII.1.6. 4 Ex Post Tracking Error 33\u003c\/p\u003e \u003cp\u003eII.1.6. 5 Ex Post Mean-Adjusted Tracking Error 36\u003c\/p\u003e \u003cp\u003eII.1.6. 6 Ex Ante Tracking Error 39\u003c\/p\u003e \u003cp\u003eII.1.6. 7 Ex Ante Mean-Adjusted Tracking Error 40\u003c\/p\u003e \u003cp\u003eII.1.6. 8 Clarification of the Definition of Active Risk 42\u003c\/p\u003e \u003cp\u003eII.1. 7 Summary and Conclusions 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. 2 Principal Component Analysis 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.2. 1 Introduction 47\u003c\/p\u003e \u003cp\u003eII.2. 2 Review of Principal Component Analysis 48\u003c\/p\u003e \u003cp\u003eII.2.2. 1 Definition of Principal Components 49\u003c\/p\u003e \u003cp\u003eII 2 Principal Component Representation 49\u003c\/p\u003e \u003cp\u003eII.2.2. 3 Frequently Asked Questions 50\u003c\/p\u003e \u003cp\u003eII.2. 3 Case Study: PCA of UK Government Yield Curves 53\u003c\/p\u003e \u003cp\u003eII.2.3. 1 Properties of UK Interest Rates 53\u003c\/p\u003e \u003cp\u003eII.2.3. 2 Volatility and Correlation of UK Spot Rates 55\u003c\/p\u003e \u003cp\u003eII.2.3. 3 PCA on UK Spot Rates Correlation Matrix 56\u003c\/p\u003e \u003cp\u003eII.2.3. 4 Principal Component Representation 58\u003c\/p\u003e \u003cp\u003eII.2.3. 5 PCA on UK Short Spot Rates Covariance Matrix 60\u003c\/p\u003e \u003cp\u003eII.2. 4 Term Structure Factor Models 61\u003c\/p\u003e \u003cp\u003eII.2.4. 1 Interest Rate Sensitive Portfolios 62\u003c\/p\u003e \u003cp\u003eII.2.4. 2 Factor Models for Currency Forward Positions 66\u003c\/p\u003e \u003cp\u003eII.2.4. 3 Factor Models for Commodity Futures Portfolios 70\u003c\/p\u003e \u003cp\u003eII.2.4. 4 Application to Portfolio Immunization 71\u003c\/p\u003e \u003cp\u003eII.2.4. 5 Application to Asset–Liability Management 72\u003c\/p\u003e \u003cp\u003eII.2.4. 6 Application to Portfolio Risk Measurement 73\u003c\/p\u003e \u003cp\u003eII.2.4. 7 Multiple Curve Factor Models 76\u003c\/p\u003e \u003cp\u003eII.2. 5 Equity PCA Factor Models 80\u003c\/p\u003e \u003cp\u003eII.2.5. 1 Model Structure 80\u003c\/p\u003e \u003cp\u003eII.2.5. 2 Specific Risks and Dimension Reduction 81\u003c\/p\u003e \u003cp\u003eII.2.5. 3 Case Study: PCA Factor Model for DJIA Portfolios 82\u003c\/p\u003e \u003cp\u003eII.2. 6 Summary and Conclusions 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. 3 Classical Models of Volatility and Correlation 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.3. 1 Introduction 89\u003c\/p\u003e \u003cp\u003eII.3. 2 Variance and Volatility 90\u003c\/p\u003e \u003cp\u003eII.3.2. 1 Volatility and the Square-Root-of-Time Rule 90\u003c\/p\u003e \u003cp\u003eII.3.3. 2 Constant Volatility Assumption 92\u003c\/p\u003e \u003cp\u003eII.3.2. 3 Volatility when Returns are Autocorrelated 92\u003c\/p\u003e \u003cp\u003eII.3.2. 4 Remarks about Volatility 93\u003c\/p\u003e \u003cp\u003eII.3. 3 Covariance and Correlation 94\u003c\/p\u003e \u003cp\u003eII.3.3. 1 Definition of Covariance and Correlation 94\u003c\/p\u003e \u003cp\u003eII.3.3. 2 Correlation Pitfalls 95\u003c\/p\u003e \u003cp\u003eII 3 Covariance Matrices 96\u003c\/p\u003e \u003cp\u003eII.3.3. 4 Scaling Covariance Matrices 97\u003c\/p\u003e \u003cp\u003eII.3. 4 Equally Weighted Averages 98\u003c\/p\u003e \u003cp\u003eII.3.4. 