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Relevant bibliographies by topics / Interest rate risk

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Academic literature on the topic 'Interest rate risk'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "Interest rate risk"

1

Pepic, Marina. "Managing interest rate risk with interest rate futures." Ekonomika preduzeca 62, no. 3-4 (2014): 201–9. http://dx.doi.org/10.5937/ekopre1404201p.

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C. Prabhavathi, C. Prabhavathi. "Impact of Interest Rate Risk In Banking System." Indian Journal of Applied Research 3, no. 6 (2011): 314–16. http://dx.doi.org/10.15373/2249555x/june2013/105.

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Ang, Andrew, and Michael Sherris. "Interest Rate Risk Management." North American Actuarial Journal 1, no. 2 (1997): 1–26. http://dx.doi.org/10.1080/10920277.1997.10595601.

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Sabovic, Serif. "Management of interest rate risk." Ekonomski signali 9, no. 1 (2014): 35–53. http://dx.doi.org/10.5937/ekonsig1401035s.

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Hoffmann, Peter, Sam Langfield, Federico Pierobon, and Guillaume Vuillemey. "Who Bears Interest Rate Risk?" Review of Financial Studies 32, no. 8 (2018): 2921–54. http://dx.doi.org/10.1093/rfs/hhy113.

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Abstract We study the allocation of interest rate risk within the European banking sector using novel data. Banks’ exposure to interest rate risk is small on aggregate, but heterogeneous in the cross-section. Contrary to conventional wisdom, net worth is increasing in interest rates for approximately half of the institutions in our sample. Cross-sectional variation in banks’ exposures is driven by cross-country differences in loan-rate fixation conventions for mortgages. Banks use derivatives to partially hedge on-balance-sheet exposures. Residual exposures imply that changes in interest rates have redistributive effects within the banking sector. Received October 31, 2017; editorial decision August 30, 2018 by Editor Philip Strahan. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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Van Hemert, Otto. "Household Interest Rate Risk Management." Real Estate Economics 38, no. 3 (2010): 467–505. http://dx.doi.org/10.1111/j.1540-6229.2010.00274.x.

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Ho, Thomas S. Y. "Managing Interest Rate Volatility Risk." Journal of Fixed Income 17, no. 3 (2007): 6–17. http://dx.doi.org/10.3905/jfi.2007.700216.

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Ehrhardt, Michael C. "Diversification and Interest Rate Risk." Journal of Business Finance & Accounting 18, no. 1 (1991): 43–59. http://dx.doi.org/10.1111/j.1468-5957.1991.tb00578.x.

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Almeida, Caio, and José Vicente. "Are interest rate options important for the assessment of interest rate risk?" Journal of Banking & Finance 33, no. 8 (2009): 1376–87. http://dx.doi.org/10.1016/j.jbankfin.2009.02.003.

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Bierwag, Gerald O. "Duration and Interest Rate Risk for a Binomial Interest Rate Stochastic Process." Canadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l'Administration 17, no. 2 (2009): 115–25. http://dx.doi.org/10.1111/j.1936-4490.2000.tb00213.x.

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Dissertations / Theses on the topic "Interest rate risk"

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Jackson, Alexander. "Interest rate and credit risk modelling." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400043.

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Zagonov, Maxim. "Financial intermediation and interest rate risk." Thesis, City University London, 2011. http://openaccess.city.ac.uk/1189/.

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This thesis analyses the link between interest rate risk faced by financial intermediaries in the G-10 countries, their balance sheet composition and national bank regulation. The regulatory authorities both in the US and in Europe increasingly emphasise the issue of bank interest rate exposure. The importance of this topic is also reasserted by recent developments in the monetary environment. The thesis offers three major contributions to the area. First, it empirically investigates the interest rate risk exposure of financial intermediaries across a large international data sample over the 1997 to 2009 time period. The results verify the importance of interest rate exposure for the majority of analysed institutions, with statistical inferences being robust to the choice of interest rate proxy, time period, and the adopted econometric methodology. Second, this research examines the underlying determinants of bank interest rate risk. Both company and market specific information is considered in the analysis. The findings suggest that national regulatory and supervisory characteristics, and notably international diversity among these provisions, are as important as firm-level accounting variables in explaining the interest rate exposures of individual banks. Finally, this work empirically addresses the impact of securitization on bank interest rate risk. In particular, the research questions whether securitization is conducive to the optimal hedging of bank interest rate risk, or is merely a funding source enabling these companies to pursue more profitable but riskier projects. The reported results imply that banks resorting to asset securitization do not, on average, achieve an unambiguous reduction in their exposure to the term structure developments.

