The current high mortgage-rates environment has reduced mobility for the majority of agency fixed mortgage borrowers, contributing to a stalled housing market. Making mortgages portable would help relieve these borrowers, and revive the housing market and national economy. By charging borrowers a portability exercising fee, paid to MBS investors, the portability option can also enhance MBS valuation, making it a win-win for borrowers and investors. Agencies and regulators can work with MBS investors, and other stakeholders, to modify existing mortgage and MBS contracts to add the portability option to existing and future agency mortgages.
{"title":"How Making Agency Mortgage-Backed Securities Portable May Impact Housing and Mortgage-Backed Securities Investors","authors":"Jiawei “David” Zhang, Yihai Yu, Joy Zhang","doi":"10.3905/jfi.2023.1.176","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.176","url":null,"abstract":"The current high mortgage-rates environment has reduced mobility for the majority of agency fixed mortgage borrowers, contributing to a stalled housing market. Making mortgages portable would help relieve these borrowers, and revive the housing market and national economy. By charging borrowers a portability exercising fee, paid to MBS investors, the portability option can also enhance MBS valuation, making it a win-win for borrowers and investors. Agencies and regulators can work with MBS investors, and other stakeholders, to modify existing mortgage and MBS contracts to add the portability option to existing and future agency mortgages.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"12 2","pages":"114 - 119"},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139167879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Terry Benzschawel, Prahlad G. Menon, Andrew Assing
Estimates of corporate default risk have improved from early agency rating scales to regression-based models, and more recently to Merton/structural and hybrid models. Despite their increasing accuracy and timeliness, access to default models is limited by high costs and computational complexity. In this study, we use extreme gradient boosting (XGBoost) to mimic the 1-year default probabilities generated by existing hybrid structural/statistical models. The dataset consists of over 1 million monthly, model-based, 1-year probability-of-default (PD) estimates from 2010 to 2019. A decision tree model with 50 input variables, including agency rating, spread-duration, industry sector, profitability, and other financial indicators is trained on PDs from 2010 to 2013, and tested on PDs from 2014 to 2019. PDs from the XGBoost model exhibit correlations of 0.8 with both DRISK and StarMine PDs, demonstrating its potential to provide consistent, timely, and accurate estimates of changes in credit risk. When PDs from the XGBoost model are substituted for hybrid-model PDs as input to relative value trading strategies, returns are similar in magnitude and monotonic, with returns increasing with relative value deciles. This is indicative of effectiveness of the XGBoost model in estimating the risk and relative value of corporate bonds.
{"title":"Gradient Boosting Model for Corporate Default","authors":"Terry Benzschawel, Prahlad G. Menon, Andrew Assing","doi":"10.3905/jfi.2023.1.175","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.175","url":null,"abstract":"Estimates of corporate default risk have improved from early agency rating scales to regression-based models, and more recently to Merton/structural and hybrid models. Despite their increasing accuracy and timeliness, access to default models is limited by high costs and computational complexity. In this study, we use extreme gradient boosting (XGBoost) to mimic the 1-year default probabilities generated by existing hybrid structural/statistical models. The dataset consists of over 1 million monthly, model-based, 1-year probability-of-default (PD) estimates from 2010 to 2019. A decision tree model with 50 input variables, including agency rating, spread-duration, industry sector, profitability, and other financial indicators is trained on PDs from 2010 to 2013, and tested on PDs from 2014 to 2019. PDs from the XGBoost model exhibit correlations of 0.8 with both DRISK and StarMine PDs, demonstrating its potential to provide consistent, timely, and accurate estimates of changes in credit risk. When PDs from the XGBoost model are substituted for hybrid-model PDs as input to relative value trading strategies, returns are similar in magnitude and monotonic, with returns increasing with relative value deciles. This is indicative of effectiveness of the XGBoost model in estimating the risk and relative value of corporate bonds.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"14 1","pages":"64 - 74"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139248470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the growth of systematic credit investing in recent years, signals have proliferated, and it is increasingly important to process and synthesize the data effectively. We discuss several important considerations faced by investors when combining signals to form a systematic credit strategy, highlighting the trade-offs and pitfalls of different methods. Using examples based on actual credit signals, we demonstrate that careful attention to the issues raised can enhance performance significantly.
