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Analysis of the Spillover Effect between Risk Shocks and Financial Assets in Turkey Using TVP-VAR-Based Wavelet Coherence Analysis

Yıl 2025, Cilt: 10 Sayı: 1, 303 - 331, 28.03.2025

Aslan Aydoğdu

https://doi.org/10.30784/epfad.1595233

Öz

This study uses wavelet coherence analysis based on the TVP-VAR extended joint approach to examine the spillover effect between risk shocks and financial assets in Turkey. For this purpose, the BIST100 Index, Brent crude oil, USD/TRY, gold ounce, Bitcoin, and Volatility Index (VIX) variables are used. The daily data set covering the period 08.11.2017-08.09.2024 is preferred. TVP-VAR extended joint approach, wavelet coherence analysis, and Hatemi-j (2012) asymmetric causality analysis were used. According to the results of the studies, dynamic spillovers between markets increase significantly during periods of volatility. Accordingly, BIST100 return and gold ounce return are determined as net shock emitters of volatility, while the dollar, Bitcoin, and Brent oil are defined as net shock receivers of volatility. Bitcoin, Brent oil, and the dollar were found to be affected by the changes in BIST100. The effect of VIX on the Total Connectivity Index (TCI) is positive and reciprocal between 2020 and 2022-2023 and intensifies in the medium and long term. A bidirectional causality relationship was found between VIX and TCI volatility, and a unidirectional causality relationship was found between VIX and gold ounce and Brent oil net spread volatility excluding Bitcoin and BIST100. It was concluded that these findings are consistent with wavelet coherence analysis.

Anahtar Kelimeler

Wavelet Coherence Analysis Volatility Spillovers TVP-VAR Stock markets Bitcoin

Kaynakça

  • Akyıldırım, E., Güneş, H. ve Çelik, İ. (2022). Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar. Gazi İktisat ve İşletme Dergisi, 8(2), 346-363. https://doi.org/10.30855/gjeb.2022.8.2.010
  • Ando, T., Greenwood-Nimmo, M. and Shin, Y. (2018). Quantile connectedness: Modelling tail behaviour in the topology of financial networks. Management Science, (68)4, 2401-2431. https://doi.org/10.1287/mnsc.2021.3984
  • Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2019). Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios. Journal of International Financial Markets, Institutions and Money, 61, 37–51. https://doi.org/10.1016/j.intfin.2019.02.003
  • Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084
  • Antonakakis, N., Gabauer, D., Gupta, R. and Plakandaras, V. (2018). Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters, 166, 63–75. https://doi.org/10.1016/j.econlet.2018.02.011
  • Aydoğdu, A. (2024). Farklı yatırım ufuklarına göre kripto para birimlerinin volatilite modellemesi (Yayımlanmamış Doktora Tezi). Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü, Denizli.
  • Aydoğdu, A. and Durmaz, S. (2024). Finansal piyasalarda volatilite yayılımı: Pay senedi, kripto para, döviz, altın ve petrol piyasaları üzerine bir araştırma. VII. Uluslararası Ekonomi, Siyaset ve Yönetim Sempozyumu’nda sunulan bildiri, Dicle Üniversitesi, Diyarbakır.
  • Balcilar, M., Gabauer, D. and Umar, Z. (2021). Crude oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219. https://doi.org/10.1016/j.resourpol.2021.102219
  • Baruník, J. and Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. https://doi.org/10.1093/jjfinec/nby001
  • Basher, S.A. and P. Sadorsky. 2016. Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235–247. https://doi.org/10.1016/j. eneco.2015.11.022
  • Başarır, Ç. (2018). Korku endeksi (VIX) ile BIST 100 arasındaki ilişki: Frekans alanı nedensellik analizi. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 19(2), 177-191. https://doi.org/10.24889/ifede.468802
  • Bouri, E., Cepni, O., Gabauer, D. and Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International Review of Financial Analysis, 73, 101646. https://doi.org/10.1016/j.irfa.2020.101646
  • Bouri, E., Gupta, R., Tiwari, A.K. and Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95. https://doi.org/10.1016/j.frl.2017.02.009
  • Chen, B.X. and Sun, Y.L. (2022). The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods. The North American Journal of Economics and Finance, 63, 101831. https://doi.org/10.1016/j.najef.2022.101831
  • Chen, Y., Zhu, X. and Li, H. (2022). The asymmetric effects of oil price shocks and uncertainty on non-ferrous metal market: Based on quantile regression. Energy, 246, 123365.https://doi.org/10.1016/j.energy.2022. 123365
  • Choudhry, T., Hassan, S.S. and Shabi, S. (2015). Relationship between gold and stock markets during the global financial crisis: Evidence from nonlinear causality tests. International Review of Financial Analysis, 41, 247-256.https://doi.org/10.1016/j.irfa.2015.03.011
  • Chung, K.H. and Chuwonganant, C. (2018). Market volatility and stock returns: The role of liquidity providers. Journal of Financial Markets, 37, 17-34. https://doi.org/10. 1016/j.finmar.2017.07.002
  • Corbet, S., Meegan, A., Larkin, C., Lucey, B. and Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics letters, 165, 28-34. https://doi.org/10.1016/j.econlet.2018.01.004
  • Crowley, P.M. (2007). A guide to wavelets for economists. Journal of Economic Surveys, 21(2), 207-267. https://doi.org/10.1111/j.1467-6419.2006.00502.x
  • Dai, Z., Zhang, X. and Liang, C. (2024). Efficient predictability of oil price: The role of VIX-based panic index shadow line difference. Energy Economics, 129, 107234. https://doi.org/10.1016/j.eneco.2023.107234
  • Diebold, F.X. and Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
  • Diebold, F.X. and Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
  • Doğan, M., Raikhan, S., Zhanar, N. and Gulbagda, B. (2023). Analysis of dynamic connectedness relationships among clean energy, carbon emission allowance, and BIST indexes. Sustainability, 15(7), 6025. https://doi.org/10.3390/su15076025
  • Erdoğan, B. (2024). Bitcoin, petrol ile borsalar arasındaki volatilite analizi. İstatistik Araştırma Dergisi, 14(1), 19-35. Erişim adresi: https://dergipark.org.tr/tr/pub/jsstr/
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Risk Şokları ve Türkiye’deki Finansal Varlıklar Arasındaki Yayılım Etkisinin TVP-VAR Dayalı Wavelet Uyum Analizi İle İncelenmesi

