[下载]Portfolio Optimization and Performance Analysis

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[下载]Portfolio Optimization and Performance Analysis
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<br/></p><p>Series Editors<br/>M.A.H. Dempster<br/>Centre for Financial<br/>Research<br/>Judge Business School<br/>University of Cambridge<br/>Dilip B. Madan<br/>Robert H. Smith School<br/>of Business<br/>University of Maryland<br/>Rama Cont<br/>Center for Financial<br/>Engineering<br/>Columbia University<br/>New York</p><p><font size="6">Contents</font></p><p>List of Tables XIII<br/>List of Figures XV<br/>I Utility and risk analysis 1<br/>1 Utility theory 5<br/>1.1 Preferences under uncertainty . . . . . . . . . . . . . . . . . 7<br/>1.1.1 Lotteries . . . . . . . . . . . . . . . . . . . . . . . . . . 7<br/>1.1.2 Axioms on pref erences . . . . . . . . . . . . . . . . . . 8<br/>1.2 Expected utility . . . . . . . . . . . . . . . . . . . . . . . . . 9<br/>1.3 Risk aversion . . . . . . . . . . . . . . . . . . . . . . . . . . . 11<br/>1.3.1 Arrow-Pratt measures ofri sk aversion . . . . . . . . . 13<br/>1.3.2 Standard utility functions . . . . . . . . . . . . . . . . 15<br/>1.3.3 Applications to portfolio allocation . . . . . . . . . . . 17<br/>1.4 Stochastic dominance . . . . . . . . . . . . . . . . . . . . . . 19<br/>1.5 Alternative expected utility theory . . . . . . . . . . . . . . . 24<br/>1.5.1 Weighted utility theory . . . . . . . . . . . . . . . . . 25<br/>1.5.2 Rank dependent expected utility theory . . . . . . . . 27<br/>1.5.3 Non-additive expected utility . . . . . . . . . . . . . . 32<br/>1.5.4 Regret theory . . . . . . . . . . . . . . . . . . . . . . . 33<br/>1.6 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 35<br/>2 Riskmeasures 37<br/>2.1 Coherent and convex risk measures . . . . . . . . . . . . . . 37<br/>2.1.1 Coherent riskmeasures . . . . . . . . . . . . . . . . . 38<br/>2.1.2 Convex riskmeasures . . . . . . . . . . . . . . . . . . 39<br/>2.1.3 Representation ofri sk measures . . . . . . . . . . . . . 40<br/>2.1.4 Risk measures and utility . . . . . . . . . . . . . . . . 41<br/>2.1.5 Dynamic riskmeasures . . . . . . . . . . . . . . . . . 43<br/>2.2 Standard riskmeasures . . . . . . . . . . . . . . . . . . . . . 48<br/>2.2.1 Value-at-Risk . . . . . . . . . . . . . . . . . . . . . . . 48<br/>2.2.2 CVaR . . . . . . . . . . . . . . . . . . . . . . . . . . . 54<br/>2.2.3 Spectral measures of risk . . . . . . . . . . . . . . . . 59<br/>2.3 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 62</p><p>II Standard portfolio optimization 65<br/>3 Static optimization 67<br/>3.1 Mean-variance analysis . . . . . . . . . . . . . . . . . . . . . 68<br/>3.1.1 Diversification effect . . . . . . . . . . . . . . . . . . . 68<br/>3.1.2 Optimal weights . . . . . . . . . . . . . . . . . . . . . 71<br/>3.1.3 Additional constraints . . . . . . . . . . . . . . . . . . 78<br/>3.1.4 Estimation problems . . . . . . . . . . . . . . . . . . . 82<br/>3.2 Alternative criteria . . . . . . . . . . . . . . . . . . . . . . . 85<br/>3.2.1 Expected utility maximization . . . . . . . . . . . . . 85<br/>3.2.2 Risk measure minimization . . . . . . . . . . . . . . . 93<br/>3.3 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 100<br/>4 Indexed funds and benchmarking 103<br/>4.1 Indexed funds . . . . . . . . . . . . . . . . . . . . . . . . . . 103<br/>4.1.1 Tracking error . . . . . . . . . . . . . . . . . . . . . . 104<br/>4.1.2 Simple index tracking methods . . . . . . . . . . . . . 105<br/>4.1.3 The threshold accepting algorithm . . . . . . . . . . . 106<br/>4.1.4 Cointegration tracking method . . . . . . . . . . . . . 112<br/>4.2 Benchmark portf olio optimization . . . . . . . . . . . . . . . 117<br/>4.2.1 Tracking-error definition . . . . . . . . . . . . . . . . . 118<br/>4.2.2 Tracking-errorminimization . . . . . . . . . . . . . . . 119<br/>4.3 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 127<br/>5 Portfolio performance 129<br/>5.1 Standard performance measures . . . . . . . . . . . . . . . . 130<br/>5.1.1 The Capital Asset Pricing Model . . . . . . . . . . . . 130<br/>5.1.2 The three standard performance measures . . . . . . . 132<br/>5.1.3 Other performance measures . . . . . . . . . . . . . . 140<br/>5.1.4 Beyond the CAPM . . . . . . . . . . . . . . . . . . . . 145<br/>5.2 Perf ormance decomposition . . . . . . . . . . . . . . . . . . . 151<br/>5.2.1 The Fama decomposition . . . . . . . . . . . . . . . . 151<br/>5.2.2 Other performance attributions . . . . . . . . . . . . . 153<br/>5.2.3 The external attribution . . . . . . . . . . . . . . . . . 153<br/>5.2.4 The internal attribution . . . . . . . . . . . . . . . . . 155<br/>5.3 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . 163<br/>III Dynamic portfolio optimization 165<br/>6 Dynamic programming optimization 169<br/>6.1 Control theory . . . . . . . . . . . . . . . . . . . . . . . . . . 169<br/>6.1.1 Calculus ofv ariations . . . . . . . . . . . . . . . . . . 