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Kladívko, Kamil. "Interest Rate Modeling." Doctoral thesis, Vysoká škola ekonomická v Praze, 2005. http://www.nusl.cz/ntk/nusl-96400.

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I study, develop and implement selected interest rate models. I begin with a simple categorization of interest rate models and with an explanation why interest rate models are useful. I explain and discuss the notion of arbitrage. I use Oldrich Vasicek's seminal model (Vasicek; 1977) to develop the idea of no-arbitrage term structure modeling. I introduce both the partial di erential equation and the risk-neutral approach to zero-coupon bond pricing. I briefly comment on affine term structure models, a general equilibrium term structure model, and HJM framework. I present the Czech Treasury yield curve estimates at a daily frequency from 1999 to the present. I use the parsimonious Nelson-Siegel model (Nelson and Siegel; 1987), for which I suggest a parameter restriction that avoids abrupt changes in parameter estimates and thus allows for the economic interpretation of the model to hold. The Nelson-Siegel model is shown to fit the Czech bond price data well without being over-parameterized. Thus, the model provides an accurate and consistent picture of the Czech Treasury yield curve evolution. The estimated parameters can be used to calculate spot rates and hence par rates, forward rates or discount function for practically any maturity. To my knowledge, consistent time series of spot rates are not available for the Czech economy. I introduce two estimation techniques of the short-rate process. I begin with the maximum likelihood estimator of a square root diff usion. A square root di usion serves as the short rate process in the famous CIR model (Cox, Ingersoll and Ross; 1985b). I develop and analyze two Matlab implementations of the estimation routine and test them on a three-month PRIBOR time series. A square root diff usion is a restricted version of, so called, CKLS di ffusion (Chan, Karolyi, Longsta and Sanders; 1992). I use the CKLS short-rate process to introduce the General Method of Moments as the second estimation technique. I discuss the numerical implementation of this method. I show the importance of the estimator of the GMM weighting matrix and question the famous empirical result about the volatility speci cation of the short-rate process. Finally, I develop a novel yield curve model, which is based on principal component analysis and nonlinear stochastic di erential equations. The model, which is not a no-arbitrage model, can be used in areas, where quantification of interest rate dynamics is needed. Examples, of such areas, are interest rate risk management, or the pro tability and risk evaluation of interest rate contingent claims, or di erent investment strategies. The model is validated by Monte Carlo simulations.

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Iqbal, Adam Saeed. "Dynamic interest rate and credit risk models." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/6851.

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This thesis studies the pricing of Treasury bonds, the pricing of corporate bonds and the modelling of portfolios of defaultable debt. By drawing on the related literature, Chapter 1 provides economic background and motivation for the study of each of these topics. Chapter 2 studies the use of Gaussian affine dynamic term structure models (GDTSMs) for forming forecasts of Treasury yields and conditional decompositions of the yield curve into expectation and risk premium components. Specifically, it proposes market prices of risk that can generate bond price time series that are consistent with the important empirical result of Cochrane and Piazzesi (2005), that a linear combination of forward rates can forecast excess returns to bonds. Since the GDTSM here falls into the essentially affine class (Duffee (2002)), it is analytically tractable. Chapter 3 studies conditional risk premia in a commonly applied default intensity based model for pricing corporate bonds. Here, I refer to such models as completely affine defaultable dynamic term structure models (DDTSMs). There are two main contributions. First, I show that completely affine DDTSMs imply that the compensation for the risk associated with shocks to default intensities (the credit spread risk premium) is related to the volatility of default intensities. Second, I run regressions to show that this relationship holds in a set of corporate bond data. Finally, Chapter 4 proposes a new dynamic model for default rates in large debt port- folios. The model is similar in principle to Duffie, Saita, and Wang (2007) and Duffie, Eckner, Horel, and Saita (2009) in that the default intensity depends on the observed macroeconomic state and unobserved frailty variables. However, the model is designed for use with more commonly available aggregate, rather than individual, default data. Fitting the model to aggregate charge-off rates in US corporate, real-estate and non- mortgage retail sectors, it is found that interest rates, industrial production and unemployment rates have quantitatively plausible effects on aggregate default rates.

