Verfügbarkeit: Market risk analysis

Market risk analysis: 1 Quantitative methods in finance

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1. Verfasser:	Alexander, Carol (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Chichester [u.a.] Wiley 2008
Online-Zugang:	Inhaltsverzeichnis Buchcover
Beschreibung:	XXVII, 290 S. Ill., graph. Darst. 1 CD-ROM (12 cm)
ISBN:	9780470998007

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adam_text	Contents List of Figures xiii List of Tables xvi List of Examples xvii Foreword xix Preface to Volume I xxiii I.I Basic Calculus for Finance 1 1.1.1 Introduction 1 1.1.2 Functions and Graphs, Equations and Roots 3 1.1.2.1 Linear and Quadratic Functions 4 1. 1.2.2 Continuous and Differentiable Real-Valued Functions 5 1.1.2.3 Inverse Functions 6 1.1.2.4 The Exponential Function 7 1. 1.2.5 The Natural Logarithm 9 1. 1.3 Differentiation and Integration 10 1.1.3.1 Definitions 10 1.1.3.2 Rules for Differentiation 11 1.1.3.3 Monotonie. Concave and Convex Functions 13 1.1.3.4 Stationary Points and Optimization 14 1.1.3.5 Integration 15 1.1.4 Analysis of Financial Returns 16 1.1.4.1 Discrete and Continuous Time Notation 16 1.1.4.2 Portfolio Holdings and Portfolio Weights 17 1.1.4.3 Profit and Loss 19 1.1.4.4 Percentage and Log Returns 19 1.1.4.5 Geometric Brownian Motion 21 1.1.4.6 Discrete and Continuous Compounding in Discrete Time 22 1. 1.4.7 Period Log Returns in Discrete Time 23 1.1.4.8 Return on a Linear Portfolio 25 1.1.4.9 Sources of Returns 25 1.1.5 Functions of Several Variables 26 1.1.5.1 Partial Derivatives: Function of Two Variables 27 1.1.5.2 Partial Derivatives: Function of Several Variables 27 Contents 1.1.6 1.1.7 .1.5.3 Stationary Points .1.5.4 Optimization .1.5.5 Total Derivatives Taylor Expansion 1.6.1 Definition and Examples 1.6.2 Risk Factors and their Sensitivities 1.1.6.3 Some Financial Applications of Taylor Expansion 1.1.6.4 Multivariate Taylor Expansion Summary and Conclusions 28 29 31 31 32 33 33 34 35 1.2 Essential Linear Algebra for Finance 1.2.1 Introduction 1.2.2 Matrix Algebra and its Mathematical Applications 1.2.2.1 Basic Terminology .2.2.2 Laws of Matrix Algebra .2.2.3 Singular Matrices .2.2.4 Determinants .2.2.5 Matrix Inversion .2.2.6 Solution of Simultaneous Linear Equations .2.2.7 Quadratic Forms .2.2.8 Definite Matrices 1.2.3 Eigenvectors and Eigenvalues 1.2.3.1 Matrices as Linear Transformations 1.2.3.2 Formal Definitions .2.3.3 The Characteristic Equation .2.3.4 Eigenvalues and Eigenvectors of a 2 χ 2 Correlation Matrix .2.3.5 Properties of Eigenvalues and Eigenvectors .2.3.6 Using Excel to Find Eigenvalues and Eigenvectors .2.3.7 Eigenvalue Test for Definiteness 1.2.4 Applications to Linear Portfolios 1.2.4.1 Covariance and Correlation Matrices 1.2.4.2 Portfolio Risk and Return in Matrix Notation 1.2.4.3 Positive Definiteness of Covariance and Correlation Matrices 1.2.4.4 Eigenvalues and Eigenvectors of Covariance and Correlation Matrices 1.2.5 Matrix Decomposition 1.2.5.1 Spectral Decomposition of a Symmetric Matrix 1.2.5.2 Similarity Transforms 1.2.5.3 