Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays in corporate finance

China has been growing rapidly over the last decades. The private sector is the driving force of this growth. This thesis focuses on firm-level investment and cash holdings in China, and the chapters are structured around the following issues. 1. Why do private firms grow so fast when they are more financially constrained? In Chapter 3, we use a panel of over 600,000 firms of different ownership types from 1998 to 2007 to find the link between investment opportunities and financial constraints. The main finding indicates that private firms, which are more likely to be financially constrained, have high investment-investment opportunity sensitivity. Furthermore, this sensitivity is relatively lower for state-owned firms in China. This shows that constrained firms value investment opportunities more than unconstrained firms. To better measure investment opportunities, we attempt to improve the Q model by considering supply and demand sides simultaneously. When we capture q from the supply side and the demand side, we find that various types of firms respond differently towards different opportunity shocks. 2. In China, there are many firms whose cash flow is far greater than their fixed capital investment. Why is their investment still sensitive to cash flow? To explain this, in Chapter 4, we attempt to introduce a new channel to find how cash flow affects firm-level investment. We use a dynamic structural model and take uncertainty and ambiguity aversion into consideration. We find that uncertainty and ambiguity aversion will make investment less sensitive to investment opportunities. However, investment-cash flow sensitivity will increase when uncertainty is high. This suggests that investment cash flow sensitivities could still be high even when the firms are not financially constrained. 3. Why do firms in China hold so much cash? How can managers’ confidence affect corporate cash holdings? In Chapter 5, we analyse corporate cash holdings in China. Firms hold cash for precautionary reasons, to hedge frictions such as financing constraints and uncertainty. In addition, firms may act differently if they are confident or not. In order to determine how confidence shocks affect precautionary savings, we develop a dynamic model taking financing constraints, uncertainty, adjustment costs and confidence shocks into consideration. We find that without confidence shocks, firms will save money in bad times and invest in good times to maximise their value. However, if managers lose their confidence, they tend to save money in good times to use in bad times, to hedge risks and financing constraint problems. This can help explain why people find different results on the cash flow sensitivity of cash. Empirically, we use a panel of Chinese listed firms. The results show that firms in China save more money in good times, and the confidence shock channel can significantly affect firms’ cash holdings policy.