Bond Prices Research Articles

Life (of a Bond) is Short: Managerial Learning from Bond Prices

Life (of a Bond) is Short: Managerial Learning from Bond Prices

Pricing of Convertible Bonds Based on the NIG Model—A Case Study of Electrical, Electronic, and Communication Equipment Manufacturing Industry

Pricing of Convertible Bonds Based on the NIG Model—A Case Study of Electrical, Electronic, and Communication Equipment Manufacturing Industry

The Unintended Impact of the Volcker Rule on Primary Market Bond Pricing: Evidence from the Rule 144A Bond Market

The Unintended Impact of the Volcker Rule on Primary Market Bond Pricing: Evidence from the Rule 144A Bond Market

Market Liquidity and Australian Government Bond Prices

Market Liquidity and Australian Government Bond Prices

Explaining Bond Pricing Principles to Undergraduates Using Historical Connections and Two New Expositions

Explaining Bond Pricing Principles to Undergraduates Using Historical Connections and Two New Expositions

Deconstruction of ESG Impacts on US Corporate Bond Pricing: The Cost of Capital Benefits Across Industry Sectors

Deconstruction of ESG Impacts on US Corporate Bond Pricing: The Cost of Capital Benefits Across Industry Sectors

Systemic Risk and Monetary Policy: The Haircut Gap Channel of the Lender of Last Resort

Abstract We show that lender of last resort (LOLR) policy exacerbates bank interconnectedness. Using novel micro-level data, we analyze LOLR’s haircut gaps: the differences between the private market and central bank haircuts. LOLR policy incentivizes banks to increase pledging and holdings of higher haircut-gap bonds, especially those issued by domestic and systemically important banks. Effects only apply to banks, not to nonbanks without LOLR access. LOLR funding revives bank bond issuance associated with higher haircut gaps and increases the subsequent correlation between pledging and issuing banks’ bond prices, in particular during periods of low-market returns and for domestic, systemically important banks.

The dynamics of bonds, commodities and bitcoin based on NARDL approach

Asset market dynamic interconnectedness poses significant challenges to investors, fund managers, and policymakers, particularly in periods of prolonged uncertainty and economic crisis. This study investigates the asymmetric connection between the Bitcoin, gold, and oil markets and the bond markets in the United States, Australia, China, and the European Union. The study employed nonlinear autoregressive distributed lag (NARDL) on data ranging from January 1, 2017, to January 26, 2023, that accounted for the COVID-19 pandemic period and the Russian-Ukraine war. The results indicate that a fall in the Bitcoin price leads to a rise in bond prices, most profoundly in the European Union, where a 1% rise in the Bitcoin price leads to a 0.032% fall in bond prices. Similarly, the oil price index indicated a negative asymmetric shock in bond prices, with the most profound rise in the US bond market. The gold market index exhibited a positive connection to the bond market (US bond market falls by 0.476%) as the market overreacts to a fall in prices rather than a rise, and often in the long run rather than the short run, except for Bitcoin. The Bitcoin and oil markets act as strong safe-havens, while gold plays the role of a weak hedge during the pandemic and the Russia-Ukraine war. While our results are consistent over multiplier impact and stability tests, fund managers may find these significant due to the involvement of Russia and Ukraine as the two largest producers and exporters of several important commodities and energy. We discuss practical implications of our findings.

Corporate bond price reversals

I demonstrate empirically that corporate bond dealers mitigate adverse selection risk by passing potentially informed transactions to institutional investors. I contrast price reversals following days with abnormal trading volume across bonds with different information asymmetry. In informed trading, the part of reversal specific to high-volume days should increase with information asymmetry. In uninformed trading, there is no such effect. Following high-volume days when investors provide liquidity, the reversals are consistent with the former case. When dealers provide liquidity, I observe the latter. The results suggest that the informational content of bond prices is higher when dealers do not take inventory.

State corporate tax changes and bond pricing: U.S. evidence

In this study, we empirically investigate whether and to what extent state tax changes may affect the cost of debt. We employ staggered changes in state corporate income tax rates in the U.S. as exogenous shocks and estimate the effects of these state tax changes on bond at-issue yield spreads. We find a significant and positive relation between bond yield spreads and state tax increases but an insignificant relation between bond yield spreads and state tax decreases. On average, tax increases result in an increase of 34 basis points in the average bond yield spread. In addition, we document that bond investors demand higher yield spreads because tax increases aggravate firms' default risk by increasing firm leverage and return volatility. Moreover, we extend our analysis to equity holders and report a negative relation between stock market reaction to bond issuances subject to state tax increases.

