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Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması

Yıl 2023, Cilt: 8 Sayı: 21, 339 - 357, 30.06.2023

Arife Özdemir Höl

https://doi.org/10.25204/iktisad.1204527
Cited By: 2

Öz

Tüm dünyayı etkisi altına alan Covid-19 pandemisi finansal piyasalar da dahil olmak üzere yaşamın her alanını olumsuz etkilemiştir. Bu çalışmanın amacı Covid-19 döneminde Türkiye’de küresel ve yerel finansal varlıklar arasındaki dinamik bağlantılılık ilişkisini araştırmaktır. Dinamik bağlantılılık ilişkisini araştırabilmek için 11.03.2020-01.02.2022 dönemine ait veriler TVP-VAR yöntemi kullanılarak analiz edilmiştir. Analiz sonucunda elde edilen bulgulara göre Bitcoin fiyatı ve ons altın fiyatının volatiliteyi yayan değişkenler olduğu; BIST 100 endeksi, dolar kuru ve WTI ham petrol fiyatının ise volatiliteyi alan değişkenler olduğu belirlenmiştir. Volatiliteyi en çok alan değişken BIST 100 endeksi olurken ikinci sırada dolar kuru üçüncü sırada ise WTI ham petrol fiyatı yer almaktadır. BIST 100 endeksinin ons altın, Bitcoin ve dolar kurunda meydana gelen değişmelerden etkilendiği görülürken, BIST 100 endeksini en fazla etkileyen değişkenin ons altın olduğu belirlenmiştir. Ulaşılan bu sonuçların portföy yöneticileri, riskten korunmak isteyenler, politika yapıcılar, yatırım stratejisi oluşturmak isteyenler açısından faydalı olacağı düşünülmektedir.

