Asset Allocation under the Influence of Long-Run Relations

134 Pages·2014·0.82 MB·German

Band 71 Schriften
zu Immobilienökonomie
und Immobilienrecht
Herausgeber:
IREIBS International Real Estate Business School
Prof. Dr. Sven Bienert
Prof. Dr. Stephan Bone-Winkel
Prof. Dr. Kristof Dascher
Prof. Dr. Dr. Herbert Grziwotz
Prof. Dr. Tobias Just
Prof. Dr. Kurt Klein
Prof. Dr. Jürgen Kühling, LL.M.
Benedikt Fleischmann
Prof. Gabriel Lee, Ph. D.
Prof. Dr. Gerit Mannsen
Asset Allocation Prof. Dr. Dr. h.c. Joachim Möller
Prof. Dr. Wolfgang Schäfers
Prof. Dr. Karl-Werner Schulte HonRICS
under the
Prof. Dr. Steffen Sebastian
Prof. Dr. Wolfgang Servatius
Influence of Prof. Dr. Frank Stellmann
Prof. Dr. Martin Wentz
Long-Run
Relations
Benedikt Fleischmann

Asset Allocation under the Influence of Long-Run Relations
Die Deutsche Bibliothek – CIP Einheitsaufnahme

Fleischmann, Benedikt
Asset Allocation under the Influence of Long-Run Relations
Benedikt Fleischmann
Regensburg: Universitätsbibliothek Regensburg 2014
(Schriften zu Immobilienökonomie und Immobilienrecht; Bd. 71)
Zugl.: Regensburg, Univ. Regensburg, Diss., 2013
ISBN: beantragt
ISBN: beantragt
© IRE|BS International Real Estate Business School, Universität Regensburg
Verlag: Universitätsbibliothek Regensburg, Regensburg 2014
Zugleich: Dissertation zur Erlangung des Grades eines Doktors der Wirtschaftswissenschaften,
eingereicht an der Fakultät für Wirtschaftswissenschaften der Universität Regensburg
Tag der mündlichen Prüfung: 24. Juli 2013
Berichterstatter: Prof. Dr. Steffen Sebastian
Prof. Dr. Rolf Tschernig
Contents
1 Research Objectives, Methods and Scientific Contributions 1
2 Inflation-Hedging, Asset Allocation and the Investment Horizon 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 VAR Model and Data . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1 VAR Specification . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.3 VAR Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Horizon Effects in Risk and Return
for Nominal and Real Returns . . . . . . . . . . . . . . . . . . . . . . 18
2.4.1 The Term Structure of Risk . . . . . . . . . . . . . . . . . . . 18
2.4.2 Inflation-Hedging . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.3 The Term Structure of Expected Returns . . . . . . . . . . . . 25
2.5 Horizon-Dependent Portfolio Optimizations . . . . . . . . . . . . . . 27
2.5.1 Mean-Variance Optimization . . . . . . . . . . . . . . . . . . . 27
2.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.A.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.A.2 Calculation of the Desmoothed Real Estate Returns . . . . . . 32
2.A.3 Robustness Regarding Different Unsmoothing Parameters . . . 33
2.A.4 Misspecification Tests . . . . . . . . . . . . . . . . . . . . . . . 35
I
Contents
3 Modeling Asset Price Dynamics under a Multivariate Cointegra-
tion Framework 37
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.1 VAR Specification . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.2 VEC Specification . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.3 Horizon Dependent Variance-Covariance . . . . . . . . . . . . 43
3.2.4 Portfolio Choice Problem . . . . . . . . . . . . . . . . . . . . . 45
3.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.3.1 Data and Time Series Properties . . . . . . . . . . . . . . . . 47
3.3.2 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . 51
3.3.3 Long Horizon Effects . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.4 Asset Allocation Decisions . . . . . . . . . . . . . . . . . . . . 67
3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.A.1 Bootstrap Method . . . . . . . . . . . . . . . . . . . . . . . . 74
3.A.2 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4 Do Stock Prices and Cash Flows Drift Apart? The Influence of
Macroeconomic Proxies 76
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.2.1 The Econometric Model . . . . . . . . . . . . . . . . . . . . . 80
4.2.2 Hypotheses Testing . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2.3 Long-Run Analyses . . . . . . . . . . . . . . . . . . . . . . . . 83
4.2.4 Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.3.1 Data and Time Series Properties . . . . . . . . . . . . . . . . 85
4.3.2 Cointegration Rank Analysis . . . . . . . . . . . . . . . . . . . 90
4.3.3 Restriction Tests . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.4 Testing the Financial Ratios . . . . . . . . . . . . . . . . . . . 96
4.3.5 Level Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
II
Contents