Valuation of Commodity-Linked Bond with Stochastic Convenience Yield, Stochastic Volatility, and Credit Risk in an Intensity-Based Model

In this study, we consider an intensity-based model for pricing a commodity-linked bond with credit risk. Recently, the pricing of a commodity-linked bond with credit risk under the structural model has been studied. We extend the result using an intensity-based model, stochastic volatility model, and stochastic convenience yield model. In the intensity-based model, the credit event by the counterparty occurs at the time of first jump in a stochastic Poisson process, in which intensity is modeled as the sum of two CIR prosesses. We assume that the underlying asset follows the stochastic volatility and convenience yield models. Using the measure change technique, we explicitly derive the commodity-linked bond pricing formula in the proposed model. As a result, we provide the explicit solution for the price of the commodity-linked bond with stochastic convenience yield, stochastic volatility, and credit risk as single integrations. In addition, we present several examples to demonstrate the effects of significant parameters on the value of commodity-linked bond using numerical integration. In particular, examples are provided, focusing on the behavior of prices based on effects of recovery rate.

Interpellating Finance – a dramaturgical model for green bond pricing

Global financial governance is turning green. Attempting to tackle climate change, financial elites seem to swing confusingly between hesitant and action aimed at relieving the contradictions that led to the crisis, and opportunistic co-optation of critical discourse. Drawing on the work of Althusser, Laclau and Butler, we describe the historical emergence of this green financial apparatus and the related proliferation of green labels and signifiers. Green labels serve as the malleable ground on which a diversity of meanings and positions are articulated and temporarily fixed. Labels are the names through which financiers are interpellated and constituted as green ideological subjects. Through an analysis of the mechanisms of green bonds pricing, and of the actors involved in a green bond boot camp, we contend that the added value of green financial instruments, called the greenium, cannot merely be attributed to the performativity of models and formulas for risk engineering. Rather, it is the material effect of a subjectivation apparatus attuned to the existing relations of production. Ultimately, the greenness which becomes encrypted in the greenium is a process of translation of language of capital valorisation, performed through the tendency of capital to reproduce relations of exploitation in transitional time.

Implicit government guarantees, media tone and bond pricing

AbstractBy exploiting the first public bond default event in China—the Chaori bond default in 2014—we examine how implicit government guarantees shape the role of media information in bond pricing. We find an insignificant association between preissuance media tones and bond issuances’ yield spreads before the event; however, the association becomes negative and significant after the event. The role of media tone in bond pricing is more pronounced for regions with greater information demand for default risk and for media outlets with stronger information provisions toward implicit government guarantees after the event. Mechanism analyses suggest increased bond investors’ risk awareness, rather than strengthened information provisions in media tones, as the force behind the more pronounced role of media tone in bond pricing. Finally, lower yield spreads driven by more positive media tone do not suggest bond overpricing. Our study reveals the role of media tone in bond pricing and displays the evolution of the media tone's role in bond pricing due to changing institutions.

Research on Pricing Methods of Convertible Bonds Based on Deep Learning GAN Models

This paper proposes two data-driven models (including LSTM pricing model, WGAN pricing model) and an improved model of LSM based on GAN to analyze the pricing of convertible bonds. In addition, the LSM model with higher precision in traditional pricing model is selected for comparative study with other pricing models. It is found that the traditional LSM pricing model has a large error in the first-day pricing, and the pricing function needs to be further improved. Among the four pricing models, LSTM pricing model and WGAN pricing model have the best pricing effect. The WGAN pricing model is better than the LSTM pricing model (0.21%), and the LSM improved model (1.17%) is better than the traditional LSM model (2.26%). Applying the generative deep learning model GAN to the pricing of convertible bonds can circumvent the harsh preconditions of assumptions, and significantly improve the pricing effect of the traditional model. The scope of application of each model is different. Therefore, this paper proves the feasibility of the GAN model applied to the pricing of convertible bonds, and enriches the pricing function of derivatives in the financial field.

