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KRİPTO PARA PİYASASINDA VOLATİL DAVRANIŞLARIN ASİMETRİK STOKASTİK VOLATİLİTE MODELİ İLE TESTİ

Yıl 2023, Cilt: 19 Sayı: 1, 61 - 82, 24.03.2023
https://doi.org/10.17130/ijmeb.1175863

Öz

Bu çalışmada, kripto piyasasının önde gelen altı kripto para biriminin (Bitcoin, Stellar, Litecoin, Ethereum, Tether ve Ripple) volatil yapısı, asimetrik ilişki ve/ve ya kaldıraç etkisinin var olup olmadığı test edilmektedir. 09/11/2017-31/07/2022 dönemini kapsayan ve WinBUGS uygulaması ile yapılan bu çalışmada öncelikle logaritmik fark alınarak getiri serisi hesaplanmıştır. Bu kapsamda 100.000 tekrarla örneklem sınaması yapılmış olup katsayıların başlangıç eğiliminden çıkması için tahminlerin ilk 10.000 örneklemi dışlanarak kalan 90.000 örneklemle analiz gerçekleştirilmiştir. Asimetrik stokastik volatilite modeli tahmin sonuçlarına göre kripto para birimlerinin oynaklık kalıcılığı, oynaklığın öngörülebilirliği ve para birimlerinin kendi getirilerinin şoku ile oynaklıklarının etkisi arasındaki korelasyon düzeyi ilgili parametreler ile değerlendirilmiştir. Belirtilen zaman aralığında çalışmamızda kullanılan tüm kripto para birimleri için yoğun bir volatilite kümelenmesi olduğu gözlemlenmiştir. Bu volatilitenin sürekli olduğu ve düşük öngörülebilirliğin varlığı ampirik olarak asimetrik stokastik volatilite modeli ile elde edilen bulgular arasındadır. Ayrıca çalışmanın sonuçlarına göre Ethereum kripto para birimi dışındaki diğer beş para biriminin hiçbirinde ne kaldıraç etkisi ne de asimetrik ilişkisinin hiçbiri gözlemlenmemiştir.

