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Journal articles on the topic 'Interest rate models'

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Published: 4 June 2021
Last updated: 8 June 2024

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1

Paseka, Alex, Theodoro Koulis, and Aerambamoorthy Thavaneswaran. "Interest Rate Models." Journal of Mathematical Finance 02, no. 02 (2012): 141–58. http://dx.doi.org/10.4236/jmf.2012.22016.

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2

Hagan, Patrick S., and Diana E. Woodward. "Markov interest rate models." Applied Mathematical Finance 6, no. 4 (December 1999): 233–60. http://dx.doi.org/10.1080/13504869950079275.

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3

Ho, Thomas S. Y. "Evolution of Interest Rate Models." Journal of Derivatives 2, no. 4 (May 31, 1995): 9–20. http://dx.doi.org/10.3905/jod.1995.407923.

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4

BRODY, DORJE C., and STALA HADJIPETRI. "COHERENT CHAOS INTEREST-RATE MODELS." International Journal of Theoretical and Applied Finance 18, no. 03 (May 2015): 1550016. http://dx.doi.org/10.1142/s0219024915500168.

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The Wiener chaos approach to interest-rate modeling arises from the observation that in the general context of an arbitrage-free model with a Brownian filtration, the pricing kernel admits a representation in terms of the conditional variance of a square-integrable generator, which in turn admits a chaos expansion. When the expansion coefficients of the random generator factorize into multiple copies of a single function, the resulting interest-rate model is called "coherent", whereas a generic interest-rate model is necessarily "incoherent". Coherent representations are of fundamental importance because an incoherent generator can always be expressed as a linear superposition of coherent elements. This property is exploited to derive general expressions for the pricing kernel and the associated bond price and short rate processes in the case of a generic nth order chaos model, for each n ∈ ℕ. Pricing formulae for bond options and swaptions are obtained in closed form for a number of examples. An explicit representation for the pricing kernel of a generic incoherent model is then obtained by use of the underlying coherent elements. Finally, finite-dimensional realizations of coherent chaos models are investigated and we show that a class of highly tractable models can be constructed having the characteristic feature that the discount bond price is given by a piecewise-flat (simple) process.
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5

Hunt, Phil, Joanne Kennedy, and Antoon Pelsser. "Markov-functional interest rate models." Finance and Stochastics 4, no. 4 (August 2000): 391–408. http://dx.doi.org/10.1007/pl00013525.

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6

Hunt, P. J., and J. E. Kennedy. "Implied interest rate pricing models." Finance and Stochastics 2, no. 3 (May 1, 1998): 275–93. http://dx.doi.org/10.1007/s007800050041.

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7

Zhu, You-Lan. "Three-factor interest rate models." Communications in Mathematical Sciences 1, no. 3 (2003): 557–73. http://dx.doi.org/10.4310/cms.2003.v1.n3.a8.

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8

Boyle, Phelim P., and Weidong Tian. "Quadratic Interest Rate Models as Approximations to Effective Rate Models." Journal of Fixed Income 9, no. 3 (December 31, 1999): 69–80. http://dx.doi.org/10.3905/jfi.1999.319221.

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9

Tse, Y. K. "Short-term interest rate models and generation of interest rate scenarios." Mathematics and Computers in Simulation 43, no. 3-6 (March 1997): 475–80. http://dx.doi.org/10.1016/s0378-4754(97)00034-7.

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10

Moh, Young-Kyu, and Yeongseop Rhee. "Continuous-time Interest Rate Differential Models." INTERNATIONAL BUSINESS REVIEW 20, no. 2 (June 30, 2016): 27. http://dx.doi.org/10.21739/ibr.2016.06.20.2.27.

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11

Park, F. C., C. M. Chun, C. W. Han, and N. Webber. "Interest rate models on Lie groups." Quantitative Finance 11, no. 4 (April 2011): 559–72. http://dx.doi.org/10.1080/14697680903468963.

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12

Beinhofer, Maximilian, Ernst Eberlein, Arend Janssen, and Manuel Polley. "Correlations in Lévy interest rate models." Quantitative Finance 11, no. 9 (September 2011): 1315–27. http://dx.doi.org/10.1080/14697688.2010.542299.

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13

Björk, Tomas, and Andrea Gombani. "Minimal realizations of interest rate models." Finance and Stochastics 3, no. 4 (August 1, 1999): 413–32. http://dx.doi.org/10.1007/s007800050069.