{"title":"Integrating Multiple Signals in Systematic Corporate Bond Selection Strategies","authors":"Arik Ben Dor, Stephan Florig","doi":"10.3905/jfi.2023.1.174","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.174","url":null,"abstract":"With the growth of systematic credit investing in recent years, signals have proliferated, and it is increasingly important to process and synthesize the data effectively. We discuss several important considerations faced by investors when combining signals to form a systematic credit strategy, highlighting the trade-offs and pitfalls of different methods. Using examples based on actual credit signals, we demonstrate that careful attention to the issues raised can enhance performance significantly.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"2005 8","pages":"5 - 22"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the impact of credit spreads on the stochastic duration and convexity of corporate bonds with respect to the very metrics for equivalent Treasury bonds. We show that the credit spread has two interacting effects on both the duration and convexity of a corporate coupon bond compared to those of an equivalent Treasury coupon bond. For bond convexity, we newly uncover that the first effect originates from the duration of the Treasuries; from both the duration and convexity of the coupon bonds’ conditional survival probability; and from the covariance between the default-free short rate and the credit spread. The second driver stems from the weighting of the convexities of the zero-coupon bonds. We provide necessary and sufficient conditions for the duration and convexity of defaultable corporate coupon bonds to be smaller than those of equivalent Treasury bonds. Since interest rates and credit spreads are, by and large, negatively correlated, our numerical results support the notion that not only durations but also convexities of defaultable corporate bonds may be smaller than those of equivalent Treasuries.
{"title":"Bond Duration and Convexity under Stochastic Interest Rates and Credit Spreads","authors":"Dione Ibrahima, Van Son Lai","doi":"10.3905/jfi.2023.1.173","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.173","url":null,"abstract":"We investigate the impact of credit spreads on the stochastic duration and convexity of corporate bonds with respect to the very metrics for equivalent Treasury bonds. We show that the credit spread has two interacting effects on both the duration and convexity of a corporate coupon bond compared to those of an equivalent Treasury coupon bond. For bond convexity, we newly uncover that the first effect originates from the duration of the Treasuries; from both the duration and convexity of the coupon bonds’ conditional survival probability; and from the covariance between the default-free short rate and the credit spread. The second driver stems from the weighting of the convexities of the zero-coupon bonds. We provide necessary and sufficient conditions for the duration and convexity of defaultable corporate coupon bonds to be smaller than those of equivalent Treasury bonds. Since interest rates and credit spreads are, by and large, negatively correlated, our numerical results support the notion that not only durations but also convexities of defaultable corporate bonds may be smaller than those of equivalent Treasuries.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using new corporate bonds issued by US industrial firms from 2001 to 2020, we compare the performance of S&P’s credit ratings with that of the Bloomberg Model Credit Default Swap (CDS) spread and the Bloomberg Market/Model CDS spread ratio. We find that: (1) while both credit ratings and CDS spread affect nominal yield spreads significantly, Bloomberg Model CDS spreads are timelier than credit ratings in updating credit risk information; (2) with regard to predicting actual defaults of the new bonds, both credit ratings and Bloomberg CDS spread are effective; and (3) S&P investment-grade credit ratings do not have any capability to predict defaults, while the Bloomberg CDS spread is effective in predicting defaults regardless of credit quality. We conclude that the Bloomberg Model CDS spread is a better indicator of default risk than the S&P’s credit rating.
