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LIBOR KAVRAMI VE LIBOR’UN MULTİ-LAYER PERCEPTRON İLE TAHMİNİ

Year 2025, Issue: 27, 34 - 53, 12.11.2025
https://doi.org/10.58724/assam.1702558

Abstract

1970'lerden beri yaşanan ekonomik gelişmeler ve faiz oranlarındaki aşırı oynaklık, uzun vadeli borçlanmalarda yeni çözümler gerektirmiştir. Bu kapsamda, değişken faiz oranları için bir karşılaştırma aracı olan Londra Bankalararası Teklif Faizi (LIBOR) önemli bir çözüm olmuştur. 1986’da BBA tarafından referans faiz oranı olarak hesaplanan LIBOR’un kullanımı 2008 Küresel Finans Krizi’ne kadar artmış, ancak kriz sonrası manipülasyon iddialarıyla sorgulanarak 2020’lerde ABD’de SOFR, Birleşik Krallık’ta SONIA ve AB’de ESTR ile değiştirilmiştir. Uzun yıllar bankalararası borçlanma maliyetlerini belirleyen LIBOR'un gelecekteki değerinin tahmini, şirketler ve ülkeler için büyük önem taşımaktadır. Faiz oranı modellemesi Louis Bachelier’in 1900’deki Aritmetik Brown Hareketi (ABM) çalışmasıyla başlamıştır. Günümüzde LIBOR Piyasa Modeli (LMM) yaygın olsa da karmaşıklığı, kalibrasyon sorunları, simülasyon gerekliliği ve LIBOR'un sona ermesiyle kullanımı sınırlıdır. Bu çalışmada, faiz hareketlerinin doğrusal ve parametrik olmaması nedeniyle geleneksel ekonometrik yöntemler yetersiz kalmıştır. Bu nedenle yapay zeka uygulaması olan Çok Katmanlı Algılayıcı (MLP) yöntemi kullanılmıştır. Yeterli SOFR verisi olmaması gibi nedenlerle, bir aylık LIBOR bağımlı, FED politika faizi ise bağımsız değişken olarak ele alınmıştır. Hem eşbütünleşme hem de MLP analizleri, LIBOR'un FED politika faizi ile birebir hareket ettiğini ortaya koymaktadır. FED Politika Faiz Oranının doğru tahmini, gelecekteki LIBOR oranının da isabetli tahminini mümkün kılacaktır.