Cholesky Decomposition 1.2.5.4 LU Decomposition 1.2.6 Principal Component Analysis 1.2.6.1 Definition of Principal Components 1.2.6.2 Principal Component Representation 1.2.6.3 Case Study: PCA of European Equity Indices 1.2.7 Summary and Conclusions 37 37 38 38 39 40 41 43 44 45 46 48 48 50 51 52 52 53 54 55 55 56 58 59 61 61 62 62 63 64 65 66 67 70 Contents 1.3 Probability and Statistics 71 1.3.1 Introduction 71 1.3.2 Basic Concepts 72 1.3.2.1 Classical versus Bayesian Approaches 72 1.3.2.2 Laws of Probability 73 1.3.2.3 Density and Distribution Functions 75 1.3.2.4 Samples and Histograms 76 1.3.2.5 Expected Value and Sample Mean 78 1.3.2.6 Variance 79 1.3.2.7 Skewness and Kurtosis 81 1.3.2.8 Quantiles, Quartiles and Percentiles 83 1.3.3 Univariate Distributions 85 1.3.3.1 Binomial Distribution 85 1.3.3.2 Poisson and Exponential Distributions 87 1.3.3.3 Uniform Distribution 89 1.3.3.4 Normal Distribution 90 1.3.3.5 Lognormal Distribution 93 1.3.3.6 Normal Mixture Distributions 94 1.3.3.7 Student t Distributions 97 1.3.3.8 Sampling Distributions 100 1.3.3.9 Generalized Extreme Value Distributions 101 1.3.3.10 Generalized Pareto Distribution 103 1.3.3.11 Stable Distributions 105 1.3.3.12 Kernels 106 1.3.4 Multivariate Distributions 107 1.3.4.1 Divariate Distributions 108 1.3.4.2 Independent Random Variables 109 1.3.4.3 Covariance 110 1.3.4.4 Correlation 111 1.3.4.5 Multivariate Continuous Distributions 114 1.3.4.6 Multivariate Normal Distributions 115 1.3.4.7 Divariate Normal Mixture Distributions 116 1.3.4.8 Multivariate Student t Distributions 117 1.3.5 Introduction to Statistical Inference 118 1.3.5.1 Quantiles, Critical Values and Confidence Intervals 118 1.3.5.2 Central Limit Theorem 120 1.3.5.3 Confidence Intervals Based on Student t Distribution 122 1.3.5.4 Confidence Intervals for Variance 123 1.3.5.5 Hypothesis Tests 124 1.3.5.6 Tests on Means 125 1.3.5.7 Tests on Variances 126 1.3.5.8 Non-Parametric Tests on Distributions 127 1.3.6 Maximum Likelihood Estimation 130 1.3.6.1 The Likelihood Function 130 1.3.6.2 Finding the Maximum Likelihood Estimates 131 1.3.6.3 Standard Errors on Mean and Variance Estimates 133 Contents 1.3.7 Stochastic Processes in Discrete and Continuous Time 134 1.3.7.1 Stationary and Integrated Processes in Discrete Time 134 1.3.7.2 Mean Reverting Processes and Random Walks in Continuous Time 136 1.3.7.3 Stochastic Models for Asset Prices and Returns 137 1.3.7.4 Jumps and the Poisson Process 139 1.3.8 Summary and Conclusions 140 1.4 Introduction to Linear Regression 143 1.4.1 Introduction 143 1.4.2 Simple Linear Regression 144 1.4.2.1 Simple Linear Model 144 1.4.2.2 Ordinary Least Squares 146 1.4.2.3 Properties of the Error Process 148 1.4.2.4 ANOVA and Goodness of Fit 149 1.4.2.5 Hypothesis Tests on Coefficients 151 1.4.2.6 Reporting the Estimated Regression Model 152 1.4.2.7 Excel Estimation of the Simple Linear Model 153 1.4.3 Properties of OLS Estimators 155 1.4.3.1 Estimates and Estimators 155 1.4.3.2 Unbiasedness and Efficiency 156 1.4.3.3 Gauss-Markov Theorem 157 1.4.3.4 Consistency and Normality