Using data on all municipal “green” bonds issued from 2010 to 2021, the authors examine whether municipal bonds issued to finance green power projects command a premium (lower yield to maturity) relative to other similar-risk municipal green bonds. Consistent with the idea that green projects are easier to identify and certify in the setting of power generation, the authors find that green power bonds command a premium of 11 basis points relative to other similar-risk municipal green bonds. Those results support a nonpecuniary utility for such easily identifiable green investments. The authors illustrate the incremental value added from this reduction in the cost of capital and a gradual move from fossil fuel to green power generation for one of the largest municipal utilities in the United States.
{"title":"Are There Different Shades of Green? The “Greenium” in Municipal Power Bonds","authors":"Karan Bhanot, C. Combs, Raj Patel","doi":"10.3905/jfi.2022.1.129","DOIUrl":"https://doi.org/10.3905/jfi.2022.1.129","url":null,"abstract":"Using data on all municipal “green” bonds issued from 2010 to 2021, the authors examine whether municipal bonds issued to finance green power projects command a premium (lower yield to maturity) relative to other similar-risk municipal green bonds. Consistent with the idea that green projects are easier to identify and certify in the setting of power generation, the authors find that green power bonds command a premium of 11 basis points relative to other similar-risk municipal green bonds. Those results support a nonpecuniary utility for such easily identifiable green investments. The authors illustrate the incremental value added from this reduction in the cost of capital and a gradual move from fossil fuel to green power generation for one of the largest municipal utilities in the United States.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"84 - 99"},"PeriodicalIF":0.0,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42365294","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 article analyzes the out-of-sample performance of portfolio optimization models in the US corporate bond universe. In our empirical study, we measure the benefits of naive diversification and find that it eventually reaches a limit as the number of bonds increases. Also, we observe substantial improvements in the risk-adjusted performance of scientific portfolio constructions when compared to simple barbell strategies for the same given duration. When duration constraints are relaxed, we find that both naively and scientifically diversified portfolios outperform cap-weighted benchmarks in terms of Sharpe ratio.
{"title":"An Empirical Analysis of the Benefits of Corporate Bond Portfolio Optimization in the Presence of Duration Constraints","authors":"Romain Deguest, L. Martellini, Vincent Milhau","doi":"10.3905/jfi.2022.1.128","DOIUrl":"https://doi.org/10.3905/jfi.2022.1.128","url":null,"abstract":"This article analyzes the out-of-sample performance of portfolio optimization models in the US corporate bond universe. In our empirical study, we measure the benefits of naive diversification and find that it eventually reaches a limit as the number of bonds increases. Also, we observe substantial improvements in the risk-adjusted performance of scientific portfolio constructions when compared to simple barbell strategies for the same given duration. When duration constraints are relaxed, we find that both naively and scientifically diversified portfolios outperform cap-weighted benchmarks in terms of Sharpe ratio.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"50 - 82"},"PeriodicalIF":0.0,"publicationDate":"2022-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47944496","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 : 2021-12-31DOI: 10.3905/jfi.2021.31.3.001
Stanley J. Kon
{"title":"Editor’s Letter","authors":"Stanley J. Kon","doi":"10.3905/jfi.2021.31.3.001","DOIUrl":"https://doi.org/10.3905/jfi.2021.31.3.001","url":null,"abstract":"","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44166351","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}
In this article, we undertake a systematic, security-level analysis of momentum and reversal strategies in US Treasuries covering more than 40 years of data. We distinguish between what we call “market” and “self” time-series momentum (reversal) strategies and present an exact identity between these two time-series and the cross-sectional momentum (reversal) strategies. This identity helps us identify the sources of profitability of the various strategies and raises an interesting question regarding the contribution to the profitability of the first and second principal components of yield changes. We find that there exist look-back and investment periods for which momentum time series strategies (both “self” and “market”) give rise to statistically and economically significant positive Sharpe ratios; but we find that after adjusting for duration, the reversal cross-sectional strategy has an even larger Sharpe ratio and is profitable over a wider range of look-back and investment periods. We find an explanation for this finding in the mean-reverting properties of the yield-curve slope. Finally, we discover that the duration-adjusted reversal cross-sectional strategy can be successfully implemented in a long-only fashion.