Kaynakça

  • Akhtaruzzaman, M., Sensoy, A., & Corbet, S. (2020). The influence of bitcoin on portfolio diversification and design. Finance Research Letters, 37, 101344.
  • Almansour, B., Alshater, M. & Almansour, A.(2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering & Management Systems, 20(2), 130-139.
  • Anavatan, A. & Kayacan, Y. (2019). Are Bitcoin returns predictable?. Journal of Current Researches on Business and Economics, 9(1), 13-22.
  • Asai, M. & McAleer, M. (2005). Dynamic asymmetric leverage in stochastic volatility models. Econometric Reviews, 24(3), 317-332.
  • Atanu, D., Kumar, D. & Basu, N. (2009). A Review on recent trends of stochastic volatility models. International Review of Applied Financial Issues and Economics, 1(1), 83-106.
  • Balcilar, M., Bouri, E., Gupta, R. & Roubaud, D. (2017), Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Baur, D. & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151.
  • Bohte, R. & Rossini, L. (2019). Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models. Journal of Risk and Financial Management, 12(3), 150-168.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Bouoiyour, J. & Selmi, R. (2016). Bitcoin: a beginning of a new phase?. Economics Bulletin, 36(3), 1430-1440.
  • Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216-221.
  • Conrad, C., Custovic, C. & Ghysels, E. (2018). Long and short-term cryptocurrency volatility components : A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23-35.
  • Das, S. & Ghanem, R. (2009). A bounded random matrix approach for stochastic upscaling. Multiscale Modeling & Simulation, 8(1), 296-325.
  • Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139-144.
  • Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
  • Göktaş, Ö. & Hepsağ, A. (2016). BIST-100 endeksinin volatil davranışlarının simetrik ve asimetrik stokastik volatilite modelleri ile analizi. Ekonomik Yaklaşım, 27(99), 1-15.
  • Harvey, A. & Shephard, N. (1996). Estimation of an asymmetric stochastic volatility model for asset returns. Journal of Business & Economic Statistics, 14(4), 429-434.
  • Haykir, O. & Yagli, I. (2022). Speculative bubbles and herding in cryptocurrencies. Financial Innovation, 8(1), 1-33.
  • https://www.tradingview.com – (Erişim tarihi : 12.08.2022).
  • https://www.investing.com – (Erişim tarihi : 12.08.2022).
  • Huang, J. & Xu, J. (2021). Sequential learning of cryptocurrency volatility dynamics: evidence based on a stochastic volatility model with jumps in returns and volatility. The Quarterly Journal of Finance, 11(2), 1-37.
  • Kahraman, K., Küçükşahin, H. & Çağlak, E. (2019). Kripto para birimlerinin volatilite yapısı: GARCH modelleri karşılaştırması. Fiscaoeconomia, 3(2), 21-45.
  • Karaağaç, G. A., & Altınırmak, S. (2018). En yüksek piyasa değerine sahip on kripto paranın birbirleriyle etkileşimi. Muhasebe ve Finansman Dergisi, 79, 123-138.
  • Katsiampa, P. (2019). An empirical investigation of volatility dynamics in the cryptocurrency market. Research in International Business and Finance, 50, 322-335.
  • Kim, J., Jun, C. & Lee, J. (2021). Forecasting the volatility of the cryptocurrency market by GARCH and stochastic volatility. Mathematics, 9(14), 1-16.
  • Kim, S., Shephard, N. & Chib, S. (1998). Stochastic volatility : likelihood inference and comparison with ARCH models. Review of Economic Studie, 65, 361-393.
  • Knight, L., Satchell, S. & Yu, J. (2002). Theory & methods: estimation of the stochastic volatility model by the empirical characteristic function method. Australian& New Zealand Journal of Statistics, 44(3), 319-335. Koy, A., Yaman, M. & Mete, S. (2021). Kripto paraların volatilite modelinde ABD borsa endekslerinin yeri : Bitcoin üzerine bir uygulama. Finansal Araştırmalar ve Çalışmalar Dergisi, 13(24), 159-170.
  • Kumah, S. P., & Mensah, J. O. (2022). Are cryptocurrencies connected to gold? A wavelet‐based quantile‐in‐quantile approach. International Journal of Finance & Economics, 27(3), 3640-3659.
  • Kumar, A. & Anandarao, S. (2019). Volatility spillover in crypto-currency markets: Some evidences from GARCH and wavelet analysis. Physica A: Statistical Mechanics and its Applications, 524, 448-458.
  • Kunimoto, N. & Kakamu, K. (2021). Is Bitcoin really a currency? A viewpoint of a stochastic volatility model. https://arxiv.org/pdf/2111.15351.pdf (Erişim Tarihi : 05/08/2022).
  • Li, Z. Z., Su, C. W. & Zhu, M. N. (2022). How Does Uncertainty Affect Volatility Correlation between Financial Assets? Evidence from Bitcoin, Stock and Gold. Emerging Markets Finance and Trade, 58(9), 2682-2694.
  • Liu, J. & Serletis, A. (2019). Volatility in the cryptocurrency market. Open Economies Review, 30, 779-811.
  • Maciel, L. (2021). Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?. International Journal of Finance & Economics, 26(3), 4840-4855.
  • Pellegrini, S. & Rodrigez, A. (2007). Financial econometrics and SV models. http://halweb.uc3m.es/esp/Personal/personas/spellegr/esp/Curso_Cordoba/Tutorial_Guide.pdf (Erişim Tarihi: 12.08.2022).
  • Poon, S.H. (2005). A practical guide to forecasting financial market volatility. John Willey.
  • Shi, S. &Shi, Y. (2021). Bitcoin futures: trade it or ban it? The European Journal of Finance, 27(4-5), 381-396.
  • Taylor, S. J. (1986). Modelling financial time series. U.K.: John Wiley.
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
  • Yu, J. & R, Meyer. (2006). Multivariate stochastic volatility models: Bayesian estimation and model comparison. Econometric Reviews, 25, 361–384.
  • Zahid, M. & Iqbal, F. (2020). Modeling the volatility of cryptocurrencies: an empirical application of stochastic volatility models. Sains Malaysiana, 49(3), 703-712.