Selective Default Expectations

Abstract This paper explores how selective default expectations affect the pricing of sovereign bonds in a historical laboratory: the German default of the 1930s. We analyze yield differentials between identical government bonds traded across various creditor countries before and after bond market segmentation. We show that, when secondary debt markets are segmented, a large selective default probability can be priced in bond yield spreads. Selective default risk accounted for one-third of the yield spread of German external bonds over the risk-free rate during the 1930s. Selective default expectations arose from differences in the creditor countries’ economic power over the debtor.

High-Frequency Trading in Bond Returns: A Comparison Across Alternative Methods and Fixed-Income Markets

AbstractA properly performing and efficient bond market is widely considered important for the smooth functioning of trading systems in general. An important feature of the bond market for investors is its liquidity. High-frequency trading employs sophisticated algorithms to explore numerous markets, such as fixed-income markets. In this trading, transactions are processed more quickly, and the volume of trades rises significantly, improving liquidity in the bond market. This paper presents a comparison of neural networks, fuzzy logic, and quantum methodologies for predicting bond price movements through a high-frequency strategy in advanced and emerging countries. Our results indicate that, of the selected methods, QGA, DRCNN and DLNN-GA can correctly interpret the expected bond future price direction and rate changes satisfactorily, while QFuzzy tend to perform worse in forecasting the future direction of bond prices. Our work has a large potential impact on the possible directions of the strategy of algorithmic trading for investors and stakeholders in fixed-income markets and all methodologies proposed in this study could be great options policy to explore other financial markets.

Computing Arbitrage-Free Yields in Multi-Factor Gaussian Shadow Rate Term Structure Models

This paper develops an approximation to arbitrage-free bond yields in Gaussian shadow rate term structure models. In this class of models, yields are constrained to be above an effective lower bound, thus rendering standard bond pricing methods inapplicable. I propose approximating the nonlinear relationship between yields and state variables using moments of the censored normal distribution. In an empirical application, this approximation technique is accurate to within a fraction of a basis point. As I show, minimizing the yield approximation error is crucial for model estimation as even seemingly small errors can lead to economically meaningful inference biases.

Nonlinear Modeling of Mortality Data and Its Implications for Longevity Bond Pricing

Human mortality has been improving faster than expected over the past few decades. This unprecedented improvement has caused significant financial stress to pension plan sponsors and annuity providers. The widely recognized Lee–Carter model often assumes linearity in its period effect as an integral part of the model. Nevertheless, deviation from linearity has been observed in historical mortality data. In this paper, we investigate the applicability of four nonlinear time-series models: threshold autoregressive model, Markov switching model, structural change model, and generalized autoregressive conditional heteroskedasticity model for mortality data. By analyzing the mortality data from England and Wales and Italy spanning the years 1900 to 2019, we compare the goodness of fit and forecasting performance of the four nonlinear models. We then demonstrate the implications of nonlinearity in mortality modeling on the pricing of longevity bonds as a practical illustration of our findings.

Equilibrium price estimation of green bonds from the perspective of resource allocation.

Green bonds offer substantial positive externalities compared to other types of bonds. This leads to a resource distribution efficiency that falls below the optimal level dictated by Pareto efficiency. It becomes essential to determine a means by which green bonds can achieve an equilibrium price, ensuring optimal public resource allocation and maximized social welfare. From the perspective of externalities, this study employs the carbon shadow price (CSP) to determine the equilibrium price of carbon emissions. Subsequently, this value aids in estimating the equilibrium price of green bonds. Firstly, we introduced an optimized bootstrap method to estimate the bias-corrected CSP at the provincial level in China from 2007 to 2020. Then, a pricing framework is developed, integrating both the carbon trading price and the estimated CSP, to determine the green bond's equilibrium price. Numerical simulations indicate that, under current conditions, green bonds cannot achieve the equilibrium price by relying solely on the carbon trading mechanism. Therefore, further development of China's carbon emissions trading market is required.

Searching for Assets to Hedge Against Inflation in the U.S. Market

This paper provides evidence regarding the relationship between asset returns and (expected) inflation in the U.S. market. Evidence indicates that inflation has a negative effect on stocks, REIT and bonds. However, its effect on housing and gold assets is positive. Evidence concludes both housing and gold tend to show a positive correlation with inflation. This study finds that inflation causes equity market volatility due to investors’ fears about the possibility of interest rate hikes by the Fed, which further aggravates the price of stocks and REIT, but helps to improve bond prices due to a flight-to-quality effect.