Anahtar Kelimeler

Covid-19 pandemisi TVP-VAR Finansal Varlıklar

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
  • Andersen, T. G. ve Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International economic review, 39(4), 885-905. https://doi.org/10.2307/2527343
  • Andersen, T. G., Bollerslev, P. Christoffersen ve F. X. Diebold. 2006. Volatility forecasting. In Handbook of economic forecasting, ed. G. Elliott, C. Granger, and A. Timmermann, 778–878. Amsterdam: North-Holland.
  • Antonakakis, N., Chatziantoniou, I. ve 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., Cuñado, J., Filis, G., Gabauer, D. ve de Gracia, F. P. (2019a). Oil and asset classes implied volatilities: dynamic connectedness and investment strategies. Available at SSRN 3399996. http://dx.doi.org/10.2139/ssrn.3399996
  • Antonakakis, N., Gabauer, D., ve Gupta, R. (2019b). International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression. International Review of Financial Analysis, 65, 101382. https://doi.org/10.1016/j.irfa.2019.101382
  • Antonakakis, N., Gabauer, D., ve Gupta, R. (2019c). Greek economic policy uncertainty: Does it matter for Europe? Evidence from a dynamic connectedness decomposition approach. Physica A: Statistical Mechanics and Its Applications, 535, 122280. https://doi.org/10.1016/j.physa.2019.122280
  • Avşarlıgil, N. (2020). Covid-19 salgının Bitcoin ve diğer finansal piyasalar ile ilişkisi üzerine bir inceleme. Alanya Akademik Bakış, 4(3), 665-682. https://doi.org/10.29023/alanyaakademik.735214
  • Ayhan, F. ve Abdullazade, M. (2021). Türkiye ekonomisinde Covid-19 salgını sonrasında petrol ve altın fiyatları ile vaka sayılarının döviz kuru üzerindeki etkileri. Yaşar Üniversitesi E-Dergisi, 16(62), 509-523. https://doi.org/10.19168/jyasar.887005
  • Ayrancı, A.E. ve Arı, G. (2021). Covid-19 Pandemisinin BIST sektör endeksleri ile ilişkisi: Bayer-Hanck (2013) eşbütünleşme analizi. İşletme Araştırmaları Dergisi, 13(4), 3770-3785. https://doi.org/10.20491/isarder.2021.1355
  • Bahrini, R. ve Filfilan, A. (2020). Impact of the novel coronavirus on stock market returns: evidence from GCC countries. Quantitative Finance and Economics, 4(4), 640-652. https://doi.org/10.3934/QFE.2020029
  • Baker, S.R., Bloom, N., Davis, S.J., Kost, K., Sammon, M. ve Viratyosin, T. (2020). The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10(4), 742-758. https://doi.org/10.1093/rapstu/raaa008
  • Baruník, J. ve 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
  • Bayer, C. ve Hanck, C. (2013). Combining non-cointegration tests. Journal of Time Series Analysis, 34(1): 83-95. https://doi.org/10.1111/j.1467-9892.2012.00814.x
  • Beirne, J., Renzhi, N., Sugandi, E. ve Volz, U. (2020). Financial market and capital flow dynamics during the COVID-19 pandemic. Asian Development Bank Institute Working Paper 1158, 1-36. https://doi.org/10.2139/ssrn.3656848
  • Bouhali, H., Dahbani, A. ve Dinar, B. (2021). COVID-19 impacts on financial markets: takeaways from the third wave. Russian Journal of Economics, 7, 200-212. https://doi.org/10.32609/j.ruje.7.65328
  • Bouri, E., Cepni, O., Gabauer, D. ve 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
  • Büyükakın, F. ve Demir, S. (2022). COVID-19 sürecinin türk finansal sistemine yönelik etkilerinin Toda-Yamamoto yöntemi ile analizi. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(4), 387-396. https://doi.org/10.52791/aksarayiibd.1053192
  • Caporale, G. M., Catik, A. N., Helmi, M. H., Akdeniz, C. ve Ilhan, A. (2021). The effects of the Covid-19 pandemic on stock markets, CDS and economic activity: Time-varying evidence from the US and Europe. CESifo Working Paper No. 9316. http://dx.doi.org/10.2139/ssrn.3932024
  • Cogley, T. ve Sargent, T.J. (2005). Drifts and volatilities: Monetary policies and outcomes ın the post WWII US. Review of Economic Dynamics, 8(2), 262-302. https://doi.org/10.1016/j.red.2004.10.009
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Spread of Volatility Among Financial Assets in Türkiye During Covid-19 Period: TVP-VAR Application

Yıl 2023, Cilt: 8 Sayı: 21, 339 - 357, 30.06.2023

Arife Özdemir Höl

https://doi.org/10.25204/iktisad.1204527
Cited By: 2

Öz

The Covid-19 pandemic, which has affected the whole world, has adversely affected all areas of life, including financial markets. The aim of this study is to investigate the dynamic connectedness between global and local financial assets in Türkiye during the Covid-19 period. Data for the period 11.03.2020-01.02.2022 were analyzed using the TVP-VAR method in order to investigate the dynamic connectivity relationship. According to the findings obtained as a result of the analysis, Bitcoin price and ounce gold price are variables that volatility transmitters; it has been determined that BIST 100 index, dollar rate and WTI crude oil price are volatility receivers. The variable with the highest volatility is the BIST 100 index, while the dollar rate is in the second place and the WTI crude oil price is in the third place. While BIST 100 index is the variable that receives the most this volatility, the dollar rate is in second place and the WTI crude oil price is in third place. While it was observed that the BIST 100 index was affected by the changes in the ounce gold, Bitcoin and dollar rates, it was determined that the variable that most affected the BIST 100 index was ounce gold. It is thought that these results will be beneficial for portfolio managers, hedgers, policymakers, and those who want to create an investment strategy.

