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THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION

Year 2022, , 85 - 91, 30.06.2022
https://doi.org/10.17261/Pressacademia.2022.1563

Abstract

Purpose- NFT is a digital token that represents a unique, one-of-a-kind asset on the blockchain. In this respect, NFTs can be used to represent
ownership of any unique asset. In this study, the volatility spillover relationship between the NFT Investment Index and the Global Technology
Index (XTEC) is investigated.
Methodology- More than one GARCH type model has been developed that reveals the relationship between assets in financial markets. The
DCC GARCH model was preferred because it is a current model that reveals the variable correlation coefficient depending on time. The DCCGARCH method was preferred for modeling the volatility spillover in the study. Daily data covering the period 19.04.2021-22.04.2022 are
used.
Findings- - According to the findings of the study; A mutual volatility spillover has been detected between the NFT Investment Index and
XTEC. Accordingly, the 1% shock in XTEC increases the NFT Investment Index volatility by 0.24%, while the 1% shock in the NFT Investment
Index increases the XTEC volatility by approximately 1.86%. The findings show that NFT Investment Index volatility is more effective on XTEC
volatility.
Conclusion- Those who invest in NFT or technology markets and those who are considering investing should also take into account the
developments in the other market in question in terms of risk management. In addition, market regulators should take a proactive approach
by considering the impact and importance of NFT markets.

References

  • Alawadhi, K. M. & Alshamali, N. (2022). NFTs Emergence in Financial Markets and their Correlation with DeFis and Cryptocurrencies. Applied Economics and Finance, 9(1), 108-120.
  • Ante, L. (2021). The Non-Fungible Token (NFT) Market and Its Relationship with Bitcoin and Ethereum. Blockchain Research Lab, BRL Working Paper Series No. 20.
  • Bao, H., & Roubaud, D. (2022). Non-Fungible Token: A Systematic Review and Research Agenda. Journal of Risk and Financial Management, 15(5), 215. https://www.mdpi.com/1911-8074/15/5/215 (Date Accessed: 29.05.2022).
  • Binance Academy (2020). Kripto Koleksiyonlukları ve Benzersiz Tokenler (NFT) Rehberi. https://academy.binance.com/tr/articles/a-guide-tocrypto-collectibles-and-non fungible-tokens-nfts (Date Accessed: 24.05.2022).
  • Binance Academy (2021). Kendi NFT’lerinizi Nasıl Üretebilirsiniz? https://academy.binance.com/tr/articles/how-to-make-your-own-nfts (Date Accessed: 24.05.2022).
  • Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, 72(3), 498-505.
  • Bollerslev, T., Engle, R. F. & Wooldridge, J. M. (1988). A Capital Asset Pricing Model with Time-Varying Covariances. Journal of Political Economy, 96(1), 116–131.
  • Diebold, F. X. & Yilmaz, K. (2012). Better to Give than to Receive_Predictive Directional Measurement of Volatility Spillover. International Journal of Forecasting, 28(1), 57-66.
  • Dowling, M. (2022a). Fertile LAND: Pricing Non-Fungible Tokens. Finance Research Letters, 44, 102096.
  • Dowling, M. (2022b). Is Non-Fungible Token Pricing Driven by Cryptocurrencies? Finance Research Letters, 44, 102097.
  • Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20(3), 339–350.
  • Engle, R. F. & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11(1), 122-150.
  • Gul Senkardes, C. (2021). Blockchain Technology and NFT’s: A Review in Music Industry. Journal of Management, Marketing and Logistics (JMML), 8(3), 154-163
  • Güven, V. & Şahinöz, E. (2018). Blokzincir - Kripto Paralar - Bitcoin: Satoshi Dünyayı Değiştiriyor. İstanbul: Kronik Kitap.
  • Han, H., Linton, O., Oka, T., & Whang, Y. J. (2016). The Cross Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series. Journal of Econometrics, 193(1), 251-270.
  • Hepsağ, A. & Akçalı, B. Y. (2016). Türk Finans Piyasasında İşlem Gören Bankalar İle ABD Finans Piyasası Arasındaki Volatilite Etkileşiminin Analizi. Avrasya Ekonometri İstatistik ve Ampirik Ekonomi Dergisi, 1(1), 54-72.
  • Ito, K., Shibano, K. & Mogi, G. (2022). Predicting the Bubble of Non-Fungible Tokens (NFTs): An Empirical Investigation. https://arxiv.org/abs/2203.12587 (Date Accessed: 26.05.2022).
  • NonFungible.com (2022). NFT Market Quarterly Report: Q1 · 2022. https://nonfungible.com/reports/2022/en/q1-quarterly-nft-marketreport-free/thank-you (Date Accessed: 29.05.2022).
  • Pinto-Gutiérrez, C., Gaitán, S., Jaramillo, D. & Velasquez, S. (2022). The NFT Hype: What Draws Attention to Non-Fungible Tokens? Mathematics, 10(3), 1-13.
  • Sattary, A. (2014). Petrol Fiyatları İle Hisse Senedi Getirileri Arasında Oynaklık Geçişkenliğinin Analizi Ve Portföy Yönetimine Yansımaları. Yayımlanmamış Doktora Tezi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü, Ekonometri Anabilim Dalı.
  • Tse, Y. & Tsui, A. (2002) A Multivariate GARCH Model with Time-Varying Correlations. Journal of Business and Economic Statistics, 20, 351- 362.
  • www.ethereum.org/en/nft/ (Date Accessed: 25.05.2022).
  • www.investing.com (Date Accessed: 25.04.2022).
  • Yousaf, I. & Yarovaya, L. (2022) Static and Dynamic Connectedness between NFTs, Defi and Other Assets: Portfolio Implication. Global Finance Journal, 53, 100719
Year 2022, , 85 - 91, 30.06.2022
https://doi.org/10.17261/Pressacademia.2022.1563

