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MODELING, FORECASTING THE CRYPTOCURRENCY MARKET VOLATILITY AND VALUE AT RISK DYNAMICS OF BITCOIN

Yıl 2020, Cilt 22, Sayı 2, 296 - 312, 30.06.2020
https://doi.org/10.31460/mbdd.726952

Öz

Bitcoin volatility was investigated with various symmetric and asymmetric models in the study. In addition, value at risk (VaR) was calculated by using the Kupiec LR test and the error prediction performances of the models were compared. As a result of the work, the long memory of volatility in Bitcoin returns was found. It means the cryptocurrency market is not efficient. According to the FIAPARCH asymmetric model, it was determined that positive information shocks reaching the Bitcoin market increased volatility more than negative information shocks. Comparing the error prediction performance of the models by calculating VaR, the HYGARCH model prediction results were found to be superior to other models included in the study. Thus, it was determined that the most suitable model in predicting the volatility, namely the risk of Bitcoin in short and long positions for those who consider investing in Bitcoin, is the asymmetric model HYGARCH.

Kaynakça

  • Aksoy, E. E. 2018. “Bitcoin: Paradan Sonraki En Büyük İcat-Blockchain Teknolojisi ve Altcoin’ler”, İstanbul: Abaküs Kitap.
  • Ardia, D., Bluteau, K. and Rüede, M. 2019. “Regime Changes in Bitcoin GARCH Volatility Dynamics”, Finance Research Letters, 29: 266-271.
  • Baillie, R. T., Bollerslev, T. and Mikkelsen, H. O. 1996. “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 74(1): 3-30.
  • Balıbey, M. and Türkyılmaz, S. (2014). “Value-at-Risk Analysis in the Presence of Asymmetry and Long Memory: The Case of Turkish Stock Market”, International Journal of Economics and Financial Issues, 4(4): 836-848.
  • Baur, D. G. and Dimpfl, T. (2018). “Asymmetric Volatility in Cryptocurrencies”, Economics Letters, 173: 148-151.
  • Bollerslev, T. and Mikkelsen, H. O. 1996. “Modeling and Pricing Long Memory in Stock Market Volatility”, Journal of Econometrics, 73(1): 151-184.
  • Bouoiyour, J. and Selmi, R. 2016. “Bitcoin: A Beginning of A New Phase?”, Bulletin, 36(3): 1430-1440.
  • Bouri, E., Azzi, G. and Dyhrberg, A. H. 2016. “On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013”, Economics: Open-Access, Open-Assessment E-Journal, Economics Discussion Papers, No: 2016-41.
  • Bozkuş Kahyaoğlu, S. 2017. “Blok Zinciri Teknolojilerinin Finansal Piyasalara Olası Etkileri Üzerine Bir Değerlendirme”, 2. International Trakya Accounting Finance and Auditing Symposium. 17-19.10.2017, Edirne-Türkiye.
  • Byström, H. and Krygier, D. 2018. “What Drives Bitcoin Volatility?”, The Knut Wicksell Centre for Financial Studies, Working Paper 2018: 3.
  • Catania, L. Grassi, S. and Ravazzolo, F. 2018. “Predicting the Volatility of Cryptocurrency Time-Series”, Centre for Applied Macro and Petroleum Economics (CAMP) Working Paper Series, No: 3/2018.
  • Chaim, P. and Laurini, M. P. 2018. “Volatility and Return Jumps in Bitcoin”, Economics Letters, 173: 158-163.
  • Charles, A. and Darné, O. 2014. “Large Shocks in the Volatility of the Dow Jones Industrial Average Index: 1928-2013”, Journal of Banking & Finance, 43: 188-199.
  • Charles, A. and Darné, O. 2019. “Volatility Estimation for Bitcoin: Replication and Robustness”, International Economics, 157: 23-32.
  • Chu, J., Chan, S., Nadarajah, S. and Osterrieder, J. 2017. “GARCH Modelling of Cryptocurrencies”, Journal of Risk and Financial Management, 10(17): 1-15.
  • CoinMarketCap. www.coinmarketcap.com (Erişim Tarihi: 25.04.2020).
  • Davidson, J. 2004. “Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and A New Model”, Journal of Business & Economic Statistics, 22: 16–29.
  • Dyhrberg, A. H. 2016a. “Bitcoin, Gold and the Dollar – A GARCH Volatility Analysis”, Finance Research Letters, 16: 85-92.
  • Dyhrberg, A. H. 2016b. “Hedging Capabilities of Bitcoin. Is It Virtual Gold?”, Finance Research Letters, 16: 139-144.
  • Katsiampa, P. 2017. “Volatility Estimation for Bitcoin: A Comparison of GARCH Models”, Economics Letters, 158: 3-6.
  • Kelly, B. 2014. “The Bitcoin Big Bang: How Alternative Currencies Are About to Change the World”, New Jersey: John Wiley & Sons.
  • Klein, T., Thu, H. P. and Walther, T. 2018. “Bitcoin Is not the New Gold – A Comparison of Volatility, Correlation, and Portfolio Performance”, International Review of Financial Analysis, 59: 105-116.
  • Mensi, W., Al-Yahyaee, K. H. and Kang, S. H. 2019. “Structural Breaks and Double Long Memory of Cryptocurrency Prices: A Comparative Analysis from Bitcoin and Ethereum”, Finance Research Letters, 29: 222-230.
  • Nakamoto, S. 2008. “Bitcoin: A Peer-to-Peer Electronic Cash System”, https://bitcoin.org/bitcoin.pdf (Erişim Tarihi: 10.10.2019).
  • Nelson, D. B. 1991. “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica: Journal of the Econometric Society, 59(2): 347-370.
  • Phillip, A., Chan, J. S. K. and Peiris, S. 2018. “A New Look at Cryptocurrencies”, Economics Letters, 163: 6-9.
  • Pichl, L. and Kaizoji, T. 2017. “Volatility Analysis of Bitcoin Price Time Series”, Quantitative Finance and Economics, 1(4): 474-485.
  • Tse, Y. K. 1998. “The Conditional Heteroscedasticity of the Yen-Dollar Exchange Rate”, Journal of Applied Econometrics, 13(1): 49-55.
  • Vigna, P. and Casey, M. J. 2017. “Kripto Para Çağı: Bitcoin ve Dijital Paranın Küresel Ekonomik Sisteme Meydan Okuması (Çev: A. Atav)”, Ankara: Buzdağı Yayınevi.