{"title":"Cross-Sectional and Time-Series Momentum in the US Sovereign Bond Market","authors":"L. Martellini, R. Rebonato, J. Maeso","doi":"10.3905/jfi.2021.1.127","DOIUrl":"https://doi.org/10.3905/jfi.2021.1.127","url":null,"abstract":"In this article, we undertake a systematic, security-level analysis of momentum and reversal strategies in US Treasuries covering more than 40 years of data. We distinguish between what we call “market” and “self” time-series momentum (reversal) strategies and present an exact identity between these two time-series and the cross-sectional momentum (reversal) strategies. This identity helps us identify the sources of profitability of the various strategies and raises an interesting question regarding the contribution to the profitability of the first and second principal components of yield changes. We find that there exist look-back and investment periods for which momentum time series strategies (both “self” and “market”) give rise to statistically and economically significant positive Sharpe ratios; but we find that after adjusting for duration, the reversal cross-sectional strategy has an even larger Sharpe ratio and is profitable over a wider range of look-back and investment periods. We find an explanation for this finding in the mean-reverting properties of the yield-curve slope. Finally, we discover that the duration-adjusted reversal cross-sectional strategy can be successfully implemented in a long-only fashion.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"20 - 40"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41468886","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}
Inability to accurately project transaction cost is one of the main drags on alpha and performance for bond investors. We introduce a framework for bond trade cost analysis that reflects bond characteristics as well as order information. This framework leverages historical and real-time data to deliver solid explanatory power. The authors goal is to help buy-side traders and dealers to build liquidity trees, while assisting portfolio managers to make investment decisions that include trade costs. We lean on 20 years of experience modeling transaction cost for equities, as well as intimate knowledge of the bond market microstructure. Our work covers investment grade and high yield corporate bonds, issued in USD, EUR, and GBP, as well as government bonds in developed and emerging markets globally.
{"title":"Occam’s Razor for Bond Trade Costs","authors":"Vlad Rashkovich, A. Iogansen","doi":"10.3905/jfi.2021.1.125","DOIUrl":"https://doi.org/10.3905/jfi.2021.1.125","url":null,"abstract":"Inability to accurately project transaction cost is one of the main drags on alpha and performance for bond investors. We introduce a framework for bond trade cost analysis that reflects bond characteristics as well as order information. This framework leverages historical and real-time data to deliver solid explanatory power. The authors goal is to help buy-side traders and dealers to build liquidity trees, while assisting portfolio managers to make investment decisions that include trade costs. We lean on 20 years of experience modeling transaction cost for equities, as well as intimate knowledge of the bond market microstructure. Our work covers investment grade and high yield corporate bonds, issued in USD, EUR, and GBP, as well as government bonds in developed and emerging markets globally.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"79 - 92"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42296181","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}
S. Cohen, Stephen Laipply, Ananth Madhavan, James Mauro
The authors provide empirical evidence on the functioning of the primary market for iShares fixed income exchange-traded funds (ETFs) during the Covid-19 crisis, the first analysis of custom redemption baskets in the growing literature on ETFs. The authors show that the primary market process worked as expected despite the high level of market stress. Contrary to recent suggestions that asset managers actively discouraged redemptions in stressed markets by offering less desirable bonds, the authors demonstrate that iShares redemption baskets during the crisis were reflective of the factor characteristics of the fund itself.
{"title":"The Primary Market Process for Fixed Income Exchange-Traded Funds Under Market Stress","authors":"S. Cohen, Stephen Laipply, Ananth Madhavan, James Mauro","doi":"10.3905/jfi.2021.1.126","DOIUrl":"https://doi.org/10.3905/jfi.2021.1.126","url":null,"abstract":"The authors provide empirical evidence on the functioning of the primary market for iShares fixed income exchange-traded funds (ETFs) during the Covid-19 crisis, the first analysis of custom redemption baskets in the growing literature on ETFs. The authors show that the primary market process worked as expected despite the high level of market stress. Contrary to recent suggestions that asset managers actively discouraged redemptions in stressed markets by offering less desirable bonds, the authors demonstrate that iShares redemption baskets during the crisis were reflective of the factor characteristics of the fund itself.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"66 - 78"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46342200","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 a Bayesian generalized linear mixed model (GLMM), we analyze Eurozone sovereign real-world default probabilities and correlations, and compare regulatory and economic capital requirements. The approach combines prior information and sparse sovereign historical default data. One main finding is that capital under the Basel internal ratings based approach (IRBA) is higher than under the standardized approach (SA) by a factor of 2.06 to 8.86, depending on the method for estimating the probability of default. This divergence is driven mainly by zero capital charges for highly rated securities under the SA. Furthermore, under the Bayesian model, Basel IRBA capital is roughly equivalent to economic capital using the expected shortfall at a 99% confidence level. The results suggest that the zero risk weights under the SA are not consistent with economic risk and offer opportunities for regulatory arbitrage.