Abstract

References

  • Alawadhi, K. M. & Alshamali, N. (2022). NFTs Emergence in Financial Markets and their Correlation with DeFis and Cryptocurrencies. Applied Economics and Finance, 9(1), 108-120.
  • Ante, L. (2021). The Non-Fungible Token (NFT) Market and Its Relationship with Bitcoin and Ethereum. Blockchain Research Lab, BRL Working Paper Series No. 20.
  • Bao, H., & Roubaud, D. (2022). Non-Fungible Token: A Systematic Review and Research Agenda. Journal of Risk and Financial Management, 15(5), 215. https://www.mdpi.com/1911-8074/15/5/215 (Date Accessed: 29.05.2022).
  • Binance Academy (2020). Kripto Koleksiyonlukları ve Benzersiz Tokenler (NFT) Rehberi. https://academy.binance.com/tr/articles/a-guide-tocrypto-collectibles-and-non fungible-tokens-nfts (Date Accessed: 24.05.2022).
  • Binance Academy (2021). Kendi NFT’lerinizi Nasıl Üretebilirsiniz? https://academy.binance.com/tr/articles/how-to-make-your-own-nfts (Date Accessed: 24.05.2022).
  • Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. The Review of Economics and Statistics, 72(3), 498-505.
  • Bollerslev, T., Engle, R. F. & Wooldridge, J. M. (1988). A Capital Asset Pricing Model with Time-Varying Covariances. Journal of Political Economy, 96(1), 116–131.
  • Diebold, F. X. & Yilmaz, K. (2012). Better to Give than to Receive_Predictive Directional Measurement of Volatility Spillover. International Journal of Forecasting, 28(1), 57-66.
  • Dowling, M. (2022a). Fertile LAND: Pricing Non-Fungible Tokens. Finance Research Letters, 44, 102096.
  • Dowling, M. (2022b). Is Non-Fungible Token Pricing Driven by Cryptocurrencies? Finance Research Letters, 44, 102097.
  • Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics, 20(3), 339–350.
  • Engle, R. F. & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11(1), 122-150.
  • Gul Senkardes, C. (2021). Blockchain Technology and NFT’s: A Review in Music Industry. Journal of Management, Marketing and Logistics (JMML), 8(3), 154-163
  • Güven, V. & Şahinöz, E. (2018). Blokzincir - Kripto Paralar - Bitcoin: Satoshi Dünyayı Değiştiriyor. İstanbul: Kronik Kitap.
  • Han, H., Linton, O., Oka, T., & Whang, Y. J. (2016). The Cross Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series. Journal of Econometrics, 193(1), 251-270.
  • Hepsağ, A. & Akçalı, B. Y. (2016). Türk Finans Piyasasında İşlem Gören Bankalar İle ABD Finans Piyasası Arasındaki Volatilite Etkileşiminin Analizi. Avrasya Ekonometri İstatistik ve Ampirik Ekonomi Dergisi, 1(1), 54-72.
  • Ito, K., Shibano, K. & Mogi, G. (2022). Predicting the Bubble of Non-Fungible Tokens (NFTs): An Empirical Investigation. https://arxiv.org/abs/2203.12587 (Date Accessed: 26.05.2022).
  • NonFungible.com (2022). NFT Market Quarterly Report: Q1 · 2022. https://nonfungible.com/reports/2022/en/q1-quarterly-nft-marketreport-free/thank-you (Date Accessed: 29.05.2022).
  • Pinto-Gutiérrez, C., Gaitán, S., Jaramillo, D. & Velasquez, S. (2022). The NFT Hype: What Draws Attention to Non-Fungible Tokens? Mathematics, 10(3), 1-13.
  • Sattary, A. (2014). Petrol Fiyatları İle Hisse Senedi Getirileri Arasında Oynaklık Geçişkenliğinin Analizi Ve Portföy Yönetimine Yansımaları. Yayımlanmamış Doktora Tezi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü, Ekonometri Anabilim Dalı.
  • Tse, Y. & Tsui, A. (2002) A Multivariate GARCH Model with Time-Varying Correlations. Journal of Business and Economic Statistics, 20, 351- 362.
  • www.ethereum.org/en/nft/ (Date Accessed: 25.05.2022).
  • www.investing.com (Date Accessed: 25.04.2022).
  • Yousaf, I. & Yarovaya, L. (2022) Static and Dynamic Connectedness between NFTs, Defi and Other Assets: Portfolio Implication. Global Finance Journal, 53, 100719
There are 24 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Articles
Authors