1 Unconditional Variance and Volatility 99\u003c\/p\u003e \u003cp\u003eII.3.4. 2 Unconditional Covariance and Correlation 102\u003c\/p\u003e \u003cp\u003eII.3.4. 3 Forecasting with Equally Weighted Averages 103\u003c\/p\u003e \u003cp\u003eII.3. 5 Precision of Equally Weighted Estimates 104\u003c\/p\u003e \u003cp\u003eII.3.5. 1 Confidence Intervals for Variance and Volatility 104\u003c\/p\u003e \u003cp\u003eII.3.5. 2 Standard Error of Variance Estimator 106\u003c\/p\u003e \u003cp\u003eII.3.5. 3 Standard Error of Volatility Estimator 107\u003c\/p\u003e \u003cp\u003eII.3.5. 4 Standard Error of Correlation Estimator 109\u003c\/p\u003e \u003cp\u003eII.3. 6 Case Study: Volatility and Correlation of US Treasuries 109\u003c\/p\u003e \u003cp\u003eII.3.6. 1 Choosing the Data 110\u003c\/p\u003e \u003cp\u003eII.3.6. 2 Our Data 111\u003c\/p\u003e \u003cp\u003eII.3.6. 3 Effect of Sample Period 112\u003c\/p\u003e \u003cp\u003eII.3.6. 4 How to Calculate Changes in Interest Rates 113\u003c\/p\u003e \u003cp\u003eII.3. 7 Equally Weighted Moving Averages 115\u003c\/p\u003e \u003cp\u003eII.3.7. 1 Effect of Volatility Clusters 115\u003c\/p\u003e \u003cp\u003eII.3.7. 2 Pitfalls of the Equally Weighted Moving Average Method 117\u003c\/p\u003e \u003cp\u003eII.3.7. 3 Three Ways to Forecast Long Term Volatility 118\u003c\/p\u003e \u003cp\u003eII.3. 8 Exponentially Weighted Moving Averages 120\u003c\/p\u003e \u003cp\u003eII.3.8. 1 Statistical Methodology 120\u003c\/p\u003e \u003cp\u003eII.3.8. 2 Interpretation of Lambda 121\u003c\/p\u003e \u003cp\u003eII.3.8. 3 Properties of EWMA Estimators 122\u003c\/p\u003e \u003cp\u003eII.3.8. 4 Forecasting with EWMA 123\u003c\/p\u003e \u003cp\u003eII.3.8. 5 Standard Errors for EWMA Forecasts 124\u003c\/p\u003e \u003cp\u003eII.3.8. 6 RiskMetrics TM Methodology 126\u003c\/p\u003e \u003cp\u003eII.3.8. 7 Orthogonal EWMA versus RiskMetrics EWMA 128\u003c\/p\u003e \u003cp\u003eII.3. 9 Summary and Conclusions 129\u003c\/p\u003e \u003cp\u003eII. 4 Introduction to GARCH Models 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII.4. 1 Introduction 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.4. 2 The Symmetric Normal GARCH Model 135\u003c\/p\u003e \u003cp\u003eII.4.2. 1 Model Specification 135\u003c\/p\u003e \u003cp\u003eII.4.2. 2 Parameter Estimation 137\u003c\/p\u003e \u003cp\u003eII.4.2. 3 Volatility Estimates 141\u003c\/p\u003e \u003cp\u003eII.4.2. 4 GARCH Volatility Forecasts 142\u003c\/p\u003e \u003cp\u003eII.4.2. 5 Imposing Long Term Volatility 144\u003c\/p\u003e \u003cp\u003eII.4.2. 6 Comparison of GARCH and EWMA Volatility Models 147\u003c\/p\u003e \u003cp\u003eII.4. 3 Asymmetric GARCH Models 147\u003c\/p\u003e \u003cp\u003eII.4.3. 1 A-garch 148\u003c\/p\u003e \u003cp\u003eII.4.3. 2 Gjr-garch 150\u003c\/p\u003e \u003cp\u003eII.4.3. 3 Exponential GARCH 151\u003c\/p\u003e \u003cp\u003eII.4.3. 4 Analytic E-GARCH Volatility Term Structure Forecasts 154\u003c\/p\u003e \u003cp\u003eII.4.3. 5 Volatility Feedback 156\u003c\/p\u003e \u003cp\u003eII.4. 4 Non-Normal GARCH Models 157\u003c\/p\u003e \u003cp\u003eII.4.4. 1 Student t GARCH Models 157\u003c\/p\u003e \u003cp\u003eII.4.4. 2 Case Study: Comparison of GARCH Models for the Ftse 100 159\u003c\/p\u003e \u003cp\u003eII.4.4. 3 Normal Mixture GARCH Models 161\u003c\/p\u003e \u003cp\u003eII 4 Markov Switching GARCH 163\u003c\/p\u003e \u003cp\u003eII.4. 