of OLS Estimators 157 1.4.3.5 Testing for Normality 158 1.4.4 Multi variate Linear Regression 158 1.4.4.1 Simple Linear Model and OLS in Matrix Notation 159 1.4.4.2 General Linear Model 161 1.4.4.3 Case Study: A Multiple Regression 162 1.4.4.4 Multiple Regression in Excel 163 1.4.4.5 Hypothesis Testing in Multiple Regression 163 1.4.4.6 Testing Multiple Restrictions 166 1.4.4.7 Confidence Intervals 167 1.4.4.8 Multicollinearity 170 1.4.4.9 Case Study: Determinants of Credit Spreads 171 1.4.4.10 Orthogonal Regression 173 1.4.5 Autocorrelation and Heteroscedasticity 175 1.4.5.1 Causes of Autocorrelation and Heteroscedasticity 175 1.4.5.2 Consequences of Autocorrelation and Heteroscedasticity 176 1.4.5.3 Testing for Autocorrelation 176 1.4.5.4 Testing for Heteroscedasticity 177 1.4.5.5 Generalized Least Squares 178 1.4.6 Applications of Linear Regression in Finance 179 1.4.6.1 Testing a Theory 179 1.4.6.2 Analysing Empirical Market Behaviour 180 1.4.6.3 Optimal Portfolio Allocation 181 ______________________________________________ Contents xi 1.4.6.4 Regression-Based Hedge Ratios 181 1.4.6.5 Trading on Regression Models 182 1.4.7 Summary and Conclusions 184 1.5 Numerical Methods in Finance 185 1.5.1 Introduction 185 1.5.2 Iteration 187 1.5.2.1 Method of Bisection 187 1.5.2.2 Newton-Raphson Iteration 188 1.5.2.3 Gradient Methods 191 1.5.3 Interpolation and Extrapolation 193 1.5.3.1 Linear and Bilinear Interpolation 193 1.5.3.2 Polynomial Interpolation: Application to Currency Options 195 1.5.3.3 Cubic Splines: Application to Yield Curves 197 1.5.4 Optimization 200 1.5.4.1 Least Squares Problems 201 1.5.4.2 Likelihood Methods 202 1.5.4.3 The EM Algorithm 203 1.5.4.4 Case Study: Applying the EM Algorithm to Normal Mixture Densities 203 1.5.5 Finite Difference Approximations 206 1.5.5.1 First and Second Order Finite Differences 206 1.5.5.2 Finite Difference Approximations for the Greeks 207 1.5.5.3 Finite Difference Solutions to Partial Differential Equations 208 1.5.6 Binomial Lattices 210 1.5.6.1 Constructing the Lattice 211 1.5.6.2 Arbitrage Free Pricing and Risk Neutral Valuation 211 1.5.6.3 Pricing European Options 212 1.5.6.4 Lognormal Asset Price Distributions 213 1.5.6.5 Pricing American Options 215 1.5.7 Monte Carlo Simulation 217 1.5.7.1 Random Numbers 217 1.5.7.2 Simulations from an Empirical or a Given Distribution 217 1.5.7.3 Case Study: Generating Time Series of Lognormal Asset Prices 218 1.5.7.4 Simulations on a System of Two Correlated Normal Returns 220 1.5.7.5 Multivariate Normal and Student f Distributed Simulations 220 1.5.8 Summary and Conclusions 223 1.6 Introduction to Portfolio Theory 225 1.6.1 Introduction 225 1.6.2 Utility Theory 226 1.6.2.1 Properties of Utility Functions 226 1.6.2.2 Risk Preference 229 1.6.2.3 How to Determine the Risk Tolerance of an Investor 230 1.6.2.4 Coefficients of Risk Aversion 231 Contents 1.6.2.5 Some Standard Utility Functions 232 1.6.2.6 Mean-Variance Criterion 234 1.6.2.7 Extension of the Mean-Variance Criterion to Higher Moments 235 1.6.3 Portfolio Allocation 237 1.6.3.1 