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14

Hull, John, and Alan White. "One-Factor Interest-Rate Models and the Valuation of Interest-Rate Derivative Securities." Journal of Financial and Quantitative Analysis 28, no. 2 (June 1993): 235. http://dx.doi.org/10.2307/2331288.

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15

Russo, Vincenzo, and Frank J. Fabozzi. "Calibrating Short Interest Rate Models in Negative Rate Environments." Journal of Derivatives 24, no. 4 (May 31, 2017): 80–92. http://dx.doi.org/10.3905/jod.2017.24.4.080.

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16

Angelini, Flavio, and Stefano Herzel. "Consistent Initial Curves for Interest Rate Models." Journal of Derivatives 9, no. 4 (May 31, 2002): 8–17. http://dx.doi.org/10.3905/jod.2002.319182.

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17

Nowman, K. Ben. "Continuous-time short term interest rate models." Applied Financial Economics 8, no. 4 (August 1998): 401–7. http://dx.doi.org/10.1080/096031098332934.

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18

EVANS, GEORGE W., and BRUCE MCGOUGH. "Interest-Rate Pegs in New Keynesian Models." Journal of Money, Credit and Banking 50, no. 5 (July 8, 2018): 939–65. http://dx.doi.org/10.1111/jmcb.12523.

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19

A. Sullivan, Michael. "Discrete-time continuous-state interest rate models." Journal of Economic Dynamics and Control 25, no. 6-7 (June 2001): 1001–17. http://dx.doi.org/10.1016/s0165-1889(00)00065-8.

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20

Flores, S., and T. E. Govindan. "Stability of some stochastic interest rate models." International Journal of Contemporary Mathematical Sciences 11 (2016): 185–96. http://dx.doi.org/10.12988/ijcms.2016.51053.

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21

Treepongkaruna, Sirimon, and Stephen Gray. "Short-term interest rate models: valuing interest rate derivatives using a Monte-Carlo approach." Accounting and Finance 43, no. 2 (July 2003): 231–59. http://dx.doi.org/10.1111/1467-629x.00090.

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22

Niizeki, Mikiyo Kii. "A comparison of short-term interest rate models: empirical tests of interest rate volatility." Applied Financial Economics 8, no. 5 (October 1998): 505–12. http://dx.doi.org/10.1080/096031098332808.

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23

Yanishevskyi, V. S., and L. S. Nodzhak. "The path integral method in interest rate models." Mathematical Modeling and Computing 8, no. 1 (2020): 125–36. http://dx.doi.org/10.23939/mmc2021.01.125.

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An application of path integral method to Merton and Vasicek stochastic models of interest rate is considered. Two approaches to a path integral construction are shown. The first approach consists in using Wieners measure with the following substitution of solutions of stochastic equations into the models. The second approach is realised by using transformation from Wieners measure to the integral measure related to the stochastic variables of Merton and Vasicek equations. The introduction of boundary conditions is considered in the second approach in order to remove incorrect time asymptotes from the classic Merton and Vasicek models of interest rates. By the example of Merton model with zero drift, a Dirichlet boundary condition is considered. A path integral representation of term structure of interest rate is obtained. The estimate of the obtained path integrals is performed, where it is shown that the time asymptote is limited.
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24

Koulis, Theodoro, and Aera Thavaneswaran. "Inference for Interest Rate Models Using Milstein’s Approximation." Journal of Mathematical Finance 03, no. 01 (2013): 110–18. http://dx.doi.org/10.4236/jmf.2013.31010.

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25

Hainaut, Donatien. "Lévy Interest Rate Models with a Long Memory." Risks 10, no. 1 (December 23, 2021): 2. http://dx.doi.org/10.3390/risks10010002.

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This article proposes an interest rate model ruled by mean reverting Lévy processes with a sub-exponential memory of their sample path. This feature is achieved by considering an Ornstein–Uhlenbeck process in which the exponential decaying kernel is replaced by a Mittag–Leffler function. Based on a representation in term of an infinite dimensional Markov processes, we present the main characteristics of bonds and short-term rates in this setting. Their dynamics under risk neutral and forward measures are studied. Finally, bond options are valued with a discretization scheme and a discrete Fourier’s transform.
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26

Sen, Amit, and R. Stafford Johnson. "Features of skewness-adjusted binomial interest rate models." International Journal of Bonds and Derivatives 4, no. 2 (2020): 126. http://dx.doi.org/10.1504/ijbd.2020.10031540.