Anahtar Kelimeler

Covid-19 pandemic Financial Assets TVP-VAR

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
  • Andersen, T. G. ve Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International economic review, 39(4), 885-905. https://doi.org/10.2307/2527343
  • Andersen, T. G., Bollerslev, P. Christoffersen ve F. X. Diebold. 2006. Volatility forecasting. In Handbook of economic forecasting, ed. G. Elliott, C. Granger, and A. Timmermann, 778–878. Amsterdam: North-Holland.
  • Antonakakis, N., Chatziantoniou, I. ve 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., Cuñado, J., Filis, G., Gabauer, D. ve de Gracia, F. P. (2019a). Oil and asset classes implied volatilities: dynamic connectedness and investment strategies. Available at SSRN 3399996. http://dx.doi.org/10.2139/ssrn.3399996
  • Antonakakis, N., Gabauer, D., ve Gupta, R. (2019b). International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression. International Review of Financial Analysis, 65, 101382. https://doi.org/10.1016/j.irfa.2019.101382
  • Antonakakis, N., Gabauer, D., ve Gupta, R. (2019c). Greek economic policy uncertainty: Does it matter for Europe? Evidence from a dynamic connectedness decomposition approach. Physica A: Statistical Mechanics and Its Applications, 535, 122280. https://doi.org/10.1016/j.physa.2019.122280
  • Avşarlıgil, N. (2020). Covid-19 salgının Bitcoin ve diğer finansal piyasalar ile ilişkisi üzerine bir inceleme. Alanya Akademik Bakış, 4(3), 665-682. https://doi.org/10.29023/alanyaakademik.735214
  • Ayhan, F. ve Abdullazade, M. (2021). Türkiye ekonomisinde Covid-19 salgını sonrasında petrol ve altın fiyatları ile vaka sayılarının döviz kuru üzerindeki etkileri. Yaşar Üniversitesi E-Dergisi, 16(62), 509-523. https://doi.org/10.19168/jyasar.887005
  • Ayrancı, A.E. ve Arı, G. (2021). Covid-19 Pandemisinin BIST sektör endeksleri ile ilişkisi: Bayer-Hanck (2013) eşbütünleşme analizi. İşletme Araştırmaları Dergisi, 13(4), 3770-3785. https://doi.org/10.20491/isarder.2021.1355
  • Bahrini, R. ve Filfilan, A. (2020). Impact of the novel coronavirus on stock market returns: evidence from GCC countries. Quantitative Finance and Economics, 4(4), 640-652. https://doi.org/10.3934/QFE.2020029
  • Baker, S.R., Bloom, N., Davis, S.J., Kost, K., Sammon, M. ve Viratyosin, T. (2020). The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10(4), 742-758. https://doi.org/10.1093/rapstu/raaa008
  • Baruník, J. ve 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
  • Bayer, C. ve Hanck, C. (2013). Combining non-cointegration tests. Journal of Time Series Analysis, 34(1): 83-95. https://doi.org/10.1111/j.1467-9892.2012.00814.x
  • Beirne, J., Renzhi, N., Sugandi, E. ve Volz, U. (2020). Financial market and capital flow dynamics during the COVID-19 pandemic. Asian Development Bank Institute Working Paper 1158, 1-36. https://doi.org/10.2139/ssrn.3656848
  • Bouhali, H., Dahbani, A. ve Dinar, B. (2021). COVID-19 impacts on financial markets: takeaways from the third wave. Russian Journal of Economics, 7, 200-212. https://doi.org/10.32609/j.ruje.7.65328
  • Bouri, E., Cepni, O., Gabauer, D. ve 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
  • Büyükakın, F. ve Demir, S. (2022). COVID-19 sürecinin türk finansal sistemine yönelik etkilerinin Toda-Yamamoto yöntemi ile analizi. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(4), 387-396. https://doi.org/10.52791/aksarayiibd.1053192
  • Caporale, G. M., Catik, A. N., Helmi, M. H., Akdeniz, C. ve Ilhan, A. (2021). The effects of the Covid-19 pandemic on stock markets, CDS and economic activity: Time-varying evidence from the US and Europe. CESifo Working Paper No. 9316. http://dx.doi.org/10.2139/ssrn.3932024
  • Cogley, T. ve Sargent, T.J. (2005). Drifts and volatilities: Monetary policies and outcomes ın the post WWII US. Review of Economic Dynamics, 8(2), 262-302. https://doi.org/10.1016/j.red.2004.10.009
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  • Elgammal, M. M., Ahmed, W. M. ve Alshami, A. (2021). Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic. Resources Policy, 74, 102334. https://doi.org/10.1016/j.resourpol.2021.102334