References

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  • Andrés, J. A., & Scudiero, F. E. (2023). Futures contracts as a means of hedging market risks. Aibi Research, Management and Engineering Journal, 11(3), 42–51. https://doi.org/10.15649/2346030X.3185
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  • Haykin, S. (2008). Neural networks and learning machines (3rd ed.). Pearson Education.
  • Heath, D., Jarrow, R., & Morton, A. (1992). Bond pricing and the term structure of interest rates: A new methodology for contingent claims valuation. Econometrica, 60(1), 77–105. https://doi.org/10.2307/2951677
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  • Huang, W., & Todorov, K. (2022, December). The post-Libor world: A global view from the BIS derivatives statistics. BIS Quarterly Review. https://www.bis.org/publ/qtrpdf/r_qt2212e.pdf
  • Hull, J. C. (2012). Options, futures, and other derivatives (8th ed.). Prentice Hall.
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  • Izgi, B., & Bakkaloglu, A. (2017). Fundamental solution of bond pricing in the Ho-Lee stochastic interest rate model under the invariant criteria. New Trends in Mathematical Sciences, 5(1), 196–203. http://dx.doi.org/10.20852/ntmsci.2017.138
  • Jackel, P. (2001). Monte Carlo methods in finance. John Wiley & Sons.
  • Jamshidian, F. (1997). Libor and swap market models and measures. Finance and Stochastics, 1(4), 293–330. https://doi.org/10.1007/s007800050026
  • Jarrow, R., & Protter, P. (2003). A short history of stochastic integration and mathematical finance: The early years, 1880–1970. Cornell University. https://www.ma.imperial.ac.uk/~ajacquie/IC_AMDP/IC_AMDP_Docs/Literature/Jarrow_Protter_History_Stochastic_Integration.pdf
  • Kiff, J. (2012, December). Back to basics: What is LIBOR? Finance & Development, 49(4). https://www.imf.org/external/pubs/ft/fandd/2012/12/basics.htm
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539
  • Leippold, M., & Strömberg, J. (2013). Time-changed Levy LIBOR market model: Pricing and joint estimation of the cap surface and swaption cube (Swiss Finance Institute Research Paper Series, No. 12–23). https://ssrn.com/abstract=2065375
  • Martinez Peria, S. M., & Schmukler, S. (2017). Understanding the use of long-term finance in developing economies (IMF Working Paper WP/17/96). International Monetary Fund. https://www.elibrary.imf.org/view/journals/001/2017/096/001.2017.issue-096-en.xml
  • Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141–183. https://doi.org/10.2307/3003143
  • Miltersen, K., Sandmann, K., & Sondermann, D. (1997). Closed form solutions for term structure derivates with log-normal interest rates. Journal of Finance, 52(1), 409–430. ttps://doi.org/10.1111/j.1540-6261.1997.tb02710.x
  • Nawalkha, S. K. (2009). The LIBOR market model: A critical review. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1396777
  • Nekrasov, V. (2013). An accompaniment to a course on interest rate modeling: With discussion of Black-76, Vasicek and HJM models and a gentle introduction to the multivariate LIBOR market model. SSRN. https://ssrn.com/abstract=2001007
  • Papapantoleon, A., Schoenmakers, J., & Skovmand, D. (2011). Efficient and accurate log-Lévy approximations to Lévy driven LIBOR models (CREATES Research Paper, 2011-22). https://www.wias-berlin.de/people/schoenma/papapan_schoen_skov.pdf
  • Papapantoleon, A., & Skovmand, D. (2011, July). Numerical methods for the Lévy LIBOR model [Paper presentation]. Conference Name, Location. arXiv:1006.3340. https://arxiv.org/pdf/1006.3340.pdf
  • Peters, O., & Adamou, A. (2018). The sum of log-normal variates in geometric Brownian motion. arXiv:1802.02939. https://arxiv.org/abs/1802.02939
  • Popescu, M.-C., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579-588. https://www.researchgate.net/publication/228340819
  • Samuelson, P. A. (1965). Rational theory of warrant pricing. Industrial Management Review, 6(2), 13–39.
  • Tomar, A., & Laxkar, P. (2022). Differences of Tanh, sigmoid and ReLu activation function in neural network. International Journal of Scientific Progress and Research, 80(6), 18-22.
  • The World Bank. (2015). Global financial development report 2015-2016: Long-term finance. https://documents1.worldbank.org/curated/en/955811467986333727/pdf/99100-PUB-REVISED-Box393195B-OUO-9-PUBLIC.pdf
  • Vasicek, O. (1977). An equilibrium characterization of the term structure. Journal of Financial Economics, 5(2), 177–188. https://doi.org/10.1016/0304-405X(77)90016-2
  • Verschuren, R. M. (2020). Stochastic interest rate modelling using a single or multiple curves: An empirical performance analysis of the Lévy forward price model. Quantitative Finance, 20(7), 1123–1148. https://doi.org/10.1080/14697688.2020.1722318
  • Wang, L., Zhang, M., & Liu, Z. (2023). The progress of Black-Scholes model and Black-Scholes-Merton model. BCP Business & Management, 38, 3405–3410. https://doi.org/10.54691/bcpbm.v38i.3980
  • West, G. (2010). Interest rate derivatives: Lecture notes. finmod. https://web.archive.org/web/20120417044831/http://www.finmod.co.za/ird.pdf Westfälische Wilhelms-Universität Münster. (2021). Advanced financial mathematics, lecture notes summer term 2021. https://www.uni-muenster.de/imperia/md/content/Stochastik/financial_mathematics.pdf
  • Wu, L., & Zhang, F. (2006). LIBOR market model with stochastic volatility. Journal of Industrial and Management Optimization, 2(2), 199–227. https://doi.org/10.3934/jimo.2006.2.199

