Прогнозирование курсов турецкой лиры с помощью одномерных методов: могут ли простые модели превзойти сложные?

На протяжении 2022 г. политика центрального банка Турции по снижению номинальной процентной ставки вызывала эпизоды сильных колебаний курса турецкой лиры. В этих условиях ежедневная доходность пары USD/TRY привлекала внимание инвесторов, склонных к риску. Поэтому неопределенность в отношении ставок подтолкнула алгоритмических трейдеров к поиску наилучшей модели прогнозирования. Несмотря на растущую тенденцию к использованию сложных моделей для прогнозирования финансовых временных рядов, в большинстве случаев простые модели могут дать более точные прогнозы. Чтобы проверить это утверждение, в данном исследовании было использовано несколько моделей для прогнозирования ежедневных валютных курсов в краткосрочной перспективе. Интересно, что простая модель экспоненциального сглаживания превзошла все остальные альтернативы. Кроме того, в отличие от первоначальных предположений, временные ряды не имели ни структурного разрыва, ни признаков эффектов ARCH и левериджа. Несмотря на такое поведение, существуют неоспоримые доказательства наличия тренда с длинной памятью. Это означает, что ряд имеет тенденцию сохранять движение, по крайней мере, в течение короткого периода. В итоге исследование пришло к выводу, что простые модели дают лучшие прогнозы для валютных курсов, чем сложные подходы.

1. Fetai B., Koku P. S., Caushi A., Fetai A. The relationship between exchange rate and inflation: The case of Western Balkans countries. Journal of Business Economics and Finance. 2016;5(4):360–364. DOI: 10.17261/Pressacademia.2017.358

2. Edison H. J. Purchasing power parity in the long run: A test of the dollar/pound exchange rate (1890–1978). Journal of Money, Credit and Banking. 1987;19(3):376–387. DOI: 10.2307/1992083

3. Thahara A. F., Rinosha K. F., Shifaniya A. J.F. The relationship between exchange rate and trade balance: Empirical evidence from Sri Lanka. Journal of Asian Finance, Economics and Business. 2021;8(5):37–41. DOI: 10.13106/jafeb.2021.vol8.no5.0037

4. Razak N., Masih M. The relationship between exchange rate and trade balance: Evidence from Malaysia based on ARDL and nonlinear ARDL. Munich Personal RePEc Archive. MPRA Paper. 2018;(112447). URL: https://mpra.ub.uni-muenchen.de/112447/1/MPRA_paper_112447.pdf

5. Kouladoum J.-C. External debts and real exchange rates in developing countries: Evidence from Chad. Munich Personal RePEc Archive. MPRA Paper. 2018;(88440). URL: https://mpra.ub.uni-muenchen.de/88440/1/MPRA_paper_88440.pdf

6. Christensen I., Dion F., Reid C. Real return bonds, inflation expectations, and the break-even inflation rate. Bank of Canada Working Paper. 2004;(43). URL: https://www.bankofcanada.ca/wp-content/uploads/2010/02/wp04–43.pdf

7. Cheung Y.-W. Long memory in foreign-exchange rates. Journal of Business & Economic Statistics. 1993;11(1):93–101. DOI: 10.2307/1391309

8. Granger C. W.J., Joyeux R. An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis. 1980;1(1):15–29. DOI: 10.1111/j.1467–9892.1980.tb00297.x

9. Bollerslev T., Wright J. H. High frequency data, frequency domain inference, and volatility forecasting. The Review of Economics and Statistics. 2001;83(4):590–602. URL: https://public.econ.duke.edu/~boller/Published_Papers/restat_01.pdf

10. Longmore R., Robinson W. Modeling and forecasting exchange rate dynamics: An application of asymmetric volatility models. Bank of Jamaica Working Paper. 2004;(03). URL: https://www.boj.org.jm/uploads/pdf/papers_pamphlets/papers_pamphlets_modelling_and_forecasting_exchange_rate_dynamicsan_application_of_aysmmetric_volatility_models.pdf

11. Pacelli V., Bevilacqua V., Azzollini M. An artificial neural network model to forecast exchange rates. Journal of Intelligent Learning Systems and Applications. 2011;3(2):57–69. DOI: 10.4236/jilsa.2011.32008

12. Khashei M., Torbat S., Haji Rahimi Z. An enhanced neural-based bi-component hybrid model for foreign exchange rate forecasting. Turkish Journal of Forecasting. 2017;1(1):16–29.

