A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model

Olcay Ölçen

TR EN

Monte Carlo Simülasyon Modeli ile Amerika Birleşik Devletleri Hava Taşıyıcıları için Finansal Getiri Analizi

Abstract

Amaç ve Yöntem: Hava taşıyıcıları, ekonomik, politik ve teknolojik olmak üzere üç önemli boyutun kavşağında oldukları için finansal ve ekonomik olarak zor zamanlara alışkındır. Bu boyutlardaki küçük değişiklikler bile bir krizin kaynağıdır. ABD Havayolu şirketlerinin yatırımcılarının motivasyonları sürekli bir değişim halindedir. Bu analiz, standart sapma değerlerinin 0,01, 0,0001 ve 0,000001 olduğu üç senaryo ile Monte Carlo Simülasyonu yöntemini kullanarak bir getiri tahmini yapmayı amaçlamaktadır. Bulgular: Bulgulara göre, standart sapmalar arttıkça kazançlar veya kayıplar artarken ortalama sabittir. Öte yandan, devletlerin hava taşıyıcılarının dinamikleri konusunda daha istikrarlı politikalar kullanmaları gerektiği sonucuna varılabilir. Finansal yönetimde oynaklık, COVID-19 ve benzeri krizlerde görüldüğü gibi hava taşıyıcılarının düşmanıdır. Hava taşıyıcıları ve devletler oynaklık şoklarına hazır olmalıdır çünkü havayolları devlet stratejilerinin ve politikalarının bir parçası olarak tasarlanmıştır. Bu nedenle, korunma stratejileri her zaman devam etmelidir. Özgünlük: Bu araştırma, deneysel koşullar altında ve küçükten büyüğe değişen şoklar altında hisse senedi getirilerinin davranışlarını ve olası sonuçlarını göstermektedir.

Keywords

Hava Taşıyıcıları, Finansal Getiriler, Monte Carlo Simülasyon Modeli, Standart Sapma

A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model

Abstract

Purpose and Method: Air carriers are accustomed to hard times financially and economically because they are at the crossroads of three important dimensions that are economic, political and technological. Even small changes in these dimensions are the source of a crisis. The motivations of the US. Air carriers' investors are in a state of continual change. This analysis aims to make a return forecast using the Monte Carlo Simulation method with three scenarios in which values of standard deviations are 0.01, 0.0001 and 0.000001. Findings: According to the findings, as standard deviations increase, the gains or losses increase while the mean is stable. On the other hand, it can be concluded that the states should utilise more stable politics regarding the dynamics of air carriers. Volatility is an enemy of air carriers in financial management, as witnessed in COVID-19 and similar crises. Air carriers and states should be ready for volatility shocks because airlines are designed as part of state strategies and politics. Therefore, hedging strategies should always continue. Originality: This research shows the behaviours of the stock returns and possible results under experimental conditions and changing shocks from small to large.

Keywords

Air Carriers, Financial Returns, Monte Carlo Simulation Model, Standard Deviation

References

Aggarwal, R., Inclan, C., & Leal, R. (1999). Volatility in emerging stock markets. Journal of Financial and Quantitative Analysis, 34(1), 33–55.
Abidovna, A. S. (2023). Monte Carlo Modelling and Its Peculiarities in the Implementation of Marketing Analysis in the Activities of the Enterprise. Gospodarka i Innowacje, 42, 375–380.
Akyildirim, E., Corbet, S., Efthymiou, M., Guiomard, C., O'Connell, J. F., & Sensoy, A. (2020). The financial market effects of international aviation disasters. International Review of Financial Analysis, 69, 101468.
Amihud, Y., & Mendelson, H. (1987). Trading mechanisms and stock returns: An empirical investigation. The Journal of Finance, 42(3), 533–553.
Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross‐section of volatility and expected returns. The Journal of Finance, 61(1), 259–299.
Angelini, G., Bacchiocchi, E., Caggiano, G., & Fanelli, L. (2019). Uncertainty across volatility regimes. Journal of Applied Econometrics, 34(3), 437–455.
Atems, B. (2021). The response of the US aviation industry to demand and supply shocks in the oil and jet fuel markets. Transportation Research Interdisciplinary Perspectives, 11, 100452.
Baele, L., & Inghelbrecht, K. (2009). Time-varying integration and international diversification strategies. Journal of Empirical Finance, 16(3), 368–387.
Bagchi, B. (2017). Volatility spillovers between crude oil price and stock markets: Evidence from BRIC countries. International Journal of Emerging Markets, 12(2), 352–365.
Baillie, R. T., & DeGennaro, R. P. (1990). Stock returns and volatility. Journal of Financial and Quantitative Analysis, 25(2), 203–214.