TEST OF VOLATILITY BEHAVIORS ON THE CRYPTO CURRENCY MARKET WITH THE ASYMMETRIC STOCHASTIC VOLATILITY MODEL

Yıl 2023, Cilt: 19 Sayı: 1, 61 - 82, 24.03.2023
https://doi.org/10.17130/ijmeb.1175863

Öz

In this study, the six major crypto currencies of the crypto market (Bitcoin, Stellar, Litecoin, Ethereum, Tether and Ripple) aims to test whether volatile structure, the asymmetric relationship and/or leverage effect exists. Our study covers the period of 09/11/2017-31/07/2022 and with WinBUGS application, the return series is calculated primarily by taking the logarithmic difference. In this context, samples were tested with 100,000 iterations and analysis was performed with the remaining 90,000 samples, excluding the first 10,000 samples of the estimates, in order for the coefficients to come out of the initial trend. According to the asymmetric effect model estimation results, the volatility persistence of cryptocurrencies, volatility predictability, and the correlation between the shock of the currencies' own returns and the effect and the shock effects of their volatility were evaluated. When we consider cryptocurrencies as a whole throughout the study, there is an intense volatility clustering for all cryptocurrencies used in our study, this volatility is continuous and the presence of low predictability is among the findings with the empirically asymmetric stochastic volatility model. In addition, according to the results of the study, neither leverage effect nor asymmetric effect relationship was observed in any of the other five currencies except Ethereum cryptocurrency.

