Fixed Income (Corporate Bonds)

Bayesian Estimation of Corporate Default Spreads ArXiv ID: 2503.02991 “View on arXiv” Authors: Unknown Abstract Risk-averse investors often wish to exclude stocks from their portfolios that bear high credit risk, which is a measure of a firm’s likelihood of bankruptcy. This risk is commonly estimated by constructing signals from quarterly accounting items, such as debt and income volatility. While such information may provide a rich description of a firm’s credit risk, the low-frequency with which the data is released implies that investors may be operating with outdated information. In this paper we circumvent this problem by developing a high-frequency credit risk proxy via corporate default spreads which are estimated from daily bond price data. We accomplish this by adapting classic yield curve estimation methods to a corporate bond setting, leveraging advances in Bayesian estimation to ensure higher model stability when working with small sample data which also allows us to directly model the uncertainty of our predictions. ...

The VIX as Stochastic Volatility for Corporate Bonds ArXiv ID: 2410.22498 “View on arXiv” Authors: Unknown Abstract Classic stochastic volatility models assume volatility is unobservable. We use the Volatility Index: S&P 500 VIX to observe it, to easier fit the model. We apply it to corporate bonds. We fit autoregression for corporate rates and for risk spreads between these rates and Treasury rates. Next, we divide residuals by VIX. Our main idea is such division makes residuals closer to the ideal case of a Gaussian white noise. This is remarkable, since these residuals and VIX come from separate market segments. Similarly, we model corporate bond returns as a linear function of rates and rate changes. Our article has two main parts: Moody’s AAA and BAA spreads; Bank of America investment-grade and high-yield rates, spreads, and returns. We analyze long-term stability of these models. ...

Quantile Regression using Random Forest Proximities ArXiv ID: 2408.02355 “View on arXiv” Authors: Unknown Abstract Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often the goal isn’t just point prediction but determining uncertainty. Quantifying uncertainty, especially the aleatoric uncertainty due to the unpredictable nature of market drivers, helps investors understand varying risk levels. Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic quantile regression methods that need separate models for each quantile, quantile regression forests estimate the entire conditional distribution of the target variable with a single model, while retaining all the salient features of a typical random forest. We introduce a novel approach to compute quantile regressions from random forests that leverages the proximity (i.e., distance metric) learned by the model and infers the conditional distribution of the target variable. We evaluate the proposed methodology using publicly available datasets and then apply it towards the problem of forecasting the average daily volume of corporate bonds. We show that using quantile regression using Random Forest proximities demonstrates superior performance in approximating conditional target distributions and prediction intervals to the original version of QRF. We also demonstrate that the proposed framework is significantly more computationally efficient than traditional approaches to quantile regressions. ...

Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective ArXiv ID: 2406.12983 “View on arXiv” Authors: Unknown Abstract A corporate bond trader in a typical sell side institution such as a bank provides liquidity to the market participants by buying/selling securities and maintaining an inventory. Upon receiving a request for a buy/sell price quote (RFQ), the trader provides a quote by adding a spread over a \textit{“prevalent market price”}. For illiquid bonds, the market price is harder to observe, and traders often resort to available benchmark bond prices (such as MarketAxess, Bloomberg, etc.). In \cite{“Bergault2023ModelingLI”}, the concept of \textit{“Fair Transfer Price”} for an illiquid corporate bond was introduced which is derived from an infinite horizon stochastic optimal control problem (for maximizing the trader’s expected P&L, regularized by the quadratic variation). In this paper, we consider the same optimization objective, however, we approach the estimation of an optimal bid-ask spread quoting strategy in a data driven manner and show that it can be learned using Reinforcement Learning. Furthermore, we perform extensive outcome analysis to examine the reasonableness of the trained agent’s behavior. ...

Corporate Green Bonds ArXiv ID: ssrn-3125518 “View on arXiv” Authors: Unknown Abstract I examine corporate green bonds, whose proceeds finance climate-friendly projects. These bonds have become more prevalent over time, especially in industries wh Keywords: Green Bonds, Sustainable Finance, Climate Finance, Bond Issuance, ESG Metrics, Fixed Income (Corporate Bonds) Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses standard econometric methods (event studies, matching) rather than advanced mathematics, but is heavily data-driven with a comprehensive dataset from Bloomberg and rigorous empirical analysis of market reactions and firm performance. flowchart TD G["Research Goal:<br/>Analyze Corporate Green Bond Issuance & Performance"] --> D["Data Collection:<br/>S&P Global & Bloomberg<br/>~500 US Corporate Bonds 2010-2020"] D --> M["Methodology:<br/>Difference-in-Differences<br>PSM Matching<br/>Regression Analysis"] M --> C["Computational Processes:<br/>1. Yield Spread Estimation<br/>2. ESG Impact Modeling<br/>3. Certification Analysis"] C --> F["Key Findings:<br/>1. Certified Green Bonds<br/> have 20-25 bps lower yields<br/>2. ESG factors drive issuance<br/>3. Liquidity premium varies<br/>4. No 'Greenium' for non-certified"]