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27

Johnson, R. Stafford, and Amit Sen. "Features of skewness-adjusted binomial interest rate models." International Journal of Bonds and Derivatives 4, no. 2 (2020): 126. http://dx.doi.org/10.1504/ijbd.2020.109333.

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28

LI, ANLONG, PETER RITCHKEN, and L. SANKARASUBRAMANIAN. "Lattice Models for Pricing American Interest Rate Claims." Journal of Finance 50, no. 2 (June 1995): 719–37. http://dx.doi.org/10.1111/j.1540-6261.1995.tb04802.x.

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29

Rainer, Martin. "Calibration of stochastic models for interest rate derivatives." Optimization 58, no. 3 (April 2009): 373–88. http://dx.doi.org/10.1080/02331930902741796.

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30

GRUNDKE, PETER. "Integrating Interest Rate Risk in Credit Portfolio Models." Journal of Risk Finance 5, no. 2 (February 2004): 6–15. http://dx.doi.org/10.1108/eb022982.

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31

Sorwar, Ghulam. "Estimating single factor jump diffusion interest rate models." Applied Financial Economics 21, no. 22 (July 21, 2011): 1679–89. http://dx.doi.org/10.1080/09603107.2011.591729.

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32

Kulish, Mariano, James Morley, and Tim Robinson. "Estimating DSGE models with zero interest rate policy." Journal of Monetary Economics 88 (June 2017): 35–49. http://dx.doi.org/10.1016/j.jmoneco.2017.05.003.

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33

Ju, Y. J., C. E. Kim, and J. C. Shim. "Genetic-based fuzzy models: Interest rate forecasting problem." Computers & Industrial Engineering 33, no. 3-4 (December 1997): 561–64. http://dx.doi.org/10.1016/s0360-8352(97)00193-9.

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34

Dellaportas, Petros, David G. T. Denison, and Chris Holmes. "Flexible Threshold Models for Modelling Interest Rate Volatility." Econometric Reviews 26, no. 2-4 (April 12, 2007): 419–37. http://dx.doi.org/10.1080/07474930701220600.

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35

Di Persio, Luca, Gregorio Pellegrini, and Michele Bonollo. "Polynomial Chaos Expansion Approach to Interest Rate Models." Journal of Probability and Statistics 2015 (2015): 1–24. http://dx.doi.org/10.1155/2015/369053.

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The Polynomial Chaos Expansion (PCE) technique allows us to recover a finite second-order random variable exploiting suitable linear combinations of orthogonal polynomials which are functions of a given stochastic quantityξ, hence acting as a kind of random basis. The PCE methodology has been developed as a mathematically rigorous Uncertainty Quantification (UQ) method which aims at providing reliable numerical estimates for some uncertain physical quantities defining the dynamic of certain engineering models and their related simulations. In the present paper, we use the PCE approach in order to analyze some equity and interest rate models. In particular, we take into consideration those models which are based on, for example, the Geometric Brownian Motion, the Vasicek model, and the CIR model. We present theoretical as well as related concrete numerical approximation results considering, without loss of generality, the one-dimensional case. We also provide both an efficiency study and an accuracy study of our approach by comparing its outputs with the ones obtained adopting the Monte Carlo approach, both in its standard and its enhanced version.
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36

Zhao, Juan. "Long time behaviour of stochastic interest rate models." Insurance: Mathematics and Economics 44, no. 3 (June 2009): 459–63. http://dx.doi.org/10.1016/j.insmatheco.2009.01.001.

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37

Jouini, Elyès, and Clotilde Napp. "Arbitrage with Fixed Costs and Interest Rate Models." Journal of Financial and Quantitative Analysis 41, no. 4 (December 2006): 889–913. http://dx.doi.org/10.1017/s0022109000002684.