Kaynakça

  • Akhtaruzzaman, M., Sensoy, A., & Corbet, S. (2020). The influence of bitcoin on portfolio diversification and design. Finance Research Letters, 37, 101344.
  • Almansour, B., Alshater, M. & Almansour, A.(2021). Performance of ARCH and GARCH models in forecasting cryptocurrency market volatility. Industrial Engineering & Management Systems, 20(2), 130-139.
  • Anavatan, A. & Kayacan, Y. (2019). Are Bitcoin returns predictable?. Journal of Current Researches on Business and Economics, 9(1), 13-22.
  • Asai, M. & McAleer, M. (2005). Dynamic asymmetric leverage in stochastic volatility models. Econometric Reviews, 24(3), 317-332.
  • Atanu, D., Kumar, D. & Basu, N. (2009). A Review on recent trends of stochastic volatility models. International Review of Applied Financial Issues and Economics, 1(1), 83-106.
  • Balcilar, M., Bouri, E., Gupta, R. & Roubaud, D. (2017), Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling, 64, 74-81.
  • Baur, D. & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151.
  • Bohte, R. & Rossini, L. (2019). Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models. Journal of Risk and Financial Management, 12(3), 150-168.
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • Bouoiyour, J. & Selmi, R. (2016). Bitcoin: a beginning of a new phase?. Economics Bulletin, 36(3), 1430-1440.
  • Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216-221.
  • Conrad, C., Custovic, C. & Ghysels, E. (2018). Long and short-term cryptocurrency volatility components : A GARCH-MIDAS analysis. Journal of Risk and Financial Management, 11(2), 23-35.
  • Das, S. & Ghanem, R. (2009). A bounded random matrix approach for stochastic upscaling. Multiscale Modeling & Simulation, 8(1), 296-325.
  • Dyhrberg, A. H. (2016). Hedging capabilities of bitcoin. Is it the virtual gold?. Finance Research Letters, 16, 139-144.
  • Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
  • Göktaş, Ö. & Hepsağ, A. (2016). BIST-100 endeksinin volatil davranışlarının simetrik ve asimetrik stokastik volatilite modelleri ile analizi. Ekonomik Yaklaşım, 27(99), 1-15.
  • Harvey, A. & Shephard, N. (1996). Estimation of an asymmetric stochastic volatility model for asset returns. Journal of Business & Economic Statistics, 14(4), 429-434.
  • Haykir, O. & Yagli, I. (2022). Speculative bubbles and herding in cryptocurrencies. Financial Innovation, 8(1), 1-33.
  • https://www.tradingview.com – (Erişim tarihi : 12.08.2022).
  • https://www.investing.com – (Erişim tarihi : 12.08.2022).
  • Huang, J. & Xu, J. (2021). Sequential learning of cryptocurrency volatility dynamics: evidence based on a stochastic volatility model with jumps in returns and volatility. The Quarterly Journal of Finance, 11(2), 1-37.
  • Kahraman, K., Küçükşahin, H. & Çağlak, E. (2019). Kripto para birimlerinin volatilite yapısı: GARCH modelleri karşılaştırması. Fiscaoeconomia, 3(2), 21-45.
  • Karaağaç, G. A., & Altınırmak, S. (2018). En yüksek piyasa değerine sahip on kripto paranın birbirleriyle etkileşimi. Muhasebe ve Finansman Dergisi, 79, 123-138.
  • Katsiampa, P. (2019). An empirical investigation of volatility dynamics in the cryptocurrency market. Research in International Business and Finance, 50, 322-335.
  • Kim, J., Jun, C. & Lee, J. (2021). Forecasting the volatility of the cryptocurrency market by GARCH and stochastic volatility. Mathematics, 9(14), 1-16.
  • Kim, S., Shephard, N. & Chib, S. (1998). Stochastic volatility : likelihood inference and comparison with ARCH models. Review of Economic Studie, 65, 361-393.
  • Knight, L., Satchell, S. & Yu, J. (2002). Theory & methods: estimation of the stochastic volatility model by the empirical characteristic function method. Australian& New Zealand Journal of Statistics, 44(3), 319-335. Koy, A., Yaman, M. & Mete, S. (2021). Kripto paraların volatilite modelinde ABD borsa endekslerinin yeri : Bitcoin üzerine bir uygulama. Finansal Araştırmalar ve Çalışmalar Dergisi, 13(24), 159-170.
  • Kumah, S. P., & Mensah, J. O. (2022). Are cryptocurrencies connected to gold? A wavelet‐based quantile‐in‐quantile approach. International Journal of Finance & Economics, 27(3), 3640-3659.
  • Kumar, A. & Anandarao, S. (2019). Volatility spillover in crypto-currency markets: Some evidences from GARCH and wavelet analysis. Physica A: Statistical Mechanics and its Applications, 524, 448-458.
  • Kunimoto, N. & Kakamu, K. (2021). Is Bitcoin really a currency? A viewpoint of a stochastic volatility model. https://arxiv.org/pdf/2111.15351.pdf (Erişim Tarihi : 05/08/2022).
  • Li, Z. Z., Su, C. W. & Zhu, M. N. (2022). How Does Uncertainty Affect Volatility Correlation between Financial Assets? Evidence from Bitcoin, Stock and Gold. Emerging Markets Finance and Trade, 58(9), 2682-2694.
  • Liu, J. & Serletis, A. (2019). Volatility in the cryptocurrency market. Open Economies Review, 30, 779-811.
  • Maciel, L. (2021). Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?. International Journal of Finance & Economics, 26(3), 4840-4855.
  • Pellegrini, S. & Rodrigez, A. (2007). Financial econometrics and SV models. http://halweb.uc3m.es/esp/Personal/personas/spellegr/esp/Curso_Cordoba/Tutorial_Guide.pdf (Erişim Tarihi: 12.08.2022).
  • Poon, S.H. (2005). A practical guide to forecasting financial market volatility. John Willey.
  • Shi, S. &Shi, Y. (2021). Bitcoin futures: trade it or ban it? The European Journal of Finance, 27(4-5), 381-396.
  • Taylor, S. J. (1986). Modelling financial time series. U.K.: John Wiley.
  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
  • Yu, J. & R, Meyer. (2006). Multivariate stochastic volatility models: Bayesian estimation and model comparison. Econometric Reviews, 25, 361–384.
  • Zahid, M. & Iqbal, F. (2020). Modeling the volatility of cryptocurrencies: an empirical application of stochastic volatility models. Sains Malaysiana, 49(3), 703-712.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Finans
Bölüm Araştırma Makaleleri
Yazarlar

Magsud Gubadlı 0000-0003-0270-9526

Vedat Sarıkovanlık 0000-0002-7152-6275

Yayımlanma Tarihi 24 Mart 2023
Gönderilme Tarihi 15 Eylül 2022
Kabul Tarihi 29 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 19 Sayı: 1

Kaynak Göster

APA Gubadlı, M., & Sarıkovanlık, V. (2023). KRİPTO PARA PİYASASINDA VOLATİL DAVRANIŞLARIN ASİMETRİK STOKASTİK VOLATİLİTE MODELİ İLE TESTİ. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 19(1), 61-82. https://doi.org/10.17130/ijmeb.1175863