KRİPTOPARA PİYASA VOLATİLİTESİNİN MODELLENMESİ, TAHMİNİ VE BİTCOİN’İN RİSKE MARUZ DEĞER DİNAMİKLERİ

Yıl 2020, Cilt 22, Sayı 2, 296 - 312, 30.06.2020
https://doi.org/10.31460/mbdd.726952

Öz

Bu çalışmada Bitcoin volatilitesi, çeşitli simetrik ve asimetrik modeller yardımıyla araştırılmaktadır. Bunun yanında Kupiec LR testi yardımıyla riske maruz değer (RMD) hesaplanarak modellerin hata öngörü performansları karşılaştırılmaktadır. Çalışma sonucunda Bitcoin getiri volatilitesinde uzun hafızanın varlığı tespit edilmiştir. Bu durum, kripto para piyasasının etkin olmadığı anlamına gelmektedir. Ayrıca FIAPARCH asimetrik model sonucuna göre Bitcoin piyasasına ulaşan pozitif bilgi şoklarının negatif bilgi şoklarına kıyasla volatiliteyi daha çok artırdığı belirlenmiştir. RMD hesaplanarak modellerin hata öngörü performansları karşılaştırıldığında, HYGARCH model tahmin sonuçlarının çalışma kapsamındaki diğer modellerden daha üstün olduğu belirlenmiştir. Böylece Bitcoin’e yatırım yapmayı düşünenlerin kısa ve uzun pozisyonlar için Bitcoin’in volatilitesini yani riskini tahmin etmede en uygun modelin asimetrik bir model olan HYGARCH modeli olduğu tespit edilmiştir.