Yıl 2025, Cilt: 10 Sayı: 1, 303 - 331, 28.03.2025

Aslan Aydoğdu

https://doi.org/10.30784/epfad.1595233

Öz

Bu çalışmanın amacı, risk şokları ile Türkiye’de finansal varlıklar arasındaki yayılım etkisinin TVP-VAR genişletilmiş ortak bağlantılılık yaklaşımına dayalı wavelet uyum analizi ile incelemektir. Bu amaç doğrultusunda, BIST100 Endeksi, Brent ham petrol, USD/TRY, altın ons, Bitcoin ve Volatilite Endeksi (VIX) değişkenleri kullanılmıştır. 08.11.2017-08.09.2024 dönemini kapsayan günlük veri seti tercih edilmiştir. TVP-VAR genişletilmiş ortak bağlantılılık yaklaşımı, wavelet uyum analizi ve Hatemi-j (2012) asimetrik nedensellik analizi kullanılmıştır. Analiz sonuçlarına göre piyasalar arasındaki dinamik yayılmaların dalgalanma dönemlerinde önemli ölçüde arttığı tespit edilmiştir. Buna göre BIST100 getirisi ve altın ons getirisi volatiliteyi net şok yayan değişkenler olarak belirlenmiş; dolar, Bitcoin ve Brent petrol ise volatiliteyi net şok alan değişkenler olarak tespit edilmiştir. Bitcoin ve Brent petrol, doların BIST100’den meydana gelen değişimlerden etkilendiği görülmüştür. VIX’in Toplam Bağlantı Endeksi (TCI) üzerindeki etkisi 2020 ve 2022-2023 yılları arasında pozitif ve karşılıklı olup orta ve uzun vadede yoğunlaşmaktadır. VIX ile TCI volatilitesi arasında çift yönlü; VIX ile Bitcoin ve BIST100 hariç altın ons ve Brent petrol net yayılma volatilitesi arasında tek yönlü nedensellik ilişkisi bulgusuna ulaşılmıştır. Bu bulguların wavelet uyum analizi ile tutarlı olduğu sonucuna varılmıştır.

