Handbook of Asset and Liability Management,9780444508751

Handbook of Asset and Liability Management

by ;
ISBN13: 9780444508751
ISBN10: 0444508759
Format: Hardcover
Pub. Date: 2006-08-28
Publisher(s): Elsevier Science
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Summary

This first volume of the Handbook of Asset and Liability Management presents the theories and methods supporting models that align a firm's operations and tactics with its uncertain environment. Detailing the symbiosis between optimization tools and financial decision-making, its original articles cover term and volatility structures, interest rates, risk-return analysis, dynamic asset allocation strategies in discrete and continuous time, the use of stochastic programming models, bond portfolio management, and the Kelly capital growth theory and practice. They effectively set the scene for Volume Two by showing how the management of risky assets and uncertain liabilities within an integrated, coherent framework remains the core problem for both financial institutions and other business enterprises as well. *Each volume presents an accurate survey of a sub-field of finance *Fills a substantial gap in this field *Broad in scope

Table of Contents

Introduction to the Series v
Contents of the Handbook vii
Preface ix
Chapter 1 Enterprise-Wide Asset and Liability Management: Issues, Institutions, and Models
DAN ROSEN and STAVROS A. ZENIOS
1(24)
Abstract
2(1)
1. Introduction
3(3)
1.1. What is enterprise risk management
4(1)
1.2. Example: Enterprise-wide view of credit risks in a bank
5(1)
2. A conceptual framework for enterprise risk management
6(11)
2.1. The management of a single line of business
7(2)
2.2. The management of a business portfolio
9(1)
2.3. Integrating design, pricing, funding, and capitalization
9(1)
2.4. Components of enterprise risk management
10(5)
2.5. Why is enterprise risk management important
15(2)
3. Asset and liability management in enterprise risk management
17(2)
3.1. Components of asset and liability management
17(2)
4. Models for asset and liability management
19(2)
References
21(4)
Chapter 2 Term and Volatility Structures
ROGER J.-B. WETS and STEPHEN W. BIANCHI
25(44)
Abstract
26(1)
Keywords
26(1)
1. Term structure
27(30)
1.1. An example
27(3)
1.2. BootStrapping
30(4)
1.3. Nelson–Siegel and Svensson's extension
34(2)
1.4. Maximum smoothness
36(1)
1.5. Forward-rates via geometric programming
37(1)
1.6. EpiCurves
38(13)
1.7. A comparison for U.S. Treasury curves
51(6)
2. Volatility structure
57(10)
2.1. Setting the stage
57(3)
2.2. Some tree-based valuation models
60(1)
2.3. The EpiVolatility model
61(1)
2.4. Implementation
62(3)
2.5. Summary
65(2)
References
67(2)
Chapter 3 Protecting Investors against Changes in Interest Rates
OLIVIER DE LA GRANDVILLE
69(70)
1. Basic concepts for valuation and immunization of bond portfolios in continuous time
73(17)
1.1. The instantaneous forward rate
73(2)
1.2. The continuously compounded spot rate
75(2)
1.3. Introducing the missing link: The continuously compounded total return
77(3)
1.4. Relationships between the total return, the forward rate and the spot rate
80(1)
1.5. Theorems on the behavior of the forward rate and the total return
81(3)
1.6. The spot rate curve as a spline and its corresponding forward rate curve
84(6)
2. Immunization: A first approach
90(12)
2.1. The continuously compounded horizon rate of return
91(1)
2.2. A geometrical representation of the horizon rate of return
91(2)
2.3. Existence and characteristics of an immunizing horizon
93(1)
2.4. The Macaulay concept of duration, its properties and uses
94(5)
2.5. A second-order condition
99(1)
2.6. The immunization problem
100(2)
3. Protecting investors against any shift in the interest rate structure A general immunization theorem
102(16)
3.1. Notation
102(2)
3.2. Present values at time 0
104(1)
3.3. Future values at time 0
105(1)
3.4. Present values at time epsilon
105(1)
3.5. Future values at time epsilon
106(1)
3.6. Further concepts for immunization: the moments of order k of a bond and a bond portfolio
