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The Volatility Relationship Among Financial Assets: TVP-VAR Model

Year 2023, Volume: 5 Issue: 4, 225 - 237, 30.12.2023

Burhan Erdoğan

https://doi.org/10.54821/uiecd.1392184
Cited By: 1

Abstract

In the post-pandemic period, intense fluctuations in interest rates, inflation, and prices were observed in many countries around the world. This study was conducted to analyze the dynamic interconnectedness between financial assets during this turbulent period. The study was conducted using TVP-VAR analysis on daily data of one-month deposit interest rate, BIST100 index return, two-year bond interest rate, USDTRY exchange rate, gold ounce price and CDS premiums between 2018 and 2023. The results of the study show that the interaction between variables reached a very high level especially in the post-pandemic period and then decreased over the years. On the other hand, the BIST100 index, gold and CDS premium are net shock emitters, while deposits, USDTRY and bonds are net shock receivers. It is aimed that the results obtained will enable investors to choose the right investment instrument in today's financial markets where prices, returns, and rates fluctuate, and on the other hand, it is aimed to benefit firms and policymakers in terms of macro problems in the current geography.

Keywords

Finance Volatility TVP-VAR

References

  • Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898.
  • Akkuş, H. T., & Doğan, M. (2023) Analysis of dynamic connectedness relationships between cryptocurrency. NFT and DeFi assets: TVP-VAR approach. Applied Economics Letters, 1-6.
  • Akyıldırım, E., Güneş, H., & Çelik, İ. (2022). Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar. Gazi İktisat ve İşletme Dergisi, 8(2), 346-363.
  • Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & De Gracia, F. P. (2019). Oil and asset classes implied volatilities: Dynamic connectedness and investment strategies. Energy Economics Forthcoming, Available at SSRN 3399996.
  • Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84.
  • Arı, Y. (2022). TVP-VAR based CARR-volatility connectedness: Evidence from the Russian-Ukraine conflict. Ekonomi Politika ve Finans Araştırmaları Dergisi, 7(3), 590-607.
  • Asl, M. G., Bouri, E., Darehshiri, S., & Gabauer, D. (2021). Good and bad volatility spillovers in the cryptocurrency market: New Evidence from a TVP-VAR asymmetric connectedness approach. Available at SSRN 3957317.
  • Cao, G., & Xie, W. (2022). Asymmetric dynamic spillover effect between cryptocurrency and China’s financial market: Evidence from TVP-VAR based connectedness approach. Finance Research Letters, 49, 103070.
  • Caporale, G. M., Catik, A. N., Helmi, M. H., Akdeniz, C., & 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.
  • Chatziantoniou, I., Floros, C., & Gabauer, D. (2022). Volatility contagion between crude oil and G7 stock markets in the light of trade wars and COVID-19: A TVP-VAR extended joint connectedness approach. In Applications in Energy Finance: The Energy Sector, Economic Activity, Financial Markets and the Environment (pp. 145-168). Cham: Springer International Publishing.
  • Dahir, A. M., Mahat, F., Amin Noordin, B. A., & Hisyam Ab Razak, N. (2020). Dynamic connectedness between Bitcoin and equity market information across BRICS countries: Evidence from TVP-VAR connectedness approach. International Journal of Managerial Finance, 16(3), 357-371.
  • Daly, K. (2008). Financial volatility: Issues and measuring techniques. Physica A: statistical mechanics and its applications, 387(11), 2377-2393.
  • Değirmenci, N. (2017). Finansal piyasalar arasındaki oynaklık yayılımı: Literatür araştırması. Akademik Sosyal Araştırmalar Dergisi, 547, 161-179.
  • Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.
  • Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134.
  • Doğan, M., Raikhan, S., Zhanar, N., & Gulbagda, B. (2023). Analysis of Dynamic Connectedness Relationships among Clean Energy, Carbon Emission Allowance, and BIST Indexes. Sustainability, 15(7), 6025.
  • Dornbusch, R., Park, Y. C., & Claessens, S. (2000). Contagion: how it spreads and how it can be stopped. World Bank Research Observer. 15(2), 177-197.
  • Erben Yavuz, A. (2023). Temiz enerji sürdürülebilir ve BIST endeksleri arasındaki ilişkilerin analizi: TVP-VAR yaklaşımı. İşletme Akademisi Dergisi, 4(3), 339–354.
  • Gökgöz, H., & Kayahan, C. (2023). Bitcoin ile gelişmiş ve gelişmekte olan ülkeler arasındaki volatilite yayılım etkisinin TVP-VAR ile analizi. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 41(1), 109-125.
  • He, X., Cai, X. J., & Hamori, S. (2018). Bank credit and housing prices in China: Evidence from a TVP-VAR model with stochastic volatility. Journal of Risk and Financial Management, 11(4), 90.
  • Höl, A. Ö. (2023). Covid-19 döneminde Türkiye’de finansal varlıklar arasındaki volatilite yayılımı: TVP-VAR uygulaması. İktisadi İdari ve Siyasal Araştırmalar Dergisi (İKTİSAD), 8(21), 339-357.
  • Huang, J., Chen, B., Xu, Y. & Xia, X. (2023). Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A novel TVP-VAR frequency connectedness approach. Finance Research Letters, 53, 103634.
  • Jebabli, I., Arouri, M. & Teulon, F. (2014). On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility. Energy Economics, 45, 66-98.
  • Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74, 119-47.
  • Koop, G., Leon-Gonzalez, R., & Strachan, R. W. (2009). On the evolution of the monetary policy transmission mechanism. Journal of Economic Dynamics and Control, 33(4), 997-1017.
  • Liu, J., Ma, F., & Zhang, Y. (2019). Forecasting the Chinese stock volatility across global stock markets. Physica A: Statistical Mechanics and Its Applications, 525, 466-477.
  • McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric reviews, 27(1-3), 10-45.
  • Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1). 17-29.
  • Şenol, Z., & Türkay, H. (2020). Gelişmiş ve gelişmekte olan borsalar arasındaki oynaklık yayılımı. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 42(2), 361-385.
  • Zhang, P., Gao, J., Zhang, Y., & Wang, T. W. (2021). Dynamic spillover effects between the US stock volatility and China’s stock market crash risk: a TVP-VAR approach. Mathematical Problems in Engineering, 1-12.
  • Zhou, M. J., Huang, J. B., & Chen, J. Y. (2020). The effects of geopolitical risks on the stock dynamics of China’s rare metals: A TVP-VAR analysis. Resources Policy, 68, 101784.
  • https://data.tuik.gov.tr/Bulten/Index?p=Finansal-Yat%C4%B1r%C4%B1m-Ara%C3%A7lar%C4%B1n%C4%B1n-Reel-Getiri-Oranlar%C4%B1-Ocak-2023-49500&dil=1, Accessed on: 01.10.2023
  • https://www.vap.org.tr/?col=114, Accessed on: 01.10.2023

