Interest Rate Models, Asset Allocation and Quantitative Techniques for Central Banks and Sovereign Wealth Funds

401 Pages·2010·3.71 MB·English

by  Arjan B. Berkelaar

Interest Rate Models, Asset Allocation and Quantitative Techniques for
Central Banks and Sovereign Wealth Funds
Also by Arjan B. Berkelaar, Joachim Coche and Ken Nyholm
CENTRAL BANK RESERVES AND SOVEREIGN WEALTH MANAGEMENT (edited)
Interest Rate Models, Asset
Allocation and Quantitative
Techniques for Central Banks
and Sovereign Wealth Funds
Edited By
Arjan B. Berkelaar
Joachim Coche
Ken Nyholm
Introduction, selection and editorial matter © Arjan B. Berkelaar, Joachim Coche
and Ken Nyholm 2010
Softcover reprint of the hardcover 1st edition 2010978-0-230-24012-4
All rights reserved. No reproduction, copy or transmission of this
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First published 2010 by
PALGRAVE MACMILLAN
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registered in England, company number 785998, of Houndmills, Basingstoke,
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175 Fifth Avenue, New York, NY 10010.
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Palgrave® and Macmillan® are registered trademarks in the United States,
the United Kingdom, Europe and other countries.
ISBN 978-1-349-31641-0 ISBN 978-0-230-25129-8 (eBook)
DOI 10.1057/9780230251298
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10 9 8 7 6 5 4 3 2 1
19 18 17 16 15 14 13 12 11 10
Contents
List of Illustrations vii
Notes on Contributors xiv
Preface xxi
Introduction xxii
Part I Interest Rate Modelling and Forecasting
1 Combining Canadian Interest Rate Forecasts 3
David Jamieson Bolder and Yuliya Romanyuk
2 Updating the Yield Curve to Analyst’s Views 31
Leonardo M. Nogueira
3 A Spread-Risk Model for Strategic Fixed-Income Investors 44
Fernando Monar Lora and Ken Nyholm
4 Dynamic Management of Interest Rate Risk for
Central Banks and Pension Funds 64
Arjan B. Berkelaar and Gabriel Petre
Part II Portfolio Optimization Techniques
5 A Strategic Asset Allocation Methodology
Using Variable Time Horizon 93
Paulo Maurício F. de Cacella, Isabela Ribeiro Damaso
and Antônio Francisco da Silva Jr.
6 Hidden Risks in Mean–Variance Optimization: An
Integrated-Risk Asset Allocation Proposal 112
José Luiz Barros Fernandes and José Renato Haas Ornelas
7 Efficient Portfolio Optimization in the Wealth
Creation and Maximum Drawdown Space 134
Alejandro Reveiz and Carlos León
8 Copulas and Risk Measures for Strategic Asset
Allocation: A Case Study for Central Banks
and Sovereign Wealth Funds 158
Cyril Caillault and Stéphane Monier
v
vi Contents
9 P ractical Scenario-Dependent Portfolio
Optimization: A Framework to Combine Investor Views and
Quantitative Discipline into Acceptable Portfolio Decisions 178
Roberts L. Grava
10 Strategic Tilting around the SAA Benchmark 189
Aaron Drew, Richard Frogley, Tore Hayward and Rishab Sethi
11 Optimal Construction of a Fund of Funds 207
Petri Hilli, Matti Koivu and Teemu Pennanen
Part III Asset Class Modelling and Quantitative Techniques
12 Mortgage-Backed Securities in a Strategic
Asset Allocation Framework 225
Myles Brennan and Adam Kobor
13 Q uantitative Portfolio Strategy – Including US MBS
in Global Treasury Portfolios 249
Lev Dynkin, Jay Hyman and Bruce Phelps
14 Volatility as an Asset Class for Long-Term Investors 265
Marie Brière, Alexander Burgues and Ombretta Signori
15 A Frequency Domain Methodology for
Time Series Modelling 280
Hens Steehouwer
16 Estimating Mixed Frequency Data: Stochastic
Interpolation with Preserved Covariance Structure 325
Tørres G. Trovik and Couro Kane-Janus
17 Statistical Inference for Sharpe Ratio 337
Friedrich Schmid and Rafael Schmidt
Index 359
List of Illustrations
Tables
I.1 The 50 largest public investment funds xxiii
I.2 Types of public investment funds xxviii
2.1 US Treasury yield curves for Example 2 36
3.1 Ratio of RMSFE for the US Treasury curve (N-S model) 58
3.2 Ratio of RMSFE for the swap-spreads 59
3.3 Ratio of RMSFE for the LIBOR-SWAP curve 60
4.1 OLS estimates of first-order autocorrelation
coefficients for interest rates 68
4.2 The ADF statistic for the null hypothesis of a unit root 69
4.3 T he KPSS statistic for the null hypothesis of a
stationary process 69
4.4 Rejection frequencies for ADF and KPSS tests when
the true series follows an AR(1) process 70
4.5 Variance ratios for the US, UK and Eurozone 71
4.6 Statistics for benchmark portfolios 73
5.1 Allocations for portfolio number 70 (%) 106
5.2 Allocations for portfolio number 70 (%) 107
5.3 Allocations for portfolio 70 (%) 109