Barigozzi, M., & Hallin, M. (2016). Generalized dynamic factor models and volatilities: Recovering the market volatility shocks. The Econometrics Journal, 19(1), C33–C60.
Beetsma, R., & Giuliodori, M. (2012). The changing macroeconomic response to stock market volatility shocks. Journal of Macroeconomics, 34(2), 281–293.
Bollerslev, T., Li, S. Z., & Zhao, B. (2020). Good volatility, bad volatility, and the cross-section of stock returns. Journal of Financial and Quantitative Analysis, 55(3), 751–781.
Bonate, P. L. (2001). A brief introduction to Monte Carlo simulation. Clinical Pharmacokinetics, 40, 15–22.
Born, B., & Pfeifer, J. (2014). Risk matters: The real effects of volatility shocks. American Economic Review, 104(12), 4231–4239.
Bouri, E., & Harb, E. (2022). The size of good and bad volatility shocks does matter for spillovers. Journal of International Financial Markets, Institutions and Money, 80, 101626.
Brenner, M., & Subrahmanyan, M. G. (1988). A simple formula to compute the implied standard deviation. Financial Analysts Journal, 44(5), 80–83.
Brueckner, J. K., & Abreu, C. (2020). Does the fuel conservation effect of higher fuel prices appear at both the aircraft model and aggregate airline levels? Economics Letters, 197, 109647.
Cai, Y., Zhang, Y., & Zhang, A. (2025). Oil price shocks and airlines' stock return and volatility–A GFEVD analysis. Economics of Transportation, 41, 100396.
Callen, J. L., Khan, M., & Lu, H. (2013). Accounting quality, stock price delay, and future stock returns. Contemporary Accounting Research, 30(1), 269–295.
Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281–318.
Capaul, C., Rowley, I., & Sharpe, W. F. (1993). International value and growth stock returns. Financial Analysts Journal, 49(1), 27–36.
Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.
Carter, D. A., Rogers, D. A., & Simkins, B. J. (2006). Does hedging affect firm value? Evidence from the US airline industry. Financial Management, 35(1), 53–86.
Chordia, T., Sarkar, A., & Subrahmanyam, A. (2005). An empirical analysis of stock and bond market liquidity. The Review of Financial Studies, 18(1), 85–129.
Connolly, R., Stivers, C., & Sun, L. (2005). Stock market uncertainty and the stock-bond return relation. Journal of Financial and Quantitative Analysis, 40(1), 161–194.
Dar, A. B. (2022). On the sustainable nexus between oil prices and aviation stocks. Sustainable Operations and Computers, 3, 168–175.
Degiannakis, S., Filis, G., & Kizys, R. (2014). The effects of oil price shocks on stock market volatility. The Energy Journal, 35(1), 35–56.
Deb, S. (2023). Analyzing airlines stock price volatility during COVID‐19 pandemic. International Journal of Finance & Economics, 28(2), 1497–1513.
Engle, R. F., & Patton, A. J. (2007). What good is a volatility model? In Forecasting volatility in the financial markets (pp. 47–63). Butterworth-Heinemann.
Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776–797.
Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.
Fama, E. F. (1990). Stock returns, expected returns, and real activity. The Journal of Finance, 45(4), 1089–1108.
Fama, E. F. (1995). Random walks in stock market prices. Financial Analysts Journal, 51(1), 75–80.
Fama, E. F., & French, K. R. (1988). Permanent and temporary components of stock prices. Journal of Political Economy, 96(2), 246–273.
Fama, E. F., & French, K. R. (1996). The CAPM is wanted, dead or alive. The Journal of Finance, 51(5), 1947–1958.
Fama, E. F., & French, K. R. (2016). Dissecting anomalies with a five-factor model. The Review of Financial Studies, 29(1), 69–103.
Fama, E. F., & French, K. R. (2020). Comparing cross-section and time-series factor models. The Review of Financial Studies, 33(5), 1891–1926.
Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. International Economic Review, 10(1), 1–21.
Flouris, T., & Walker, T. (2005). Confidence in airline performance in difficult market conditions. Journal of Air Transportation, 10(1).
French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3–29.