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Dozzi, Anna <1993&gt. "Prosper: Interest Rate and Credit Risk Analysis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/14422.

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The technological development of recent years has allowed a revolution in the financial sector with the introduction of new financing methods including Peer to Peer Lending. In this study it will be discussed the business model of Prosper, which is a Peer to Peer Lending platform that aims to facilitate the connection between borrowers and creditors by implementing the disintermediation process. To get a general idea of the theme that will be discussed later, the most discussed topics in recent literature regarding P2P platforms have been reported. In particular, it will be analysed the topics of the relationship with the banks, the importance of the information related to the users of the platform, the credit risk and the interest rate. Subsequently, the Prosper database will be studied in order to understand the real advantages of this platform. In particular, from the data provided by Prosper will be studied the various information concerning the borrowers and creditors. Then, the loans provided by the platform will be examined, and the risk adjusted interest rate will be calculated from their interest rate and credit risk. The objective of this document is to determine the adequacy of the interest rates proposed by Prosper and to establish the variables that influence them.

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Chui, Hiu-fai Sam. "Evaluation of measures taken by financial institutes under the interest rate swing caused by the currency attack /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19882117.

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Berg, Simon, and Victor Elfström. "IRRBB in a Low Interest Rate Environment." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273589.

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Financial institutions are exposed to several different types of risk. One of the risks that can have a significant impact is the interest rate risk in the bank book (IRRBB). In 2018, the European Banking Authority (EBA) released a regulation on IRRBB to ensure that institutions make adequate risk calculations. This article proposes an IRRBB model that follows EBA's regulations. Among other things, this framework contains a deterministic stress test of the risk-free yield curve, in addition to this, two different types of stochastic stress tests of the yield curve were made. The results show that the deterministic stress tests give the highest risk, but that the outcomes are considered less likely to occur compared to the outcomes generated by the stochastic models. It is also demonstrated that EBA's proposal for a stress model could be better adapted to the low interest rate environment that we experience now. Furthermore, a discussion is held on the need for a more standardized framework to clarify, both for the institutions themselves and the supervisory authorities, the risks that institutes are exposed to.<br>Finansiella institutioner är exponerade mot flera olika typer av risker. En av de risker som kan ha en stor påverkan är ränterisk i bankboken (IRRBB). 2018 släppte European Banking Authority (EBA) ett regelverk gällande IRRBB som ska se till att institutioner gör tillräckliga riskberäkningar. Detta papper föreslår en IRRBB modell som följer EBAs regelverk. Detta regelverk innehåller bland annat ett deterministiskt stresstest av den riskfria avkastningskurvan, utöver detta så gjordes två olika typer av stokastiska stresstest av avkastningskurvan. Resultatet visar att de deterministiska stresstesten ger högst riskutslag men att utfallen anses vara mindre sannolika att inträffa jämfört med utfallen som de stokastiska modellera genererade. Det påvisas även att EBAs förslag på stressmodell skulle kunna anpassas bättre mot den lågräntemiljö som vi för tillfället befinner oss i. Vidare förs en diskussion gällande ett behov av ett mer standardiserat ramverk för att tydliggöra, både för institutioner själva och samt övervakande myndigheter, vilka risker institutioner utsätts för.

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Hegre, Håvard. "Interest rate modeling with applications to counterparty risk." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9470.

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<p>This thesis studies the estimation of credit exposure arising from a portfolio of interest rate derivatives. The estimation is performed using a Monte Carlo simulation. The results are compared to the exposure obtained under the current exposure method provided by the Bank for International Settlements (BIS). We show that the simulation method provides a much richer set of information for credit risk managers. Also, depending on the current exposure and the nature of the transactions, the BIS method can fail to account for potential exposure. All test portfolios benefit significantly from a netting agreement, but the BIS approach tends to overestimate the risk reduction due to netting. In addition we examine the impact of antithetic variates and different time-discretizations. We find that a discretization based on derivatives' start and maturity dates may reduce simulation time significantly without loosing generality in exposure profiles. Antithetic variates have a small effect.</p>

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Nguyen, Hai Nam. "Contributions to credit risk and interest rate modeling." Thesis, Evry-Val d'Essonne, 2014. http://www.theses.fr/2013EVRY0038.

