MEASURING THE SENSITIVITY OF DIFFERENT MONTE CARLO MODELS IN FORECASTING AIRLINE STOCK PRICES

Year 2024, Volume: 20 Issue: 1, 56 - 65, 31.12.2024

Olcay Ölçen

Abstract

Purpose- Monte Carlo Models are widely utilised by scientific research in a variety. Two research models are argued and designed regarding the Quasi and Pseudo Monte Carlo models in this paper.
Methodology- The main research questions are formed here as “Which Monte Carlo model can give more effective results to USA Airline investors?”. There is a utilisation problem of Monte Carlo Models by investors. The research also will help to fill this gap. On the other hand, Sobol and Halton sequences are utilized to develop Quasi Monte Carlo Model.
Findings- Quasi-Monte Carlo Models are given more real results than Pseudo Monte Carlo Models, especially in high number (5000) iterations. The results are specifically important for investors. The main disadvantage of the research is a random timespan that is out of a crisis or special event.
Conclusion- According to research results of bias (the approximation to reality), the Quasi-Monte Carlo Model gives more efficient results than the Pseudo Monte Carlo Model regarding accuracy and sensitivity. Investors in the American Air Carriers financial market should be aware of this important reality.

Keywords

Monte Carlo models, American Airlines, stock prices

References

• Atanassov, E., & Dimov, I. T. (2008). What Monte Carlo models can do and cannot do efficiently?. Applied Mathematical Modelling, 32(8), 1477-1500.
• Atanassov, E., & Ivanovska, S. (2022, June). On the use of Sobol’sequence for high dimensional simulation. In International Conference on Computational Science (pp. 646-652). Cham: Springer International Publishing.
• Berblinger, M., & Schlier, C. (1991). Monte Carlo integration with quasi-random numbers: some experience. Computer Physics Communications, 66(2-3), 157-166.
• Bonate, P. L. (2001). A brief introduction to Monte Carlo simulation. Clinical Pharmacokinetics, 40, 15-22.
• Chen, N., & Hong, L. J. (2007, December). Monte Carlo simulation in financial engineering. In 2007 Winter Simulation Conference (pp. 919-931). IEEE.
• Dimov, I., Todorov, V., & Georgiev, S. (2023). A Super-convergent stochastic method based on the sobol sequence for multidimensional sensitivity analysis in environmental protection. Axioms, 12(2), 146.
• Dong, G. Y., & Lemieux, C. (2022). Dependence properties of scrambled Halton sequences. Mathematics and Computers in Simulation, 200, 240-262.
• Drukker, D. M., & Gates, R. (2006). Generating halton sequences using mata. The Stata Journal, 6(2), 214-228.
• Faure, H., & Lemieux, C. (2009). Generalized Halton sequences in 2008: A comparative study. ACM Transactions on Modeling and Computer Simulation (TOMACS), 19(4), 1-31.
• Faure, H., & Lemieux, C. (2010). Improved Halton sequences and discrepancy bounds. Monte Carlo Methods Appl, 16, 231-250.
• Faure, H., & Lemieux, C. (2016). Irreducible Sobol’sequences in prime power bases. Acta Arithmetica, 173(1), 59-80.
• Ferson, S. (1996). What Monte Carlo methods cannot do. Human and Ecological Risk Assessment: An International Journal, 2(4), 990-1007.
• Gómez-Pérez, D., Hofer, R., & Niederreiter, H. (2013). A general discrepancy bound for hybrid sequences involving Halton sequences. Unif. Distrib. Theory, 8(1), 31-45.
• Halton, J. H. (1994). Sequential Monte Carlo techniques for the solution of linear systems. Journal of Scientific Computing, 9(2), 213-257.
• Harase, S. (2019). Comparison of consequences in financial applications. Monte Carlo Methods and Applications, 25(1), 61-74.
• Murtha, J. A. (1997). Monte Carlo simulation: its status and future. Journal of Petroleum Technology, 49(04), 361-373.
• Nawrocki, D. (2001). The Problems with Monte Carlo Simulation. Journal of Financial Planning, 14(11), 155-167.
• Owen, A. B. (2020, August). On dropping the first Sobol’point. In International Conference on Monte Carlo and quasi-Monte Carlo methods in scientific computing (pp. 71-86). Cham: Springer International Publishing.
• Renardy, M., Joslyn, L. R., Millar, J. A., & Kirschner, D. E. (2021). To Sobol or not to Sobol? The effects of sampling schemes in systems biology applications. Mathematical biosciences, 337, 108593.
• Train, K. Halton Sequences for Mixed Logit. http://elsa. Berkeley. edu/wp/train0899. pdf.
• Wang, X., & Hickernell, F. J. (2000). Randomized Halton sequences. Mathematical and Computer Modelling, 32(7-8), 887-899.
• Weerasinghe, G., Chi, H., & Cao, Y. (2016). Particle swarm optimization simulation via optimal Halton sequences. Procedia Computer Science, 80, 772-781.