Risky Bonds Research Articles

The dynamics of leverage and the belief distribution of wealth

The scale and terms of borrowing in an economy depend on the manner in which wealth is distributed across potential creditors with heterogeneous beliefs about the future. This distribution evolves over time as uncertainty is resolved, in favor of optimists if loans are repaid in full, and in favor of pessimists if there is widespread default. We model this process in an economy with two assets—risky bonds and risk-free cash. Within periods, given the inherited distribution of wealth across belief types, the scale and terms of borrowing are endogenously determined. Following good states, aggregate borrowing and the face value of debt both rise, and the interest rate falls. In the absence of noise, wealth converges to beliefs that differ systematically from the objective probability governing state realizations, with greater risk-aversion associated with greater optimism. In the presence of noise, the economy exhibits periods of high performance, punctuated by periods of crisis and stagnation.

Static Markowitz mean-variance portfolio selection model with long-term bonds

We propose a static Markowitz mean-variance portfolio selection model suitable for long-term zero-coupon bonds. The model uses a multi-factor term structure model of Vasicek (Ornstein-Uhlenbeck) type to compute the portfolio's expected return and its variance in the model. German Government zero-coupon bonds with short to very long time to maturity are considered; the data spans August 2002 to December 2020. The main investment assumption is the re-investment of cash flows of zero-coupon bonds with maturities less than the planning horizon at the current spot interest rate. Solutions for the zero-coupon holding vector and the tangency portfolio are obtained in closed form. Model parameters are estimated under an assumption of modeling ambiguity which takes the form of Knightian uncertainties at the level of the latent factors, allowing the use of a Kalman filter. Different investment strategies are examined on various risk portfolios. Results show that one- and two-factor Vasicek models produce attractive out-of-sample portfolio predictions in terms of the Sharpe ratio especially on long-term investments. It is also noted that a small number of risky bonds can adequately produce very attractive portfolio risk-return profiles.

Pricing risky corporate bonds: An empirical study

AbstractThis paper empirically studies a model for pricing risky corporate bonds proposed by Baaquie—based on the seminal Merton. The proposed model provides an exact solution for the price of a risky corporate bond with a finite maturity and explains the market price of corporate fixed coupon bonds as being the result of the market risk that is carried by the bond. Baaquie's model is empirically tested using 42 fixed coupon bonds issued by 23 US corporations, between 2011 and 2017. It is found that the proposed model estimates most bond prices quite accurately. Market time (similar to the concept of psychological time), which is distinct from calendar time, is quantified in the paper and is an exogenous behavioral parameter that plays a pivotal role in improving the accuracy of the pricing model for long‐maturity risky bonds.

Abnormal Downside Tail Risk as a Predictor of Risky Bond Returns

Abnormal Downside Tail Risk as a Predictor of Risky Bond Returns

Risky Corporate Bonds in 2021: A Bubble, or Rational Underwriting in a Low-Rate Environment?

In response to the pandemic, the Federal Reserve and US Treasury aggressively supported the corporate debt markets in 2020, both through easy monetary policy and direct participation. As the economy recovered, the Fed maintained an easy monetary policy in pursuit of more robust employment, gross domestic product growth, and inflation targets. Many observers have asserted that these policy actions and other factors have driven a bubble in risk assets, such as equities, special purpose acquisition companies, real estate, commodities, cryptocurrencies, nonfungible tokens, and risky bonds. Others believe that the valuations for at least some of these assets reflect investors’ rational incorporation of the current interest rate environment and economic outlook into their underwriting assumptions. Here, the authors analyze this question with regard to one of the riskiest classes of debt securities—the CCC-rated portion of the corporate high-yield debt market—to draw broader conclusions about the leveraged credit markets in the United States. Using historical market metrics, this portion of the market at its recent peak offered almost no excess return to compensate investors for the risk taken relative to low-risk alternatives. From this, the authors conclude that investors are underwriting significantly more optimistic outcomes than those reflected in historical averages. Although the authors believe that this segment of the market falls short of a true bubble, as they define it, they warn that current conditions pose key risks to investors and to the broader economy. <b>Key Findings</b> ▪ The US high-yield bond market, like other asset classes in the wake of the current easy monetary environment, has reached peak levels. ▪ Consistent with past bubbles, investors have moved up the risk curve, disproportionately bidding up the riskiest portion of this market. ▪ Although this reflects excessive risk appetite on the part of investors, it falls short of a true bubble as we define it. ▪ That said, these conditions pose key risks to investors and to the broader economy.

DEFAULTABLE TERM STRUCTURES DRIVEN BY SEMIMARTINGALES

In this paper, we consider a market with a term structure of credit risky bonds in the single-name case. We aim at minimal assumptions extending existing results in this direction: first, the random field of forward rates is driven by a general semimartingale. Second, the Heath–Jarrow–Morton (HJM) approach is extended with an additional component capturing those future jumps in the term structure which are visible from the current time. Third, the associated recovery scheme is as general as possible, it is only assumed to be nonincreasing. In this general setting, we derive generalized drift conditions which characterize when a given measure is a local martingale measure, thus yielding no asymptotic free lunch with vanishing risk (NAFLVR), the right notion for this large financial market to be free of arbitrage.