13. Pfahler J. F. Exchange rate forecasting with advanced machine learning methods. Journal of Risk and Financial Management. 2022;15(1):2. DOI: 10.3390/jrfm15010002

14. Friedman M. The methodology of positive economics. In: Essays in positive economics. Chicago, IL: University of Chicago Press; 1953:3–16;30–43. URL: https://www.wiwiss.fu-berlin.de/fachbereich/bwl/pruefungs-steuerlehre/loeffler/Lehre/bachelor/investition/Friedman_the_methology_of_positive_economics.pdf

15. Fama E. F. Efficient capital markets: A review of theory and empirical work. The Journal of Finance. 1970;25(2):383–417. DOI: 10.2307/2325486

16. Jarqu C. M., Bera A. K. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters. 1980;6(3):255–259. DOI: 10.1016/0165–1765(80)90024–5

17. Grigoletto M., Lisi F. Looking for skewness in financial time series. The Econometrics Journal. 2009;12(2):310– 323. DOI: 10.1111/j.1368–423X.2009.00281.x

18. Box G., Jenkins G. Time series analysis: Forecasting and control. San Francisco, CA: Holden-Day; 1970. 553 p.

19. Engle R. E. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica. 1982;50(4):987–1007. DOI: 10.2307/1912773

20. Blanchard G. J., Watson M. W. Bubbles, rational expectations and financial market. NBER Working Paper. 1982;(945). URL: https://www.nber.org/system/files/working_papers/w0945/w0945.pdf

21. Wald A., Wolfowitz J. On a test whether two samples are from the same population. Annals of Mathematical Statistics. 1940;11(2):147–162. DOI: 10.1214/aoms/1177731909

22. Baillie R. T. Long memory processes and fractional integration in econometrics. Journal of Econometrics. 1996;73(1):5–59. DOI: 10.1016/0304–4076(95)01732–1

23. Said E. S., Dickey D. A. Testing for unit roots in autoregressive moving average models of unknown order. Biometrika. 1984;71(3):599–607. DOI: 10.1093/biomet/71.3.599

24. Phillips P. C.B., Perron P. Testing for a unit root in time series regression. Biometrika. 1988;75(2):335–346. DOI: 10.1093/biomet/75.2.335

25. Kwiatkowski D., Phillips P. C.B., Schmidt P., Shin Y. Testing the null hypothesis of stationary against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics. 1992;54(1–3):159–178. DOI: 10.1016/0304–4076(92)90104-Y

26. Perron P. The great crash, the oil price shocks, and the unit root hypothesis. Econometrica. 1989;57(6):1361–1401. DOI: 10.2307/1913712

27. Chow G. C. Tests of equality between sets of coefficients in two linear regressions. Econometrica. 1960;28(3):591–605. DOI: 10.2307/1910133

28. Bai J., Perron P. Estimating and testing linear models with multiple structural changes. Econometrica. 1998;66(1):47–78. DOI: 10.2307/2998540

29. Bai J., Perron P. Computation and analysis of multiple structural change models. Applied Econometrics. 2003;18(1):1–22. DOI: 10.1002/jae.659

30. Enders W. Applied econometric time series. 4th ed. New York, NY: John Wiley & Sons, Inc.; 2014. 496 p. (Wiley Series in Probability and Statistics).

31. Ljung M. G., Box G. E.P. On a measure of lack of fit in time series models. Biometrika. 1978;65(2):297–303. DOI: 10.1093/biomet/65.2.297

32. Brown R. G. Exponential smoothing for predicting demand. In: Proc. 10th Nat. meet. Operations Research Society of America (San Francisco, CA, November 16, 1956). Cambridge, MA: Arthur D. Little Inc.; 1956:1–15.

33. Holt C. C. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting. 2004;20(1):5–10. DOI: 10.1016/j.ijforecast.2003.09.015

34. Jacobs F. R., Chase R. B. Operation and supply chain management: The core. 3rd ed. New York, NY: McGraw-Hill Education; 2013. 552 p.

35. Heizer J., Render B. Operation management. 10th ed. Upper Saddle River, NJ: Prentice-Hall; 2011. 806 p.

36. Chopra S., Meindl P. Supply chain management: Strategy, planning and operation. 5th ed. Upper Saddle River, NJ: Prentice-Hall; 2013. 528 p.

37. Raeisi Sarkandiz M., Bahlouli R. The stock market between classical and behavioral hypotheses: An empirical investigation of the Warsaw Stock Exchange. Econometric Research in Finance. 2019;4(2):67–88. DOI: 10.33119/ERFIN.2019.4.2.1