Finansal Varlıklar Arasındaki Volatilite İlişkisi: TVP-VAR Modeli

Year 2023, Volume: 5 Issue: 4, 225 - 237, 30.12.2023

Burhan Erdoğan

https://doi.org/10.54821/uiecd.1392184
Cited By: 1

Abstract

Pandemi sonrası dönemde birçok dünya ülkesinde faiz, enflasyon ve fiyatlarda yoğun dalgalanmalar görülmüştür. Bu çalışma yaşanan bu çalkantılı dönemde finansal varlıklar arasındaki dinamik bağlantılılığın analizi amacıyla gerçekleştirilmiştir. Çalışma 2018-2023 yılları arasındaki bir aylık mevduat faiz oranı, BİST100 endeksi getirisi, iki yıl vadeli tahvil faiz oranı, USDTRY kuru, altın ons fiyatı ve CDS primlerinin günlük verileri üzerinden TVP-VAR analizi kullanılarak gerçekleştirilmiştir. Çalışma sonuçları özellikle pandemi sonrası dönemde değişkenler arasındaki etkileşimin çok yüksek bir seviyeye çıktığını daha sonra yıllar itibariyle azaldığını göstermektedir. Diğer taraftan BİST100 endeksi, altın ve CDS priminin net şok yayıcı varlıklar olurken mevduat, USDTRY ve tahvil değişkenlerinin ise net şok alıcı değişkenler olduğu tespit edilmiştir. Elde edilen sonuçların fiyat, getiri ve oranların dalgalı bir görünüm sergilediği günümüz finansal piyasalarında özellikle yatırımcıların doğru yatırım aracını seçmesine imkan vermesi ve diğer taraftan bulunulan coğrafyadaki makro problemler açısından firmalara ve politika yapıcılara fayda sağlaması amaçlanmaktadır.