5 GARCH Covariance Matrices 164\u003c\/p\u003e \u003cp\u003eII.4.5. 1 Estimation of Multivariate GARCH Models 165\u003c\/p\u003e \u003cp\u003eII.4.5. 2 Constant and Dynamic Conditional Correlation GARCH 166\u003c\/p\u003e \u003cp\u003eII.4.5. 3 Factor GARCH 169\u003c\/p\u003e \u003cp\u003eII.4. 6 Orthogonal GARCH 171\u003c\/p\u003e \u003cp\u003eII.4.6. 1 Model Specification 171\u003c\/p\u003e \u003cp\u003eII.4.6. 2 Case Study: A Comparison of RiskMetrics and O-GARCH 173\u003c\/p\u003e \u003cp\u003eII.4.6. 3 Splicing Methods for Constructing Large Covariance Matrices 179\u003c\/p\u003e \u003cp\u003eII.4. 7 Monte Carlo Simulation with GARCH Models 180\u003c\/p\u003e \u003cp\u003eII.4.7. 1 Simulation with Volatility Clustering 180\u003c\/p\u003e \u003cp\u003eII.4.7. 2 Simulation with Volatility Clustering Regimes 183\u003c\/p\u003e \u003cp\u003eII.4.7. 3 Simulation with Correlation Clustering 185\u003c\/p\u003e \u003cp\u003eII.4. 8 Applications of GARCH Models 188\u003c\/p\u003e \u003cp\u003eII.4.8. 1 Option Pricing with GARCH Diffusions 188\u003c\/p\u003e \u003cp\u003eII.4.8. 2 Pricing Path-Dependent European Options 189\u003c\/p\u003e \u003cp\u003eII.4.8. 3 Value-at-Risk Measurement 192\u003c\/p\u003e \u003cp\u003eII.4.8. 4 Estimation of Time Varying Sensitivities 193\u003c\/p\u003e \u003cp\u003eII.4.8. 5 Portfolio Optimization 195\u003c\/p\u003e \u003cp\u003eII.4. 9 Summary and Conclusions 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. 5 Time Series Models and Cointegration 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.5. 1 Introduction 201\u003c\/p\u003e \u003cp\u003eII.5. 2 Stationary Processes 202\u003c\/p\u003e \u003cp\u003eII.5.2. 1 Time Series Models 203\u003c\/p\u003e \u003cp\u003eII.5.2. 2 Inversion and the Lag Operator 206\u003c\/p\u003e \u003cp\u003eII.5.2. 3 Response to Shocks 206\u003c\/p\u003e \u003cp\u003eII.5.2. 4 Estimation 208\u003c\/p\u003e \u003cp\u003eII.5.2. 5 Prediction 210\u003c\/p\u003e \u003cp\u003eII.5.2. 6 Multivariate Models for Stationary Processes 211\u003c\/p\u003e \u003cp\u003eII.5. 3 Stochastic Trends 212\u003c\/p\u003e \u003cp\u003eII.5.3. 1 Random Walks and Efficient Markets 212\u003c\/p\u003e \u003cp\u003eII.5.3. 2 Integrated Processes and Stochastic Trends 213\u003c\/p\u003e \u003cp\u003eII.5.3. 3 Deterministic Trends 214\u003c\/p\u003e \u003cp\u003eII.5.3. 4 Unit Root Tests 215\u003c\/p\u003e \u003cp\u003eII.5.3. 5 Unit Roots in Asset Prices 218\u003c\/p\u003e \u003cp\u003eII.5.3. 6 Unit Roots in Interest Rates, Credit Spreads and Implied Volatility 220\u003c\/p\u003e \u003cp\u003eII.5.3. 7 Reconciliation of Time Series and Continuous Time Models 223\u003c\/p\u003e \u003cp\u003eII.5.3. 8 Unit Roots in Commodity Prices 224\u003c\/p\u003e \u003cp\u003eII.5. 4 Long Term Equilibrium 225\u003c\/p\u003e \u003cp\u003eII.5.4. 1 Cointegration and Correlation Compared 225\u003c\/p\u003e \u003cp\u003eII.5.4. 2 Common Stochastic Trends 227\u003c\/p\u003e \u003cp\u003eII.5.4. 3 Formal Definition of Cointegration 228\u003c\/p\u003e \u003cp\u003eII.5.4. 4 Evidence of Cointegration in Financial Markets 229\u003c\/p\u003e \u003cp\u003eII.5.4. 5 Estimation and Testing in Cointegrated Systems 231\u003c\/p\u003e \u003cp\u003eII.5.4. 6 Application to Benchmark Tracking 239\u003c\/p\u003e \u003cp\u003eII.5.4. 