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We offer several suggestions for researchers using corporate bond return data. First, despite clear instructions from older papers (e.g., Bessembinder et al., The Review of Financial Studies 22:4219–4258, 2009) about ways to compute credit excess returns, a lot of recent research simply subtracts a Treasury Bill return. We show that this imprecision is likely to contaminate inferences, as the rate component of returns is negatively correlated to the spread component. This is a problem for all research looking at corporate bond returns, especially time series analysis and safer corporate bonds (e.g., investment grade). We provide a simple approach using Wharton Research Data Services (WRDS) data to remove the interest rate component of corporate bond returns. Second, we note significant differences in the coverage of corporate bonds across the Trade Reporting and Compliance Engine (TRACE) platform and typical corporate bond indices. We provide some simple rules for researchers who are using TRACE to select a subset of bonds closest to those contained inside corporate bond indices used by institutional investors. Third, we note differential quality in the prices and hence returns between TRACE and typical corporate bond indices. Corporate bond returns provided by corporate bond indices (i) correctly estimate credit excess returns, (ii) are synchronous for the entire set of bonds, allowing for consistent cross-sectional comparability, and (iii) suffer less from stale pricing issues. Due to these coverage and data quality issues, researchers should try, where possible, to source return data from multiple sources to ensure the robustness of their results.

Studies of bond return predictability find a puzzling disparity between strong statistical evidence of return predictability and the failure to convert return forecasts into economic gains. We show that resolving this puzzle requires accounting for important features of bond return models such as volatility dynamics and unspanned macro factors. A three-factor model comprising a forward spread, a weighted combination of forward rates, and a macro factor generates notable gains in out-of-sample forecast accuracy compared with a model based on the expectations hypothesis. Such gains in predictive accuracy translate into higher risk-adjusted portfolio returns after accounting for estimation error and model uncertainty. Consistent with models featuring unspanned macro factors, our forecasts of future bond excess returns are strongly negatively correlated with survey forecasts of short rates. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2829 This paper was accepted by Gustavo Manso, finance.

A structural model for green bonds is developed to explain the formation and dynamics of green bond prices and to address the issue of the so-called 'greenium', that is, the difference between the yields on a conventional bond and a green bond with the same characteristics. We provide answers to the following questions: What are the determinants of the green bond value? Do green bonds enhance the credit quality of the issuer? Are green bonds a relatively cheap tool to fund sustainable investments? We also study the effect of investors' environmental concern on portfolio allocation. Our results have direct policy implications and suggest that an improvement in credit quality could ultimately lead to a lower cost of capital for green bond issuers and that governmental tax-based incentives and an increase in investors' green awareness play a significant role in scaling up the green bonds market.

We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock-and labor-market-related variables are more relevant for shortterm maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels.

What makes an asset a “safe” asset? We study a model where two countries each issue sovereign bonds to satisfy investors’ safe asset demands. The countries differ in the float of their bonds and the fundamental resources available to rollover debts. A sovereign’s debt is safer if its fundamentals are strong relative to other possible safe assets, not merely strong on an absolute basis. If demand for safe assets is high, a large float enhances safety through a market depth benefit. If demand for safe assets is low, then large debt size is a negative as rollover risk looms large.

Standard optimal Debt Management (DM) models prescribe a dominant role for long bonds and advocate against issuing short bonds. They require very large positions in order to complete markets and assume each period that governments repurchase all outstanding bonds and reissue (r/r) new ones. These features of DM are inconsistent with U.S. data. We introduce incomplete markets via small transaction costs which serves to make optimal DM more closely resemble the data : r/r are negligible, short bond issuance substantial and persistent and short and long bonds positively co-vary. Intuitively, long bonds help smooth taxes over states and short bonds over time. Solving incomplete market models with multiple assets is challenging so a further contribution of this article is introducing a novel computational method to find global solutions.

Green bonds (or climate bonds) are one of the most used sustainable investment instruments, and under the Paris Climate Agreement of 2015, the climate bond market is expected to thrive in the near future. Green bonds are gaining increasing popularity between environmentally responsible investors, as well as investors who 'simply' attempt to benefit from portfolio diversification, including green issuances, that are close to other fixed bonds. This paper aims to take advantage of previous literature contributions on the green bond market to indicate the way forward for future research. Herein, through a systematic literature review on the green bond market, our ultimate goal is to provide investors, main markets actors, and policymakers with some helpful insight on the role of environmental investments in reshaping the financial markets and fostering the sustainability of the economy.

We establish currency as an important factor shaping global portfolios. Using a new security-level data set, we demonstrate that investor holdings are biased toward their own currencies to such an extent that countries typically hold most of the foreign-debt securities denominated in their currency. While large firms issue in foreign currency and borrow from foreigners, most firms issue only in local currency and do not directly access foreign capital. These patterns hold broadly across countries except for the United States, as foreign investors hold significant shares of US dollar bonds. The share of dollar-denominated cross-border holdings surged after 2008.

Intro -- List of Figures -- List of Tables -- List of Symbols -- 1 Introduction -- 1.1 Motivation and Aims of the Thesis -- 1.2 Course of Investigation -- 2 Scientific Background -- 2.1 CAT Bonds -- 2.2 Machine Learning -- 3 Improving CAT Bond Pricing Models via AdvancedMachine Learning in the Primary Market -- 3.1 Introduction -- 3.2 Data -- 3.3 Empirical Analysis -- 3.4 Interim Conclusion -- 4 Forecasting Accuracy of Advanced Machine Learningand Linear Regression - Evidence from the SecondaryCAT Bond Market -- 4.1 Introduction -- 4.2 Data -- 4.3 Empirical Analysis -- 4.4 Interim Conclusion -- 5 Superior Forecasting with simple AR(1) Models in alow-volatility Environment: Evidence from the CATBond Market -- 5.1 Introduction -- 5.2 Procedure -- 5.3 Empirical Analysis -- 5.4 Interim Conclusion -- 6 Conclusion -- Bibliography.

We examine the influence of social capital in the municipal bond market. Defined as the norms and networks that encourage cooperation, social capital is a social construct which captures a region's level of altruism, trustworthiness, and propensity to honor obligations. We expect that municipalities with high social capital are more trustworthy and likely to honor their debt obligations, which will result in lower bond yields. Our findings confirm that the bonds issued by municipalities located in high social capital counties exhibit lower yields compared to the municipalities located in low social capital counties. Our findings are also supported by bond prices in the secondary market, which shows that bonds from the municipalities located in high social capital regions have higher prices. Additional tests reveal that the influence of social capital is stronger for general obligation bonds, suggesting that social capital matters more for bonds where the willingness of municipalities to pay taxes is an important factor. Lastly, we document that the bonds of municipalities in high social capital areas are less likely to have insurance, suggesting that social capital may act as a substitute for bond insurance.