Year 2025, Issue: 27, 34 - 53, 12.11.2025
https://doi.org/10.58724/assam.1702558

Abstract

References

  • Alexander, C., & Lvov, D. (2003). Statistical properties of forward Libor rates (ISMA Discussion Papers in Finance 2003-03). ICMA Centre. http://www.icmacentre.ac.uk/pdf/discussion/DP2003-03.pdf
  • Alternative Reference Rates Committee. (2017, June 22). The ARRC selects a broad repo rate as its preferred alternative reference rate. Federal Reserve Bank of New York. https://www.newyorkfed.org/medialibrary/microsites/arrc/files/2017/ARRC-press-release-Jun-22-2017.pdf
  • Alternative Reference Rates Committee. (2018, March). Second report. Federal Reserve Bank of New York. https://www.sec.gov/spotlight/fixed-income-advisory-committee/arrc-second-report-041519.pdf
  • Alternative Reference Rates Committee. (2019, January 31). Frequently asked questions. Federal Reserve Bank of New York. https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/banking-and-capital-markets/ey-arrc-faq.pdf
  • Andersen, L., & Andersen, J. (2000). Volatility skews and extensions of the Libor market model. Applied Mathematical Finance, 7(1), 1–32. https://doi.org/10.1080/13504860050036110
  • Andrés, J. A., & Scudiero, F. E. (2023). Futures contracts as a means of hedging market risks. Aibi Research, Management and Engineering Journal, 11(3), 42–51. https://doi.org/10.15649/2346030X.3185
  • Arteta, C., Kamin, S., & Ruch, F. U. (2022). How do rising U.S. interest rates affect emerging and developing economies? (World Bank Policy Research Working Paper, 10258). World Bank. https://documents1.worldbank.org/curated/en/099036212082239238/pdf/IDU032d1feef0db0d0480e0b3190f92d87c50de8.pdf
  • Astray, G. F. J., Rodriguez-Rajo, A., Ferreiro-Lage, M., Fernandez-Gonzalez, D., Jato, V., & Mejutoa, J. C. (2010). The use of artificial neural networks to forecast biological atmospheric allergens or pathogens only as Alternaria spores. Journal of Environmental Monitoring, 12(11), 2145–2152. https://doi.org/10.1039/c0em00248h
  • Bachelier, L. (1900). Théorie de la spéculation [Theory of speculation]. Gauthier-Villars.
  • Belomestny, D., & Schoenmakers, J. (2009). A jump-diffusion Libor model and its robust calibration. The Weierstrass Institute. https://www.wias-berlin.de/people/schoenma/RQUF-2008-0135_Final.pdf
  • Brace, A., Gatarek, D., & Musiela, M. (1997). The market model of interest rate dynamics. Mathematical Finance, 7(2), 127–154. https://doi.org/10.1111/1467-9965.00028
  • Cheng, L. (2016). On the calibration of the SABR model and its extensions (Master's thesis, Imperial College London). https://www.imperial.ac.uk/media/imperial-college/faculty-of-natural-sciences/department-of-mathematics/math-finance/Cheng_Luo-thesis.pdf
  • Congressional Budget Office. (2024, February). The budget and economic outlook: 2024 to 2034. https://www.cbo.gov/publication/59946
  • Devineau, L., Arrouy, P.-E., Bonnefoy, P., & Boumezoued, A. (2017). Fast calibration of the Libor market model with stochastic volatility and displaced diffusion. arXiv:1706.00263. https://arxiv.org/pdf/1706.00263.pdf
  • Durré, A., Evjen, S., & Pialgaard, R. (2003). Estimating risk premia in money markets (ECB Working Paper No. 221). European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp221.pdf
  • Federal Reserve Bank of St. Louis. (2024). Federal Reserve Economic Data (FRED). https://fred.stlouisfed.org
  • Financial Conduct Authority. (2023, April 3). FCA announces decision on synthetic US dollar LIBOR. https://www.fca.org.uk/news/news-stories/fca-announces-decision-synthetic-us-dollar-libor
  • Gatarek, D., Bachert, P., & Maksymiuk, R. (2006). The LIBOR market model in practice. John Wiley & Sons. global-rates.com. (2024). Historical LIBOR rates. https://www.global-rates.com/en/interest-rates/libor/
  • Greene, W. H. (2012). Econometric analysis (7th ed.). Prentice Hall.
  • Haugh, M. (2010). Market models, term structure models: IEOR E4710. Columbia University. http://www.columbia.edu/~mh2078/market_models.pdf
  • Haykin, S. (2008). Neural networks and learning machines (3rd ed.). Pearson Education.
  • Heath, D., Jarrow, R., & Morton, A. (1992). Bond pricing and the term structure of interest rates: A new methodology for contingent claims valuation. Econometrica, 60(1), 77–105. https://doi.org/10.2307/2951677
  • Ho, T. S. Y., & Lee, S.-B. (1986). Term structure movements and pricing interest rate contingent claims. The Journal of Finance, 41(5), 1011–1029. https://doi.org/10.1111/j.1540-6261.1986.tb04524.x
  • Hou, D., & Skeie, D. (2014, March). LIBOR: Origins, economics, crisis, scandal, and reform (Federal Reserve Bank of New York Staff Reports, No. 667). http://hdl.handle.net/10419/120778
  • Huang, W., & Todorov, K. (2022, December). The post-Libor world: A global view from the BIS derivatives statistics. BIS Quarterly Review. https://www.bis.org/publ/qtrpdf/r_qt2212e.pdf