6.1 Main characteristics of the sample 119
6.2 Composition of the optimal portfolios according
to different criteria(%) 131
8.1 Reserves estimates for several central Banks 159
8.2 Reserves estimates for ten SWFs 159
8.3 List of asset classes for CBs and SWFs 165
8.4 F irst four moments, parameters estimates, AIC
and KS test for each asset class of CBs’ universe 166
8.5 First four moments, parameters estimates, AIC
and KS test for each asset class of SWFs’ universe 168
8.6 N umber of asset class pairs selected by copulas
according to the AIC (CB case) 169
8.7 Number of asset class pairs selected by
copulas according to the AIC (SWF case) 169
10.1 Equities versus bonds historical back-test 195
10.2 Strategic tilting historical back-test: summary of results 197
10.3 Historical back-test of tilting as a package 198
vii
viii List of Illustrations
10.4 Monte Carlo simulation of tilting strategy 200
10.5 Long-run returns and regrets 201
11.1 Optimally constructed fund of funds 216
11.A.1 Data series used in the estimation 219
12.1 Historical performance statistics of selected
bond indices in % (Jan. 1990–Sept. 2008) 230
12.2 Composition of the US high grade fixed income
universe (as of 30 June 2008) 231
12.3 In-sample and out-of-sample estimations 242
12.4 O ut-of-sample total return estimation
in % (Dec. 2006–Sept. 2008) 246
13.1 P erformance comparison of TBA proxy and MBS
Fixed-Rate Index, Sept. 2001–Sept. 2008 253
13.2 TBA proxy portfolio holdings as of 30 Sept. 2008 256
13.3 Summary of historical performance, Sep. 2000–Sep. 2008 258
13.4 Ex-ante TEV between benchmark and
GlobalAgg (projected by GRM as of Mar. 2008) 259
13.5 E x-ante TEV between Benchmark 2 and GlobalAgg
(projected by GRM as of Mar. 2008) 260
13.6 Performance of Benchmark 1.3 before and after
credit crisis, relative to G7 Treasuries and GlobalAgg 261
14.1 Portfolio allocation: minimum modified VaR 273
14.A.1 Descriptive statistics 278
14.A.2 Correlation matrix 279
14.A.3 Coskewness matrix 279
14.A.4 Cokurtosis matrix 279
15.1 20th century averages and (geometric)
average growth rates. 297
15.2 S tatistics of trend, low frequency and high
frequency components of five economic
and financial variables. 300
15.3 M ean and variance of low, high frequency
and total model from (5). 317
15.4 Complex roots of low and high frequency
models from (5). 317
15.5 Six combinations of DGP representation
and frequency ranges for which error (6) is calculated 319
15.6 Mean errors (6) for each of the six combinations
in Table 15.5 based on 1000 simulations 321
17.1 S tatistical impact of the variance stabilizing
transformation on the estimation of the Sharpe ratio 344
17.2 E stimated Sharpe ratio S(cid:109)Rn, mean, standard
deviation, maximum, minimum and length n of
the excess returns time series for different GBP 345
List of Illustrations ix
17.3 E stimated Sharpe ratio S(cid:109)Rn, mean, standard
deviation, maximum, minimum and length n of
the excess return time series for different ETFs 350
17.4 Estimates of the parameters μ, ω, α, and β of the
GARCH(1,1) model as defined in Formula (10)
for distinct time series of ETF excess returns 350
17.5 The one-sided hypothesis H0: SRX > SRY,
as defined in (16), is tested 356
Figures
I.1 Reserves growth and the number of academic
publications on reserves and sovereign
wealth management xxxii
I.2 Research fields in economics and
finance: number of publications xxxii
1.1 Zero-coupon rates from January 1973 to August 2007 8
1.2 Forecasting interest rates 9
1.3 Predictive performance for frequentist forecasts
relative to random walk 10
1.4 P redictive performance for Bayesian forecasts
relative to random walk 11
1.5 L og predictive likelihood weights over the
training period of 120 points 17
1.6 L og marginal model likelihood weights over
the training period of 120 points 17
1.7 Dynamic model averaging 19
1.8 Static model averaging 19
1.9 Dynamic predictive performance for frequentist
combinations relative to random walk 20
1.10 D ynamic predictive performance for Bayesian
combinations relative to random walk 21
1.11 D ynamic predictive performance for Bayesian
log combinations relative to random walk 22
1.12 S tatic predictive performance for frequentist
combinations relative to random walk 23
1.13 Static predictive performance for Bayesian
combinations relative to random walk 24
1.14 S tatic predictive performance for Bayesian log
combinations relative to random walk 25
1.15 P redictive performance of best individual
models and best combinations relative to random
walk, static setting 26
2.1 US Treasury yield curves for Example 2 37