106(3)
3.7. A general immunization theorem
109(9)
3.8. The nature of the cash flows of an immunizing portfolio
118(1)
4. Applications
118(15)
4.1. The spot structures and their shifts
119(3)
4.2. Building immunizing portfolios
122(2)
4.3. Immunization results
124(2)
4.4. How large should we set the immunization parameter K?
126(2)
4.5. Infinity of solutions
128(2)
4.6. How sensitive are immunizing portfolios to changes in horizon H?
130(1)
4.7. Flow sensitive are immunizing portfolios to a change in the basket of available bonds?
131(2)
5. Conclusion and suggestions
133(4)
6. Notes to references
137(1)
References
137(2)
Chapter 4 Risk-Return Analysis
HARRY M. MARKOWITZ and ERIK VAN DIJK
139(60)
Abstract
140(1)
Keywords
141(51)
1. Introduction
142(2)
2. The "general" mean-variance model
144(2)
3. Applications of the general model
146(3)
3.1. Asset liability modeling
146(1)
3.2. Factor models
147(1)
3.3. Other constraints
148(1)
3.4. Tracking error
148(1)
4. Examples of mean-variance efficient sets
149(7)
4.1. Critical lines and corner portfolios
149(1)
4.2. Efficient EV and Eσ combinations
150(2)
4.3. All feasible Eσ combinations
152(1)
4.4. Possible features
153(3)
5. Solution to the "general" mean-variance problem
156(10)
5.1. Preliminaries
156(1)
5.2. The critical line algorithm
156(2)
5.3. Getting started
158(3)
5.4. The critical line algorithm with upper bounds
161(1)
5.5. The critical line algorithm with factor and scenario models of covariance
162(3)
5.6. Notes on computing
165(1)
6. Separation theorems
166(7)
6.1. The Tobin–Sharpe separation theorems
166(3)
6.2. Two-funds separation
169(1)
6.3. Separation theorems not true in general
169(1)
6.4. The Elton, Gruber, Padberg algorithm
170(1)
6.5. An alternate EGP-like algorithm
171(2)
7. Alternate risk measures
173(7)
7.1. Semideviation
173(2)
7.2. Mean absolute deviation (MAD)
175(1)
7.3. Probability of loss and value at risk (Gaussian Rp)
176(2)
7.4. Probability of loss and Value at Risk (non-Gaussian Rp)
178(2)
7.5. Conditional value at risk (CVaR)
180(1)
8. Choice of criteria
180(4)
8.1. Exact conditions
180(1)
8.2. Mean-variance approximations to expected utility
181(3)
8.3. Significance of MV approximations to EU
184(1)
9. Risk-return analysis in practice
184(202)
9.1. Choice of criteria
185(1)
9.2. Tracking error or total variability
186(1)
9.3. Estimates for asset classes
187(1)
9.4. Estimation of expected returns for individual equities
187(1)
9.5. Black—Litterman
188(1)
9.6. Security analyst recommendations
189(1)
9.7. Estimates of covariance
189(1)
9.8. Parameter uncertainty
190(2)
10. Epilogue
192(1)
References
193(6)
Chapter 5 Dynamic Asset Allocation Strategies Using a Stochastic Dynamic Programming Approach
GERD INFANGER
199(54)
Abstract
200(1)
Keywords
200(1)
1. Introduction
201(3)
2. Approaches for dynamic asset allocation
204(3)
2.1. Multi-stage stochastic programming
204(2)
2.2. Stochastic dynamic programming
206(1)
3. Single-period portfolio choice
207(2)
4. Utility functions
209(2)
5. A general approach to modeling utility
211(3)
6. Dynamic portfolio choice
214(3)
6.1. Dynamic stochastic programming and Monte Carlo sampling
215(1)
6.2. Serially dependent asset returns
216(1)
6.3. A fast method for normally distributed asset returns
217(1)
7. Numerical results
217(30)
7.1. Data assumptions
217(5)
7.2. An investment example
222(12)
7.3. The performance of dynamic strategies
234(4)
7.4. Dynamic strategies for hedging downside risk
238(3)
7.5. Downside risk protection at every period
241(5)
7.6. Computation times
246(1)
8. Comparison to multi-stage stochastic programming
247(1)
Acknowledgements
248(1)
References
248(5)
Chapter 6 Stochastic Programming Models for Asset Liability Management
ROY KOUWENBERG and STAVROS A. ZENIOS
253(52)
Abstract
254(1)
1. Introduction
255(1)
2. Stochastic programming
256(11)
2.1. Basic concepts in stochastic programming
256(5)
2.2. Stochastic programming model for portfolio management
261(6)
3. Scenario generation and tree construction
267(20)
3.1. Scenarios for the liabilities
267(3)
