Extreme Events : Robust Portfolio Construction in the Presence of Fat Tails
| Year of publication: |
2011 ; 2nd ed
|
|---|---|
| Authors: | Kemp, Malcolm |
| Publisher: |
Hoboken : John Wiley & Sons |
| Subject: | Portfolio-Management | Portfolio selection | Statistische Verteilung | Statistical distribution | Theorie | Theory | Ausreißer | Outliers | Robustes Verfahren | Robust statistics |
| Description of contents: | Table of Contents [external.dandelon.com] |
| Extent: | Online-Ressource (336 p.) |
|---|---|
| Series: | The Wiley Finance Series ; v.617 The Wiley Finance Ser ; v.617 |
| Type of publication: | Book / Working Paper |
| Language: | English |
| Notes: | Description based upon print version of record Extreme Events; Contents; Preface; Acknowledgements; Abbreviations; Notation; 1 Introduction; 1.1 Extreme events; 1.2 The portfolio construction problem; 1.3 Coping with really extreme events; 1.4 Risk budgeting; 1.5 Elements designed to maximise benefit to readers; 1.6 Book structure; 2 Fat Tails - In Single (i.e., Univariate) Return Series; 2.1 Introduction; 2.2 A fat tail relative to what?; 2.3 Empirical examples of fat-tailed behaviour in return series; 2.3.1 Introduction; 2.3.2 Visualising fat tails; 2.3.3 Behaviour of individual bonds and bond indices; 2.3.4 Behaviour of equity indices 2.3.5 Currencies and other asset types2.4 Characterising fat-tailed distributions by their moments; 2.4.1 Introduction; 2.4.2 Skew and kurtosis; 2.4.3 The (fourth-moment) Cornish-Fisher approach; 2.4.4 Weaknesses of the Cornish-Fisher approach; 2.4.5 Improving on the Cornish-Fisher approach; 2.4.6 Statistical tests for non-Normality; 2.4.7 Higher order moments and the Omega function; 2.5 What causes fat tails?; 2.5.1 Introduction; 2.5.2 The Central Limit Theorem; 2.5.3 Ways in which the Central Limit Theorem can break down; 2.6 Lack of diversification; 2.7 A time-varying world 2.7.1 Introduction2.7.2 Distributional mixtures; 2.7.3 Time-varying volatility; 2.7.4 Regime shifts; 2.8 Stable distributions; 2.8.1 Introduction; 2.8.2 Defining characteristics; 2.8.3 The Generalised Central Limit Theorem; 2.8.4 Quantile-quantile plots of stable distributions; 2.9 Extreme value theory (EVT); 2.9.1 Introduction; 2.9.2 Extreme value distributions; 2.9.3 Tail probability densities; 2.9.4 Estimation of and inference from tail index values; 2.9.5 Issues with extreme value theory; 2.10 Parsimony; 2.11 Combining different possible source mechanisms 2.12 The practitioner perspective2.12.1 Introduction; 2.12.2 Time-varying volatility; 2.12.3 Crowded trades; 2.12.4 Liquidity risk; 2.12.5 'Rational' behaviour versus 'bounded rational' behaviour; 2.12.6 Our own contribution to the picture; 2.13 Implementation challenges; 2.13.1 Introduction; 2.13.2 Smoothing of return series; 2.13.3 Time clocks and non-constant time period lengths; 2.13.4 Price or other data rather than return data; 2.13.5 Economic sensitivities that change through time; 3 Fat Tails - In Joint (i.e., Multivariate) Return Series; 3.1 Introduction 3.2 Visualisation of fat tails in multiple return series3.3 Copulas and marginals - Sklar's theorem; 3.3.1 Introduction; 3.3.2 Fractile-fractile (i.e., quantile-quantile box) plots; 3.3.3 Time-varying volatility; 3.4 Example analytical copulas; 3.4.1 Introduction; 3.4.2 The Gaussian copula; 3.4.3 The t-copula; 3.4.4 Archimedean copulas; 3.5 Empirical estimation of fat tails in joint return series; 3.5.1 Introduction; 3.5.2 Disadvantages of empirically fitting the copula; 3.5.3 Multi-dimensional quantile-quantile plots; 3.6 Causal dependency models; 3.7 The practitioner perspective 3.8 Implementation challenges |
| ISBN: | 978-0-470-75013-1 ; 978-0-470-97679-1 ; 978-0-470-75013-1 |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012676499