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Cette thèse traite de plusieurs sujets en mathématiques financières: risque de crédit, optimisation de portefeuille et modélisation des taux d’intérêts. Le chapitre 1 consiste en trois études dans le domaine du risque de crédit. La plus innovante est la première dans laquel nous construisons un modèle tel que la propriété d’immersion n’est vérifiée sous aucune mesure martingale équivalente. Le chapitre 2 étudie le problème de maximisation de la somme d’une utilité de la richesse terminale et d’une utilité de la consommation. Le chapitre 3 étudie l’évaluation des produits dérivés de taux d’intérêt dans un cadre multicourbe, qui prend en compte la différence entre une courbe de taux sans risque et des courbes de taux Libor de différents tenors<br>This thesis deals with several topics in mathematical finance: credit risk, portfolio optimization and interest rate modeling. Chapter 1 consists of three studies in the field of credit risk. The most innovative is the first one, where we construct a model such that the immersion property does not hold under any equivalent martingale measure. Chapter 2 studies the problem of maximization of the sum of the utility of the terminal wealth and the utility of the consumption, in a case where a sudden jump in the risk-free interest rate induces market incompleteness. Chapter 3 studies the valuation of Libor interest rate derivatives in a multiple-curve setup, which accounts for the spreads between a risk-free discount curve and Libor curves of different tenors

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Klaassen, Pieter. "Stochastic programming models for interest-rate risk management." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/11913.

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Books on the topic "Interest rate risk"

1

Managing interest rate risk. Woodhead-Faulkner, 1987.

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Damian, Kissane, ed. Interest rate risk management. Eurostudy, 1988.

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Matz, Leonard M. Interest rate risk management. Sheshunoff, 2006.

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Nawalkha, Sanjay K. Interest Rate Risk Modeling. John Wiley & Sons, Ltd., 2005.

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M, Gardener Edward P., and University College of North Wales. Institute of European Finance., eds. Interest rate risk and banks. Institute of European Finance, University College of North Wales, 1987.

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M, Gardener Edward P., and University College of North Wales. Institute of European Finance., eds. Interest rate risk and banks. Institute of European Finance, University College of North Wales, 1987.

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Boris, Antl, ed. Management of interest rate risk. Euromoney Publications, 1990.

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Boris, Antl, ed. Management of interest rate risk. Euromoney, 1988.

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Hanweck, Gerald A. Interest rate volatility: Understanding, analyzing, and managing interest rate risk and risk-based capital. Irwin Professional Pub., 1996.

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Myers, Cliff. Interest rate risk policy and control. Sendero Corp., 1989.

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Book chapters on the topic "Interest rate risk"

1

García, Francisco Javier Población. "Interest Rate Risk." In Financial Risk Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41366-2_5.

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Willsher, Richard. "Interest Rate Risk." In Export Finance. Palgrave Macmillan UK, 1995. http://dx.doi.org/10.1007/978-1-349-13980-4_17.

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Bilan, Andrada, Hans Degryse, Kuchulain O’Flynn, and Steven Ongena. "Interest Rate Risk." In Banking and Financial Markets. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26844-2_3.

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Bingham, Nicholas H., and Rüdiger Kiesel. "Interest Rate Theory." In Risk-Neutral Valuation. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3619-4_8.

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Bingham, Nicholas H., and Rüdiger Kiesel. "Interest Rate Theory." In Risk-Neutral Valuation. Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3856-3_8.

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Zagst, Rudi. "Risk Measures." In Interest-Rate Management. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-12106-1_6.

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Zagst, Rudi. "Risk Management." In Interest-Rate Management. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-12106-1_7.

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Chen, Lin. "Managing Interest Rate Risk." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-46825-4_7.

