9780387989969

Measuring Risk in Complex Stochastic Systems

by ; ; ; ; ;
  • ISBN13:

    9780387989969

  • ISBN10:

    038798996X

  • Format: Paperback
  • Copyright: 2000-05-01
  • Publisher: Springer Verlag

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Supplemental Materials

What is included with this book?

Summary

This collection of articles by leading researchers will be of interest to people working in the area of mathematical finance.

Table of Contents

Preface v
Contributors vii
Allocation of Economic Capital in loan portfolios
1(18)
Ludger Overbeck
Introduction
1(1)
Credit portfolios
2(3)
Ability to Pay Process
3(1)
Loss distribution
4(1)
Economic Capital
5(1)
Capital allocation
6(1)
Capital allocation based on Var/Covar
6(2)
Allocation of marginal capital
8(1)
Contributory capital based on coherent risk measures
8(2)
Coherent risk measures
8(1)
Capital Definition
9(1)
Contribution to Shortfall-Risk
10(1)
Comparision of the capital allocation methods
10(3)
Analytic Risk Contribution
10(1)
Simulation procedure
10(1)
Comparison
11(1)
Portfolio size
12(1)
Summary
13(6)
Bibliography
15(4)
Estimating Volatility for Long Holding Periods
19(14)
Rudiger Kiesel
William Perraudin
Alex Taylor
Introduction
19(2)
Construction and Properties of the Estimator
21(3)
Large Sample Properties
22(1)
Small Sample Adjustments
23(1)
Monte Carlo Illustrations
24(3)
Applications
27(3)
Conclusion
30(3)
Bibliography
30(3)
A Simple Approach to Country Risk
33(36)
Frank Lehrbass
Introduction
33(1)
A Structural No-Arbitrage Approach
34(13)
Structural versus Reduced-Form Models
34(1)
Applying a Structural Model to Sovereign Debt
35(1)
No-Arbitrage vs Equilibrium Term Structure
36(1)
Assumptions of the Model
37(2)
The Arbitrage-Free Value of a Eurobond
39(6)
Possible Applications
45(1)
Determination of Parameters
46(1)
Description of Data and Parameter Setting
47(4)
DM-Eurobonds under Consideration
47(2)
Equity Indices and Currencies
49(1)
Default-Free Term Structure and Correlation
50(1)
Calibration of Default-Mechanism
50(1)
Pricing Capability
51(2)
Test Methodology
52(1)
Inputs for the Closed-Form Solution
52(1)
Model versus Market Prices
53(1)
Hedging
53(5)
Static part of Hedge
54(1)
Dynamic Part of Hedge
55(1)
Evaluation of the Hedging Strategy
56(2)
Management of a Portfolio
58(6)
Set Up of the Monte Carlo Approach
58(2)
Optimality Condition
60(2)
Application of the Optimality Condition
62(2)
Modification of the Optimality Condition
64(1)
Summary and Outlook
64(5)
Bibliography
65(4)
Predicting Bank Failures in Transition
69(14)
Jan Hanousek
Motivation
69(2)
Improving ``Standard'' Models of Bank Failures
71(2)
Czech banking sector
73(2)
Data and the Results
75(2)
Conclusions
77(6)
Bibliography
80(3)
Credit Scoring using Semiparametric Methods
83(16)
Marlene Muller
Bernd Ronz
Introduction
83(1)
Data Description
84(3)
Logistic Credit Scoring
87(1)
Semiparametric Credit Scoring
88(4)
Testing the Semiparametric Model
92(1)
Misclassification and Performance Curves
93(6)
Bibliography
96(3)
On the (Ir) Relevancy of Value-at-Risk Regulation
99(20)
Phornchanok J. Cumperayot
Jon Danielsson
Bjorn N. Jorgensen
Caspar G. de Vries
Introduction
99(2)
VaR and other Risk Measures
101(5)
VaR and Other Risk Measures
102(3)
VaR as a Side Constraint
105(1)
Economic Motives for VaR Management
