Essays on Macroeconomics and Finance Open Access

Li, Zhao (2016)

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This dissertation documents important issues on finance and macroeconomics. The first two chapters study the impact of uncertainty, which has recently become a hot research topic, although the mechanism of how it works remains an open question. I investigate the relationship between uncertainty and lending behavior in the syndicated loan market in order to propose new micro mechanisms. The Dealscan, Compustat, and CRSP databases are merged and the firm-level uncertainty is calculated from stock return volatility. Through comprehensive empirical studies, I ascertain that uncertainty substantially affects the quantity and price of the market. When uncertainty is higher, the loan shares tend to concentrate in the hands of lead lenders and the credit spread is higher. Along with empirical analyses, I propose a simple model to explain why higher uncertainty leaves the lead lenders with higher loan shares. The mechanism relies on the fact that uncertainty raises the unobservable investigation effort, which makes the benefit of shirking for the lead lenders higher. As a result, the participant lenders have to pull back their investment in order to leave the lead lenders with a higher share, which reduces the incentive of lead lenders to shirk responsibility. The first chapters thus propose new transmission channels of uncertainty's impact and sheds light on further theoretical and empirical works in this topic. The third chapter explores the BVAR forecasting methodologies for China. While it is well known that many models used for Western economies do not perform well in explaining and forecasting China's economic data, I challenge this convention by building rigorous econometric forecasting models for the Chinese economy. Different state-of-the-art Bayesian Vector-autoregression (BVAR) models are built, revised, and evaluated. It is found that the richer data set of additional macroeconomic data and sectoral data helps forecast the GDP, CPI, and interest rate. The large-scale BVAR model with 124 variables turns out to be the champion of forecasting models and it is more effective in extracting information from a large database than factor models and hierarchical models.

Table of Contents

1 Uncertainty and Syndicated Loan Structure. 4

1.1 Introduction. 4

1.2 Literature Review. 8

1.3 Theory. 10

1.3.1 Environment Overview. 10

1.3.2 Timeline of Movement. 12

1.3.3 Lead Lender's Problem. 13

1.3.4 Participant Lender's Problem. 17

1.4 Empirical Evidences. 19

1.4.1 Data. 19

1.4.2 Measure of Uncertainty. 20

1.4.3 Measure of Loan Structure. 22

1.4.4 Descriptive Statistics. 23

1.4.5 Uncertainty and Loan Shares. 24

1.4.6 Uncertainty and Loan Size. 25

1.5 Conclusion. 26

2 Uncertainty and Credit Spread. 47

2.1 Introduction. 47

2.2 Literature Review. 49

2.3 Theoretical Explanation. 51

2.3.1 Characteristic Functions. 52

2.3.2 Bank's Problem. 53

2.3.3 Entrepreneur's Problem. 54

2.3.4 Impact of Uncertainty. 54

2.4 Statistical Methods. 55

2.4.1 Measure of Uncertainty. 55

2.4.2 Data. 56

2.4.3 Variables of Interest. 58

2.5 Regression Evidence. 60

2.5.1 Uncertainty and Credit Spread. 60

2.5.2 Size Effect. 62

2.5.3 Uncertainty from the Lender Side. 63

2.5.4 Cyclical Impact. 65

2.5.5 Different Measures of Uncertainty. 66

2.6 Conclusion. 67

3 Forecasting China's Economy: A Bayesian Approach. 82

3.1 Introduction. 82

3.2 Literature Review. 85

3.3 Methodology. 89

3.3.1 Benchmark BVAR Model. 89

3.3.2 Dummy Observations. 93

3.3.3 Large BVAR Model. 95

3.3.4 FAVAR Model. 97

3.3.5 Hierarchical Model. 98

3.4 Data. 100

3.5 Empirical Evidence. 103

3.5.1 Benchmark Model. 103

3.5.2 Evaluation of Large BVAR Model. 104

3.5.3 Evaluation of Factor Model. 107

3.5.4 Evaluation of Hierarchical Model. 108

3.5.5 Champions of Models. 109

3.6 Conclusion. 111

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