Dergiler / Ege Akademik Bakış Dergisi / 2024 / Cilt: 24 Sayı: 4

Determinants of Exchange Rate Jumps in Türkiye

Erkan Ağaslan(DUMLUPINAR ÜNİVERSİTESİ)0000-0001-8118-7222
Savaş Gayaker(Ankara Hacı Bayram Veli Üniversitesi)0000-0002-7186-1532
Erol Bulut(ANKARA HACI BAYRAM VELİ ÜNİVERSİTESİ)0000-0002-9293-9052
Sayfa
621–638
Yayın Tarihi
2024-11-01
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The findings indicate that increased dollarization raises the likelihood of an exchange rate jump, while higher deposit rates and central bank reserves reduce the probability of a jump.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study examines the exchange rate jumps in Türkiye between 2013 and 2021 and the factors influencing these jumps.
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Temel Bulgular & Veri:The primary objective of this research is to identify these episodes of spikes, which can be characterized as jumps in exchange rates amid regular increases.
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Sonuç & Katkı:The findings indicate that increased dollarization raises the likelihood of an exchange rate jump, while higher deposit rates and central bank reserves reduce the probability of a jump.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2013 Verileri
⚙️Değişkenler:Exchange Rate · Dollarization · PELT Algorithm

Abstract

This study examines the exchange rate jumps in Türkiye between 2013 and 2021 and the factors influencing these jumps. The Turkish Lira experienced a consistent depreciation against other currencies during this period. A closer examination of the depreciation timeline revealed that the Turkish Lira's depreciation was occasionally abrupt and exceedingly pronounced. The primary objective of this research is to identify these episodes of spikes, which can be characterized as jumps in exchange rates amid regular increases. To achieve this, the Pruned Exact Linear Time (PELT) algorithm was employed to detect sudden shifts in the exchange rate. Taking these points as dependent variables, a rare event logistic regression model was utilized to determine the probability of an exchange rate jump. The findings indicate that increased dollarization raises the likelihood of an exchange rate jump, while higher deposit rates and central bank reserves reduce the probability of a jump.

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Bu Makaleye Atıf Yap

Ağaslan, E., Gayaker, S., Bulut, E. (2024). Determinants of Exchange Rate Jumps in Türkiye. Ege Akademik Bakış Dergisi, Cilt: 24 Sayı: 4, 621–638. https://doi.org/10.21121/eab.20240409

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Kaynakça (63)

