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I examine the effect of the disclosure of pending lawsuits in 10-K/Q filings on the contractual terms of newly issued bonds. I find that firms' decisions to disclose pending lawsuits and the amount of disclosed information (i.e., the level of disclosure) have opposite effects. Specifically, firms that disclose a higher proportion of their pending lawsuits face higher yields and are more likely to include default clauses pertaining to court judgments in the bond prospectuses. However, within the subsample of firms that disclose their lawsuits, I find that firms with a higher level of disclosure regarding their pending lawsuits are rewarded with lower yields. This evidence suggests that bond investors interpret the decision to disclose pending lawsuits as a sign that the potential losses due to these lawsuits are material and reasonably possible and thus demand more stringent bond terms. However, bond investors associate a higher level of disclosure with a lower likelihood of withholding bad news and thus accept lower yields.

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.

We examine whether climate change news risk is priced in corporate bonds. We estimate bond covariance with a climate change news index and find that bonds with a higher climate change news beta earn lower future returns, consistent with the asset pricing implications of demand for bonds with high potential to hedge against climate risk. Moreover, when investors are concerned about climate risk, they are willing to pay higher prices for bonds issued by firms with better environmental performance. Our findings suggest that corporate policies aimed at improving environmental performance pay off when the market is concerned about climate change risk.

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.

This paper proposes a new explanation for the failure of Uncovered Interest Parity (UIP) that rationalizes both the classic UIP puzzle and the evidence that the puzzle reverses direction at longer horizons. In the model, excess currency returns arise as compensation for endogenous fluctuations in bond convenience yield differentials. Due to the interaction of monetary and fiscal policy, the impulse response of the equilibrium convenience yield is nonmonotonic, which generates the reversal of the puzzle. The calibrated model fits exchange rate dynamics very well. I also find direct evidence linking convenience yields to excess currency returns.

In diesem Beitrag werden Marktreaktionen auf die erstmalige Emission von Green Bonds und die erstmalige Offenlegung von Green Bond Rahmenwerken von 45 großen, börsennotierten europäischen Banken und Versicherungen mit einer Ereignisstudie empirisch analysiert. Unter Berücksichtigung der aktuellen regulatorischen Entwicklungen des European Green Bond Standards untersuchen wir, wie der Markt auf die Emission von Green Bonds und die Offenlegung von Green Bond Rahmenwerken von 2015 bis 2022 reagiert. Es wird auch analysiert, welche finanziellen Kennzahlen sowie Green Bond- und Rahmenwerks-bezogene Merkmale die Marktreaktionen beeinflussen, gemessen an kumulativen abnormalen Renditen. Wir stellen fest, dass die Marktreaktionen aufgrund sich ausgleichender positiver und negativer Effekte nicht signifikant sind. Allerdings können statistisch signifikante kumulative abnormale Renditen in absoluten Werten beobachtet werden. Schließlich identifizieren wir die Größe eines Unternehmens, den Verschuldungsgrad und den angebotenen Kupon (Textlänge und förderfähige grüne Projekte) als wesentliche Einflussfaktoren für positive und negative Marktreaktionen auf die erstmalige Emission grüner Anleihen (erstmalige Offenlegung von grünen Anleiherahmenwerken).

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.

Everything on bonds, bond funds, and more! Updated for the new economy Whether you're looking for income, diversification, or protection from stock market volatility, bonds can play an important role in any portfolio. Newly updated, Bond Investing For Dummies covers the essentials of getting started and ways to select and purchase bonds for your needs. You'll get up to speed on the different bond varieties and see how to get the best prices when you sell. We'll help you wrap your mind around bond returns and risk and recognize the major factors that influence bond performance. With easily understandable explanations and examples, you can understand bonds from every angle-yield, credit risk, callability, fund selection, bond broker-dealers, web portals, and beyond. This is the expert information and advice you need to invest in bonds in today's environment. Learn what bonds are and how you can use them to strengthen and protect your portfolio Understand how interest rates and other shifting sands affect bond investing Minimize your risk and maximize your returns with proven advice from an expert financial advisor Use online investing and apps to buy bonds and bond funds with confidence and easeNovice and experienced investors alike will love this quick-and-easy approach to bond investing.

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.