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AbstractWe study securities market models with fixed costs. We first characterize the absence of arbitrage opportunities and provide fair pricing rules. We then apply these results to extend some popular interest rate and option pricing models that present arbitrage opportunities in the absence of fixed costs. In particular, we prove that the quite striking result obtained by Dybvig, Ingersoll, and Ross (1996), which asserts that under the assumption of absence of arbitrage long zero-coupon rates can never fall, is no longer true in models with fixed costs, even arbitrarily small fixed costs. For instance, models in which the long-term rate follows a diffusion process are arbitrage-free in the presence of fixed costs (including arbitrarily small fixed costs). We also rationalize models with partially absorbing or reflecting barriers on the price processes. We propose a version of the Cox, Ingersoll, and Ross (1985) model which, consistent with Longstaff (1992), produces yield curves with realistic humps, but does not assume an absorbing barrier for the short-term rate. This is made possible by the presence of (even arbitrarily small) fixed costs.
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38

Canabarro, Eduardo. "Wher do One-Factor Interest Rate Models Fail?" Journal of Fixed Income 5, no. 2 (September 30, 1995): 31–52. http://dx.doi.org/10.3905/jfi.1995.408145.

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39

Easton, Malcolm C. "Binary Tree Interest Rate Models with Risk Premiums." Journal of Fixed Income 8, no. 2 (September 30, 1998): 53–59. http://dx.doi.org/10.3905/jfi.1998.408237.

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40

Akahori, Jirô, Yuji Hishida, Josef Teichmann, and Takahiro Tsuchiya. "A heat kernel approach to interest rate models." Japan Journal of Industrial and Applied Mathematics 31, no. 2 (May 17, 2014): 419–39. http://dx.doi.org/10.1007/s13160-014-0147-3.

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41

Booth, GG, and W. Bessler. "Goal programming models for managing interest-rate risk." Omega 17, no. 1 (January 1989): 81–89. http://dx.doi.org/10.1016/0305-0483(89)90023-6.

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42

Zhang, Yonghui, Zhongtian Chen, and Yong Li. "Bayesian testing for short term interest rate models." Finance Research Letters 20 (February 2017): 146–52. http://dx.doi.org/10.1016/j.frl.2016.09.020.

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43

Deelstra, G., and F. Delbaen. "Long-term returns in stochastic interest rate models." Insurance: Mathematics and Economics 17, no. 2 (October 1995): 163–69. http://dx.doi.org/10.1016/0167-6687(95)00018-n.

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44

Kuan, Grace C. H., and Nick Webber. "Pricing Barrier Options with One-Factor Interest Rate Models." Journal of Derivatives 10, no. 4 (May 31, 2003): 33–50. http://dx.doi.org/10.3905/jod.2003.319204.

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45

Ishimura, Naoyuki, Bold Javkhlan, MasaAki Nakamura, and Zheng Wei. "Models of the Short Interest Rate in Discrete Processes." Open Journal of Applied Sciences 03, no. 01 (2013): 12–14. http://dx.doi.org/10.4236/ojapps.2013.31b1003.

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46

Deelstra, Griselda. "Long-Term Returns in Stochastic Interest Rate Models: Applications." ASTIN Bulletin 30, no. 1 (May 2000): 123–40. http://dx.doi.org/10.2143/ast.30.1.504629.

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AbstractWe extend the Cox-Ingersoll-Ross (1985) model of the short interest rate by assuming a stochastic reversion level, which better reflects the time dependence caused by the cyclical nature of the economy or by expectations concerning the future impact of monetary policies. In this framework, we have studied the convergence of the long-term return by using the theory of generalised Bessel-square processes. We emphasize the applications of the convergence results. A limit theorem proves evidence of the use of a Brownian motion with drift instead of the integral . For practice, however, this approximation turns out to be only appropriate when there are no explicit formulae and calculations are very time-consuming.
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47

Khramov, Vadim. "Estimating Parameters of Short-Term Real Interest Rate Models." IMF Working Papers 13, no. 212 (2013): 1. http://dx.doi.org/10.5089/9781475594645.001.

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48

Treepongkaruna, Sirimon, and Stephen Gray. "On the robustness of short–term interest rate models." Accounting & Finance 43, no. 1 (February 4, 2003): 87–121. http://dx.doi.org/10.1111/1467-629x.00084.

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49

Jamshidian, F. "A simple class of square-root interest-rate models." Applied Mathematical Finance 2, no. 1 (March 1995): 61–72. http://dx.doi.org/10.1080/13504869500000004.

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50

Byun, Jong-Cook, and Son-Nan Chen. "International real interest rate parity with error correction models." Global Finance Journal 7, no. 2 (September 1996): 129–51. http://dx.doi.org/10.1016/s1044-0283(96)90001-0.

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