Portfolio Diversification 238 1.6.3.2 Minimum Variance Portfolios 240 1.6.3.3 The Markowitz Problem 244 1.6.3.4 Minimum Variance Portfolios with Many Constraints 245 1.6.3.5 Efficient Frontier 246 1.6.3.6 Optimal Allocations 247 1.6.4 Theory of Asset Pricing 250 1.6.4.1 Capital Market Line 250 1.6.4.2 Capital Asset Pricing Model 252 1.6.4.3 Security Market Line 253 1.6.4.4 Testing the CAPM 254 1.6.4.5 Extensions to CAPM 255 1.6.5 Risk Adjusted Performance Measures 256 1.6.5.1 CAPM RAPMs 257 1.6.5.2 Making Decisions Using the Sharpe Ratio 258 1.6.5.3 Adjusting the Sharpe Ratio for Autocorrelation 259 1.6.5.4 Adjusting the Sharpe Ratio for Higher Moments 260 1.6.5.5 Generalized Sharpe Ratio 262 1.6.5.6 Kappa Indices, Omega and Sortino Ratio 263 1.6.6 Summary and Conclusions 266 References 269 Statistical Tables 273 Index 279
adam_txt	Contents List of Figures xiii List of Tables xvi List of Examples xvii Foreword xix Preface to Volume I xxiii I.I Basic Calculus for Finance 1 1.1.1 Introduction 1 1.1.2 Functions and Graphs, Equations and Roots 3 1.1.2.1 Linear and Quadratic Functions 4 1. 1.2.2 Continuous and Differentiable Real-Valued Functions 5 1.1.2.3 Inverse Functions 6 1.1.2.4 The Exponential Function 7 1. 1.2.5 The Natural Logarithm 9 1. 1.3 Differentiation and Integration 10 1.1.3.1 Definitions 10 1.1.3.2 Rules for Differentiation 11 1.1.3.3 Monotonie. Concave and Convex Functions 13 1.1.3.4 Stationary Points and Optimization 14 1.1.3.5 Integration 15 1.1.4 Analysis of Financial Returns 16 1.1.4.1 Discrete and Continuous Time Notation 16 1.1.4.2 Portfolio Holdings and Portfolio Weights 17 1.1.4.3 Profit and Loss 19 1.1.4.4 Percentage and Log Returns 19 1.1.4.5 Geometric Brownian Motion 21 1.1.4.6 Discrete and Continuous Compounding in Discrete Time 22 1. 1.4.7 Period Log Returns in Discrete Time 23 1.1.4.8 Return on a Linear Portfolio 25 1.1.4.9 Sources of Returns 25 1.1.5 Functions of Several Variables 26 1.1.5.1 Partial Derivatives: Function of Two Variables 27 1.1.5.2 Partial Derivatives: Function of Several Variables 27 Contents 1.1.6 1.1.7 .1.5.3 Stationary Points .1.5.4 Optimization .1.5.5 Total Derivatives Taylor Expansion 1.6.1 Definition and Examples 1.6.2 Risk Factors and their Sensitivities 1.1.6.3 Some Financial Applications of Taylor Expansion 1.1.6.4 Multivariate Taylor Expansion Summary and Conclusions 28 29 31 31 32 33 33 34 35 1.2 Essential Linear Algebra for Finance 1.2.1 Introduction 1.2.2 Matrix Algebra and its Mathematical Applications 1.2.2.1 Basic Terminology .2.2.2 Laws of Matrix Algebra .2.2.3 Singular Matrices .2.2.4 Determinants .2.2.5 Matrix Inversion .2.2.6 Solution of Simultaneous Linear Equations .2.2.7 Quadratic Forms .2.2.8 Definite Matrices 1.2.3 Eigenvectors and Eigenvalues 1.2.3.1 Matrices as Linear Transformations 1.2.3.2 Formal Definitions .2.3.3 The Characteristic Equation .2.3.4 Eigenvalues and Eigenvectors of a 2 χ 2 Correlation Matrix .2.3.5 Properties of Eigenvalues and Eigenvectors .2.3.6 Using Excel to Find Eigenvalues and Eigenvectors .2.3.7 Eigenvalue Test for Definiteness 1.2.4 