Anahtar Kelimeler

Wavelet Uyum Analizi Volatilite Yayılımı Pay Senedi Piyasaları TVP-VAR Bitcoin

Kaynakça

  • Akyıldırım, E., Güneş, H. ve Çelik, İ. (2022). Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar. Gazi İktisat ve İşletme Dergisi, 8(2), 346-363. https://doi.org/10.30855/gjeb.2022.8.2.010
  • Ando, T., Greenwood-Nimmo, M. and Shin, Y. (2018). Quantile connectedness: Modelling tail behaviour in the topology of financial networks. Management Science, (68)4, 2401-2431. https://doi.org/10.1287/mnsc.2021.3984
  • Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2019). Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios. Journal of International Financial Markets, Institutions and Money, 61, 37–51. https://doi.org/10.1016/j.intfin.2019.02.003
  • Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84. https://doi.org/10.3390/jrfm13040084
  • Antonakakis, N., Gabauer, D., Gupta, R. and Plakandaras, V. (2018). Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics Letters, 166, 63–75. https://doi.org/10.1016/j.econlet.2018.02.011
  • Aydoğdu, A. (2024). Farklı yatırım ufuklarına göre kripto para birimlerinin volatilite modellemesi (Yayımlanmamış Doktora Tezi). Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü, Denizli.
  • Aydoğdu, A. and Durmaz, S. (2024). Finansal piyasalarda volatilite yayılımı: Pay senedi, kripto para, döviz, altın ve petrol piyasaları üzerine bir araştırma. VII. Uluslararası Ekonomi, Siyaset ve Yönetim Sempozyumu’nda sunulan bildiri, Dicle Üniversitesi, Diyarbakır.
  • Balcilar, M., Gabauer, D. and Umar, Z. (2021). Crude oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219. https://doi.org/10.1016/j.resourpol.2021.102219
  • Baruník, J. and Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. https://doi.org/10.1093/jjfinec/nby001
  • Basher, S.A. and P. Sadorsky. 2016. Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235–247. https://doi.org/10.1016/j. eneco.2015.11.022
  • Başarır, Ç. (2018). Korku endeksi (VIX) ile BIST 100 arasındaki ilişki: Frekans alanı nedensellik analizi. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 19(2), 177-191. https://doi.org/10.24889/ifede.468802
  • Bouri, E., Cepni, O., Gabauer, D. and Gupta, R. (2021). Return connectedness across asset classes around the COVID-19 outbreak. International Review of Financial Analysis, 73, 101646. https://doi.org/10.1016/j.irfa.2020.101646
  • Bouri, E., Gupta, R., Tiwari, A.K. and Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95. https://doi.org/10.1016/j.frl.2017.02.009
  • Chen, B.X. and Sun, Y.L. (2022). The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods. The North American Journal of Economics and Finance, 63, 101831. https://doi.org/10.1016/j.najef.2022.101831
  • Chen, Y., Zhu, X. and Li, H. (2022). The asymmetric effects of oil price shocks and uncertainty on non-ferrous metal market: Based on quantile regression. Energy, 246, 123365.https://doi.org/10.1016/j.energy.2022. 123365
  • Choudhry, T., Hassan, S.S. and Shabi, S. (2015). Relationship between gold and stock markets during the global financial crisis: Evidence from nonlinear causality tests. International Review of Financial Analysis, 41, 247-256.https://doi.org/10.1016/j.irfa.2015.03.011
  • Chung, K.H. and Chuwonganant, C. (2018). Market volatility and stock returns: The role of liquidity providers. Journal of Financial Markets, 37, 17-34. https://doi.org/10. 1016/j.finmar.2017.07.002
  • Corbet, S., Meegan, A., Larkin, C., Lucey, B. and Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics letters, 165, 28-34. https://doi.org/10.1016/j.econlet.2018.01.004
  • Crowley, P.M. (2007). A guide to wavelets for economists. Journal of Economic Surveys, 21(2), 207-267. https://doi.org/10.1111/j.1467-6419.2006.00502.x
  • Dai, Z., Zhang, X. and Liang, C. (2024). Efficient predictability of oil price: The role of VIX-based panic index shadow line difference. Energy Economics, 129, 107234. https://doi.org/10.1016/j.eneco.2023.107234
  • Diebold, F.X. and Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
  • Diebold, F.X. and Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
  • Doğan, M., Raikhan, S., Zhanar, N. and Gulbagda, B. (2023). Analysis of dynamic connectedness relationships among clean energy, carbon emission allowance, and BIST indexes. Sustainability, 15(7), 6025. https://doi.org/10.3390/su15076025