Keywords

Finans Oynaklık TVP-VAR

References

  • Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898.
  • Akkuş, H. T., & Doğan, M. (2023) Analysis of dynamic connectedness relationships between cryptocurrency. NFT and DeFi assets: TVP-VAR approach. Applied Economics Letters, 1-6.
  • Akyıldırım, E., Güneş, H., & Çelik, İ. (2022). Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar. Gazi İktisat ve İşletme Dergisi, 8(2), 346-363.
  • Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & De Gracia, F. P. (2019). Oil and asset classes implied volatilities: Dynamic connectedness and investment strategies. Energy Economics Forthcoming, Available at SSRN 3399996.
  • Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84.
  • Arı, Y. (2022). TVP-VAR based CARR-volatility connectedness: Evidence from the Russian-Ukraine conflict. Ekonomi Politika ve Finans Araştırmaları Dergisi, 7(3), 590-607.
  • Asl, M. G., Bouri, E., Darehshiri, S., & Gabauer, D. (2021). Good and bad volatility spillovers in the cryptocurrency market: New Evidence from a TVP-VAR asymmetric connectedness approach. Available at SSRN 3957317.
  • Cao, G., & Xie, W. (2022). Asymmetric dynamic spillover effect between cryptocurrency and China’s financial market: Evidence from TVP-VAR based connectedness approach. Finance Research Letters, 49, 103070.
  • Caporale, G. M., Catik, A. N., Helmi, M. H., Akdeniz, C., & 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.
  • Chatziantoniou, I., Floros, C., & Gabauer, D. (2022). Volatility contagion between crude oil and G7 stock markets in the light of trade wars and COVID-19: A TVP-VAR extended joint connectedness approach. In Applications in Energy Finance: The Energy Sector, Economic Activity, Financial Markets and the Environment (pp. 145-168). Cham: Springer International Publishing.
  • Dahir, A. M., Mahat, F., Amin Noordin, B. A., & Hisyam Ab Razak, N. (2020). Dynamic connectedness between Bitcoin and equity market information across BRICS countries: Evidence from TVP-VAR connectedness approach. International Journal of Managerial Finance, 16(3), 357-371.
  • Daly, K. (2008). Financial volatility: Issues and measuring techniques. Physica A: statistical mechanics and its applications, 387(11), 2377-2393.
  • Değirmenci, N. (2017). Finansal piyasalar arasındaki oynaklık yayılımı: Literatür araştırması. Akademik Sosyal Araştırmalar Dergisi, 547, 161-179.
  • Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.
  • Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134.
  • Doğan, M., Raikhan, S., Zhanar, N., & Gulbagda, B. (2023). Analysis of Dynamic Connectedness Relationships among Clean Energy, Carbon Emission Allowance, and BIST Indexes. Sustainability, 15(7), 6025.
  • Dornbusch, R., Park, Y. C., & Claessens, S. (2000). Contagion: how it spreads and how it can be stopped. World Bank Research Observer. 15(2), 177-197.
  • Erben Yavuz, A. (2023). Temiz enerji sürdürülebilir ve BIST endeksleri arasındaki ilişkilerin analizi: TVP-VAR yaklaşımı. İşletme Akademisi Dergisi, 4(3), 339–354.
  • Gökgöz, H., & Kayahan, C. (2023). Bitcoin ile gelişmiş ve gelişmekte olan ülkeler arasındaki volatilite yayılım etkisinin TVP-VAR ile analizi. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 41(1), 109-125.
  • He, X., Cai, X. J., & Hamori, S. (2018). Bank credit and housing prices in China: Evidence from a TVP-VAR model with stochastic volatility. Journal of Risk and Financial Management, 11(4), 90.
  • Höl, A. Ö. (2023). Covid-19 döneminde Türkiye’de finansal varlıklar arasındaki volatilite yayılımı: TVP-VAR uygulaması. İktisadi İdari ve Siyasal Araştırmalar Dergisi (İKTİSAD), 8(21), 339-357.
  • Huang, J., Chen, B., Xu, Y. & Xia, X. (2023). Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A novel TVP-VAR frequency connectedness approach. Finance Research Letters, 53, 103634.
  • Jebabli, I., Arouri, M. & Teulon, F. (2014). On the effects of world stock market and oil price shocks on food prices: An empirical investigation based on TVP-VAR models with stochastic volatility. Energy Economics, 45, 66-98.
  • Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74, 119-47.
  • Koop, G., Leon-Gonzalez, R., & Strachan, R. W. (2009). On the evolution of the monetary policy transmission mechanism. Journal of Economic Dynamics and Control, 33(4), 997-1017.
  • Liu, J., Ma, F., & Zhang, Y. (2019). Forecasting the Chinese stock volatility across global stock markets. Physica A: Statistical Mechanics and Its Applications, 525, 466-477.
  • McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric reviews, 27(1-3), 10-45.
  • Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1). 17-29.
  • Şenol, Z., & Türkay, H. (2020). Gelişmiş ve gelişmekte olan borsalar arasındaki oynaklık yayılımı. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 42(2), 361-385.
  • Zhang, P., Gao, J., Zhang, Y., & Wang, T. W. (2021). Dynamic spillover effects between the US stock volatility and China’s stock market crash risk: a TVP-VAR approach. Mathematical Problems in Engineering, 1-12.
  • Zhou, M. J., Huang, J. B., & Chen, J. Y. (2020). The effects of geopolitical risks on the stock dynamics of China’s rare metals: A TVP-VAR analysis. Resources Policy, 68, 101784.
  • https://data.tuik.gov.tr/Bulten/Index?p=Finansal-Yat%C4%B1r%C4%B1m-Ara%C3%A7lar%C4%B1n%C4%B1n-Reel-Getiri-Oranlar%C4%B1-Ocak-2023-49500&dil=1, Accessed on: 01.10.2023
  • https://www.vap.org.tr/?col=114, Accessed on: 01.10.2023
There are 33 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Research Articles
Authors

Burhan Erdoğan 0000-0002-6171-0554

Publication Date December 30, 2023
Submission Date November 17, 2023
Acceptance Date December 23, 2023
Published in Issue Year 2023 Volume: 5 Issue: 4

Cite

APA Erdoğan, B. (2023). The Volatility Relationship Among Financial Assets: TVP-VAR Model. International Journal of Business and Economic Studies, 5(4), 225-237. https://doi.org/10.54821/uiecd.1392184

Cited By

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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