  • Elliot, G., Rothenberg T. J. ve Stock, J.H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64, 813-836. https://doi.org/10.2307/2171846
  • Ghorbel, A. ve Jeribi, A. (2021). Contagion of COVID-19 pandemic between oil and financial assets: The evidence of multivariate Markov switching GARCH models. Journal of Investment Compliance, 22(2), 151-169. https://doi.org/10.1108/JOIC-01-2021-0001
  • Goldstein, I., Koijen, R. S. ve Mueller, H. M. (2021). COVID-19 and its impact on financial markets and the real economy. The Review of Financial Studies, 34(11), 5135-5148. http://dx.doi.org/10.2139/ssrn.3895134
  • Güneş, H. (2022). Covid döneminde finansal varlıklar arasındaki nedensellik farklılaşması. Aurum Journal of Social Sciences, 7(1), 49-64. Retrieved from https://dergipark.org.tr/en/pub/aurum/issue/70478/1108588
  • Gülhan, Ü. (2020). Covid-19 pandemisine BIST 100 reaksiyonu: ekonometrik bir analiz. Electronic Turkish Studies, 15(4), 497-509. http://dx.doi.org/10.7827/TurkishStudies.44122
  • Gümüş, U. T. ve Can Öziç, H. (2020). BİST100 endeksinin covid 19 öncesi ve covid 19’la mücadele sürecinde volatilite yapısının incelenmesi. Journal of Current Researches on Business and Economics, 10(1), 43-58. https://doi.org/10.26579/jocrebe.69
  • Günsoy, B. ve Yıldız, Ü. (2021). Türkiye için Covid-19 pandemisi ile döviz kuru arasındaki frekans alanı nedensellik analizi. International Conference on Economics Turkish Economic Association, 1-11.
  • Hacıevliyagil, N. ve Gümüş, A. (2020). Covid-19’un en etkili olduğu ülkelerde salgın-borsa ilişkisi. Gaziantep University Journal of Social Sciences, 19(COVID-19 Special Issue), 354-364. https://doi.org/10.21547/jss.742893
  • Hong, H., Bian, Z. ve Lee, C. C. (2021). COVID-19 and instability of stock market performance: evidence from the US. Financial Innovation, 7(1), 1-18. https://doi.org/10.1186/s40854-021-00229-1
  • İlhan, A. ve Akdeniz, C. (2020). The impact of macroeconomic variables on the stock market in the time of Covid-19: The case of Turkey. Ekonomi Politika ve Finans Araştırmaları Dergisi, 5(3), 893-912. https://doi.org/10.30784/epfad.810630
  • Kakinuma, Y. (2021). Nexus between Southeast Asian stock markets, bitcoin and gold: spillover effect before and during the COVID-19 pandemic. Journal of Asia Business Studies. https://doi.org/10.1108/JABS-02-2021-0050
  • Kartal, M. ve Dağlı, Ü. (2021). Covid-19 salgınının BIST-100 endeksi üzerindeki etkisi: Türkiye özelinde ampirik bir araştırma. Avrupa Bilim ve Teknoloji Dergisi, (31), 815-822. https://doi.org/10.31590/ejosat.981801
  • Kayral, İ. E. ve Tandoğan, N. Ş. (2020). Covid-19 pandemisinin BİST100 endeksi, döviz kurları, altın getiri ve volatilitelerine etkisi. Gaziantep University Journal of Social Sciences, 19(COVID-19 Special Issue), 687-701. https://doi.org/10.21547/jss.786384
  • Khan, K., Zhao, H., Zhang, H., Yang, H., Shah, M. H. ve Jahanger, A. (2020). The impact of COVID-19 pandemic on stock markets: an empirical analysis of world major stock indices. The Journal of Asian Finance, Economics, and Business, 7(7), 463-474. https://doi.org/10.13106/jafeb.2020.vol7.no7.463
  • Kılcı, E. N. (2021). COVID-19 salgını döneminde Türkiye finansal piyasalarındaki değişimlerin tahmin edilmesinde volatilite endeksinin rolünün analizi. Mali Cözüm Dergisi, 31, 25-43. https://archive.ismmmo.org.tr/docs/malicozum/165malicozum/4.pdf
  • Koop, Gary, Pesaran, M.H. ve Potter, S.M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74, 119-47. https://doi.org/10.1016/0304-4076(95)01753-4
  • Koop, G., Leon-Gonzalez, R. ve Strachan, R.W. (2009). On the evolution of the monetary policy transmission mechanism. Journalof Economic Dynamics and Control, 33(4), 997-1017. https://doi.org/10.1016/j.jedc.2008.11.003
  • Koop, G. ve Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Econometrics, 177(2), 185-98. https://doi.org/10.1016/j.jeconom.2013.04.007
  • Koop, G. ve Korobilis, D. (2014). A new ındex of financial conditions. European Economic Review, 71, 101-116. https://doi.org/10.1016/j.euroecorev.2014.07.002
  • Kuloğlu, A. (2021). Covıd-19 krizinin petrol fiyatları üzerine etkisi. Ekonomi Politika ve Finans Araştırmaları Dergisi, 6(3), 710-727. https://doi.org/10.30784/epfad.996706