Applications to Linear Portfolios 1.2.4.1 Covariance and Correlation Matrices 1.2.4.2 Portfolio Risk and Return in Matrix Notation 1.2.4.3 Positive Definiteness of Covariance and Correlation Matrices 1.2.4.4 Eigenvalues and Eigenvectors of Covariance and Correlation Matrices 1.2.5 Matrix Decomposition 1.2.5.1 Spectral Decomposition of a Symmetric Matrix 1.2.5.2 Similarity Transforms 1.2.5.3 Cholesky Decomposition 1.2.5.4 LU Decomposition 1.2.6 Principal Component Analysis 1.2.6.1 Definition of Principal Components 1.2.6.2 Principal Component Representation 1.2.6.3 Case Study: PCA of European Equity Indices 1.2.7 Summary and Conclusions 37 37 38 38 39 40 41 43 44 45 46 48 48 50 51 52 52 53 54 55 55 56 58 59 61 61 62 62 63 64 65 66 67 70 Contents 1.3 Probability and Statistics 71 1.3.1 Introduction 71 1.3.2 Basic Concepts 72 1.3.2.1 Classical versus Bayesian Approaches 72 1.3.2.2 Laws of Probability 73 1.3.2.3 Density and Distribution Functions 75 1.3.2.4 Samples and Histograms 76 1.3.2.5 Expected Value and Sample Mean 78 1.3.2.6 Variance 79 1.3.2.7 Skewness and Kurtosis 81 1.3.2.8 Quantiles, Quartiles and Percentiles 83 1.3.3 Univariate Distributions 85 1.3.3.1 Binomial Distribution 85 1.3.3.2 Poisson and Exponential Distributions 87 1.3.3.3 Uniform Distribution 89 1.3.3.4 Normal Distribution 90 1.3.3.5 Lognormal Distribution 93 1.3.3.6 Normal Mixture Distributions 94 1.3.3.7 Student t Distributions 97 1.3.3.8 Sampling Distributions 100 1.3.3.9 Generalized Extreme Value Distributions 101 1.3.3.10 Generalized Pareto Distribution 103 1.3.3.11 Stable Distributions 105 1.3.3.12 Kernels 106 1.3.4 Multivariate Distributions 107 1.3.4.1 Divariate Distributions 108 1.3.4.2 Independent Random Variables 109 1.3.4.3 Covariance 110 1.3.4.4 Correlation 111 1.3.4.5 Multivariate Continuous Distributions 114 1.3.4.6 Multivariate Normal Distributions 115 1.3.4.7 Divariate Normal Mixture Distributions 116 1.3.4.8 Multivariate Student t Distributions 117 1.3.5 Introduction to Statistical Inference 118 1.3.5.1 Quantiles, Critical Values and Confidence Intervals 118 1.3.5.2 Central Limit Theorem 120 1.3.5.3 Confidence Intervals Based on Student t Distribution 122 1.3.5.4 Confidence Intervals for Variance 123 1.3.5.5 Hypothesis Tests 124 1.3.5.6 Tests on Means 125 1.3.5.7 Tests on Variances 126 1.3.5.8 Non-Parametric Tests on Distributions 127 1.3.6 Maximum Likelihood Estimation 130 1.3.6.1 The Likelihood Function 130 1.3.6.2 Finding the Maximum Likelihood Estimates 131 1.3.6.3 Standard Errors on Mean and Variance Estimates 133 Contents 1.3.7 Stochastic Processes in Discrete and Continuous Time 134 1.3.7.1 Stationary and Integrated Processes in Discrete Time 134 1.3.7.2 Mean Reverting Processes and Random Walks in Continuous Time 136 1.3.7.3 Stochastic Models for Asset Prices and Returns 137 1.3.7.4 Jumps and the Poisson Process 139 1.3.8 Summary and Conclusions 140 1.4 Introduction to Linear Regression 143 1.4.1 Introduction 143 1.4.2 Simple Linear Regression 144 1.4.2.1 Simple Linear Model 144 1.4.2.2 Ordinary Least Squares 146 1.4.2.3 Properties of the