7 Case Study: Cointegration Index Tracking in the Dow Jones Index 240\u003c\/p\u003e \u003cp\u003eII.5.5 Modelling Short Term Dynamics 243\u003c\/p\u003e \u003cp\u003eII.5.5.1 Error Correction Models 243\u003c\/p\u003e \u003cp\u003eII.5.5. 2 Granger Causality 246\u003c\/p\u003e \u003cp\u003eII.5.5. 3 Case Study: Pairs Trading Volatility Index Futures 247\u003c\/p\u003e \u003cp\u003eII.5. 6 Summary and Conclusions 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. 6 Introduction to Copulas 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.6. 1 Introduction 253\u003c\/p\u003e \u003cp\u003eII.6. 2 Concordance Metrics 255\u003c\/p\u003e \u003cp\u003eII.6.2. 1 Concordance 255\u003c\/p\u003e \u003cp\u003eII.6.2. 2 Rank Correlations 256\u003c\/p\u003e \u003cp\u003eII.6. 3 Copulas and Associated Theoretical Concepts 258\u003c\/p\u003e \u003cp\u003eII.6.3. 1 Simulation of a Single Random Variable 258\u003c\/p\u003e \u003cp\u003eII.6.3. 2 Definition of a Copula 259\u003c\/p\u003e \u003cp\u003eII.6.3. 3 Conditional Copula Distributions and their Quantile Curves 263\u003c\/p\u003e \u003cp\u003eII.6.3. 4 Tail Dependence 264\u003c\/p\u003e \u003cp\u003eII.6.3. 5 Bounds for Dependence 265\u003c\/p\u003e \u003cp\u003eII.6. 4 Examples of Copulas 266\u003c\/p\u003e \u003cp\u003eII.6.4. 1 Normal or Gaussian Copulas 266\u003c\/p\u003e \u003cp\u003eII.6.4. 2 Student t Copulas 268\u003c\/p\u003e \u003cp\u003eII.6.4. 3 Normal Mixture Copulas 269\u003c\/p\u003e \u003cp\u003eII.6.4. 4 Archimedean Copulas 271\u003c\/p\u003e \u003cp\u003eII.6. 5 Conditional Copula Distributions and Quantile Curves 273\u003c\/p\u003e \u003cp\u003eII.6.5. 1 Normal or Gaussian Copulas 273\u003c\/p\u003e \u003cp\u003eII.6.5. 2 Student t Copulas 274\u003c\/p\u003e \u003cp\u003eII.6.5. 3 Normal Mixture Copulas 275\u003c\/p\u003e \u003cp\u003eII.6.5. 4 Archimedean Copulas 275\u003c\/p\u003e \u003cp\u003eII.6.5. 5 Examples 276\u003c\/p\u003e \u003cp\u003eII.6. 6 Calibrating Copulas 279\u003c\/p\u003e \u003cp\u003eII.6.6. 1 Correspondence between Copulas and Rank Correlations 280\u003c\/p\u003e \u003cp\u003eII.6.6. 2 Maximum Likelihood Estimation 281\u003c\/p\u003e \u003cp\u003eII.6.6. 3 How to Choose the Best Copula 283\u003c\/p\u003e \u003cp\u003eII.6. 7 Simulation with Copulas 285\u003c\/p\u003e \u003cp\u003eII.6.7. 1 Using Conditional Copulas for Simulation 285\u003c\/p\u003e \u003cp\u003eII.6.7. 2 Simulation from Elliptical Copulas 286\u003c\/p\u003e \u003cp\u003eII.6.7. 3 Simulation with Normal and Student t Copulas 287\u003c\/p\u003e \u003cp\u003eII.6.7. 4 Simulation from Archimedean Copulas 290\u003c\/p\u003e \u003cp\u003eII.6. 8 Market Risk Applications 290\u003c\/p\u003e \u003cp\u003eII.6.8. 1 Value-at-Risk Estimation 291\u003c\/p\u003e \u003cp\u003eII.6.8. 2 Aggregation and Portfolio Diversification 292\u003c\/p\u003e \u003cp\u003eII.6.8. 3 Using Copulas for Portfolio Optimization 295\u003c\/p\u003e \u003cp\u003eII.6. 9 Summary and Conclusions 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. 7 Advanced Econometric Models 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.7. 1 Introduction 301\u003c\/p\u003e \u003cp\u003eII.7. 2 Quantile Regression 303\u003c\/p\u003e \u003cp\u003eII.7.2. 1 Review of Standard Regression 304\u003c\/p\u003e \u003cp\u003eII.7.2. 2 What is Quantile Regression? 