Portfolio Construction and Analytics

AvFrank J. Fabozzi,Dessislava A. Pachamanova

Inbunden, Engelska, 2016

1 434 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

Beskrivning

A detailed, multi-disciplinary approach to investment analytics Portfolio Construction and Analytics provides an up-to-date understanding of the analytic investment process for students and professionals alike. With complete and detailed coverage of portfolio analytics and modeling methods, this book is unique in its multi-disciplinary approach. Investment analytics involves the input of a variety of areas, and this guide provides the perspective of data management, modeling, software resources, and investment strategy to give you a truly comprehensive understanding of how today's firms approach the process. Real-world examples provide insight into analytics performed with vendor software, and references to analytics performed with open source software will prove useful to both students and practitioners.Portfolio analytics refers to all of the methods used to screen, model, track, and evaluate investments. Big data, regulatory change, and increasing risk is forcing a need for a more coherent approach to all aspects of investment analytics, and this book provides the strong foundation and critical skills you need. Master the fundamental modeling concepts and widely used analyticsLearn the latest trends in risk metrics, modeling, and investment strategiesGet up to speed on the vendor and open-source software most commonly usedGain a multi-angle perspective on portfolio analytics at today's firmsIdentifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring risk and performance are all major functions of an investment firm that relies heavily on analytics output. This reliance will only increase in the face of market changes and increased regulatory pressure, and practitioners need a deep understanding of the latest methods and models used to build a robust investment strategy. Portfolio Construction and Analytics is an invaluable resource for portfolio management in any capacity.

Produktinformation

Utgivningsdatum:2016-05-24
Mått:163 x 231 x 51 mm
Vikt:862 g
Format:Inbunden
Språk:Engelska
Serie:Frank J. Fabozzi Series
Antal sidor:624
Förlag:John Wiley & Sons Inc
ISBN:9781118445594

Utforska kategorier

Finansiering inom Ekonomi och Ledarskap

Mer om författaren

DESSISLAVA A. PACHAMANOVA is professor of analytics and computational finance and Zwerling Family Endowed Research Scholar at Babson College.FRANK J. FABOZZI is professor of finance at EDHEC Business School, a senior scientific adviser at the EDHEC-Risk Institute, and editor of the Journal of Portfolio Management.