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Bascom, Wilbert O. "Managing Interest Rate Risk." In The Economics of Financial Reform in Developing Countries. Palgrave Macmillan UK, 1994. http://dx.doi.org/10.1007/978-1-349-23372-4_9.

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Sherris, Michael. "Interest rate risk analysis." In Money and Capital Markets, 2nd ed. Routledge, 2023. http://dx.doi.org/10.4324/9781003416517-5.

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Conference papers on the topic "Interest rate risk"

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Zhou, Yun, and Xiaosong Zheng. "A Study of Commercial Banks Interest Rate Risk Management under Interest Rates Liberalization." In International Conference on Transformations and Innovations in Management (ictim-17). Atlantis Press, 2017. http://dx.doi.org/10.2991/ictim-17.2017.70.

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Cui, Wei, Min Dai, Steven Kou, Yaquan Zhang, Chengxi Zhang, and Xianhao Zhu. "Interest Rate Swap Valuation in the Chinese Market." In Innovations in Insurance, Risk- and Asset Management. WORLD SCIENTIFIC, 2018. http://dx.doi.org/10.1142/9789813272569_0013.

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Stádník, Bohumil. "IMPROVING THE QUANTIFICATION OF INTEREST RATE RISK." In 12th International Scientific Conference „Business and Management 2022“. Vilnius Gediminas Technical University, 2022. http://dx.doi.org/10.3846/bm.2022.762.

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The value of Macaulay duration, probably the most widely used quantification method for measuring interest rate sensitivity of bonds, could roughly be financially interpreted as a percentage change of the bond price if the paral-lel shift of the interest rate equals 1 percentage point along the entire zero-coupon curve and the initial bond price is equal to 100%. The main problem of its practical application lies in the fact that parallel curve shift is a very rare case, and we are more often concerned with predicting short-term rate shifts and considering their consequences for the rest of the yield curve and thus also for bonds with longer maturities. Therefore, it is useful to find a certain value that represents a quantification of the impact of short rate shifts on bond prices with respect to the parameters of bonds. So, the main contribution of this financial engineering research is to design a measure that can be used in the same way as Macaulay duration, but as a response to the change of the short interest rate, for example: in the equation for chang-ing ΔP of a bond, in the equation of the volatility ratio of two bonds, or in the equation for bond portfolio sensitivity. Such a measure is still lacking in finance. We refer to this measure as the “short rate-shift duration”. Since the effect of the short rate shift on the entire yield curve, and thus especially on the price of long-term bonds, is very difficult to predict analytically, we use empirical data to calculate the duration value of the short-term shift and also to calculate its values for the USD and EUR interest markets.

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Yu, Yue, and Liu Lan. "The Impact of Interest Rate Marketization on the Interest Rate Risk of Commercial Banks." In 2019 3rd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2019. http://dx.doi.org/10.1109/icdsba48748.2019.00045.

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He, Haixia. "Interest Rate Risk Management of Commercial Bank under the Background of Interest Rate Liberalization." In 2015 International Conference on Economics, Management, Law and Education. Atlantis Press, 2015. http://dx.doi.org/10.2991/emle-15.2015.70.

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Huo, Yunlei. "Risk Management of the Bank Interest Rates under the Background of Interest Rate Marketization." In 4th International Conference on Management Science, Education Technology, Arts, Social Science and Economics 2016. Atlantis Press, 2016. http://dx.doi.org/10.2991/msetasse-16.2016.215.

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Sheng, Xiaokang. "Research on the Interest Rate Risk Management of Commercial Banks in China under Interest Rate Liberalization." In 2018 2nd International Conference on Education Science and Economic Management (ICESEM 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/icesem-18.2018.33.

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Balz, Felix Florian. "How did the interest rate risk influence the German real estate economy?" In Konfrence doktorandů. Vysoká škola finanční a správní, 2023. http://dx.doi.org/10.37355/kd-2023-01.

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The German residential real estate market has thrived in recent decades, driven by low-interest rates, demand and supply factors. However, concerns arise about the impact of changing interest rates. This paper explores the nature of interest rate risk and its effects on the German real estate market. Interest rate risk, affecting instruments and portfolios, extends to residential real estate. Interest rate changes can affect mortgage costs, influencing demand and prices. It can alter interest rate risk can lead to immune to interest rate risk, as research suggests an 11% overvaluation. Understanding the interplay between interest rate risk and the German real estate market is vital for risk interest rate risk are key factors in its future. In conclusion, this paper underscores the intricate relationship between interest rate risk and the German residential real estate market, interest rates and demographic shifts. Such analyses will provide a more comprehensive understanding of how interest rate risk influences real estate pricing and market dynamics.