Kaynakça

  • Aksoy, E. E. 2018. “Bitcoin: Paradan Sonraki En Büyük İcat-Blockchain Teknolojisi ve Altcoin’ler”, İstanbul: Abaküs Kitap.
  • Ardia, D., Bluteau, K. and Rüede, M. 2019. “Regime Changes in Bitcoin GARCH Volatility Dynamics”, Finance Research Letters, 29: 266-271.
  • Baillie, R. T., Bollerslev, T. and Mikkelsen, H. O. 1996. “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 74(1): 3-30.
  • Balıbey, M. and Türkyılmaz, S. (2014). “Value-at-Risk Analysis in the Presence of Asymmetry and Long Memory: The Case of Turkish Stock Market”, International Journal of Economics and Financial Issues, 4(4): 836-848.
  • Baur, D. G. and Dimpfl, T. (2018). “Asymmetric Volatility in Cryptocurrencies”, Economics Letters, 173: 148-151.
  • Bollerslev, T. and Mikkelsen, H. O. 1996. “Modeling and Pricing Long Memory in Stock Market Volatility”, Journal of Econometrics, 73(1): 151-184.
  • Bouoiyour, J. and Selmi, R. 2016. “Bitcoin: A Beginning of A New Phase?”, Bulletin, 36(3): 1430-1440.
  • Bouri, E., Azzi, G. and Dyhrberg, A. H. 2016. “On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013”, Economics: Open-Access, Open-Assessment E-Journal, Economics Discussion Papers, No: 2016-41.
  • Bozkuş Kahyaoğlu, S. 2017. “Blok Zinciri Teknolojilerinin Finansal Piyasalara Olası Etkileri Üzerine Bir Değerlendirme”, 2. International Trakya Accounting Finance and Auditing Symposium. 17-19.10.2017, Edirne-Türkiye.
  • Byström, H. and Krygier, D. 2018. “What Drives Bitcoin Volatility?”, The Knut Wicksell Centre for Financial Studies, Working Paper 2018: 3.
  • Catania, L. Grassi, S. and Ravazzolo, F. 2018. “Predicting the Volatility of Cryptocurrency Time-Series”, Centre for Applied Macro and Petroleum Economics (CAMP) Working Paper Series, No: 3/2018.
  • Chaim, P. and Laurini, M. P. 2018. “Volatility and Return Jumps in Bitcoin”, Economics Letters, 173: 158-163.
  • Charles, A. and Darné, O. 2014. “Large Shocks in the Volatility of the Dow Jones Industrial Average Index: 1928-2013”, Journal of Banking & Finance, 43: 188-199.
  • Charles, A. and Darné, O. 2019. “Volatility Estimation for Bitcoin: Replication and Robustness”, International Economics, 157: 23-32.
  • Chu, J., Chan, S., Nadarajah, S. and Osterrieder, J. 2017. “GARCH Modelling of Cryptocurrencies”, Journal of Risk and Financial Management, 10(17): 1-15.
  • CoinMarketCap. www.coinmarketcap.com (Erişim Tarihi: 25.04.2020).
  • Davidson, J. 2004. “Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and A New Model”, Journal of Business & Economic Statistics, 22: 16–29.
  • Dyhrberg, A. H. 2016a. “Bitcoin, Gold and the Dollar – A GARCH Volatility Analysis”, Finance Research Letters, 16: 85-92.
  • Dyhrberg, A. H. 2016b. “Hedging Capabilities of Bitcoin. Is It Virtual Gold?”, Finance Research Letters, 16: 139-144.
  • Katsiampa, P. 2017. “Volatility Estimation for Bitcoin: A Comparison of GARCH Models”, Economics Letters, 158: 3-6.
  • Kelly, B. 2014. “The Bitcoin Big Bang: How Alternative Currencies Are About to Change the World”, New Jersey: John Wiley & Sons.
  • Klein, T., Thu, H. P. and Walther, T. 2018. “Bitcoin Is not the New Gold – A Comparison of Volatility, Correlation, and Portfolio Performance”, International Review of Financial Analysis, 59: 105-116.
  • Mensi, W., Al-Yahyaee, K. H. and Kang, S. H. 2019. “Structural Breaks and Double Long Memory of Cryptocurrency Prices: A Comparative Analysis from Bitcoin and Ethereum”, Finance Research Letters, 29: 222-230.
  • Nakamoto, S. 2008. “Bitcoin: A Peer-to-Peer Electronic Cash System”, https://bitcoin.org/bitcoin.pdf (Erişim Tarihi: 10.10.2019).
  • Nelson, D. B. 1991. “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica: Journal of the Econometric Society, 59(2): 347-370.
  • Phillip, A., Chan, J. S. K. and Peiris, S. 2018. “A New Look at Cryptocurrencies”, Economics Letters, 163: 6-9.
  • Pichl, L. and Kaizoji, T. 2017. “Volatility Analysis of Bitcoin Price Time Series”, Quantitative Finance and Economics, 1(4): 474-485.
  • Tse, Y. K. 1998. “The Conditional Heteroscedasticity of the Yen-Dollar Exchange Rate”, Journal of Applied Econometrics, 13(1): 49-55.
  • Vigna, P. and Casey, M. J. 2017. “Kripto Para Çağı: Bitcoin ve Dijital Paranın Küresel Ekonomik Sisteme Meydan Okuması (Çev: A. Atav)”, Ankara: Buzdağı Yayınevi.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm ANABOLUM
Yazarlar

Hilmi Tunahan AKKUŞ (Sorumlu Yazar)
BALIKESİR ÜNİVERSİTESİ, SAVAŞTEPE MESLEK YÜKSEKOKULU
0000-0002-8407-1580
Türkiye


İsmail ÇELİK
MEHMET AKİF ERSOY ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ
0000-0002-6330-754X
Türkiye

Yayımlanma Tarihi 30 Haziran 2020
Başvuru Tarihi 25 Nisan 2020
Kabul Tarihi 8 Haziran 2020
Yayınlandığı Sayı Yıl 2020, Cilt 22, Sayı 2

Kaynak Göster

APA Akkuş, H. T. & Çelik, İ. (2020). MODELING, FORECASTING THE CRYPTOCURRENCY MARKET VOLATILITY AND VALUE AT RISK DYNAMICS OF BITCOIN . Muhasebe Bilim Dünyası Dergisi , 22 (2) , 296-312 . DOI: 10.31460/mbdd.726952

Cited By

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