106(6)
Policy Implications
112(2)
Conclusion
114(5)
Bibliography
115(4)
Backesting beyond VaR
119(12)
Wolfgang Hardle
Gerhard Stahl
Forecast tasks and VaR Models
119(3)
Backtesting based on the expected shortfall
122(1)
Backtesting in Action
123(6)
Conclusions
129(2)
Bibliography
129(2)
Measuring Implied Volatility Surface Risk using PCA
131(18)
Alpha Sylla
Christophe Villa
Introduction
131(1)
PCA of Implicit Volatility Dynamics
132(5)
Data and Methodology
133(1)
The results
133(4)
Smile-consistent pricing models
137(6)
Local Volatility Models
137(2)
Implicit Volatility Models
139(1)
The volatility models implementation
140(3)
Measuring Implicit Volatility Risk using VaR
143(6)
VaR Origins and definition
143(2)
VaR and Principal Components Analysis
145(2)
Bibliography
147(2)
Detection and estimation of changes in ARCH processes
149(12)
Piotr Kokoszka
Remigijus Leipus
Introduction
149(3)
Testing for change-point in ARCH
152(4)
Asymptotics under null hypothesis
153(2)
Asymptotics under local alternatives
155(1)
Change-point estimation
156(5)
ARCH model
156(2)
Extensions
158(1)
Bibliography
159(2)
Behaviour of Some Rank Statistics for Detecting Changes
161(14)
Ales Slaby
Introduction
161(1)
Limit Theorems
162(1)
Simulations
163(6)
Comments
169(4)
Acknowledgements
173(2)
Bibliography
173(2)
A stable CAPM in the presence of heavy-tailed distributions
175(14)
Stefan Huschens
Jeong-Ryeol Kim
Introduction
175(1)
Empirical evidence for the stable Paretian hypothesis
176(3)
Empirical evidence
176(1)
Univariate und multivariate β-stable distributions
177(2)
Stable CAPM and estimation for β-coefficients
179(4)
Stable CAPM
180(2)
Estimation of the β-coefficient in stable CAPM
182(1)
Empirical analysis of bivariate symmetry test
183(2)
Test for bivariate symmetry
183(1)
Estimates for the β-coefficient in stable CAPM
184(1)
Summary
185(4)
Bibliography
185(4)
A Tailored Suit for Risk Management: Hyperbolic Model
189(14)
Jens Breckling
Ernst Eberlein
Philip Kokic
Introduction
189(1)
Advantages of the Proposed Risk Management Approach
190(1)
Mathematical Definition of the P & L Distribution
191(2)
Estimation of the P & L using the Hyperbolic Model
193(3)
How well does the Approach Conform with Reality
196(1)
Extension to Credit Risk
196(2)
Application
198(5)
Bibliography
201(2)
Computational Resources for Extremes
203(12)
Torsten Kleinow
Michael Thomas
Introduction
203(1)
Computational Resources
204(4)
XploRe
204(1)
Xtremes
205(1)
Extreme Value Analysis with XploRe and Xtremes
205(3)
Differences between XploRe and Xtremes
208(1)
Client/Server Architectures
208(4)
Client/Server Architecture of XploRe
209(2)
Xtremes Corba Server
211(1)
Conclusion
212(3)
Bibliography
212(3)
Confidence intervals for tail index estimator
215(8)
Sergei Y. Novak
Confidence intervals for a tail index estimator
215(8)
Bibliography
221(2)
Extremes of alpha-ARCH Models
223(22)
Christian Robert
Introduction
223(1)
The model and its properties
224(2)
The tails of the stationary distribution
226(3)
Extreme value results
229(3)
Normalizing factors
229(1)
Computation of the extremal index
230(2)
Empirical study
232(7)
Distribution of extremes
235(1)
Tail behavior
236(2)
The extremal index
238(1)
Proofs
239(5)
Conclusion
244(1)
Bibliography
245

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