Hilmi Tunahan Akkus This is me 0000-0002-8407-1580

Samet Gursoy This is me 0000-0003-1020-7438

Mesut Dogan This is me 0000-0001-6879-1361

Publication Date June 30, 2022
Published in Issue Year 2022

Cite

APA Akkus, H. T., Gursoy, S., & Dogan, M. (2022). THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION. Research Journal of Business and Management, 9(2), 85-91. https://doi.org/10.17261/Pressacademia.2022.1563
AMA Akkus HT, Gursoy S, Dogan M. THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION. RJBM. June 2022;9(2):85-91. doi:10.17261/Pressacademia.2022.1563
Chicago Akkus, Hilmi Tunahan, Samet Gursoy, and Mesut Dogan. “THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION”. Research Journal of Business and Management 9, no. 2 (June 2022): 85-91. https://doi.org/10.17261/Pressacademia.2022.1563.
EndNote Akkus HT, Gursoy S, Dogan M (June 1, 2022) THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION. Research Journal of Business and Management 9 2 85–91.
IEEE H. T. Akkus, S. Gursoy, and M. Dogan, “THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION”, RJBM, vol. 9, no. 2, pp. 85–91, 2022, doi: 10.17261/Pressacademia.2022.1563.
ISNAD Akkus, Hilmi Tunahan et al. “THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION”. Research Journal of Business and Management 9/2 (June 2022), 85-91. https://doi.org/10.17261/Pressacademia.2022.1563.
JAMA Akkus HT, Gursoy S, Dogan M. THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION. RJBM. 2022;9:85–91.
MLA Akkus, Hilmi Tunahan et al. “THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION”. Research Journal of Business and Management, vol. 9, no. 2, 2022, pp. 85-91, doi:10.17261/Pressacademia.2022.1563.
Vancouver Akkus HT, Gursoy S, Dogan M. THE VOLATILITY SPILLOVER BETWEEN NFT INVESTMENT INDEX AND GLOBAL TECHNOLOGY INDEX: DCC-GARCH APPLICATION. RJBM. 2022;9(2):85-91.

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