  • Liu, L., Wang, E. Z. ve Lee, C. C. (2020). Impact of the COVID-19 pandemic on the crude oil and stock markets in the US: A time-varying analysis. Energy Research Letters, 1(1), 13154. https://doi.org/10.46557/001c.13154
  • Maki D. (2012). Tests for cointegration allowing for an unknown number of breaks. Economic Modelling, 29 (5): 2011-2015. https://doi.org/10.1016/j.econmod.2012.04.022
  • Nakajima, J. (2011). Time-varying parameter VAR model with stochastic volatility: An overview of methodology and empirical applications. Institute for Monetary and Economic Studies, Bank of Japan, 29, 107-142.
  • Nielsen, M.Ø. (2010). Nonparametric cointegration analysis of fractional systems with unknown integration orders. Journal of Econometrics, 155, 170-187. http://dx.doi.org/10.2139/ssrn.1326422
  • Ozturk, M. ve Cavdar, S. C. (2021). The contagion of COVID-19 pandemic on the volatilities of international crude oil prices, gold, exchange rates and Bitcoin. The Journal of Asian Finance, Economics and Business, 8(3), 171-179. https://doi.org/10.13106/jafeb.2021.vol8.no3.0171
  • Özkan, N. ve Ünlü, U. (2021). Bölgesel COVID-19 vaka sayıları, altın fiyatları, euro ve BIST şehir endeksleri arasındaki ilişki: bir ARDL sınır testi yaklaşımı. Ekonomi Politika ve Finans Araştırmaları Dergisi, 6(1), 240-253. https://doi.org/10.30784/epfad.880244
  • Patton, A. J. (2006). Volatility forecast comparison using imperfect volatility proxies. Quantitative Finance Research Centre, University of Technology Sydney, Research Paper 175, 1-45. http://dx.doi.org/10.2139/ssrn.932890
  • Petrova, K. (2019). A quasi-bayesian local likelihood approach to time varying parameter VAR models. Journal of Econometrics, 212(1), 286-306. https://doi.org/10.1016/j.jeconom.2019.04.031
  • Pesaran, H. Hashem ve Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58, 17-29. https://doi.org/10.1016/S0165-1765(97)00214-0
  • Phillips, P.C.B. ve Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75, 335-346.http://dx.doi.org/10.1093/biomet/75.2.335
  • Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-52. https://doi.org/10.1111/j.1467-937X.2005.00353.x
  • Shehzad, K., Xiaoxing, L., Arif, M., Rehman, K. U. ve Ilyas, M. (2020). Investigating the psychology of financial markets during covid-19 era: a case study of the us and european markets. Frontiers in Psychology, 11, Article 1924, 1-13. https://doi.org/10.3389/fpsyg.2020.01924
  • Spiegel, S., Kaldewei, C. ve Huzel, M. (2020). Corona crisis causes turmoil in financial markets. United Nations Department of Economic and Social Affairs. Polıcy Brıef, 59, 1-4. https://doi.org/10.18356/baf30ff5-en
  • Suyadal, M. (2021). Covıd-19 pandemisinde piyasa etkinliği ve davranışsal finans teorilerinin geçerliliği: uluslararası piyasalarda bir uygulama. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (60), 519-546. https://doi.org/10.18070/erciyesiibd.994139
  • Wang, D., Li, P. ve Huang, L. (2022). Time-frequency volatility spillovers between major international financial markets during the COVID-19 pandemic. Finance Research Letters, 46, 102244, 1-8. https://doi.org/10.1016/j.frl.2021.102244
  • Yıldız, S. N. ve Aydın, Ü. (2022). Covid-19 salgınının Türkiye’de finansal yatırım araçları üzerindeki etkisi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 23 (1), 294-316. https://doi.org/10.37880/cumuiibf.1012964
  • Yiğit, M. ve Yiğit, A.G. (2021). Türkiye’de Bitcoin’in finansal piyasalarla entegrasyonuna yönelik bir araştırma: covıd-19 öncesi ve sonrası için bir uzun dönem analizi. Journal of Academic Value Studies, 7(2), 177-193. http://dx.doi.org/10.29228/javs.51673
  • Zhang, H., Hong, H., Guo, Y. ve Yang, C. (2022). Information spillover effects from media coverage to the crude oil, gold, and Bitcoin markets during the COVID-19 pandemic: Evidence from the time and frequency domains. International Review of Economics & Finance, 78, 267-285. https://doi.org/10.1016/j.iref.2021.12.005
Toplam 65 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonometrik ve İstatistiksel Yöntemler, Finans
Bölüm Araştırma Makaleleri
Yazarlar