305\u003c\/p\u003e \u003cp\u003eII.7.2. 3 Parameter Estimation in Quantile Regression 305\u003c\/p\u003e \u003cp\u003eII.7.2. 4 Inference in Linear Quantile Regression 307\u003c\/p\u003e \u003cp\u003eII.7.2. 5 Using Copulas for Non-linear Quantile Regression 307\u003c\/p\u003e \u003cp\u003eII.7. 3 Case Studies on Quantile Regression 309\u003c\/p\u003e \u003cp\u003eII.7.3. 1 Case Study 1: Quantile Regression of Vftse on FTSE 100 Index 309\u003c\/p\u003e \u003cp\u003eII.7.3. 2 Case Study 2: Hedging with Copula Quantile Regression 314\u003c\/p\u003e \u003cp\u003eII.7. 4 Other Non-Linear Regression Models 319\u003c\/p\u003e \u003cp\u003eII.7.4. 1 Non-linear Least Squares 319\u003c\/p\u003e \u003cp\u003eII.7.4. 2 Discrete Choice Models 321\u003c\/p\u003e \u003cp\u003eII.7. 5 Markov Switching Models 325\u003c\/p\u003e \u003cp\u003eII.7.5. 1 Testing for Structural Breaks 325\u003c\/p\u003e \u003cp\u003eII.7.5. 2 Model Specification 327\u003c\/p\u003e \u003cp\u003eII.7.5. 3 Financial Applications and Software 329\u003c\/p\u003e \u003cp\u003eII.7. 6 Modelling Ultra High Frequency Data 330\u003c\/p\u003e \u003cp\u003eII.7.6. 1 Data Sources and Filtering 330\u003c\/p\u003e \u003cp\u003eII.7.6. 2 Modelling the Time between Trades 332\u003c\/p\u003e \u003cp\u003eII.7.6. 3 Forecasting Volatility 334\u003c\/p\u003e \u003cp\u003eII.7. 7 Summary and Conclusions 337\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII. 8 Forecasting and Model Evaluation 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eII.8. 1 Introduction 341\u003c\/p\u003e \u003cp\u003eII.8. 2 Returns Models 342\u003c\/p\u003e \u003cp\u003eII.8.2. 1 Goodness of Fit 343\u003c\/p\u003e \u003cp\u003eII.8.2. 2 Forecasting 347\u003c\/p\u003e \u003cp\u003eII.8.2. 3 Simulating Critical Values for Test Statistics 348\u003c\/p\u003e \u003cp\u003eII.8.2. 4 Specification Tests for Regime Switching Models 350\u003c\/p\u003e \u003cp\u003eII.8. 3 Volatility Models 350\u003c\/p\u003e \u003cp\u003eII.8.3. 1 Goodness of Fit of GARCH Models 351\u003c\/p\u003e \u003cp\u003eII.8.3. 2 Forecasting with GARCH Volatility Models 352\u003c\/p\u003e \u003cp\u003eII.8.3. 3 Moving Average Models 354\u003c\/p\u003e \u003cp\u003eII.8. 4 Forecasting the Tails of a Distribution 356\u003c\/p\u003e \u003cp\u003eII.8.4. 1 Confidence Intervals for Quantiles 356\u003c\/p\u003e \u003cp\u003eII.8.4. 2 Coverage Tests 357\u003c\/p\u003e \u003cp\u003eII.8.4. 3 Application of Coverage Tests to GARCH Models 360\u003c\/p\u003e \u003cp\u003eII.8.4. 4 Forecasting Conditional Correlations 361\u003c\/p\u003e \u003cp\u003eII.8. 5 Operational Evaluation 363\u003c\/p\u003e \u003cp\u003eII.8.5. 1 General Backtesting Algorithm 363\u003c\/p\u003e \u003cp\u003eII.8.5. 2 Alpha Models 365\u003c\/p\u003e \u003cp\u003eII.8.5. 3 Portfolio Optimization 366\u003c\/p\u003e \u003cp\u003eII.8.5. 4 Hedging with Futures 366\u003c\/p\u003e \u003cp\u003eII.8.5. 5 Value-at-Risk Measurement 367\u003c\/p\u003e \u003cp\u003eII.8.5. 6 Trading Implied Volatility 370\u003c\/p\u003e \u003cp\u003eII.8.5. 7 Trading Realized Volatility 372\u003c\/p\u003e \u003cp\u003eII.8.5. 8 Pricing and Hedging Options 373\u003c\/p\u003e \u003cp\u003eII.8. 