A Probability Metrics Approach to Financial Risk Measures (eBook, ePUB)
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A Probability Metrics Approach to Financial Risk Measures (eBook, ePUB)
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A Probability Metrics Approach to Financial Risk Measures relates the field of probability metrics and risk measures to one another and applies them to finance for the first time. * Helps to answer the question: which risk measure is best for a given problem? * Finds new relations between existing classes of risk measures * Describes applications in finance and extends them where possible * Presents the theory of probability metrics in a more accessible form which would be appropriate for non-specialists in the field * Applications include optimal portfolio choice, risk theory, and numerical…mehr
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- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 392
- Erscheinungstermin: 10. März 2011
- Englisch
- ISBN-13: 9781444392708
- Artikelnr.: 38309890
- Verlag: John Wiley & Sons
- Seitenzahl: 392
- Erscheinungstermin: 10. März 2011
- Englisch
- ISBN-13: 9781444392708
- Artikelnr.: 38309890
finance. Chapter 2 Probability distances and metrics. 2.1 Introduction. 2.2
Some examples of probability metrics. 2.3 Distance and semidistance spaces.
2.4 Definitions of probability distances and metrics. 2.5 Summary. 2.6
Technical appendix. Chapter 3 Choice under uncertainty. 3.1 Introduction.
3.2 Expected utility theory. 3.3 Stochastic dominance. 3.4 Probability
metrics and stochastic dominance. 3.5 Cumulative Prospect Theory. 3.6
Summary. 3.7 Technical appendix. Chapter 4 A classification of probability
distances. 4.1 Introduction. 4.2 Primary distances and primary metrics. 4.3
Simple distances and metrics. 4.4 Compound distances and moment functions.
4.5 Ideal probability metrics. 4.6 Summary. 4.7 Technical appendix. Chapter
5 Risk and uncertainty. 5.1 Introduction. 5.2 Measures of dispersion. 5.3
Probability metrics and dispersion measures. 5.4 Measures of risk. 5.5 Risk
measures and dispersion measures. 5.6 Risk measures and stochastic orders.
5.7 Summary. 5.8 Technical appendix. Chapter 6 Average value-at-risk. 6.1
Introduction. 6.2 Average value-at-risk. 6.2.1 AVaR for stable
distributions. 6.3 AVaR estimation from a sample. 6.4 Computing portfolio
AVaR in practice. 6.5 Back-testing of AVaR. 6.6 Spectral risk measures. 6.7
Risk measures and probability metrics. 6.8 Risk measures based on
distortion functionals. 6.9 Summary. 6.10 Technical appendix. Chapter 7
Computing AVaR through Monte Carlo. 7.1 Introduction. 7.2 An illustration
of Monte Carlo variability. 7.3 Asymptotic distribution, classical
conditions. 7.4 Rate of convergence to the normal distribution. 7.5
Asymptotic distribution, heavy-tailed returns. 7.6 Rate of convergence,
heavy-tailed returns. 7.7 On the choice of a distributional model. 7.8
Summary. 7.9 Technical appendix. Chapter 8 Stochastic dominance revisited.
8.1 Introduction. 8.2 Metrization of preference relations. 8.3 The
Hausdorff metric structure. 8.4 Examples. 8.5 Utility-type representations.
8.6 Almost stochastic orders and degree of violation. 8.7 Summary. 8.8
Technical appendix.
finance. Chapter 2 Probability distances and metrics. 2.1 Introduction. 2.2
Some examples of probability metrics. 2.3 Distance and semidistance spaces.
2.4 Definitions of probability distances and metrics. 2.5 Summary. 2.6
Technical appendix. Chapter 3 Choice under uncertainty. 3.1 Introduction.
3.2 Expected utility theory. 3.3 Stochastic dominance. 3.4 Probability
metrics and stochastic dominance. 3.5 Cumulative Prospect Theory. 3.6
Summary. 3.7 Technical appendix. Chapter 4 A classification of probability
distances. 4.1 Introduction. 4.2 Primary distances and primary metrics. 4.3
Simple distances and metrics. 4.4 Compound distances and moment functions.
4.5 Ideal probability metrics. 4.6 Summary. 4.7 Technical appendix. Chapter
5 Risk and uncertainty. 5.1 Introduction. 5.2 Measures of dispersion. 5.3
Probability metrics and dispersion measures. 5.4 Measures of risk. 5.5 Risk
measures and dispersion measures. 5.6 Risk measures and stochastic orders.
5.7 Summary. 5.8 Technical appendix. Chapter 6 Average value-at-risk. 6.1
Introduction. 6.2 Average value-at-risk. 6.2.1 AVaR for stable
distributions. 6.3 AVaR estimation from a sample. 6.4 Computing portfolio
AVaR in practice. 6.5 Back-testing of AVaR. 6.6 Spectral risk measures. 6.7
Risk measures and probability metrics. 6.8 Risk measures based on
distortion functionals. 6.9 Summary. 6.10 Technical appendix. Chapter 7
Computing AVaR through Monte Carlo. 7.1 Introduction. 7.2 An illustration
of Monte Carlo variability. 7.3 Asymptotic distribution, classical
conditions. 7.4 Rate of convergence to the normal distribution. 7.5
Asymptotic distribution, heavy-tailed returns. 7.6 Rate of convergence,
heavy-tailed returns. 7.7 On the choice of a distributional model. 7.8
Summary. 7.9 Technical appendix. Chapter 8 Stochastic dominance revisited.
8.1 Introduction. 8.2 Metrization of preference relations. 8.3 The
Hausdorff metric structure. 8.4 Examples. 8.5 Utility-type representations.
8.6 Almost stochastic orders and degree of violation. 8.7 Summary. 8.8
Technical appendix.