{"title":"Is Bloomberg’s Credit Default Swaps Model Superior in Predicting Defaults?","authors":"Seung Hun Han, Karyl B. Leggio, Yoon S. Shin","doi":"10.3905/jfi.2023.1.172","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.172","url":null,"abstract":"Using new corporate bonds issued by US industrial firms from 2001 to 2020, we compare the performance of S&P’s credit ratings with that of the Bloomberg Model Credit Default Swap (CDS) spread and the Bloomberg Market/Model CDS spread ratio. We find that: (1) while both credit ratings and CDS spread affect nominal yield spreads significantly, Bloomberg Model CDS spreads are timelier than credit ratings in updating credit risk information; (2) with regard to predicting actual defaults of the new bonds, both credit ratings and Bloomberg CDS spread are effective; and (3) S&P investment-grade credit ratings do not have any capability to predict defaults, while the Bloomberg CDS spread is effective in predicting defaults regardless of credit quality. We conclude that the Bloomberg Model CDS spread is a better indicator of default risk than the S&P’s credit rating.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135967890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We illustrate why constraining turnover as a mechanism for controlling t-costs in the implementation of systematic strategies is suboptimal. We examine two alternative approaches to incorporating t-costs into the optimization process and find that they could improve strategies’ net performance.
{"title":"Incorporating Transaction Costs in Credit Portfolio Optimization: Implementation and Practical Considerations","authors":"Arik Ben Dor, Jingling Guan","doi":"10.3905/jfi.2023.1.171","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.171","url":null,"abstract":"We illustrate why constraining turnover as a mechanism for controlling t-costs in the implementation of systematic strategies is suboptimal. We examine two alternative approaches to incorporating t-costs into the optimization process and find that they could improve strategies’ net performance.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135146920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The USD LIBOR panel has ceased as of June 30, 2023, and market participants have been transitioning to the Secured Overnight Financing Rate (SOFR) as the alternative benchmark. In this article, we examine the relation between SOFR and LIBOR as well as analyze various additional benchmark rates that were considered by regulators, academics, and industry experts. We conduct statistical analysis to evaluate how well the adjusted benchmark rates have tracked 1-month LIBOR using historical data. First, we use the mean absolute error to quantify the distance between 1-month LIBOR and each benchmark rate, after adjusting for term and spread. Next, we employ a time-series analysis to assess the degree to which each benchmark co-moved with 1-month LIBOR. We find that although benchmark rates, including SOFR, have generally tracked 1-month LIBOR rates well in the long run, the relation weakens in times of market dislocation, such as during the 2007–2009 global financial crisis and the 2020 COVID-19 pandemic.
{"title":"How Do Alternatives to LIBOR Measure Up?","authors":"Faten Sabry, Frank J. Fabozzi, Ramisa Roya","doi":"10.3905/jfi.2023.1.170","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.170","url":null,"abstract":"The USD LIBOR panel has ceased as of June 30, 2023, and market participants have been transitioning to the Secured Overnight Financing Rate (SOFR) as the alternative benchmark. In this article, we examine the relation between SOFR and LIBOR as well as analyze various additional benchmark rates that were considered by regulators, academics, and industry experts. We conduct statistical analysis to evaluate how well the adjusted benchmark rates have tracked 1-month LIBOR using historical data. First, we use the mean absolute error to quantify the distance between 1-month LIBOR and each benchmark rate, after adjusting for term and spread. Next, we employ a time-series analysis to assess the degree to which each benchmark co-moved with 1-month LIBOR. We find that although benchmark rates, including SOFR, have generally tracked 1-month LIBOR rates well in the long run, the relation weakens in times of market dislocation, such as during the 2007–2009 global financial crisis and the 2020 COVID-19 pandemic.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135481031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study develops and evaluates a model that generates synthetic credit ratings using accounting and market-based information. The model performs well in explaining agency ratings, suggesting that fitted values for unrated companies are likely to be reasonably precise. Moreover, the synthetic ratings explain cross sectional differences in credit default swap (CDS) spreads, even after controlling for contemporaneous agency ratings. Compared with synthetic ratings, agency ratings explain a greater proportion of the variation in CDS spreads, but their differential informativeness is relatively small and has declined substantially over the past decade. This decline is possibly due to post-crisis Securities and Exchange Commission regulation that limits rating agencies’ ability to obtain confidential information from rated companies. Consistent with the finding that agency ratings do not fully impound the information in synthetic ratings, the difference between synthetic and agency ratings predicts changes in agency ratings in subsequent months, especially for small companies. There is no evidence of substantial improvement over the past 4 decades in the timeliness of agency ratings with respect to the information in synthetic ratings. Investors in large companies appear to process the synthetic rating information in a timely fashion, as the difference between synthetic and agency ratings does not predict changes in CDS spreads or in the stock prices of these companies. For small companies, however, there is some predictability.
