Essays on Non-linearities in Stock and Bond Returns: A Density-Based Approach Open Access
Pan, Jiening (2015)
Abstract
The dissertation consists of three essays that revolve around non-linearities embedded in asset returns. In the first essay "The Role of Slope Heterogeneity in Bond Excess Returns Predictability", I investigates bond excess return forecastability using current forward rates. The dynamics of excess return are modeled non-parametrically. Estimation shows heterogeneous slopes for independent variables, indicating the existence of non-linearity. Empirically, I find this non-linearity plays an important role in excess return prediction both in- and out-of-sample. By including non-linearity in the model, the in-sample R2 jumps to as high as 91%. Meanwhilelagged forward rates are no longer statistically significant, in contrast to the results documented in previous research. The out-of-sample forecasts also favor the non-parametric model. Findings in this paper suggest a potential important information source embedded in the current forward rates cross-section. Information associated with non-linearity is largely ignored in the existing literature as it is averaged out by linear model settings. The second essay "Do Non-Linearities Matter in the Yield Curve?" tries to answer the question that do non-yield variables contain information beyond what is contained in the yield curve? Using a non-linear factor extracted from the yield curve, I find nonyield factors, which are constructed from a large panel of macro-finance data, are no longer significant in predicting future bond excess returns both in- and out-of-sample. Moreover, my non-linear factor generates countercyclical and business cycle frequency bond risk premia. The findings underscore the importance of non-linearities embedded in the term structure, suggesting a fully spanned term structure model with non-linear state factors may be capable of matching features observed in the data. In the third essay "A Test on Asymmetric Dependence" (joint with Prof. Maasoumi, Lei Jiang and Ke Wu), we provide a model-free test for asymmetric dependence between stock and market returns, based on the Kullback-Leibler mutual information measure. Our test has greater power in small samples than previous tests of asymmetric correlation proposed by Hong, Tu and Zhou (2007). Empirically, we find that asymmetric dependence is a prevailing phenomenon in most commonly used portfolios.
Table of Contents
Preface 1
1 The role of slope heterogeneity in bond excess return predictability 10
I Introduction 11
II Econometric framework 13
II.1 Notation 13
II.2 The model 14
II.3 Test joint signicance of predictors 15
III Excess return forecasts 17
III.1 In-sample analysis: current forward rates only 17
III.2 In-sample analysis: with lagged variables 19
III.3 Summary and implications 20
IV Out-of-sample prediction 20
V Concluding remarks 23
1.A Appendix: Local constant estimator 25
1.B Appendix: Bootstrap algorithm and decision rule 26
1.C Appendix: Diebold and Mariano (D-M) test 28
2 Do non-linearities matter in the yield curve? 39
I Introduction 40
II The factors 43
II.1 Factors from the yield curve 43
II.2 The non-yield factors b Ft 46
II.3 The econometric model 46
III Empirical results 47
III.1 Data 47
III.2 In-sample analysis: NPt only 48
III.3 In-sample analysis: non-yield factors 49
III.4 Out-of-sample results 50
III.5 Implications of the ndings 53
IV Risk premia decomposition 53
V Concluding remarks 58
3 A Test on Asymmetric Dependence 72
I Introduction 73
II A Relative Entropy Based Test on Asymmetric Dependence 75
II.1 A relative entropy based measure of exceedance dependence 75
II.2 The non-parametric estimator 78
II.3 Test statistic and its sampling distribution 79
II.4 Asymmetric Dependence vs. Asymmetry in Distribution 81
III Simulation results 82
III.1 Simulation setup 82
III.2 Asymptotic size 84
III.3 Finite sample performance 85
III.4 Robustness of results 87
IV Asymmetric dependence in stock returns 89
IV.1 Data 89
IV.2 Empirical results 89
V Conclusion 90
3.A Appendix: Proofs 92
About this Dissertation
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