Innehållsförteckning

Preface xixAbout the Authors xxvAcknowledgments xviiChapter 1 Introduction to Portfolio Management and Analytics 11.1 Asset Classes and the Asset Allocation Decision 11.2 The Portfolio Management Process 41.2.1 Setting the Investment Objectives 41.2.2 Developing and Implementing a Portfolio Strategy 61.2.3 Monitoring the Portfolio 81.2.4 Adjusting the Portfolio 91.3 Traditional versus Quantitative Asset Management 91.4 Overview of Portfolio Analytics 101.4.1 Market Analytics 121.4.2 Financial Screening 151.4.3 Asset Allocation Models 161.4.4 Strategy Testing and Evaluating Portfolio Performance 171.4.5 Systems for Portfolio Analytics 201.5 Outline of Topics Covered in the Book 22Part One Statistical Models of Risk and UncertaintyChapter 2 Random Variables, Probability Distributions, and Important Statistical Concepts 312.1 What Is a Probability Distribution? 312.2 The Bernoulli Probability Distribution and Probability Mass Functions 322.3 The Binomial Probability Distribution and Discrete Distributions 342.4 The Normal Distribution and Probability Density Functions 382.5 The Concept of Cumulative Probability 412.6 Describing Distributions 442.6.1 Measures of Central Tendency 442.6.2 Measures of Risk 472.6.3 Skew 542.6.4 Kurtosis 552.7 Dependence between Two Random Variables: Covariance and Correlation 552.8 Sums of Random Variables 572.9 Joint Probability Distributions and Conditional Probability 612.10 Copulas 642.11 From Probability Theory to Statistical Measurement: Probability Distributions and Sampling 662.11.1 Central Limit Theorem 702.11.2 Confidence Intervals 712.11.3 Bootstrapping 722.11.4 Hypothesis Testing 73Chapter 3 Important Probability Distributions 773.1 Examples of Probability Distributions 793.1.1 Notation Used in Describing Continuous Probability Distributions 793.1.2 Discrete and Continuous Uniform Distributions 803.1.3 Student’s t Distribution 823.1.4 Lognormal Distribution 833.1.5 Poisson Distribution 853.1.6 Exponential Distribution 873.1.7 Chi-Square Distribution 883.1.8 Gamma Distribution 903.1.9 Beta Distribution 903.2 Modeling Financial Return Distributions 913.2.1 Elliptical Distributions 923.2.2 Stable Paretian Distributions 943.2.3 Generalized Lambda Distribution 963.3 Modeling Tails of Financial Return Distributions 983.3.1 Generalized Extreme Value Distribution 983.3.2 Generalized Pareto Distribution 993.3.3 Extreme Value Models 101Chapter 4 Statistical Estimation Models 1064.1 Commonly Used Return Estimation Models 1064.2 Regression Analysis 1084.2.1 A Simple Regression Example 1094.2.2 Regression Applications in the Investment Management Process 1144.3 Factor Analysis 1164.4 Principal Components Analysis 1184.5 Autoregressive Conditional Heteroscedastic Models 125Part Two Simulation and Optimization ModelingChapter 5 Simulation Modeling 1335.1 Monte Carlo Simulation: A Simple Example 1335.1.1 Selecting Probability Distributions for the Inputs 1355.1.2 Interpreting Monte Carlo Simulation Output 1375.2 Why Use Simulation? 1405.2.1 Multiple Input Variables and Compounding Distributions 1415.2.2 Incorporating Correlations 1425.2.3 Evaluating Decisions 1445.3 How Many Scenarios? 1475.4 Random Number Generation 149Chapter 6 Optimization Modeling 1516.1 Optimization Formulations 1526.1.1 Minimization versus Maximization 1546.1.2 Local versus Global Optima 1556.1.3 Multiple Objectives 1566.2 Important Types of Optimization Problems 1576.2.1 Convex Programming 1576.2.2 Linear Programming 1586.2.3 Quadratic Programming 1596.2.4 Second-Order Cone Programming 1606.2.5 Integer and Mixed Integer Programming 1616.3 A Simple Optimization Problem Formulation Example: Portfolio Allocation 1616.4 Optimization Algorithms 1666.5 Optimization Software 1686.6 A Software Implementation Example 1706.6.1 Optimization with Excel Solver 1716.6.2 Solution to the Portfolio Allocation Example 175Chapter 7 Optimization under Uncertainty 1807.1 Dynamic Programming 1817.2 Stochastic Programming 1837.2.1 Multistage Models 