{"title":"Synthetic Credit Ratings and the Inefficiency of Agency Ratings","authors":"Nissim Doron","doi":"10.3905/jfi.2023.1.169","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.169","url":null,"abstract":"This study develops and evaluates a model that generates synthetic credit ratings using accounting and market-based information. The model performs well in explaining agency ratings, suggesting that fitted values for unrated companies are likely to be reasonably precise. Moreover, the synthetic ratings explain cross sectional differences in credit default swap (CDS) spreads, even after controlling for contemporaneous agency ratings. Compared with synthetic ratings, agency ratings explain a greater proportion of the variation in CDS spreads, but their differential informativeness is relatively small and has declined substantially over the past decade. This decline is possibly due to post-crisis Securities and Exchange Commission regulation that limits rating agencies’ ability to obtain confidential information from rated companies. Consistent with the finding that agency ratings do not fully impound the information in synthetic ratings, the difference between synthetic and agency ratings predicts changes in agency ratings in subsequent months, especially for small companies. There is no evidence of substantial improvement over the past 4 decades in the timeliness of agency ratings with respect to the information in synthetic ratings. Investors in large companies appear to process the synthetic rating information in a timely fashion, as the difference between synthetic and agency ratings does not predict changes in CDS spreads or in the stock prices of these companies. For small companies, however, there is some predictability.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135645545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-30DOI: 10.3905/jfi.2023.33.2.001
Stanley J. Kon
{"title":"Editor’s Letter","authors":"Stanley J. Kon","doi":"10.3905/jfi.2023.33.2.001","DOIUrl":"https://doi.org/10.3905/jfi.2023.33.2.001","url":null,"abstract":"","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136343405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multiple-liability immunization strategies require that three conditions are satisfied. These conditions are based on the value, duration, and dispersion of the cash flow stream. The validity of immunization strategies depends on assumptions about how the term structure changes over time. Given that actual term structure changes may violate these assumptions, the performance of these strategies is an empirical question. Using historical weekly changes in the spot rate curve over a 32-year period applied to a large number of simulated portfolios, this study backtests the performance of multiple-liability immunization strategies. The author finds that the dispersion condition, in various forms, does not improve the performance of duration-matched portfolios. Statistical tests of portfolio performance do not depend on whether the dispersion condition is satisfied. Further, duration is a fairly good measure of interest rate risk. Only one duration-targeted portfolio out of 50,000 has a statistically significant historical median return at the 10 percent level.
{"title":"Empirical Test of Multiple-Liability Immunization Conditions","authors":"Joel R. Barber","doi":"10.3905/jfi.2023.1.168","DOIUrl":"https://doi.org/10.3905/jfi.2023.1.168","url":null,"abstract":"Multiple-liability immunization strategies require that three conditions are satisfied. These conditions are based on the value, duration, and dispersion of the cash flow stream. The validity of immunization strategies depends on assumptions about how the term structure changes over time. Given that actual term structure changes may violate these assumptions, the performance of these strategies is an empirical question. Using historical weekly changes in the spot rate curve over a 32-year period applied to a large number of simulated portfolios, this study backtests the performance of multiple-liability immunization strategies. The author finds that the dispersion condition, in various forms, does not improve the performance of duration-matched portfolios. Statistical tests of portfolio performance do not depend on whether the dispersion condition is satisfied. Further, duration is a fairly good measure of interest rate risk. Only one duration-targeted portfolio out of 50,000 has a statistically significant historical median return at the 10 percent level.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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