Guo, H., & Savickas, R. (2006). Idiosyncratic volatility and expected stock returns. Journal of Business & Economic Statistics, 24(1), 43–56.
Hamilton, J. D., & Lin, G. (1996). Stock market volatility and the business cycle. Journal of Applied Econometrics, 11(5), 573–593.
Harrison, R. L. (2010). Introduction to Monte Carlo simulation. AIP Conference Proceedings, 1204, 17.
Hjalmarsson, E. (2010). Predicting global stock returns. Journal of Financial and Quantitative Analysis, 45(1), 49–80.
Horobet, A., Zlatea, M. L. E., Belascu, L., & Dumitrescu, D. G. (2022). Oil price volatility and airlines' stock returns. Journal of Business Economics and Management, 23(2), 284–304.
Hou, K., Karolyi, G. A., & Kho, B. C. (2011). What factors drive global stock returns? The Review of Financial Studies, 24(8), 2527–2574.
Joy, D. C. (1991). An introduction to Monte Carlo simulations. Scanning Microscopy, 5(2), 4.
Kang, W., de Gracia, F. P., & Ratti, R. A. (2021). Economic uncertainty and US airline stock returns. The North American Journal of Economics and Finance, 57, 101388.
Kar, S., & Khandelwal, P. (2020). Cross-hedging aviation fuel price exposures. IIMB Management Review, 32(4), 389–401.
Kaufmann, R. K. (2017). Airfares and oil prices. Energy Economics, 68, 515–521.
Kon, S. J. (1984). Models of stock returns. The Journal of Finance, 39(1), 147–165.
Kristjanpoller, W., & Concha, D. (2016). Impact of fuel price fluctuations on airline stock returns. Applied Energy, 178, 496–504.
Lanne, M., & Lütkepohl, H. (2008). Identifying monetary policy shocks via changes in volatility. Journal of Money, Credit and Banking, 40(6), 1131–1149.
Lee, C. F., Chen, G. M., & Rui, O. M. (2001). Stock returns and volatility on China's stock markets. Journal of Financial Research, 24(4), 523–543.
Lewis, (2021). Identifying shocks via time-varying volatility. The Review of Economic Studies, 88(6), 3086–3124.
Li, L. (2002). Macroeconomic factors and the correlation of stock and bond returns. SSRN.
Li, X., & Su, C. W. (2024). Uncertainty shocks and airline stocks. Journal of Air Transport Management, 121, 102688.
Majka, M. (2024). Market Risk Analysis in Investment Risk Management. ResearchGate.
Maneenop, S., & Kotcharin, S. (2020). COVID-19 and airline industry. Journal of Air Transport Management, 89, 101920.
Meher, P., & Mishra, R. K. (2024). Risk-adjusted portfolio optimization. Australasian Accounting, Business and Finance Journal, 18(3).
Mumtaz, H., & Theodoridis, K. (2015). International transmission of volatility shocks. Journal of the European Economic Association, 13(3), 512–533.
Mundform, D. J., et al. (2011). Number of replications in Monte Carlo simulation. Journal of Modern Applied Statistical Methods, 10, 19–28.
Murtha, J. A. (1997). Monte Carlo simulation. Journal of Petroleum Technology, 49(4), 361–373.
Nowak, S. B. (2008). Public announcements and airline stocks. CAMA Working Paper Series.
Officer, R. R. (1972). The distribution of stock returns. Journal of the American Statistical Association, 67(340), 807–812.
Raychaudhuri, S. (2008). Introduction to Monte Carlo Simulation. Winter Simulation Conference Proceedings, 91–100.
Schwert, G. W. (1990). Stock returns and real activity. The Journal of Finance, 45(4), 1237–1257.
Schwert, G. W. (2011). Stock volatility during the financial crisis. European Financial Management, 17(5), 789–805.
Shiller, R. J. (1987). The volatility of stock market prices. Science, 235(4784), 33–37.
Smid, J. H., et al. (2010). Monte Carlo simulation models. International Journal of Food Microbiology, 139, S57–S63.
Stoll, H. R., & Whaley, R. E. (1990). Stock market structure and volatility. The Review of Financial Studies, 3(1), 37–71.
Treanor, S., Rogers, D., Carter, D., & Simkins, B. (2014). Exposure, hedging, and value. International Review of Financial Analysis, 34, 200–211.
Xiao, J., Hu, C., Ouyang, G., & Wen, F. (2019). Oil volatility and stock volatility. Energy Economics, 80, 297–309.
Yun, X., & Yoon, S.-M. (2019). Oil price change and airline stocks. Energy Economics, 78, 668–679.