4.3.6 Horizon-Dependent Analysis . . . . . . . . . . . . . . . . . . . 100
4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.A.1 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.A.2 Univariate Stationarity of the Financial Ratios . . . . . . . . . 107
4.A.3 Stability of the Long-Run Matrices across the Models . . . . . 108
4.A.4 Bootstrap Method . . . . . . . . . . . . . . . . . . . . . . . . 110
Bibliography 111
III
List of Figures
2.1 Conditional Standard Deviations of the Assets . . . . . . . . . . . . . 20
2.2 Conditional Standard Deviation of the Inflation . . . . . . . . . . . . 21
2.3 Conditional Correlations of Asset Returns and Inflation . . . . . . . . 24
4.1 Level Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.2 Differenced Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.3 Impulse Response Functions . . . . . . . . . . . . . . . . . . . . . . . 101
4.4 Impulse Response Functions of Returns . . . . . . . . . . . . . . . . . 103
IV
List of Tables
2.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 VAR Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Term Structure of Expected Returns . . . . . . . . . . . . . . . . . . 26
2.4 Portfolio Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 Data Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.6 Results Regarding Different Unsmoothing Parameters . . . . . . . . . 34
2.7 VAR Order Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.8 Residual Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1 Abbreviationsg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3 Simultaneous and Lagged Correlations . . . . . . . . . . . . . . . . . 50
3.4 Unit Root and Cointegration Rank Test . . . . . . . . . . . . . . . . 51
3.5 VAR Parameter Estimatesg . . . . . . . . . . . . . . . . . . . . . . . 52
3.6 VEC Parameter Estimatesg . . . . . . . . . . . . . . . . . . . . . . . 55
3.7 Term Structure of Risk and Correlationsg . . . . . . . . . . . . . . . . 60
3.8 Variance Decomposition for Treasury Bills . . . . . . . . . . . . . . . 62
3.9 Variance Decomposition for Stock Returns . . . . . . . . . . . . . . . 63
3.10 Variance Decomposition for Bond Returns . . . . . . . . . . . . . . . 66
3.11 Global Minimum Variance Portfoliosg . . . . . . . . . . . . . . . . . . 69
3.12 Optimal Portfolio Holdings for γ = 20 . . . . . . . . . . . . . . . . . . 70
3.13 Lag Length Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.1 Univariate Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
V
Tables
4.3 Cointegration Rank . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.4 β Restriction Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.5 α Restriction Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.6 Financial Ratio Stationarity Tests . . . . . . . . . . . . . . . . . . . . 97
4.7 Π Matrix of Model M and r = 4 . . . . . . . . . . . . . . . . . . . 99
r 6
4.8 Lag Length Selection and Cointegration Rank Stability . . . . . . . . 106
4.9 Univariate Stationarity of Financial Ratios . . . . . . . . . . . . . . . 107
4.10 Stability Analysis of the Long-Run Matrices . . . . . . . . . . . . . . 109
VI
Chapter 1
Research Objectives, Methods and
Scientific Contributions
Inflation-Hedging, Asset Allocation and the Invest-
ment Horizon
Most of the empirical studies falsify the inflation-hedging hypothesis of stocks and
realestate, buttheytypicallyestablishtheirresearchmethodsonquarterlytoannual
returns and, therefore, contrast with the fact that most investors have much longer
investment horizons. Additionally, the perverse inflation-hedging characteristics of
stocks and real estate run contrary to the general economic theory that these assets
should be a good hedge against inflation. The residual claims of the investors are
the cash flows and retained earnings, and these are derived from real assets.
In our paper ’Inflation-Hedging, Asset Allocation and the Investment Horizon’,
my coauthors, Christian Rehring and Steffen Sebastian, and I link the inflation-
hedging analysis to the mixed asset allocation analysis focusing on the role of the
investment horizon for a buy-and-hold investor. Using a vector auto-regression
(VAR) for the UK market, we estimate correlations of nominal returns with infla-
tion to analyze how the inflation-hedging abilities of cash, bonds, stocks and direct
commercial real estate change with the investment horizon. In doing so, we find
that the inflation-hedging characteristics of all assets improve with the investment
horizon. Cash is clearly the best inflation hedge at short and medium horizons. For
1