  1. Ağaslan, E., & Alkan, B. (2021). Döviz kuru oynaklığı ve ekonomik büyüme ilişkisine yeniden bakış. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(3), 1001–1016. https://doi.org/10.26745/AHBVUIBFD.1001886
  2. Ağaslan, E., & Gayaker, S. (2019). Türkiye’de para ikamesinin belirleyicileri doğrusal olmayan bir yaklaşım. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(2), 362–387.
  3. Agresti, A. (2007). An introduction to categorical data analysis. Wiley-Interscience.
  4. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2003). Micro effects of macro announcements: Real-time price discovery in foreign exchange. American Economic Review, 93(1), 38–62. https://doi.org/10.1257/000282803321455151
  5. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2007). Real-time price discovery in global stock, bond and foreign exchange markets. Journal of International Economics, 73(2), 251–277. https://doi.org/10.1016/J.JINTECO.2007.02.004
  6. Andersen, T. G., Bollerslev, T., Frederiksen, P., & Nielsen, M. Ø. (2010). Continuous-time models, realized volatilities, and testable distributional implications for daily stock returns. Journal of Applied Econometrics, 25(2), 233–261. https://doi.org/10.1002/JAE.1105
  7. Arita, S. (2003). The growth of the Korean middle class and its social consciousness. The Developing Economies, 41(2), 201–220. https://doi.org/10.1111/j.1746-1049.2003.tb00938.x
  8. Auger, I. E., & Lawrance, C. E. (1989). Algorithms for the optimal identification of segment neighborhoods. Bulletin of Mathematical Biology, 51(1), 39–54. https://doi.org/10.1016/S0092-8240(89)80047-3
  9. Ayadi, M. A., Omrane, W. B., & Das, D. K. (2024). Macroeconomic news, senior officials' speeches, and emerging currency markets: An intraday analysis of price jump reaction. Emerging Markets Review, 60, 101147.
  10. Bian, H., Fattah, R., Zhong, S., & Zhang, X. (2020). An efficient rectilinear Gaussian beam tracing method for sound propagation modelling in a non-turbulent medium. The Journal of the Acoustical Society of America, 148(6), 4037. https://doi.org/10.1121/10.0002966
  11. Bilson, J. F. O. (1978). The current experience with floating exchange rates: An appraisal of the monetary approach. The American Economic Review, 68(2), 392–397. https://www.jstor.org/stable/1816727
  12. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud fetection: A review. Https://Doi.Org/10.1214/Ss/1042727940, 17(3), 235–255. https://doi.org/10.1214/SS/1042727940
  13. Boudt, K., Croux, C., & Laurent, S. (2011). Robust estimation of intraweek periodicity in volatility and jump detection. Journal of Empirical Finance, 18(2), 353–367. https://doi.org/10.1016/J.JEMPFIN.2010.11.005
  14. Brown, R. G. (1963). Smoothing, forecasting and prediction of discrete time series. Prentice-Hall.
  15. Bujang, M. A., Sa’at, N., & Bakar, T. M. I. T. A. (2017). Determination of minimum sample size requirement for multiple linear regression and analysis of covariance based on experimental and non-experimental studies. Epidemiology, biostatistics, and public health, 14(3).
  16. Bulut, E. (2005). Döviz ekonomisi, (Piyasanın mikro yapısı). Platin Yayınları.
  17. Cassel, G. (1918). Abnormal Deviations in International Exchanges. The Economic Journal, 28(112), 413–415. https://doi.org/10.2307/2223329
  18. Chae-Deug, Y. (2024). Realized normal volatility and maximum outlying jumps in high frequency returns for Korean won–US Dollar. International Review of Financial Analysis, 95, 103344.
  19. Chatrath, A., Miao, H., Ramchander, S., & Villupuram, S. (2014). Currency jumps, cojumps and the role of macro news. Journal of International Money and Finance, 40, 42–62. https://doi.org/10.1016/J.JIMONFIN.2013.08.018
  20. Chen, J. (2012). Real exchange rate and economic growth: Evidence from Chinese provincial data (1992 - 2008). https://shs.hal.science/halshs-00667467
  21. Claasen, E. M. (1996). Global monetary economics. Oxford University Press.
  22. Copeland, L. S. (1989). Exchange rates and international finance. Addison Wesley.
  23. Duffie, D., Pan, J., & Singleton, K. (2000). Transform analysis and asset pricing for affine jump-diffusions. Econometrica, 68(6), 1343–1376. https://doi.org/10.1111/1468-0262.00164
  24. Eraker, B., Johannes, M., & Polson, N. (2003). The impact of jumps in volatility and returns. The Journal of Finance, 58(3), 1269–1300. https://doi.org/10.1111/1540-6261.00566
  25. Frankel, J. A., Galli, Giampaolo., & Giovannini, Alberto. (1996). Why we need for microstructure? In The microstructure of foreign exchange markets (p. 346). University of Chicago Press.
  26. Fuentes, M. (2009). Dollarization of debt contracts: Evidence from Chilean firms. The Developing Economies, 47(4), 458–487. https://doi.org/10.1111/j.1746-1049.2009.00094.x
  27. Gala, P., & Libânio, G. (2010). Exchange rate policies, patterns of specialization and economic development: theory and evidence in developing countries. http://bibliotecadigital.fgv.br:80/dspace/handle/10438/6875
  28. Gelbard, E., & Nagayasu, J. (2004). Determinants of Angola’s Real Exchange Rate, 1992–2002. The Developing Economies, 42(3), 392–404. https://doi.org/10.1111/j.1746-1049.2004.tb00944.x
  29. Habib, M. M., Mileva, E., & Stracca, L. (2017). The real exchange rate and economic growth: Revisiting the case using external instruments. Journal of International Money and Finance, 73, 386–398. https://doi.org/10.1016/J.JIMONFIN.2017.02.014
  30. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (Second Edition). Springer Verlag.
  31. Hilbe, J. M. (2009). Logistic regression models. CRC Press.
  32. Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. John Wiley & Sons, Inc. https://doi.org/10.1002/0471722146