Four-factor model of Quanto CDS with jumps-at-default and stochastic recovery

We modify the model of Itkin, Shcherbakov and Veygman (ISV), proposed for pricing Quanto CDS and risky bonds, in several ways. First, the recovery rate could significantly vary right before or at default, therefore, here we treat it as stochastic. Second, we assume the domestic interest rate to be deterministic, because, as shown by ISV, its volatility does not contribute much to the Quanto CDS spread. Finally, to solve the corresponding systems of 4D PDEs we use a flavor of the RBF method which is a combination of localized RBF and finite-difference methods. Results of our numerical experiments demonstrate that the influence of volatility of the recovery rate is significant if the correlation between the recovery rate and the log-intensity of the default is non-zero. Also, the impact of the recovery mean-reversion rate on the Quanto CDS spread could be comparable with the impact due to jump-at-default in the FX rate.

Don't Take Their Word for It: The Misclassification of Bond Mutual Funds

ABSTRACTWe provide evidence that bond fund managers misclassify their holdings, and that these misclassifications have a real and significant impact on investor capital flows. The problem is widespread, resulting in up to 31.4% of funds being misclassified with safer profiles, compared to their true, publicly reported holdings. “Misclassified funds”—those that hold risky bonds but claim to hold safer bonds—appear to on‐average outperform lower risk funds in their peer groups. Within category groups, misclassified funds receive more Morningstar stars and higher investor flows. However, when we correctly classify them based on actual risk, these funds are mediocre performers.

Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds

We provide evidence that bond fund managers misclassify their holdings, and that these misclassifications have a real and significant impact on investor capital flows. In particular, many funds report more investment grade assets than are actually held in their portfolios to important information intermediaries, making these funds appear significantly less risky. This results in pervasive misclassification across the universe of US fixed income mutual funds. The problem is widespread - resulting in up to 31.4% of funds being misclassified with safer profiles, when compared against their true, publicly reported holdings. “Misclassified funds” – i.e., those that hold risky bonds, but claim to hold safer bonds – appear to on-average outperform the low-risk funds in their peer groups. Within category groups, “Misclassified funds” moreover receive higher Morningstar Ratings (significantly more Morningstar Stars) and higher investor flows due to this perceived on-average outperformance. However, when we correctly classify them based on their actual risk, these funds are mediocre performers. These Misclassified funds also significantly underperform precisely when junk-bonds crash in returns. Misreporting is stronger following several quarters of large negative returns. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Risky Corporate Bonds in 2021: A Bubble, or Rational Underwriting in a Low-Rate Environment?

Risky Corporate Bonds in 2021: A Bubble, or Rational Underwriting in a Low-Rate Environment?

Non-Homogeneous Semi-Markov and Markov Renewal Processes and Change of Measure in Credit Risk

For a G-inhomogeneous semi-Markov chain and G-inhomogeneous Markov renewal processes, we study the change from real probability measure into a forward probability measure. We find the values of risky bonds using the forward probabilities that the bond will not default up to maturity time for both processes. It is established in the form of a theorem that the forward probability measure does not alter the semi Markov structure. In addition, foundation of a G-inhohomogeneous Markov renewal process is done and a theorem is provided where it is proved that the Markov renewal process is maintained under the forward probability measure. We show that for an inhomogeneous semi-Markov there are martingales that characterize it. We show that the same is true for a Markov renewal processes. We discuss in depth the calibration of the G-inhomogeneous semi-Markov chain model and propose an algorithm for it. We conclude with an application for risky bonds.

Do bond yields follow the hierarchy of risk post BRRD?

AbstractWith a sample of 4,065 bonds issued by 63 banks from 12 euro area countries during 2013–2017, this study investigates how introducing bail‐in regulation has influenced bond yields in secondary markets, by distinguishing between non‐bail‐inable and different classes of bail‐inable bonds. The bail‐in risk premium does not follow the hierarchy of risk; it is stronger for less risky bonds. The effect on the spread between senior unsecured and non‐bail‐inable bonds is much higher than for subordinated bonds. Regarding subordinated bonds, the impact is higher for securities excluded from regulatory capital than for those included.

Portfolio optimization of credit risky bonds: a semi-Markov process approach

This article presents a semi-Markov process based approach to optimally select a portfolio consisting of credit risky bonds. The criteria to optimize the credit portfolio is based on l∞-norm risk measure and the proposed optimization model is formulated as a linear programming problem. The input parameters to the optimization model are rate of returns of bonds which are obtained using credit ratings assuming that credit ratings of bonds follow a semi-Markov process. Modeling credit ratings by semi-Markov processes has several advantages over Markov chain models, i.e., it addresses the ageing effect present in the credit rating dynamics. The transition probability matrices generated by semi-Markov process and initial credit ratings are used to generate rate of returns of bonds. The empirical performance of the proposed model is analyzed using the real data. Further, comparison of the proposed approach with the Markov chain approach is performed by obtaining the efficient frontiers for the two models.

