MEVDUAT FAİZ ORANLARININ ARİMA YÖNTEMİ İLE TAHMİNİ: 2010-2022 DÖNEMİ TÜRKİYE UYGULAMASI

Abstract

Finansal verilerin en dikkate değer özelliklerinden biri zamana bağlı biçimde bir dizi teşkil etmeleridir. Bundan ötürü zaman serilerinin unsurları, söz konusu verilerin ifade ettiği ekonomik ve finansal parametreler hakkındaki bilgiyi de kapsamaktadır. Finans çalışmalarında değişkenlere ilişkin öngörü ya da tahmin hayati bir öneme sahiptir. Finansal değişkenlerin doğru, sağlıklı tahmin edilebilmesi, finansal piyasalardaki paydaşların açısından vazgeçilmez bir durumdur. Tahmin yapmada en sık kullanılan yöntemlerden birisi de ARİMA modelidir. Auto Regressive Integrated Moving Average (Otoregresif entegre hareketli ortalama) (ARIMA) modeli, tek değişkenli zaman serisi verilerini, transfer fonksiyonu verilerini ve ayrıca müdahale verilerini eşit şekilde dağıtan verilerde analiz ve tahminler için kullanılmaktadır. ARIMA yöntemi, ilk olarak Box ve Jenkins (1976) tarafından açıklamıştır, bu nedenle ARIMA modelleri genellikle Box-Jenkins modelleri olarak anılmaktadır. Bu çalışmada 2010-2022 yılı arasındaki dönem itibariyle Türkiye’de 1 yıl vadeli TL mevduat faiz oranları ARİMA yöntemi ile tahmin edilmeye çalışılmıştır. Analiz sonuçlarına göre araştırmada kullanılan Box-Jenkins (ARIMA) modelinin geçerli olduğu sonucuna varılmıştır. ARIMA (1,1,1) modelinin hem model uyum düzeyi ve modelin açıklama gücü, hem de tahmin değerleri ile gerçek değerler, modelin tahminde kullanılabilecek en doğru sonuçları veren, sağlam ve güvenilir bir model olduğunu gözlenmiştir.

Keywords

• ARİMA
• Tahmin
• Mevduat faiz oranı
• Türkiye

Forecasting The Deposit Interest Rates with The ARIMA Method: Turkish Application for The Period 2010-2022

Abstract

One of the most remarkable characteristics of financial data is that they form a series in a time-dependent manner. For this reason, the elements of the time series also include the information on the economic and financial parameters expressed by the data in question. Forecasting variables has a vital importance in finance studies. Accurate and healthy forecasting of financial variables is an indispensable condition for stakeholders in financial markets. One of the most frequently used methods in making predictions is the ARIMA Method. The Auto Regressive Integrated Moving Average (ARIMA) Method is used for analysis and forecasting on data that evenly distributes univariate time series data, transfer function data, as well as intervention data. An ARIMA Model predicts a value in the response time series as a linear combination of its past values, errors or shocks, as well as the current and past values of other time series. The ARIMA Method was first described by Box and Jenkins (1976). For this reason, ARIMA Models are often referred to as Box-Jenkins Models. In the present study, 1-year TL deposit interest rates for the period between 2010 and 2022 in Turkey were predicted with the ARIMA Method. According to the results of the analysis, it was concluded that the Box-Jenkins (ARIMA) Model used in the study is valid. It was found that the ARIMA (1, 1, 1) Model is a robust and reliable model that yields the most accurate results that can be used in prediction, both the level of model fit and the explanatory power of the model, as well as the forecasting and actual values.

Keywords

• ARIMA
• Forecasting
• Deposit interest rate
• Turkey

References

1. Abu Bakar, N., & Rosbi, S. (2017). Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction. International Journal of Ad-vanced Engineering Research and Science (IJAERS), 4(11),130-137.
2. Adhikari, R., & Agrawal, R. K. (2014). A combination of artifi cial neural network and random walk models for financial time series forecasting. Neural Computing and Applications, 24(6), 1441-1449.
3. Ahmed, R.A., Shabri, A.B. (2014). DAILY CRUDE OIL PRICE FORECASTING MODEL USING ARIMA, GENERALIZED AUTOREGRESSIVE CONDITIONAL HET-EROSCEDASTIC AND SUPPORT VECTOR MACHINES. American Journal of Applied Sciences, 11 (3), 425-432.
4. Ahmed, R.R., Vveinhardt, J., Ahmad, N. & Štreimikienė, D. (2017). KARACHI INTER-BANK OFFERED RATE (KIBOR) FORECASTING: BOX-JENKINS (ARIMA) TESTING APPROACH. E&M Economics and Management, 20(2),188-198.
5. Ahoniemi, K. (2006). Modeling and forecasting implied volatility - an econometric analysis of the VIX. Helsinki Center of Economic Research [Discussion Paper.
6. Akaike, H. (1974). A New Look at Statistical Model Identification. IEEE Transactions on Automatic Control, AC-19, 716-723.
7. Al-Gounmeein, R.S., & İsmail, M.T. (2020). Forecasting the Exchange Rate of the Jordani-an Dinar versus the US Dollar Using a Box-Jenkins Seasonal ARIMA Model. In-ternational Journal of Mathematics and Computer Science, 15(1), 27–40.
8. Almasarweh, M., & Wadi, S. (2018). ARIMA model in predicting banking stock market data. Modern Applied Science, 12, 309–312.

