MODELING, FORECASTING THE CRYPTOCURRENCY MARKET VOLATILITY AND VALUE AT RISK DYNAMICS OF BITCOIN
Abstract
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.
Keywords
References
- 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.
Details
Primary Language
English
Subjects
Business Administration
Journal Section
Research Article
Publication Date
June 30, 2020
Submission Date
April 25, 2020
Acceptance Date
June 8, 2020
Published in Issue
Year 2020 Volume: 22 Number: 2
Cited By
Dört Büyük Kriptoparanın Piyasa Riskinde Covid-19 Pandemi Etkisi
Ekonomi, Politika & Finans Araştırmaları Dergisi
https://doi.org/10.30784/epfad.811219YÜKSEK FREKANSLI KRİPTO VARLIK OYNAKLIĞININ UZUN HAFIZA VE STOKASTİK ÖZELLİKLERİNİN FIGARCH MODELİ İLE İNCELENMESİ
Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
https://doi.org/10.53092/duiibfd.1124966Piyasa riski ölçümü olarak riske maruz değer: Finansal yatırım araçları üzerine bir uygulama
Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
https://doi.org/10.25287/ohuiibf.837953Kriptopara Getirilerinin Piyasa Risklerinin Karşılaştırılması
Bingöl Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
https://doi.org/10.33399/biibfad.811774Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model
Discrete Dynamics in Nature and Society
https://doi.org/10.1155/2023/7657430Evaluation of Cryptocurrencies for Investment Decisions in the Era of Industry 4.0: A Borda Count-Based Intuitionistic Fuzzy Set Extensions EDAS-MAIRCA-MARCOS Multi-Criteria Methodology
Axioms
https://doi.org/10.3390/axioms11080404The Volatility Spillover in Metaverse Token Market: TVP-VAR Model Application
Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi
https://doi.org/10.17153/oguiibf.1399452KRİPTO VARLIKLARDA UZUN HAFIZA VE VOLATİLİTE DİNAMİKLERİ: BITCOIN, ETHEREUM VE BINANCE COIN ÖRNEĞİ
Nişantaşı Üniversitesi Sosyal Bilimler Dergisi
https://doi.org/10.52122/nisantasisbd.1648751Empirical Calibration of XGBoost Model Hyperparameters Using the Bayesian Optimisation Method: The Case of Bitcoin Volatility
Journal of Risk and Financial Management
https://doi.org/10.3390/jrfm18090487