1847.2.2 Mean-Risk Stochastic Models 1897.2.3 Chance-Constrained Models 1917.3 Robust Optimization 194Part Three Portfolio TheoryChapter 8 Asset Diversification 2038.1 The Case for Diversification 2048.2 The Classical Mean-Variance Optimization Framework 2088.3 Efficient Frontiers 2128.4 Alternative Formulations of the Classical Mean-Variance Optimization Problem 2158.4.1 Expected Return Formulation 2158.4.2 Risk Aversion Formulation 2158.5 The Capital Market Line 2168.6 Expected Utility Theory 2208.6.1 Quadratic Utility Function 2218.6.2 Linear Utility Function 2238.6.3 Exponential Utility Function 2248.6.4 Power Utility Function 2248.6.5 Logarithmic Utility Function 2248.7 Diversification Redefined 226Chapter 9 Factor Models 2329.1 Factor Models in the Financial Economics Literature 2339.2 Mean-Variance Optimization with Factor Models 2369.3 Factor Selection in Practice 2399.4 Factor Models for Alpha Construction 2439.5 Factor Models for Risk Estimation 2459.5.1 Macroeconomic Factor Models 2459.5.2 Fundamental Factor Models 2469.5.3 Statistical Factor Models 2489.5.4 Hybrid Factor Models 2509.5.5 Selecting the "Right" Factor Model 2509.6 Data Management and Quality Issues 2519.6.1 Data Alignment 2529.6.2 Survival Bias 2539.6.3 Look-Ahead Bias 2539.6.4 Data Snooping 2549.7 Risk Decomposition, Risk Attribution, and Performance Attribution 2549.8 Factor Investing 256Chapter 10 Benchmarks and the Use of Tracking Error in Portfolio Construction 26010.1 Tracking Error versus Alpha: Calculation and Interpretation 26110.2 Forward-Looking versus Backward-Looking Tracking Error 26410.3 Tracking Error and Information Ratio 26510.4 Predicted Tracking Error Calculation 26510.4.1 Variance-Covariance Method for Tracking Error Calculation 26610.4.2 Tracking Error Calculation Based on a Multifactor Model 26610.5 Benchmarks and Indexes 26810.5.1 Market Indexes 26810.5.2 Noncapitalization Weighted Indexes 27010.6 Smart Beta Investing 272Part Four Equity Portfolio ManagementChapter 11 Advances in Quantitative Equity Portfolio Management 28111.1 Portfolio Constraints Commonly Used in Practice 28211.1.1 Long-Only (No-Short-Selling) Constraints 28311.1.2 Holding Constraints 28311.1.3 Turnover Constraints 28411.1.4 Factor Constraints 28411.1.5 Cardinality Constraints 28611.1.6 Minimum Holding and Transaction Size Constraints 28711.1.7 Round Lot Constraints 28811.1.8 Tracking Error Constraints 29011.1.9 Soft Constraints 29111.1.10 Misalignment Caused by Constraints 29111.2 Portfolio Optimization with Tail Risk Measures 29111.2.1 Portfolio Value-at-Risk Optimization 29211.2.2 Portfolio Conditional Value-at-Risk Optimization 29411.3 Incorporating Transaction Costs 29711.3.1 Linear Transaction Costs 29911.3.2 Piecewise-Linear Transaction Costs 30011.3.3 Quadratic Transaction Costs 30211.3.4 Fixed Transaction Costs 30211.3.5 Market Impact Costs 30311.4 Multiaccount Optimization 30411.5 Incorporating Taxes 30811.6 Robust Parameter Estimation 31211.7 Portfolio Resampling 31411.8 Robust Portfolio Optimization 317Chapter 12 Factor-Based Equity Portfolio Construction and Performance Evaluation 32512.1 Equity Factors Used in Practice 32512.1.1 Fundamental Factors 32612.1.2 Macroeconomic Factors 32712.1.3 Technical Factors 32712.1.4 Additional Factors 32712.2 Stock Screens 32812.3 Portfolio Selection 33112.3.1 Ad-Hoc Portfolio Selection 33112.3.2 Stratification 33212.3.3 Factor Exposure Targeting 33312.4 Risk Decomposition 33412.5 Stress Testing 34312.6 Portfolio Performance Evaluation 34612.7 Risk Forecasts and Simulation 350Part Five Fixed Income Portfolio ManagementChapter 13 Fundamentals of Fixed Income Portfolio Management 36113.1 Fixed Income Instruments and Major Sectors of the Bond Market 36113.1.1 Treasury Securities 36213.1.2 Federal Agency Securities 36313.1.3 Corporate Bonds 36313.1.4 Municipal Bonds 36413.1.5 Structured Products 36413.2 Features of Fixed Income Securities 36513.2.1 Term to Maturity and Maturity 36513.2.2 Par Value 36613.2.3 Coupon Rate 36613.2.4 Bond