Error Process 148 1.4.2.4 ANOVA and Goodness of Fit 149 1.4.2.5 Hypothesis Tests on Coefficients 151 1.4.2.6 Reporting the Estimated Regression Model 152 1.4.2.7 Excel Estimation of the Simple Linear Model 153 1.4.3 Properties of OLS Estimators 155 1.4.3.1 Estimates and Estimators 155 1.4.3.2 Unbiasedness and Efficiency 156 1.4.3.3 Gauss-Markov Theorem 157 1.4.3.4 Consistency and Normality of OLS Estimators 157 1.4.3.5 Testing for Normality 158 1.4.4 Multi variate Linear Regression 158 1.4.4.1 Simple Linear Model and OLS in Matrix Notation 159 1.4.4.2 General Linear Model 161 1.4.4.3 Case Study: A Multiple Regression 162 1.4.4.4 Multiple Regression in Excel 163 1.4.4.5 Hypothesis Testing in Multiple Regression 163 1.4.4.6 Testing Multiple Restrictions 166 1.4.4.7 Confidence Intervals 167 1.4.4.8 Multicollinearity 170 1.4.4.9 Case Study: Determinants of Credit Spreads 171 1.4.4.10 Orthogonal Regression 173 1.4.5 Autocorrelation and Heteroscedasticity 175 1.4.5.1 Causes of Autocorrelation and Heteroscedasticity 175 1.4.5.2 Consequences of Autocorrelation and Heteroscedasticity 176 1.4.5.3 Testing for Autocorrelation 176 1.4.5.4 Testing for Heteroscedasticity 177 1.4.5.5 Generalized Least Squares 178 1.4.6 Applications of Linear Regression in Finance 179 1.4.6.1 Testing a Theory 179 1.4.6.2 Analysing Empirical Market Behaviour 180 1.4.6.3 Optimal Portfolio Allocation 181 _ Contents xi 1.4.6.4 Regression-Based Hedge Ratios 181 1.4.6.5 Trading on Regression Models 182 1.4.7 Summary and Conclusions 184 1.5 Numerical Methods in Finance 185 1.5.1 Introduction 185 1.5.2 Iteration 187 1.5.2.1 Method of Bisection 187 1.5.2.2 Newton-Raphson Iteration 188 1.5.2.3 Gradient Methods 191 1.5.3 Interpolation and Extrapolation 193 1.5.3.1 Linear and Bilinear Interpolation 193 1.5.3.2 Polynomial Interpolation: Application to Currency Options 195 1.5.3.3 Cubic Splines: Application to Yield Curves 197 1.5.4 Optimization 200 1.5.4.1 Least Squares Problems 201 1.5.4.2 Likelihood Methods 202 1.5.4.3 The EM Algorithm 203 1.5.4.4 Case Study: Applying the EM Algorithm to Normal Mixture Densities 203 1.5.5 Finite Difference Approximations 206 1.5.5.1 First and Second Order Finite Differences 206 1.5.5.2 Finite Difference Approximations for the Greeks 207 1.5.5.3 Finite Difference Solutions to Partial Differential Equations 208 1.5.6 Binomial Lattices 210 1.5.6.1 Constructing the Lattice 211 1.5.6.2 Arbitrage Free Pricing and Risk Neutral Valuation 211 1.5.6.3 Pricing European Options 212 1.5.6.4 Lognormal Asset Price Distributions 213 1.5.6.5 Pricing American Options 215 1.5.7 Monte Carlo Simulation 217 1.5.7.1 Random Numbers 217 1.5.7.2 Simulations from an Empirical or a Given Distribution 217 1.5.7.3 Case Study: Generating Time Series of Lognormal Asset Prices 218 1.5.7.4 Simulations on a System of Two Correlated Normal Returns 220 1.5.7.5 Multivariate Normal and Student f Distributed Simulations 220 1.5.8 Summary and Conclusions 223 1.6 Introduction to Portfolio Theory 225 1.6.1 Introduction 225 1.6.2 Utility Theory 226 1.6.2.1 Properties of Utility Functions 