6 Summary and Conclusions 375\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003eIndex 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCarol Alexander\u003c\/b\u003e is a Professor of Risk Management at the ICMA Centre, University of Reading, and Chair of the Academic Advisory Council of the Professional Risk Manager’s International Association (PRMIA). She is the author of \u003ci\u003eMarket Models: A Guide to Financial Data Analysis\u003c\/i\u003e(John Wiley \u0026amp; Sons Ltd, 2001) and has been editor and contributor of a very large number of books in finance and mathematics, including the multi-volume \u003ci\u003eProfessional Risk Manager's Handbook\u003c\/i\u003e(McGraw-Hill, 2008 and PRMIA Publications). Carol has published nearly 100 academic journal articles, book chapters and books, the majority of which focus on financial risk management and mathematical finance. Professor Alexander is one of the world's leading authorities on market risk analysis. For further details, see \u003cb\u003ewww.icmacentre.rdg.ac.uk\/alexander\u003c\/b\u003e.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMarket Risk Analysis\u003c\/b\u003e is a series of four volumes:\u003c\/p\u003e \u003cp\u003eVolume I: \u003ci\u003eQuantitative Methods in Finance\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eVolume II: \u003ci\u003ePractical Financial Econometrics\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eVolume III: \u003ci\u003ePricing, Hedging and Trading Financial Instruments\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eVolume IV: \u003ci\u003eValue at Risk Models\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003eAlthough the four volumes are very much interlinked, each containing numerous cross-references to other volumes, they are written as self-contained texts.\u003c\/p\u003e \u003cp\u003eVolume I covers the essential mathematical and financial background for subsequent volumes. There are six comprehensive chapters covering all the calculus, linear algebra, probability and statistics, numerical methods and portfolio mathematics that are necessary for market risk analysis. It is a complete and pedagogical introduction to quantitative methods applied to finance.\u003c\/p\u003e \u003cp\u003eVolume II provides a detailed understanding of financial econometrics, with a unique focus on applications to asset pricing, fund management and market risk analysis. It covers equity factor models, including a detailed analysis of the Barra model and tracking error, principal component analysis, volatility and correlation, GARCH, cointegration, copulas, Markov switching, quantile regression, discrete choice models, non-linear regression, forecasting and model evaluation.\u003c\/p\u003e \u003cp\u003eVolume III has five extensive chapters on the pricing, hedging and trading of bonds and swaps, futures and forwards, options and volatility, and detailed descriptions of mapping portfolios of these financial instruments to their risk factors. There are numerous examples, all coded in interactive Excel spreadsheets, including many pricing formulae for exotic options but excluding the calibration of stochastic volatility models, for which Matlab code is provided.