3.2. Scenarios for economic factors and asset returns
270(2)
3.3. Methods for generating scenarios
272(5)
3.4. Constructing event trees
277(6)
3.5. Options, bonds and arbitrage
283(4)
4. Comparison of stochastic programming with other methods
287(4)
4.1. Mean-variance models and downside risk
287(1)
4.2. Discrete-time multi-period models
288(2)
4.3. Continuous-time models
290(1)
4.4. Stochastic programming
291(1)
5. Applications of stochastic programming to ALM
291(5)
6. Solution methods and computations
296(1)
7. Summary and open issues
297(2)
References
299(6)
Chapter 7 Bond Portfolio Management via Stochastic Programming
M. BERTOCCHI, V. MORIGGIA and J. DUPACOVÁ
305(32)
Abstract
306(1)
1. Introduction
307(4)
2. The bond portfolio management model
311(4)
3. Input data
315(5)
4. Scenario reduction and scenario tree construction
320(1)
5. Numerical results
321(4)
6. Stress testing via contamination: Add worst-case scenarios
325(9)
7. Conclusions
334(1)
Acknowledgements
335(1)
References
335(2)
Chapter 8 Perturbation Methods for Dynamic Portfolio Allocation Problems
GEORGE CHACKO and KARL NEUMAR
337(48)
Abstract
338(1)
1. Introduction
339(1)
2. General problem formulation
340(5)
2.1. Investment opportunity set
341(3)
2.2. Utility function
344(1)
3. Exact solution for unit elasticity of intertemporal substitution
345(8)
3.1. General results
345(4)
3.2. Example 1: Time-varying expected returns (finite horizon)
349(3)
3.3. Example 2: Time-varying expected returns (infinite horizon)
352(1)
4. Approximate solution for general elasticity of intertemporal substitution
353(13)
4.1. Perturbation around unit elasticity of substitution
353(7)
4.2. Perturbation around mean of consumption/wealth ratio
360(6)
5. Example
366(16)
5.1. Time-varying volatility
367(11)
5.2. Time-varying interest rates
378(4)
6. Conclusions
382(1)
References
383(2)
Chapter 9 The Kelly Criterion in Blackjack Sports Betting, and the Stock Market
EDWARD O. THORP
385(44)
Abstract
386(1)
Keywords
386(34)
1. Introduction
387(1)
2. Coin tossing
388(4)
3. Optimal growth: Kelly criterion formulas for practitioners
392(6)
3.1. The probability of reaching a fixed goal on or before II trials
392(2)
3.2. The probability of ever being reduced to a fraction .v of this initial bankroll
394(1)
3.3. The probability of being at or above a specified value at the end of a specified number of trials
395(1)
3.4. Continuous approximation of expected time to reach a goal
396(1)
3.5. Comparing fixed fraction strategies: the probability that one strategy leads another after of trials
396(2)
4. The long run: when will the Kelly strategy "dominate"?
398(1)
5. Blackjack
399(2)
6. Sports betting
401(4)
7. Wall street: the biggest game
405(10)
7.1. Continuous approximation
406(3)
7.2. The (almost) real world
409(2)
7.3. The case for "fractional Kelly"
411(3)
7.4. A remarkable formula
414(1)
8. A case study
415(4)
8.1. The constraints
416(1)
8.2. The analysis and results
416(1)
8.3. The recommendation and the result
417(1)
8.4. The theory for a portfolio of securities
418(1)
9. My experience with the Kelly approach
419(1)
10. Conclusion
420(1)
Acknowledgements
420(8)
References
428(1)
Chapter 10 Capital Growth: Theory and Practice
LEONARD C. MACLEAN and WILLIAM T. ZIEMBA
429(46)
Abstract
430(1)
Keywords
431(1)
1. Introduction
432(2)
2. Capital accumulation
434(2)
2.1. Asset prices
435(1)
2.2. Decision criteria
435(1)
2.3. Timing of decisions
436(1)
3. Asset prices
436(8)
3.1. Pricing model
438(2)
3.2. Estimation
440(2)
3.3. Comparison
442(2)
4. Growth strategies
444(19)
4.1. The Kelly strategy
445(4)
4.2. Stochastic dominance
449(2)
4.3. Bi-criteria problems: Fractional Kelly strategies
451(6)
4.4. Growth-security trade-off
457(6)
5. Timing of decisions
463(2)
5.1. Control limits
463(2)
6. Legends of capital growth
465(4)
6.1. Princeton Newport Partners
466(1)
6.2. Kings College Chest Fund
466(2)
6.3. Berkshire–Hathaway
468(1)
6.4. Hong Kong Betting Syndicate
469(1)
References
469(6)
Author Index 475(8)
Subject Index 483

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