Geometric Brownian Motion Based on Stochastic Differential Equation Modeling Considering the Change Point Estimation for the Fluctuation of the Turkish Lira Against the US Dollar
Abstract
Keywords
Change point estimation, Euler-Maruyama approximation method, Geometric Brownian Motion, Itô stochastic differential equation, Maximum likelihood estimation method
References
- [1] E. Kostrista, D. Çibuku, Introduction to stochastic differential equations, Journal of Natural Sciences and Mathematics of UT, 3(5-6) (2018), 189-195.
- [2] R. Rezaeyan, R. Farnoosh, Stochastic differential equations and application of the Kalman-Bucy filter in the modeling of RC Circuit, Appl. Math. Sci., 4(23) (2010), 1119-1127.
- [3] S. M. Iacus, N. Yoshida, Simulation and Inference for Stochastic Processes with YUIMA: A Comprehensive R Framework for SDEs and Other Stochastic Processes, Springer, 2018.
- [4] Z. Yang, D. Aldous, Geometric Brownian Motion Model in Financial Market, University of California, Berkeley, 2015.
- [5] K. Reddy, V. Clinton, Simulating stock prices using geometric Brownian motion: Evidence from Australian companies, Australas. Account. Bus. Finance J., 10(3) (2016), 23-47. http://dx.doi.org/10.14453/aabfj.v10i3.3
- [6] C. Lausberg, F. Brandt, Forecasting risk and return of listed real estate: A simulation approach with geometric Brownian motion for the German stock market, Zeitschrift für Immobilienökonomie, 10 (2024), 1–38. https://doi.org/10.1365/s41056-024-00070-4
- [7] S. N. Z. Abidin, M. M. Jaffar, A review on geometric Brownian motion in forecasting the share prices in Bursa Malaysia, World Appl. Sci. J., 17(1) (2012), 82-93.
- [8] S. E. Shreve, Stochastic Calculus for Finance II: Continuous-Time Models, Springer, 2004.
- [9] Y. Aıt-Sahalia, Maximum likelihood estimation of discretely sampled diffusions: A closed-form approximation approach, Econometrica, 70(1) (2002), 223–262. https://doi.org/10.1111/1468-0262.00274
- [10] S. M. Iacus, Simulation and Inference for Stochastic Differential Equations: With R Examples, Springer, New York, 2008. http://dx.doi.org/10.1007/ 978-0-387-75839-8