  • Hull, J. C. (2012). Options, futures, and other derivatives (8th ed.). Prentice Hall.
  • IBM. (2023, June 27). What is machine learning? IBM. https://www.ibm.com/topics/machine-learning Intercontinental Exchange. (2018, April 25). ICE LIBOR evolution. https://www.theice.com/publicdocs/ICE_LIBOR_Evolution_Report_25_April_2018.pdf
  • Izgi, B., & Bakkaloglu, A. (2017). Fundamental solution of bond pricing in the Ho-Lee stochastic interest rate model under the invariant criteria. New Trends in Mathematical Sciences, 5(1), 196–203. http://dx.doi.org/10.20852/ntmsci.2017.138
  • Jackel, P. (2001). Monte Carlo methods in finance. John Wiley & Sons.
  • Jamshidian, F. (1997). Libor and swap market models and measures. Finance and Stochastics, 1(4), 293–330. https://doi.org/10.1007/s007800050026
  • Jarrow, R., & Protter, P. (2003). A short history of stochastic integration and mathematical finance: The early years, 1880–1970. Cornell University. https://www.ma.imperial.ac.uk/~ajacquie/IC_AMDP/IC_AMDP_Docs/Literature/Jarrow_Protter_History_Stochastic_Integration.pdf
  • Kiff, J. (2012, December). Back to basics: What is LIBOR? Finance & Development, 49(4). https://www.imf.org/external/pubs/ft/fandd/2012/12/basics.htm
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539
  • Leippold, M., & Strömberg, J. (2013). Time-changed Levy LIBOR market model: Pricing and joint estimation of the cap surface and swaption cube (Swiss Finance Institute Research Paper Series, No. 12–23). https://ssrn.com/abstract=2065375
  • Martinez Peria, S. M., & Schmukler, S. (2017). Understanding the use of long-term finance in developing economies (IMF Working Paper WP/17/96). International Monetary Fund. https://www.elibrary.imf.org/view/journals/001/2017/096/001.2017.issue-096-en.xml
  • Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141–183. https://doi.org/10.2307/3003143
  • Miltersen, K., Sandmann, K., & Sondermann, D. (1997). Closed form solutions for term structure derivates with log-normal interest rates. Journal of Finance, 52(1), 409–430. ttps://doi.org/10.1111/j.1540-6261.1997.tb02710.x
  • Nawalkha, S. K. (2009). The LIBOR market model: A critical review. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1396777
  • Nekrasov, V. (2013). An accompaniment to a course on interest rate modeling: With discussion of Black-76, Vasicek and HJM models and a gentle introduction to the multivariate LIBOR market model. SSRN. https://ssrn.com/abstract=2001007
  • Papapantoleon, A., Schoenmakers, J., & Skovmand, D. (2011). Efficient and accurate log-Lévy approximations to Lévy driven LIBOR models (CREATES Research Paper, 2011-22). https://www.wias-berlin.de/people/schoenma/papapan_schoen_skov.pdf
  • Papapantoleon, A., & Skovmand, D. (2011, July). Numerical methods for the Lévy LIBOR model [Paper presentation]. Conference Name, Location. arXiv:1006.3340. https://arxiv.org/pdf/1006.3340.pdf
  • Peters, O., & Adamou, A. (2018). The sum of log-normal variates in geometric Brownian motion. arXiv:1802.02939. https://arxiv.org/abs/1802.02939
  • Popescu, M.-C., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579-588. https://www.researchgate.net/publication/228340819
  • Samuelson, P. A. (1965). Rational theory of warrant pricing. Industrial Management Review, 6(2), 13–39.
  • Tomar, A., & Laxkar, P. (2022). Differences of Tanh, sigmoid and ReLu activation function in neural network. International Journal of Scientific Progress and Research, 80(6), 18-22.
  • The World Bank. (2015). Global financial development report 2015-2016: Long-term finance. https://documents1.worldbank.org/curated/en/955811467986333727/pdf/99100-PUB-REVISED-Box393195B-OUO-9-PUBLIC.pdf
  • Vasicek, O. (1977). An equilibrium characterization of the term structure. Journal of Financial Economics, 5(2), 177–188. https://doi.org/10.1016/0304-405X(77)90016-2
  • Verschuren, R. M. (2020). Stochastic interest rate modelling using a single or multiple curves: An empirical performance analysis of the Lévy forward price model. Quantitative Finance, 20(7), 1123–1148. https://doi.org/10.1080/14697688.2020.1722318
  • Wang, L., Zhang, M., & Liu, Z. (2023). The progress of Black-Scholes model and Black-Scholes-Merton model. BCP Business & Management, 38, 3405–3410. https://doi.org/10.54691/bcpbm.v38i.3980
  • West, G. (2010). Interest rate derivatives: Lecture notes. finmod. https://web.archive.org/web/20120417044831/http://www.finmod.co.za/ird.pdf Westfälische Wilhelms-Universität Münster. (2021). Advanced financial mathematics, lecture notes summer term 2021. https://www.uni-muenster.de/imperia/md/content/Stochastik/financial_mathematics.pdf
  • Wu, L., & Zhang, F. (2006). LIBOR market model with stochastic volatility. Journal of Industrial and Management Optimization, 2(2), 199–227. https://doi.org/10.3934/jimo.2006.2.199