Details

Primary Language

English

Subjects

Finance

Journal Section

Research Article

Authors

Olcay Ölçen ^*
0000-0002-4835-1171
Türkiye

Publication Date

December 26, 2025

Submission Date

May 20, 2025

Acceptance Date

December 19, 2025

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Year 2025 Volume: 7 Number: 2

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https://izlik.org/JA83ZR24LL

APA

Ölçen, O. (2025). A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model. Uluslararası Muhasebe Ve Finans Araştırmaları Dergisi, 7(2), 75-90. https://izlik.org/JA83ZR24LL

AMA

1.Ölçen O. A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model. IJAFR. 2025;7(2):75-90. https://izlik.org/JA83ZR24LL

Chicago

Ölçen, Olcay. 2025. “A Financial Return Analysis for the United States Air Carriers With a Monte Carlo Simulation Model”. Uluslararası Muhasebe Ve Finans Araştırmaları Dergisi 7 (2): 75-90. https://izlik.org/JA83ZR24LL.

EndNote

Ölçen O (December 1, 2025) A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model. Uluslararası Muhasebe ve Finans Araştırmaları Dergisi 7 2 75–90.

IEEE

[1]O. Ölçen, “A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model”, IJAFR, vol. 7, no. 2, pp. 75–90, Dec. 2025, [Online]. Available: https://izlik.org/JA83ZR24LL

ISNAD

Ölçen, Olcay. “A Financial Return Analysis for the United States Air Carriers With a Monte Carlo Simulation Model”. Uluslararası Muhasebe ve Finans Araştırmaları Dergisi 7/2 (December 1, 2025): 75-90. https://izlik.org/JA83ZR24LL.

JAMA

1.Ölçen O. A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model. IJAFR. 2025;7:75–90.

MLA

Ölçen, Olcay. “A Financial Return Analysis for the United States Air Carriers With a Monte Carlo Simulation Model”. Uluslararası Muhasebe Ve Finans Araştırmaları Dergisi, vol. 7, no. 2, Dec. 2025, pp. 75-90, https://izlik.org/JA83ZR24LL.

Vancouver

1.Olcay Ölçen. A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model. IJAFR [Internet]. 2025 Dec. 1;7(2):75-90. Available from: https://izlik.org/JA83ZR24LL

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Monte Carlo Simülasyon Modeli ile Amerika Birleşik Devletleri Hava Taşıyıcıları için Finansal Getiri Analizi

Abstract

Keywords

A Financial Return Analysis for the United States Air Carriers with a Monte Carlo Simulation Model

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

IZ

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