  • Erdoğan, B. (2024). Bitcoin, petrol ile borsalar arasındaki volatilite analizi. İstatistik Araştırma Dergisi, 14(1), 19-35. Erişim adresi: https://dergipark.org.tr/tr/pub/jsstr/
  • Ferrer, R., Bolós, V.J. and Benítez, R. (2016). Interest rate changes and stock returns: A European multi-country study with wavelets. International Review of Economics and Finance, 44, 1–12. https://doi.org/10.1016/j.iref.2016.03.001
  • Gkillas, K., Tsagkanos, A. and Vortelinos, D.I. (2019). Integration and risk contagion in financial crises: Evidence from international stock markets. Journal of Business Research, 104, 350-365. https://doi.org/10.1016/j.jbusres.2019.07.031
  • Golitsis, P., Gkasis, P. and Bellos, S. K. (2022). Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables: Evidence from the USA. The North American Journal of Economics and Finance, 63, 101785. https://doi.org/10.1016/j.najef.2022.101785
  • Guan, B., Mazouz, K. and Xu, Y.D. (2024). Asymmetric volatility spillover between crude oil and other asset markets. Energy Economics, 130, 107305. https://doi.org/10.1016/j.eneco.2024.107305
  • Hatipoğlu, M. ve Tekin, B. (2017). VIX endeksi, döviz kuru ve petrol fiyatlarının BIST 100 endeksi üzerindeki etkileri: Bir kuantil regresyon yaklaşımı. Ordu Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Araştırmaları Dergisi, 7(3), 627-634. Erişim adresi: https://dergipark.org.tr/tr/pub/odusobiad/
  • Investing. (2024). S&P 500. Erişim adresi: https://www.investing.com/
  • Iqbal, J. (2017). Does gold hedge stock market, inflation and exchange rate risks? An econometric investigation. International Review of Economics & Finance, 48, 1-17. https://doi.org/10.1016/j.iref.2016.11.005
  • Izzeldin, M., Muradoğlu, Y.G., Pappas, V., Petropoulou, A. and Sivaprasad, S. (2023). The impact of the Russian-Ukrainian war on global financial markets. International Review of Financial Analysis, 87, 102598. https://doi.org/10.1016/j.irfa.2023.102598
  • Jana, S., Nandi, A. and Sahu, T.N. (2024). Can cryptocurrencies provide better diversification benefits? evidence from the indian stock market. Journal of Interdisciplinary Economics. Advance online publication. https://doi.org/10.1177/02601079231214859
  • Kangallı Uyar, S.G. (2021). Uluslararası döviz piyasalarında finansal bulaşıcılık ve karşılıklı bağımlılık: Wavelet uyum analizi. Finans Politik ve Ekonomik Yorumlar, 656, 115-147. Erişim adresi: https://www.ekonomikyorumlar.com.tr/
  • Kılıç, Y. ve Çütcü, İ. (2018). Bitcoin fiyatları ile Borsa İstanbul endeksi arasındaki eşbütünleşme ve nedensellik ilişkisi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 13(3), 235-250. https://doi.org/10.17153/oguiibf.455083
  • Kilian, L. (2009). Oil price shocks, monetary policy and stagflation (CEPR Discussion Papers No. 7324). Retrieved from https://www.rba.gov.au/publications/confs/2009/pdf/kilian.pdf
  • Koanker, R. and Bassett, G.Jr. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46(1), 33-50. https://doi.org/10.2307/1913643
  • Kök, D. ve Nazlıoğlu, E.H. (2020). Finansal piyasalarda asimetrik nedensellik: BIST100, VIX ve döviz kuru örneği. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 55, 245-262. https://doi.org/10.18070/erciyesiibd.659871
  • Kuzu, S. (2019). Volatilite endeksi (VIX) ile BIST 100 arasındaki Johansen eş-bütünleşme ve frekans alanı nedensellik analizi. Electronic Turkish Studies, 14(1), 479. http://dx.doi.org/10.7827/TurkishStudies.14943
  • Lastrapes, W.D. and Wiesen, T.F. (2021). The joint spillover index. Economic Modelling, 94, 681–691. https://doi.org/10.1016/j.econmod.2020.02.010
  • Malik, F. and Umar, Z. (2019). Dynamic connectedness of oil price shocks and exchange rates. Energy Economics, 84, 104501. https://doi.org/10.1016/j.eneco.2019.104501
  • Mensi, W., Al Rababa’a, A.R., Vo, X.V. and Kang, S.H. (2021). Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets. Energy Economics, 98, 105262. https://doi.org/10.1016/j.eneco.2021.105262
  • Mensi, W., Yousaf, I., Vo, X.V. and Kang, S.H. (2022). Asymmetric spillover and network connectedness between gold, BRENT oil and EU subsector markets. Journal of International Financial Markets, Institutions and Money, 76, 101487. https://doi.org/10.1016/j.intfin.2021.101487