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Mao, H., K. M. Ostaszewski, and Y. L. Wang. "Pricing annuity insurance integrating mortality improvement risk, interest rate risk, insolvency risk and insurance demand." In 2011 IEEE MTT-S International Microwave Workshop Series on Innovative Wireless Power Transmission: Technologies, Systems, and Applications (IMWS 2011). IEEE, 2011. http://dx.doi.org/10.1109/imws.2011.6115240.

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Mao, H., K. M. Ostaszewski, and Y. L. Wang. "Pricing annuity insurance integrating mortality improvement risk, interest rate risk, insolvency risk and insurance demand." In 2011 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2011. http://dx.doi.org/10.1109/ieem.2011.6118008.

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Reports on the topic "Interest rate risk"

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Rosenberger, Grant E., and Peter Zimmerman. Interest Rate Risk at US Credit Unions. Federal Reserve Bank of Cleveland, 2024. http://dx.doi.org/10.26509/frbc-wp-202403.

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Rising interest rates have prompted concerns about losses on bank assets, especially following the failure of Silicon Valley Bank (SVB) in March 2023. In this working paper, we examine whether US credit unions could be subject to similar losses as banks and analyze how their regulatory capital would be affected. We estimate that after realizing losses from assets that have decreased in value and not yet been sold the overall net worth of the credit union industry would have fallen by 40 percent in 2023:Q1. Unrealized losses were most severe at the largest credit unions. Nonetheless, the bulk of deposits at credit unions were insured, suggesting limited risk of an SVB-style run. In addition, credit union deposit rates are relatively insensitive to market interest rates, providing credit unions with a hedge against a rising rate environment. Overall, credit unions’ balance sheet positions seemed to be more resilient to unrealized interest rate risk than banks’.

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Johri, Alok, Shahed Khan, and César Sosa-Padilla. Interest Rate Uncertainty and Sovereign Default Risk. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27639.

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Carpenter, Jennifer, Fangzhou Lu, and Robert Whitelaw. The Price and Quantity of Interest Rate Risk. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28444.

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Engel, Charles. The Real Exchange Rate, Real Interest Rates, and the Risk Premium. National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w17116.

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Drechsler, Itamar, Alexi Savov, and Philipp Schnabl. Banking on Deposits: Maturity Transformation without Interest Rate Risk. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24582.

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Ayres, João, and Radoslaw Paluszynski. Rollover and Interest-Rate Risks in Self-Fulfilling Debt Models. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005361.

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Abstract:

This paper proposes a model of sovereign default that features interest rate multiplicity driven by rollover risk. Our core mechanism shows that the possibility of a rollover crisis by itself can lead to high interest rates, which in turn reinforces the rollover risk. By exploiting complementarity between the traditional notions of slow- and fast-moving crises, our model generates a rich simulated dynamics that features frequent defaults and a volatile bond spread even in the absence of shocks to fundamentals. In the presence of risky income, our mechanism amplifies the dynamics of debt and spreads relative to model benchmarks where equilibrium multiplicity relies on the underlying shocks to income.

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Landier, Augustin, David Sraer, and David Thesmar. Banks' Exposure to Interest Rate Risk and The Transmission of Monetary Policy. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w18857.

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8

Belongia, Michael T., and Mack Ott. The U. S. Monetary Policy Regime, Interest Differentials and Dollar Exchange Rate Risk Premia. Federal Reserve Bank of St. Louis, 1987. http://dx.doi.org/10.20955/wp.1987.009.

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9

Liu, Jun, Francis Longstaff, and Ravit Mandell. The Market Price of Credit Risk: An Empirical Analysis of Interest Rate Swap Spreads. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/w8990.

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10

Hall, George, and Thomas Sargent. Interest Rate Risk and Other Determinants of Post-WWII U.S. Government Debt/GDP Dynamics. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w15702.

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