多组份资产调拨价格模型

书籍目录

About the ContributorsForewordPreface1 Theoretical Foundations of Asset Allocation and Pricing Models with Higher-order Moments  Emmanuel Jurczenko and Bertrand Maillet  1.1 Introduction  1.2 Expected utility and higher-order moments  1.3 Expected utility as an exact function of the first four moments  1.4 Expected utility as an approximating function of the first four moments  1.5 Conclusion  Appendix A  Appendix B  Appendix C  Appendix D  Appendix E  Appendix F  Acknowledgements  References2 On Certain Geometric Aspects of Portfolio Optimisation with Higher Moments  Gustavo M. de Athayde and Renato G. Fl6res Jr  2.1 Introduction  2.2 Minimal variances and kurtoses subject to the first two odd moments    2.2.1 Homothetic properties of the minimum variance set    2.2.2 The minimum kurtosis case  2.3 Generalising for higher even moments  2.4 Further properties and extensions  2.5 Concluding remarks  Appendix: The matrix notation for the higher-moments arrays  Acknowledgements  References3 Hedge Fund Portfolio Selection with Higher-order Moments: A Nonparametric Mean-Variance-Skewness-Kurtosis Efficient Frontier  Emmanuel Jurczenko, Bertrand Maillet and Paul Merlin  3.1 Introduction  3.2 Portfolio selection with higher-order moments  3.3 The shortage function and the mean-variance-skewness-kurtosis efficient frontier  3.4 Data and empirical results  3.5 Conclusion  Appendix  Acknowledgements  References4 Higher-order Moments and Beyond  Luisa Tibiletti  4.1 Introduction  4.2 Higher-order moments and simple algebra  4.3 Higher moments: Noncoherent risk measures  4.4 One-sided higher moments    4.4.1 Portfolio left-sided moment bounds    4.4.2 Properties of the upper bound UP(S_)  4.5 Preservation of marginal ordering under portfolios    4.5.1 Drawbacks in using higher moments    4.5.2 Advantages in using left-sided higher moments  4.6 Conclusion  Appendix  References5 Gram-Charlier Expansions and Portfolio Selection in Non-Gaussian Universes  Franfois Desmoulins-Lebeault  5.1 Introduction  5.2 Attempts to extend the CAPM    5.2.1 Extensions based on preferences    5.2.2 Extensions based on return distributions  5.3 An example of portfolio optimisation    5.3.1 Portfolio description    5.3.2 The various "optimal" portfolios  ……6 The Four-moment Capital Asset Pricing Model: between Asset Pricing and Asset Allocation7 Multi-Moments Method For Portfolio Management: Generalized Capital Asset Pricing Model in Homogeneous and Heterogeneous Markets8 Modeling the Dynamics of Conditional Dependency Between Financial Series9 A Test of the Homogeneity of Asset Pricing ModelsIndex

作者简介

While mainstream financial theories and applications assume that asset returns are normally distributed and individual preferences are quadratic, the overwhelming empirical evidence shows otherwise. Indeed, most of the asset returns exhibit “fat-tails” distributions and investors exhibit asymmetric preferences. These empirical findings lead to the development of a new area of research dedicated to the introduction of higher order moments in portfolio theory and asset pricing models. Multi-moment asset pricing is a revolutionary new way of modeling time series in finance which allows various degrees of long-term memory to be generated. It allows risk and prices of risk to vary through time enabling the accurate valuation of long-lived assets. This book presents the state-of-the art in multi-moment asset allocation and pricing models and provides many new developments in a single volume, collecting in a unified framework theoretical results and applications previously scattered throughout the financial literature. The topics covered in this comprehensive volume include: four-moment individual risk preferences, mathematics of the multi-moment efficient frontier, coherent asymmetric risks measures, hedge funds asset allocation under higher moments, time-varying specifications of (co)moments and multi-moment asset pricing models with homogeneous and heterogeneous agents. Written by leading academics, Multi-moment Asset Allocation and Pricing Models offers a unique opportunity to explore the latest findings in this new field of research.

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