Arife Özdemir Höl 0000-0002-9902-9174

Erken Görünüm Tarihi 24 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 15 Kasım 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 8 Sayı: 21

Kaynak Göster

APA 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
AMA Özdemir Höl A. Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması. İKTİSAD. Haziran 2023;8(21):339-357. doi:10.25204/iktisad.1204527
Chicago Özdemir Höl, Arife. “Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi 8, sy. 21 (Haziran 2023): 339-57. https://doi.org/10.25204/iktisad.1204527.
EndNote Özdemir Höl A (01 Haziran 2023) Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması. İktisadi İdari ve Siyasal Araştırmalar Dergisi 8 21 339–357.
IEEE A. Özdemir Höl, “Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması”, İKTİSAD, c. 8, sy. 21, ss. 339–357, 2023, doi: 10.25204/iktisad.1204527.
ISNAD Özdemir Höl, Arife. “Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması”. İktisadi İdari ve Siyasal Araştırmalar Dergisi 8/21 (Haziran 2023), 339-357. https://doi.org/10.25204/iktisad.1204527.
JAMA Özdemir Höl A. Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması. İKTİSAD. 2023;8:339–357.
MLA Özdemir Höl, Arife. “Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması”. İktisadi İdari Ve Siyasal Araştırmalar Dergisi, c. 8, sy. 21, 2023, ss. 339-57, doi:10.25204/iktisad.1204527.
Vancouver Özdemir Höl A. Covid-19 Döneminde Türkiye’de Finansal Varlıklar Arasındaki Volatilite Yayılımı: TVP-VAR Uygulaması. İKTİSAD. 2023;8(21):339-57.

Cited By

The Volatility Relationship Among Financial Assets: TVP-VAR Model
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https://doi.org/10.54821/uiecd.1392184
TVP-VAR YÖNTEMİ İLE DİNAMİK BAĞLANTILILIK TESPİTİ: CIVETS ÜLKELERİ ÜZERİNE UYGULAMA
Pamukkale University Journal of Social Sciences Institute
https://doi.org/10.30794/pausbed.1413704
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