\u003c\/p\u003e \u003cp\u003eVolume IV builds on the three previous volumes to provide a comprehensive and detailed treatment of market VaR models. The exposition starts at an elementary level but, as in all the other volumes, the pedagogical approach accompanied by numerous interactive Excel spreadsheets allows readers to experience the application of parametric linear, historical simulation and Monte Carlo VaR models to increasingly complex portfolios. Starting with simple positions, readers are soon applying risk models to large international securities portfolios, commodity futures, path dependent options and much else. This rigorous treatment includes many new results and applications to regulatory and economic capital allocation, measurement of VaR model risk and stress testing.\u003c\/p\u003e \u003cp\u003eEach volume is accompanied by a CD-ROM which features numerous interactive Excel spreadsheets that illustrate the vast majority of the problems and case studies in these texts. For further information see the accompanying CD-ROM\u003c\/p\u003e \u003cp\u003eWritten by leading market risk academic, Professor Carol Alexander, \u003ci\u003ePractical Financial Econometrics\u003c\/i\u003e forms part two of the \u003ci\u003eMarket Risk Analysis\u003c\/i\u003e four volume set. It introduces the econometric techniques that are commonly applied to finance with a critical and selective exposition, emphasising the areas of econometrics, such as GARCH, cointegration and copulas that are required for resolving problems in market risk analysis. The book covers material for a one-semester graduate course in applied financial econometrics in a very pedagogical fashion as each time a concept is introduced an empirical example is given, and whenever possible this is illustrated with an Excel spreadsheet.\u003c\/p\u003e \u003cp\u003eAll together, the Market Risk Analysis four volume set illustrates virtually \u003ci\u003eevery\u003c\/i\u003e concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in \u003ci\u003einteractive Excel spreadsheets\u003c\/i\u003e available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eFactor analysis with orthogonal regressions and using principal component factors;\u003c\/li\u003e \u003cli\u003eEstimation of symmetric and asymmetric, normal and Student \u003ci\u003et\u003c\/i\u003eGARCH and E-GARCH parameters;\u003c\/li\u003e \u003cli\u003eNormal, Student \u003ci\u003et\u003c\/i\u003e, Gumbel, Clayton, normal mixture copula densities, and simulations from these copulas with application to VaR and portfolio optimization;\u003c\/li\u003e \u003cli\u003ePrincipal component analysis of yield curves with applications to portfolio immunization and asset\/liability management;\u003c\/li\u003e \u003cli\u003eSimulation of normal mixture and Markov switching GARCH returns;\u003c\/li\u003e \u003cli\u003eCointegration based index tracking and pairs trading, with error correction and impulse response modelling;\u003c\/li\u003e \u003cli\u003eMarkov switching regression models (Eviews code);\u003c\/li\u003e \u003cli\u003eGARCH term structure forecasting with volatility targeting;\u003c\/li\u003e \u003cli\u003eNon-linear quantile regressions with applications to hedging.\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989574271205,"sku":"NP9780470998014","price":105.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470998014.jpg?v=1761784656","url":"https:\/\/k12savings.com\/products\/market-risk-analysis-practical-financial-econometrics-isbn-9780470998014","provider":"K12savings","version":"1.0","type":"link"}