{"title":"Eurozone Sovereign Default Risk and Capital: A Bayesian Approach","authors":"Rainer Jobst, Daniel Rösch","doi":"10.3905/jfi.2021.1.124","DOIUrl":"https://doi.org/10.3905/jfi.2021.1.124","url":null,"abstract":"Using a Bayesian generalized linear mixed model (GLMM), we analyze Eurozone sovereign real-world default probabilities and correlations, and compare regulatory and economic capital requirements. The approach combines prior information and sparse sovereign historical default data. One main finding is that capital under the Basel internal ratings based approach (IRBA) is higher than under the standardized approach (SA) by a factor of 2.06 to 8.86, depending on the method for estimating the probability of default. This divergence is driven mainly by zero capital charges for highly rated securities under the SA. Furthermore, under the Bayesian model, Basel IRBA capital is roughly equivalent to economic capital using the expected shortfall at a 99% confidence level. The results suggest that the zero risk weights under the SA are not consistent with economic risk and offer opportunities for regulatory arbitrage.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"41 - 65"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43613684","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}
Key to the valuation of agency residential mortgage-backed securities (MBSs) is the modeling of voluntary prepayment and default behaviors of the underlying borrowers in the mortgage pool. The proliferation of pool- and loan-level data coupled with access to advanced machine learning algorithms has opened the door to the application of machine learning to mortgage prepayment modeling. The modular prepayment model, one that relies on defined functions to predict mortgage prepayment, has dominated the MBS market nearly since its inception. However, machine learning models are beginning to make inroads and, in some cases, are replacing traditional modular prepayment models. The modular and machine learning model differ in the following ways: In the case of modular prepayment models, either added or multiplicative, the modeler defines the functional form of each feature as well as the “tuning” of the parameters passed to each. Machine learning or “second generation” mortgage prepayment models differ in the sense that the modeler “tunes” the hyperparameters that determine the bias variance tradeoff while the machine determines the functional form of each feature of the model. In this article, the authors propose a machine learning mortgage prepayment model using a boosted gradient classifier, trained at the loan level and generalized to the pool level. A gradient boosted classifier is a tree-based model using an ensemble of weak learners to create a strong committee for prediction.
{"title":"Rise of the Machines: Application of Machine Learning to Mortgage Prepayment Modeling","authors":"Glenn M. Schultz, F. Fabozzi","doi":"10.3905/jfi.2021.1.123","DOIUrl":"https://doi.org/10.3905/jfi.2021.1.123","url":null,"abstract":"Key to the valuation of agency residential mortgage-backed securities (MBSs) is the modeling of voluntary prepayment and default behaviors of the underlying borrowers in the mortgage pool. The proliferation of pool- and loan-level data coupled with access to advanced machine learning algorithms has opened the door to the application of machine learning to mortgage prepayment modeling. The modular prepayment model, one that relies on defined functions to predict mortgage prepayment, has dominated the MBS market nearly since its inception. However, machine learning models are beginning to make inroads and, in some cases, are replacing traditional modular prepayment models. The modular and machine learning model differ in the following ways: In the case of modular prepayment models, either added or multiplicative, the modeler defines the functional form of each feature as well as the “tuning” of the parameters passed to each. Machine learning or “second generation” mortgage prepayment models differ in the sense that the modeler “tunes” the hyperparameters that determine the bias variance tradeoff while the machine determines the functional form of each feature of the model. In this article, the authors propose a machine learning mortgage prepayment model using a boosted gradient classifier, trained at the loan level and generalized to the pool level. A gradient boosted classifier is a tree-based model using an ensemble of weak learners to create a strong committee for prediction.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"6 - 19"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47897364","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 propose a modification of the classical Black–Derman–Toy (BDT) interest rate tree model, which includes the possibility of a jump with a small probability at each step to a practically zero interest rate. The corresponding BDT algorithms are consequently modified to calibrate the tree containing zero interest rate scenarios. This modification is motivated by the recent 2007–2008 crisis in the United States, and it quantifies the risk of future crises in bond prices and derivatives. The proposed model can be useful to price derivatives. A comparison of option prices and implied volatilities on US Treasury bonds computed with both the proposed and the classical tree model is provided in six different scenarios along the different periods comprising the years 2002–2017.
{"title":"Zero Black–Derman–Toy Interest Rate Model","authors":"G. Krzyzanowski, E. Mordecki, Andr'es Sosa","doi":"10.3905/jfi.2021.1.122","DOIUrl":"https://doi.org/10.3905/jfi.2021.1.122","url":null,"abstract":"We propose a modification of the classical Black–Derman–Toy (BDT) interest rate tree model, which includes the possibility of a jump with a small probability at each step to a practically zero interest rate. The corresponding BDT algorithms are consequently modified to calibrate the tree containing zero interest rate scenarios. This modification is motivated by the recent 2007–2008 crisis in the United States, and it quantifies the risk of future crises in bond prices and derivatives. The proposed model can be useful to price derivatives. A comparison of option prices and implied volatilities on US Treasury bonds computed with both the proposed and the classical tree model is provided in six different scenarios along the different periods comprising the years 2002–2017.","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":"31 1","pages":"93 - 111"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49375656","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 : 2021-09-30DOI: 10.3905/jfi.2021.31.2.001
Stanley J. Kon
{"title":"Editor’s Letter","authors":"Stanley J. Kon","doi":"10.3905/jfi.2021.31.2.001","DOIUrl":"https://doi.org/10.3905/jfi.2021.31.2.001","url":null,"abstract":"","PeriodicalId":53711,"journal":{"name":"Journal of Fixed Income","volume":" ","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46351394","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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