  • Mensi, W., Ziadat, S.A., Al Rababa’a, A.R., Vo, X.V. and Kang, S.H. (2024). Oil, gold and international stock markets: Extreme spillovers, connectedness and its determinants. The Quarterly Review of Economics and Finance, 95, 1-17. https://doi.org/10.1016/j.qref.2024.03.002
  • Özdemir-Höl, A. (2023). Covid-19 döneminde Türkiye’de finansal varlıklar arasındaki volatilite yayılımı: TVP-VAR uygulaması. İktisadi İdari ve Siyasal Araştırmalar Dergisi, 8(21), 339-357. https://doi.org/10.25204/iktisad.1204527
  • Rua, A. and Nunes, L.C. (2009). International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16(4), 632-639. https://doi.org/10.1016/j.jempfin.2009.02.002
  • Sakarya, Ş. ve Akkuş, H.T. (2018). BIST-100 ve BIST sektör endeksleri ile VIX endeksi arasındaki ilişkisinin analizi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 21(40), 351-374. https://doi.org/10.31795/baunsobed.492470
  • Sarıtaş, H. ve Nazlıoğlu, E.H. (2019). Korku endeksi, hisse senedi piyasası ve döviz kuru ilişkisi: Türkiye için ampirik bir analiz. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 12(4), 542-551. https://doi.org/10.25287/ohuiibf.538592
  • Shaik, M., Jamil, S.A., Hawaldar, I.T., Sahabuddin, M., Rabbani, M.R. and Atif, M. (2023). Impact of geo-political risk on stocks, oil, and gold returns during GFC, COVID-19, and Russian–Ukraine War. Cogent Economics and Finance, 11(1), 2190213. https://doi.org/10.1080/23322039.2023.2190213
  • Shang, J. and Hamori, S. (2024). Quantile time-frequency connectedness analysis between crude oil, gold, financial markets, and macroeconomic indicators: Evidence from the US and EU. Energy Economics, 132, 107473. https://doi.org/10.1016/j.eneco.2024.107473
  • Sharif, A., Aloui, C., Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market and policy uncertainty Nexus in the US economy: Fresh evidence from the wavelet-based approach (SSRN Paper No. 3574699). https://doi.org/10.2139/ssrn.3574699
  • Torrence, C. and Compo, G.P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61-78. https://doi.org/10.1175/1520-0477(1998)079%3C0061:APGTWA%3E2.0.CO;2
  • Torrence, C. and Webster, P.J. (1999). Interdecadal changes in the ENSO–monsoon system. Journal of Climate, 12(8), 2679–2690. https://doi.org/10.1175/1520-0442(1999)012%3C2679:ICITEM%3E2.0.CO;2
  • Tunçel, M.B. ve Gürsoy, S. (2020). Korku endeksi (VIX), Bitcoin fiyatları ve BIST100 endeksi arasındaki nedensellik ilişkisi üzerine ampirik bir uygulama. Elektronik Sosyal Bilimler Dergisi, 19(76), 1999-2011. https://doi.org/10.17755/esosder.712702
  • Vukovic, D., Maiti, M., Grubisic, Z., Grigorieva, E.M. and Frömmel, M. (2021). COVID-19 pandemic: Is the crypto market a safe haven? The impact of the first wave. Sustainability, 13(15), 8578. https://doi.org/10.3390/su13158578
  • Wang, J., Lu, X., He, F. and Ma, F. (2020). Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU? International Review of Financial Analysis, 72, 101596. https://doi.org/10.1016/j.irfa.2020.101596
  • Yahoo Finance. (2024). S&P 500. Retrieved from https://finance.yahoo.com/
  • Yoshizaki, Y. and Hamori, S. (2013). On the influence of oil price shocks on economic activity, inflation, and exchange rates. International Journal of Financial Research, 4(2), 33. http://dx.doi.org/10.5430/ijfr.v4n2p33
  • Zhao, D., Li, P., Yang, M. and Lian, Y. (2024). How do risk shocks reshape the spillovers among the oil, gold, emerging, and developed markets? Evidence from a new TVP-VAR-based wavelet coherence framework. Applied Economics, Advance online publication. https://doi.org/10.1080/00036846.2024.2386862
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Finansal Öngörü ve Modelleme
Bölüm Makaleler
Yazarlar

Aslan Aydoğdu 0000-0001-9732-0614

Yayımlanma Tarihi 28 Mart 2025
Gönderilme Tarihi 2 Aralık 2024
Kabul Tarihi 19 Şubat 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 10 Sayı: 1

Kaynak Göster

APA Aydoğdu, A. (2025). Risk Şokları ve Türkiye’deki Finansal Varlıklar Arasındaki Yayılım Etkisinin TVP-VAR Dayalı Wavelet Uyum Analizi İle İncelenmesi. Ekonomi Politika Ve Finans Araştırmaları Dergisi, 10(1), 303-331. https://doi.org/10.30784/epfad.1595233
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