169<br/>6.1.2 Pontryagin and Bellman principles . . . . . . . . . . . 175<br/>6.1.3 Stochastic optimal control . . . . . . . . . . . . . . . . 182<br/>6.2 Lifetime portfolio selection . . . . . . . . . . . . . . . . . . . 187</p><p>6.2.1 The optimization problem . . . . . . . . . . . . . . . . 187<br/>6.2.2 The deterministic coefficients case . . . . . . . . . . . 188<br/>6.2.3 The general case . . . . . . . . . . . . . . . . . . . . . 195<br/>6.2.4 Recursive utility in continuous-time . . . . . . . . . . 203<br/>6.3 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 205<br/>7 Optimal payoff profiles and long-term management 207<br/>7.1 Optimal payoffs as functions of a benchmark . . . . . . . . . 207<br/>7.1.1 Linear versus option-based strategy . . . . . . . . . . 207<br/>7.2 Application to long-term management . . . . . . . . . . . . . 214<br/>7.2.1 Assets dynamics and optimal portfolios . . . . . . . . 214<br/>7.2.2 Exponential utility . . . . . . . . . . . . . . . . . . . . 220<br/>7.2.3 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . 223<br/>7.2.4 Distribution ofthe optimal portfolio return . . . . . . 225<br/>7.3 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 226<br/>8 Optimization within specific markets 229<br/>8.1 Optimization in incomplete markets . . . . . . . . . . . . . . 230<br/>8.1.1 General result based on martingale method . . . . . . 230<br/>8.1.2 Dynamic programming and viscosity solutions . . . . 238<br/>8.2 Optimization with constraints . . . . . . . . . . . . . . . . . 242<br/>8.2.1 General result . . . . . . . . . . . . . . . . . . . . . . . 242<br/>8.2.2 Basic examples . . . . . . . . . . . . . . . . . . . . . . 249<br/>8.3 Optimization with transaction costs . . . . . . . . . . . . . . 256<br/>8.3.1 The infinite-horizon case . . . . . . . . . . . . . . . . . 256<br/>8.3.2 The finite-horizon case . . . . . . . . . . . . . . . . . . 260<br/>8.4 Other f rameworks . . . . . . . . . . . . . . . . . . . . . . . . 263<br/>8.4.1 Labor income . . . . . . . . . . . . . . . . . . . . . . . 263<br/>8.4.2 Stochastic horizon . . . . . . . . . . . . . . . . . . . . 272<br/>8.5 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 276<br/>IV Structured portfolio management 279<br/>9 Portfolio insurance 281<br/>9.1 The Option Based Portfolio Insurance . . . . . . . . . . . . . 282<br/>9.1.1 The standard OBPI method . . . . . . . . . . . . . . . 284<br/>9.1.2 Extensions oft he OBPI method . . . . . . . . . . . . 286<br/>9.2 The Constant Proportion Portfolio Insurance . . . . . . . . . 294<br/>9.2.1 The standard CPPI method . . . . . . . . . . . . . . . 295<br/>9.2.2 CPPI extensions . . . . . . . . . . . . . . . . . . . . . 303<br/>9.3 Comparison between OBPI and CPPI . . . . . . . . . . . . . 305<br/>9.3.1 Comparison at maturity . . . . . . . . . . . . . . . . . 305<br/>9.3.2 The dynamic behavior ofOBPI and CPPI . . . . . . . 310<br/>9.4 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 318</p><p>10 Optimal dynamic portfolio with risk limits 319<br/>10.1 Optimal insured portfolio: discrete-time case . . . . . . . . . 321<br/>10.1.1 Optimal insured portfolio with a fixed number of assets 321<br/>10.1.2 Optimal insured payoffs as functions of a benchmark . 326<br/>10.2 Optimal Insured Portfolio: the dynamically complete case . . 333<br/>10.2.1 Guarantee atmaturity . . . . . . . . . . . . . . . . . . 333<br/>10.2.2 Risk exposure and utility function . . . . . . . . . . . 335<br/>10.2.3 Optimal portfolio with controlled drawdowns . . . . . 337<br/>10.3 Value-at-Risk and expected shortfall based management . . . 340<br/>10.3.1 Dynamic saf ety criteria . . . . . . . . . . . . . . . . . 340<br/>10.3.2 Expected utility under VaR/CVaR constraints . . . . 347<br/>10.4 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 350<br/>11 Hedge funds 351<br/>11.1 The hedge funds industry . . . . . . . . . . . . . . . . . . . . 351<br/>11.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 351<br/>11.1.2 Main strategies . . . . . . . . . . . . . . . . . . . . . . 352<br/>11.2 Hedge f und perf ormance . . . . . . . . . . . . . . . . . . . . 354<br/>11.2.1 Return distributions . . . . . . . . . . . . . . . . . . . 354<br/>11.2.2 Sharpe ratio limits . . . . . . . . . . . . . . . . . . . . 355<br/>11.2.3 Alternative performance measures . . . . . . . . . . . 362<br/>11.2.4 Benchmarks for alternative investment . . . . . . . . . 368<br/>11.2.5 Measure oft he performance persistence . . . . . . . . 369<br/>11.3 Optimal allocation in hedge funds . . . . . . . . . . . . . . . 370<br/>11.4 Further reading . . . . . . . . . . . . . . . . . . . . . . . . . 371<br/>A Appendix A: Arch Models 373<br/>B Appendix B: Stochastic Processes 381<br/>References 397<br/>Symbol Description 431<br/>Index 433</p><p>很好的一本书。</p>

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关键词：Optimization performance Portfolio Performan Analysis Business Series

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