  33. Jackson, B., Scargle, J. D., Barnes, D., Arabhi, S., Alt, A., Gioumousis, P., Gwin, E., San, P., Tan, L., & Tun Tao Tsai. (2005). An algorithm for optimal partitioning of data on an interval. IEEE Signal Processing Letters, 12(2), 105–108. https://doi.org/10.1109/LSP.2001.838216
  34. Karsmakers, P., Pelckmans, K., & Suykens, J. A. K. (2007). Multi-class kernel logistic regression: A fixed-size implementation. IEEE International Conference on Neural Networks - Conference Proceedings, 1756–1761. https://doi.org/10.1109/IJCNN.2007.4371223
  35. Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Http://Dx.Doi.Org/10.1080/01621459.2012.737745, 107(500), 1590–1598. https://doi.org/10.1080/01621459.2012.737745
  36. Kim, A. Y., Marzban, C., Percival, D. B., & Stuetzle, W. (2009). Using labeled data to evaluate change detectors in a multivariate streaming environment. Signal Processing, 89(12), 2529–2536. https://doi.org/10.1016/J.SIGPRO.2009.04.011
  37. King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9(2), 137–163. https://doi.org/10.1093/oxfordjournals.pan.a004868
  38. Kleinbaum, D. G., & Klein, M. (2010). Ordinal Logistic Regression (pp. 463–488). https://doi.org/10.1007/978-1-4419-1742-3_13
  39. Kleinbaum, David. G., Kupper, L. L., Nizam, A., & Rosenberg, S. E. (2007). Applied regression analysis and multivariable methods (4.th Edition). Duxbury Press.
  40. Krugman, R. P., Obstfeld, M., & Melitz, M. (2014). International economics: Theory and policy (10th ed.). Pearson.
  41. Lahaye, J., Laurent, S., & Neely, C. J. (2011). Jumps, cojumps and macro announcements. Journal of Applied Econometrics, 26(6), 893–921. https://doi.org/10.1002/JAE.1149
  42. Lee, S. S., & Mykland, P. A. (2008). Jumps in financial markets: A new nonparametric test and jump dynamics. The Review of Financial Studies, 21(6), 2535–2563. https://doi.org/10.1093/RFS/HHM056
  43. Lin, C.-J., Weng, R. C., & Keerthi, S. S. (2007). Trust region Newton methods for large-scale logistic regression. Proceedings of the 24th International Conference on Machine Learning, 561–568. https://doi.org/10.1145/1273496.1273567
  44. Liu, J., Longstaff, F. A., & Pan, J. (2003). Dynamic asset allocation with event risk. The Journal of Finance, 58(1), 231–259. https://doi.org/10.1111/1540-6261.00523
  45. Mampaey, M., Tatti, N., & Vreeken, J. (2011). Tell me what I need to know: Succinctly summarizing data with itemsets. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 573–581. https://doi.org/10.1145/2020408.2020499
  46. Manner, H., Rodríguez, G., & Stöckler, F. (2024). A changepoint analysis of exchange rate and commodity price risks for Latin American stock markets. International Review of Economics & Finance, 89, 1385-1403.
  47. McKinnon, R. I. (1982). Currency substitution and instability in the world dollar standard. The American Economic Review, 72(3), 320–333. https://www.jstor.org/stable/1831535
  48. Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14(1–2), 3–24. https://doi.org/10.1016/0022-1996(83)90017-X
  49. Meese, R. A., & Rose, A. K. (1990). Nonlinear, nonparametric, nonessential exchange rate estimation. American Economic Association, 80(2), 192–296. https://www.jstor.org/stable/2006568
  50. Muggeo, V. M. R., & Adelfio, G. (2011). Efficient change point detection for genomic sequences of continuous measurements. Bioinformatics, 27(2), 161–166. https://doi.org/10.1093/BIOINFORMATICS/BTQ647
  51. Mussa, M. (1986). Nominal exchange rate regimes and the behavior of real exchange rates: Evidence and implications. Carnegie-Rochester Conference Series on Public Policy, 25(C), 117–214. https://doi.org/10.1016/0167-2231(86)90039-4
  52. Obstfeld, M., Dornbusch, R., & McKinnon, R. (1995). International currency experience: new lessons and lessons relearned. Brookings Papers on Economic Activity, 1995(1), 119. https://doi.org/10.2307/2534574
  53. Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), 1373–1379. https://doi.org/10.1016/S0895-4356(96)00236-3
  54. Peruga, R. (1996). Learning and the exchange rate: Exchange rate responses to money announcements in the early 1980s. Journal of International Money and Finance, 15(2), 167–190. https://doi.org/10.1016/0261-5606(96)00004-6
  55. Piazzesi, M. (2005). Bond yields and the federal reserve. Journal of Political Economy, 113(2), 311–344. https://doi.org/10.1086/427466
  56. Rodrik, D. (2009). The real exchange rate and economic growth. Brookings Papers on Economic Activity, 2008(2), 365–412. https://doi.org/10.1353/ECA.0.0020
  57. Scott, A. J., & Knott, M. (1974). A cluster analysis method for grouping means in the analysis of variance. Biometrics, 30(3), 507. https://doi.org/10.2307/2529204
  58. Talih, M., & Hengartner, N. (2005). Structural learning with time-varying components: tracking the cross-section of financial time series. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(3), 321–341. https://doi.org/10.1111/J.1467-9868.2005.00504.X
  59. Tauchen, G., & Zhou, H. (2011). Realized jumps on financial markets and predicting credit spreads. Journal of Econometrics, 160(1), 102–118. https://doi.org/10.1016/J.JECONOM.2010.03.023
  60. Trigg, D. W., & Leach, A. G. (1967). Exponential smoothing with an adaptive response rate. OR, 18(1), 53. https://doi.org/10.2307/3010768
  61. Truong, C., Oudre, L., & Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167, 107299. https://doi.org/10.1016/J.SIGPRO.2019.107299
  62. Uzun, S., Sensoy, A., & Nguyen, D. K. (2023). Jump forecasting in foreign exchange markets: A high‐frequency analysis. Journal of Forecasting, 42(3), 578-624.
  63. Yao, Y.-C. (1984). Estimation of a noisy discrete-time step function: Bayes and empirical Bayes approaches. The Annals of Statistics, 12(4), 1434–1447. https://www.jstor.org/stable/2241012