Abstract
References
- Aguira, C.L.veSoares, M. J. (2014). “The continuous wavelet transform: Moving beyond uni‐ and bivariate analysis”, Journal of Economic Surveys, 28: 2, 344-375
- Antonakakis N., Chang T., Cunado J. veGupta R. (2018). “The relationship between commodity markets and commodity mutual funds: A wavelet-based analysis”, Finance Research Letters, 24, 1-9
- Conraria, L. A. Azevedo N ve Soares, M. J. (2008). “Using wavelets to decompose the time–frequency effects of monetary policy”, Physica A, 387, 2863–2878
- Conraria, L. A. ve Soares, M. J. (2011). “Oil shocks and the Macroeconomy: Econometric estimation, economic modeling and policy implications”. PTDC/ECO/64750/2006.
- Li X.L., Chang, T, Miller, S. M., Balcilar ve M., Gupta R. (2015). “The co-movement and causality between the U.S. housing and stock markets in the time and frequency domains”, International Review of Economics and Finance, 38,220–233
- Olayeni, O.R. (2016). “Causality in Continuous Wavelet Transform Without Spectral Matrix Factorization: Theory and Application”, Computational Economics, 47(3), 321.
- Rua, A. (2013). “Worldwide synchronization since the nineteenth century: A wavelet based view.” Applied Economics Letters, 20(8), 773–776.
- Rua, A. ve Nunes, L.C. (2012). “A wavelet-based assessment of market risk: The emerging markets case”, The Quarterly Review of Economics and Finance 52,84– 92.
- Tiwari, A.K., Mutascu, M. ve Andries, A. M.,(2013). “Decomposing time-frequency relationship between producer price and consumer price indices in Romania through wavelet analysis”, Economic Modelling, 31, 151–159.
- Tiwari, A.K. (2013). “Oil prices and the macroeconomy reconsideration for Germany: Using continuous wavelet”, Economic Modelling, 30, 636–642.
- Tiwari, A.K. (2018). Finance Research Letters,https://doi.org/10.1016/j.frl.2018.02.005
- Yang,L., Cai,X. J. ve Hamori S. (2017). “Does the crude oil price influence the exchange rates of oil importing and oil-exporting countries differently? A wavelet coherence analysis”, International Review of Economics and Finance 49, 536-547.
Abstract
Finansal
veriler genellikle iç içe geçmiş salınımlar, ani değişimler ve görece olarak
daha yavaş değişen trend bileşenlerini içeren karmaşık bir yapıya sahiptir.
Dalgacık analizi ile söz konusu bileşenler ayrıştırılarak verinin sahip olduğu
bileşenlerdeki değişmeleri içeren zaman-frekans grafikleri oluşturulmaktadır.
Böylelikle verideki dinamiklerin ortaya çıkarılması amacıyla salınımların
zamana, döneme ve salınım şiddetine göre değişiminin analizi mümkün olmaktadır.
Bu çalışmada küresel kriz sonrası 03.01.2008 -
09.11.2018 dönemi için, BIST (Borsa İstanbul AŞ.) 100 Endeksi, Dolar (USD), Euro (EUR) ve serbest piyasa altın getirileri
(GOLD) arasındaki ilişkiler analize konu edilmiştir. Çalışma kapsamında;
BIST-100 Endeksi ile Dolar, Euro ve Altın getiri verileri arasındaki
nedensellik ilişkisi, sürekli dalgacık dönüşümünü (Continuous Wavelet Transform - CWT) temel alan Granger
nedensellik testi ile analiz edilmiştir. CWT Granger nedensellik
testi, parametrik olmayan nedensellik testi olup Rua (2003)
CWT korelasyon ölçütünün Olayeni (2016) tarafından faz farkı gösterge
fonksiyonu kullanılarak geliştirilmesiyle oluşturulmuş ve literatürde son
dönemlerde kullanılmaya başlanmıştır.
Çalışma sonucunda BIST 100 Endeks - Dolar, BIST 100 Endeks - Euro
arasında negatif ve çift yönlü bir nedensellik saptanmakla birlikte, BIST 100
Endeks - Altın getiri serileri arasında kalıcı, baskın bir nedensellik
görülmemiştir.
References
- Aguira, C.L.veSoares, M. J. (2014). “The continuous wavelet transform: Moving beyond uni‐ and bivariate analysis”, Journal of Economic Surveys, 28: 2, 344-375
- Antonakakis N., Chang T., Cunado J. veGupta R. (2018). “The relationship between commodity markets and commodity mutual funds: A wavelet-based analysis”, Finance Research Letters, 24, 1-9
- Conraria, L. A. Azevedo N ve Soares, M. J. (2008). “Using wavelets to decompose the time–frequency effects of monetary policy”, Physica A, 387, 2863–2878
- Conraria, L. A. ve Soares, M. J. (2011). “Oil shocks and the Macroeconomy: Econometric estimation, economic modeling and policy implications”. PTDC/ECO/64750/2006.
- Li X.L., Chang, T, Miller, S. M., Balcilar ve M., Gupta R. (2015). “The co-movement and causality between the U.S. housing and stock markets in the time and frequency domains”, International Review of Economics and Finance, 38,220–233
- Olayeni, O.R. (2016). “Causality in Continuous Wavelet Transform Without Spectral Matrix Factorization: Theory and Application”, Computational Economics, 47(3), 321.
- Rua, A. (2013). “Worldwide synchronization since the nineteenth century: A wavelet based view.” Applied Economics Letters, 20(8), 773–776.
- Rua, A. ve Nunes, L.C. (2012). “A wavelet-based assessment of market risk: The emerging markets case”, The Quarterly Review of Economics and Finance 52,84– 92.
- Tiwari, A.K., Mutascu, M. ve Andries, A. M.,(2013). “Decomposing time-frequency relationship between producer price and consumer price indices in Romania through wavelet analysis”, Economic Modelling, 31, 151–159.
- Tiwari, A.K. (2013). “Oil prices and the macroeconomy reconsideration for Germany: Using continuous wavelet”, Economic Modelling, 30, 636–642.
- Tiwari, A.K. (2018). Finance Research Letters,https://doi.org/10.1016/j.frl.2018.02.005
- Yang,L., Cai,X. J. ve Hamori S. (2017). “Does the crude oil price influence the exchange rates of oil importing and oil-exporting countries differently? A wavelet coherence analysis”, International Review of Economics and Finance 49, 536-547.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Research Article |
| Authors | |
| Publication Date | September 13, 2019 |
| Submission Date | December 25, 2018 |
| Published in Issue | Year 2019 Volume: 17 Issue: 34 |