Valuation and Yield 36713.2.5 Provisions for Paying Off Bonds 36813.2.6 Bondholder Option Provisions 37013.3 Major Risks Associated with Investing in Bonds 37113.3.1 Interest Rate Risk 37113.3.2 Call and Prepayment Risk 37213.3.3 Credit Risk 37313.3.4 Liquidity Risk 37413.4 Fixed Income Analytics 37513.4.1 Measuring Interest Rate Risk 37513.4.2 Measuring Spread Risk 38313.4.3 Measuring Credit Risk 38413.4.4 Estimating Fixed Income Portfolio Risk Using Simulation 38413.5 The Spectrum of Fixed Income Portfolio Strategies 38613.5.1 Pure Bond Indexing Strategy 38713.5.2 Enhanced Indexing/Primary Factor Matching 38813.5.3 Enhanced Indexing/Minor Factor Mismatches 38913.5.4 Active Management/Larger Factor Mismatches 38913.5.5 Active Management/Full-Blown Active 39013.5.6 Smart Beta Strategies for Fixed Income Portfolios 39013.6 Value-Added Fixed Income Strategies 39113.6.1 Interest Rate Expectations Strategies 39113.6.2 Yield Curve Strategies 39213.6.3 Inter- and Intra-sector Allocation Strategies 39313.6.4 Individual Security Selection Strategies 394Chapter 14 Factor-Based Fixed Income Portfolio Construction and Evaluation 39814.1 Fixed Income Factors Used in Practice 39814.1.1 Term Structure Factors 39914.1.2 Credit Spread Factors 40014.1.3 Currency Factors 40114.1.4 Emerging Market Factors 40114.1.5 Volatility Factors 40214.1.6 Prepayment Factors 40214.2 Portfolio Selection 40214.2.1 Stratification Approach 40314.2.2 Optimization Approach 40514.2.3 Portfolio Rebalancing 40814.3 Risk Decomposition 410Chapter 15 Constructing Liability-Driven Portfolios 42015.1 Risks Associated with Liabilities 42115.1.1 Interest Rate Risk 42115.1.2 Inflation Risk 42215.1.3 Longevity Risk 42315.2 Liability-Driven Strategies of Life Insurance Companies 42315.2.1 Immunization 42415.2.2 Advanced Optimization Approaches 43515.2.3 Constructing Replicating Portfolios 43715.3 Liability-Driven Strategies of Defined Benefit Pension Funds 43815.3.1 High-Grade Bond Portfolio Solution 43915.3.2 Including Other Assets 44215.3.3 Advanced Modeling Strategies 443Part Six Derivatives and Their Application to Portfolio ManagementChapter 16 Basics of Financial Derivatives 44916.1 Overview of the Use of Derivatives in Portfolio Management 44916.2 Forward and Futures Contracts 45116.2.1 Risk and Return of Forward/Futures Position 45316.2.2 Leveraging Aspect of Futures 45316.2.3 Pricing of Futures and Forward Contracts 45416.3 Options 45916.3.1 Risk and Return Characteristics of Options 46016.3.2 Option Pricing Models 47016.4 Swaps 48516.4.1 Interest Rate Swaps 48516.4.2 Equity Swaps 48616.4.3 Credit Default Swaps 487Chapter 17 Using Derivatives in Equity Portfolio Management 49017.1 Stock Index Futures and Portfolio Management Applications 49017.1.1 Basic Features of Stock Index Futures 49017.1.2 Theoretical Price of a Stock Index Futures Contract 49117.1.3 Portfolio Management Strategies with Stock Index Futures 49417.2 Equity Options and Portfolio Management Applications 50417.2.1 Types of Equity Options 50417.2.2 Equity Portfolio Management Strategies with Options 50617.3 Equity Swaps 511Chapter 18 Using Derivatives in Fixed Income Portfolio Management 51518.1 Controlling Interest Rate Risk Using Treasury Futures 51518.1.1 Strategies for Controlling Interest Rate Risk with Treasury Futures 51818.1.2 Pricing of Treasury Futures 52018.2 Controlling Interest Rate Risk Using Treasury Futures Options 52118.2.1 Strategies for Controlling Interest Rate Risk Using Treasury Futures Options 52418.2.2 Pricing Models for Treasury Futures Options 52618.3 Controlling Interest Rate Risk Using Interest Rate Swaps 52718.3.1 Strategies for Controlling Interest Rate Risk Using Interest Rate Swaps 52818.3.2 Pricing of Interest Rate Swaps 53018.4 Controlling Credit Risk with Credit Default Swaps 53218.4.1 Strategies for Controlling Credit Risk with Credit Default Swaps 53418.4.2 General Principles for Valuing a Single-Name Credit Default Swap 535Appendix: Basic Linear Algebra Concepts 541References 549Index 563