9. Bağcı, B. (2021). ARIMA VE GRİ TAHMİN MODELLERİNDE FOURİER SERİSİ MODİFİKASYONU: TÜRKİYE ENFLASYONU UYGULAMASI. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(2), 559-577.
10. Balli, F., & Elsamadisy, E.M. (2012). Modelling the currency in circulation for the State of Qatar. International Journal of Islamic and Middle Eastern Finance and Man-agement, 5 (4),321-339.
11. Banerjee, D. (2014). Forecasting of Indian stock market using time-series ARIMA model. In Proc. Conference Paper, ICBIM-14, 2014.
12. Bilgili, F. (2001). ARİMA ve VAR Modellerinin Tahmin Başarılarının Karşılaştırılması. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17, 37-53.
13. Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problem. Journal of the American Statistical Association, 70(349), 70-79.
14. Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control (3rd ed.). Prentice-Hall.
15. Chinn, M.D., LeBlanc, M.R., & Coibion, O. (2005). The Predictive Content of Energy Fu-tures: An Update on Petroleum, Natural Gas, Heating Oil and Gasoline. 1st Edn., National Bureau of Economic Research, Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. Power Systems, IEEE Transactions on Power Systems, 18(3), 1014-1020.
16. Dinh, D.V. (2020). Forecasting domestic credit growth based on ARIMA model: Evidence from Vietnam and China. Management Science Letters, 1001–1010.
17. Dua, P., Raje, N., & Sahoo, S. (2004). Interest Rate Modeling and Forecasting in India. Occasional paper no. 3. Centre for Development Economics, Delhi School of Economics.
18. Dua, P., Raje, N., & Sahoo, S. (2008). Forecasting Interest Rates in India. Margin-The Journal of Applied Economic Research, 2 ,1, 1–41.
19. Etuk, E.H. (2016). Box-Jenkins Method Based Additive Simulating Model for Daily Ugx-Ngn Exchange Rates. Academic Journal of Applied Mathematical Sciences, 2(2),11-18.
20. Gough, O., Nowman, K.B., & Van Dellen, S. (2014). Modelling and forecasting internation-al interest rate spreads: UK, Germany, Japan and the USA. International Journal of Financial Engineering and Risk Management, 1(4),309-333.
21. Guha, B., & Bandyopadhyay, G. (2016). Gold Price Forecasting Using ARIMA Model. Journal of Advanced Management Science, 4(2), 117-121.
22. Hage, R.S., & Mghames, S.J. (2020). Modelling and Estimating Interest Rate: A Compara-tive Study of ARIMA, and ARIMA Kalman Model. European Journal of Scien-tific Research, 155(4),440-454.
23. Hannan, E. (1980). The Estimation of the Order of ARMA Process. Annals of Statistics, 8(5), 1071-1081.
24. Irfan, M., Maria, M., & Awais, M. (2010). Modeling Conditional Heteroscedasticity and Forecasting in Short Term Interest Rate of KIBOR. International Journal of Eco-nomic Perspectives, 4(4), 635-654.
25. Jadevicius, A., & Huston, S. (2015). ARIMA modelling of Lithuanian house price index. International Journal of Housing Markets and Analysis, 8 (1),135-147.
26. Makridakis, M., & Hibon, M. (2000). The M3-Competition: results, conclusions and impli-cations. International Journal of Forecasting, 16(4), 451-476.
27. Mallick, A.K., & Mishra, A.K. (2019). Interest rate forecasting and stress testing in India: PCA-ARIMA approach. Palgrave Communications, 5(32),1-17.
28. Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting irish infl ation using ARIMA mod-els. Central Bank and Financial Services Authority of Ireland Technical Paper Se-ries, 3, 46.
29. Mustafa, A., Ahmad, M. H., & Ismail, N. (2017). Modelling and forecasting US Dol-lar/Malaysian ringgit exchange rate. Reports on Economics and Finance, 3, 1-13.
30. Ngan, T. M. U. (2016). Forecasting foreign exchange rate by using ARIMA model: A case of VND/USD exchange rate. Research Journal of Finance and Accounting, 7, 38–44.
31. Omekara, C.O., Okereke,O.E., & Ehighibe, S.E. (2016). Time Series Analysis of Interest Rate in Nigeria: A Comparison of Arima and State Space Models. International Journal of Probability and Statistics, 5(2), 33-47.
32. Pankratz, A. (1991). Forecasting with Dynamic Regression Models. New York: John Wiley & Sons.
33. Radha, S., & Thenmozhi, M. (2005). Forecasting Short Term Interest Rates Using Arma-Garch and Arma-Egarch Models. Indian Institute of Capital Markets 9th Capital Markets Conference Paper.
34. Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6(2), 461-464.
35. Sehgal, S., Bijoy, K., & Deisying, F. (2011). Modeling and forecasting debt market yields: evidence from India. Banks and Bank Systems, 6(4), 49-63.
36. Toor, S., & Ali, M. (2013). Forecasting of Deposit Rates and Time Series Analysis Technical Report, BS Actuarial Sciences and Risk Management. University of Karachi. Re-trieved from https://www.academia. edu/6054561/FORECASTING_OF_DEPOSIT_ RATES_AND_TIME_SERIES_ANALYSIS.
37. Wang, Y., & Guo, Y. (2020). Forecasting Method of Stock Market Volatility in Time Series Data Based on Mixed Model of ARIMA and XGBoost. China Communications Emerging Technologies & Applications, 17(3), 205-221.
38. Yıldıran, C. U., & Fettahoğlu, A. (2017). Forecasting USD/TRY rate by ARIMA method. Cogent Economics and Finance, 5, 1–11.
39. https://evds2.tcmb.gov.tr/index.php?/evds/portlet/lrcsQFWXtqo%3D/tr (Erişim: 07.12.2022)