Libor Concept and Libor’s Estimation With Multi-Layer Perceptron

Year 2025, Issue: 27, 34 - 53, 12.11.2025
https://doi.org/10.58724/assam.1702558

Abstract

Economic developments since the 1970s and excessive volatility in interest rates have necessitated new solutions for long-term borrowing. In this context, the London Interbank Offered Rate (LIBOR), a comparison tool for variable interest rates, has been an important solution. The use of LIBOR, calculated as a reference interest rate by the BBA in 1986, increased until the 2008 Global Financial Crisis but was subsequently questioned due to post-crisis manipulation allegations and replaced in the 2020s by SOFR in the US, SONIA in the UK, and ESTR in the EU. The estimation of the future value of LIBOR, which determined interbank borrowing costs for many years, is of great importance for companies and countries. Interest rate modeling began with Louis Bachelier's 1900 work on Arithmetic Brownian Motion (ABM). Although the LIBOR Market Model (LMM) is widely used today, its application is limited due to its complexity, calibration problems, simulation requirements, and the discontinuation of LIBOR. In this study, traditional econometric methods were insufficient as interest rate movements were not linear and parametric. Therefore, the Multi-Layer Perceptron (MLP) method, an artificial intelligence application, was used. Due to reasons such as the lack of sufficient SOFR data, one-month LIBOR was taken as the dependent variable and the FED policy rate as the independent variable. Both cointegration and MLP analyses reveal that LIBOR moves one-to-one with the FED policy rate. The correct estimation of the FED Policy Rate will enable an accurate estimation of the future LIBOR rate.

References

  • Alexander, C., & Lvov, D. (2003). Statistical properties of forward Libor rates (ISMA Discussion Papers in Finance 2003-03). ICMA Centre. http://www.icmacentre.ac.uk/pdf/discussion/DP2003-03.pdf
  • Alternative Reference Rates Committee. (2017, June 22). The ARRC selects a broad repo rate as its preferred alternative reference rate. Federal Reserve Bank of New York. https://www.newyorkfed.org/medialibrary/microsites/arrc/files/2017/ARRC-press-release-Jun-22-2017.pdf
  • Alternative Reference Rates Committee. (2018, March). Second report. Federal Reserve Bank of New York. https://www.sec.gov/spotlight/fixed-income-advisory-committee/arrc-second-report-041519.pdf
  • Alternative Reference Rates Committee. (2019, January 31). Frequently asked questions. Federal Reserve Bank of New York. https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/banking-and-capital-markets/ey-arrc-faq.pdf
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Details

Primary Language English
Subjects International Foundation
Journal Section Article
Authors

Ahmet Yüzbaşıoğulları 0000-0001-7206-743X

Early Pub Date November 12, 2025
Publication Date November 12, 2025
Submission Date May 20, 2025
Acceptance Date October 6, 2025
Published in Issue Year 2025 Issue: 27

Cite

APA Yüzbaşıoğulları, A. (2025). Libor Concept and Libor’s Estimation With Multi-Layer Perceptron. ASSAM Uluslararası Hakemli Dergi(27), 34-53. https://doi.org/10.58724/assam.1702558

ASSAM-UHAD is an internationally indexed peer-reviewed journal published in April and November.