226 1.6.2.2 Risk Preference 229 1.6.2.3 How to Determine the Risk Tolerance of an Investor 230 1.6.2.4 Coefficients of Risk Aversion 231 Contents 1.6.2.5 Some Standard Utility Functions 232 1.6.2.6 Mean-Variance Criterion 234 1.6.2.7 Extension of the Mean-Variance Criterion to Higher Moments 235 1.6.3 Portfolio Allocation 237 1.6.3.1 Portfolio Diversification 238 1.6.3.2 Minimum Variance Portfolios 240 1.6.3.3 The Markowitz Problem 244 1.6.3.4 Minimum Variance Portfolios with Many Constraints 245 1.6.3.5 Efficient Frontier 246 1.6.3.6 Optimal Allocations 247 1.6.4 Theory of Asset Pricing 250 1.6.4.1 Capital Market Line 250 1.6.4.2 Capital Asset Pricing Model 252 1.6.4.3 Security Market Line 253 1.6.4.4 Testing the CAPM 254 1.6.4.5 Extensions to CAPM 255 1.6.5 Risk Adjusted Performance Measures 256 1.6.5.1 CAPM RAPMs 257 1.6.5.2 Making Decisions Using the Sharpe Ratio 258 1.6.5.3 Adjusting the Sharpe Ratio for Autocorrelation 259 1.6.5.4 Adjusting the Sharpe Ratio for Higher Moments 260 1.6.5.5 Generalized Sharpe Ratio 262 1.6.5.6 Kappa Indices, Omega and Sortino Ratio 263 1.6.6 Summary and Conclusions 266 References 269 Statistical Tables 273 Index 279
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id	DE-604.BV023295515
illustrated	Illustrated
index_date	2024-07-02T20:44:39Z
indexdate	2024-07-09T21:15:12Z
institution	BVB
isbn	9780470998007
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-016480055
oclc_num	612076456
open_access_boolean
owner	DE-91G DE-BY-TUM DE-1050 DE-355 DE-BY-UBR DE-945 DE-91 DE-BY-TUM DE-703 DE-384 DE-11
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physical	XXVII, 290 S. Ill., graph. Darst. 1 CD-ROM (12 cm)
publishDate	2008
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publishDateSort	2008
publisher	Wiley
record_format	marc
spelling	Alexander, Carol Verfasser aut Market risk analysis 1 Quantitative methods in finance Carol Alexander Chichester [u.a.] Wiley 2008 XXVII, 290 S. Ill., graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier (DE-604)BV023295503 1 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016480055&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016480055&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Buchcover
spellingShingle	Alexander, Carol Market risk analysis
title	Market risk analysis
title_auth	Market risk analysis
title_exact_search	Market risk analysis
title_exact_search_txtP	Market risk analysis
title_full	Market risk analysis 1 Quantitative methods in finance Carol Alexander
title_fullStr	Market risk analysis 1 Quantitative methods in finance Carol Alexander
title_full_unstemmed	Market risk analysis 1 Quantitative methods in finance Carol Alexander
title_short	Market risk analysis
title_sort	market risk analysis quantitative methods in finance
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016480055&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016480055&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV023295503
work_keys_str_mv	AT alexandercarol marketriskanalysis1

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