Does operating risk affect portfolio risk? Evidence from insurers' securities holding

This study empirically examines, in the setting of insurance companies, the hypothesis that investors facing more operating risk may behave as if they were more risk averse in investment decisions. Specifically, we study how operating risk from underwriting insurance policies affects insurers' risk taking behavior in their portfolio investments. We find that insurers with higher volatilities in underwriting incomes and cash flows are more conservative in their financial investment risk taking – they have lower credit risk exposure in their bond investments, as well as lower portfolio weights on risky bonds and equities. Further, insurers' portfolio risk exposure is sensitive to the risk of permanent underwriting income shocks but insensitive to the risk of transitory shocks. Transitory operating risk, however, is significantly related to portfolio risk when insurers face tight financing constraints. Our findings suggest a substitutive effect of operating risk on investment decisions by financial institutions.

Precautionary Savings and the Stock-Bond Covariance

I show that the precautionary savings motive can account for high-frequency variation in the stock-bond covariance. An increase in the price of risk lowers risky asset prices on account of an increase in risk premia; it lowers bond yields on account of the precautionary savings component. Consequently, a price of risk shock moves risky and safe asset prices in the opposite direction. Times when the price of risk is volatile see a more negative stock-bond covariance. I demonstrate that a model calibrated to match the equity risk premium fits well the recent evidence on stock-bond covariance. Empirically, I show that stock-bond covariance co-moves with credit spreads and can predict excess returns on risky bonds and on bond-like stocks. The calibrated model underlines the first-order effect of risk compensation on safe rates.

Sovereign Bond Spreads and Credit Sensitivity

Expectations of risky bond payments are unobservable and recovery rates for sovereigns are hard to estimate because they have no contractual claims to defined assets and samples of defaults are limited. A geometric version of credit spread is used to derive expected payments, dependent on idiosyncratic risk and unrelated to interest rates. The expectations are used to define a measure of price sensitivity to credit risk perceptions, or credit duration, improving the ambiguity of modified yield duration.

Improving Evaluation Techniques for Credit Risky Securities

The present value of bonds and loans is commonly calculated as a sum of contractual cash flows discounted by default-free interest rates added by an appropriate credit spread. The evidence that investors require higher nominal returns for credit risky securities is commonly invoked as the main argument in support of this approach. However, although it is widely applied in the common practice, it remains a quick-fix solution for several reasons. First, it does not properly account for the probability of collecting contractual cash flows. Second, it leads to the wrong conclusion that the present value of financial instruments remains unchanged when fluctuations of risk-free rates are perfectly balanced by opposite changes in the relevant credit spread. Third, it makes the more risky bonds appear more profitable because credit spread, when included into discount rates, inevitably leads to calculate internal rates of return inversely correlated to the borrower’s creditworthiness. This work shows how refined cash-flow mapping techniques may be used in order to design a double-leg approach which properly accounts for expected credit losses and credit survival probabilities. This approach leads to an accurate evaluation of traded bonds on the sole basis of market information. In addition, it defines complete non-arbitrage conditions and overcomes the flaws of the most common pricing models for credit risky securities.

Does Operating Risk Affect Portfolio Risk? Evidence from Insurers' Securities Holding

This study empirically examines, in the setting of insurance companies, the hypothesis that investors facing more operating risk may behave as if they were more risk averse in investment decisions. Specifically, we study how operating risk from underwriting insurance policies affects insurers' risk taking behavior in their portfolio investments. We find that insurers with higher volatilities in underwriting incomes and cash flows are more conservative in their financial investment risk -- they have lower credit risk exposure in their bond investments, as well as lower portfolio weights on risky bonds and equities. Further, insurers' portfolio risk exposure is sensitive to the risk of permanent underwriting income shocks but insensitive to the risk of transitory shocks. Transitory operating risk, however, is significantly related to portfolio risk when insurers face tight financing constraints. Our findings suggest a substitutive effect of operating risk on investment decisions by financial institutions.

Dealer Inventory and the Linkage between Primary and Secondary Markets for Eurozone Sovereign Debt

We study the impact of sovereign bond auctions on secondary markets and their feedback to the sovereigns’ debt cost. This linkage is established through the actions of primary dealers, participating in the auctions, and also acting as market-makers. We model financially-constrained primary dealers who buy newly issued bonds and manage their inventory, while maintaining market liquidity. We find empirical support for our model and its resulting hypotheses: primary dealers tend to liquidate more liquid and more risky bonds from their inventory in order to be able to participate in sovereign bond auctions and minimize the impact of their portfolios.

Contracting on Credit Ratings: Adding Value to Public Information

Abstract We consider the role of credit ratings when contracts between investors and portfolio managers are incomplete. In our model, a credit rating and a price on a risky bond both provide verifiable signals about a non-contractible state. We allow the investor to both impose ex ante restrictions on the manager’s action and provide outcome-based compensation. The optimal contract is a prohibitive one when the rating and price indicate a high likelihood of a low state, and relies on wages when the low state is less likely. We provide some observable implications of our contracting approach to ratings. (JEL G24, D86)