پیش ‌بینی نوسانات قیمت بیت ‌کوین با استفاده از مدل‌های خودرگرسیون ناهمگن (HAR)

نویسندگان
دانشگاه آزاد
چکیده
پیش‌بینی نوسانات دارایی‌های مالی به‌ویژه در بازارهای پرنوسان مانند ارزهای دیجیتال، یکی از چالش‌های مهم در تحلیل مالی است. این پیش‌بینی‌ها نه تنها می‌توانند به سرمایه‌گذاران کمک کنند تا تصمیمات بهتری در زمینه خرید و فروش اتخاذ کنند، بلکه امکان مدیریت مؤثرتر ریسک‌ها و شناسایی فرصت‌های سودآوری را نیز فراهم می‌آورند. در نهایت، توانایی پیش‌بینی نوسانات بازار می‌تواند موجب بهبود استراتژی‌های مدیریت پرتفوی و کاهش ضررهای غیرمنتظره برای سرمایه‌گذاران شود. این تحقیق به بررسی و پیش‌بینی نوسانات قیمت بیت‌کوین به‌عنوان یکی از مهم‌ترین ارزهای دیجیتال پرداخته است. مدل‌های خودرگرسیون ناهمگن (HAR) و خانواده‌های آن به‌عنوان ابزارهای اصلی برای مدل‌سازی نوسانات در این پژوهش انتخاب شدند. این مدل‌ها به‌دلیل قابلیت بالای خود در تحلیل نوسانات در مقیاس‌های زمانی مختلف، برای مطالعه داده‌های نوسانی از اهمیت ویژه‌ای برخوردارند. با توجه به ویژگی‌های خاص بازار ارزهای دیجیتال، که شامل تغییرات سریع و غیرقابل پیش‌بینی در قیمت‌ها است، استفاده از مدل‌هایی که می‌توانند نوسانات کوتاه‌مدت و بلندمدت را همزمان مدل‌سازی کنند، ضروری به نظر می‌رسد. در این مطالعه، داده‌های تاریخی با فراوانی بالا در بازه‌های زمانی 60 دقیقه‌ای، روزانه، هفتگی و ماهانه از قیمت بیت‌کوین در دوره زمانی 2018 تا 2022 مورد تحلیل قرار گرفتند. نتایج حاصل از تحلیل‌ها نشان می‌دهد که مدل‌های خودرگرسیون ناهمگن (HAR) و نسخه‌های گسترش‌یافته آن، مانند HARJ، HARQ و HARQJ، توانایی بالایی در پیش‌بینی نوسانات قیمت بیت‌کوین دارند. علاوه بر این، وارد کردن عامل پرش به این مدل‌ها باعث افزایش دقت پیش‌بینی‌ها و بهبود نتایج شده است. این یافته‌ها بر اهمیت استفاده از مدل‌های پیشرفته و ترکیبی در پیش‌بینی نوسانات بازارهای مالی تأکید می‌کند و می‌تواند راهگشای توسعه استراتژی‌های بهینه برای سرمایه‌گذاران در بازار ارزهای دیجیتال باشد.
کلیدواژه‌ها

عنوان مقاله English

Predicting Bitcoin Price Volatility Using Heterogeneous Autoregressive (HAR) models

نویسندگان English

fakhrodin fakhrehosseini
meysam kaviani
Azad Islamic university karaj branch
چکیده English

Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.

Predicting financial asset volatility is highly important because this information can help investors make more informed decisions regarding buying and selling. Accurate predictions can also reduce financial risks and identify profitable opportunities. Ultimately, the ability to forecast market changes improves portfolio management strategies and minimizes unexpected losses for investors. This study examines and predicts Bitcoin price volatility by using innovative data analysis models. The Heterogeneous Autoregressive (HAR) model and its variants were selected as the primary tools for modeling volatility because of their high capability to analyze volatility data across different time scales. Given the unique characteristics of cryptocurrency markets and rapid, unpredictable price fluctuations, the use of models that can simultaneously capture both short- and long-term volatility is of significant importance. In this study, high-frequency historical Bitcoin price data from 2018 to 2022, covering 60-minute, daily, weekly, and monthly intervals, were analyzed using the HAR, HARJ, HARQ, and HARQJ models. The results indicate that heterogeneous models have strong predictive power for Bitcoin price volatility, and incorporating jump factors into these models further improves their forecasting accuracy.

کلیدواژه‌ها English

Heterogeneous Regressions
Bitcoin
Volatility Prediction
Andersen, T. G., & Bollerslev, T. (1998). "Answering the Sceptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts." International Economic Review.
Andersen, T. G., Benzoni, L., Lund, J. (2002). An empirical investigation of continuous-time equity return models. Journal of Finance, 57, 1239-1284. Available at SSRN: https://ssrn.com/abstract=285640
Barndorff-Nielson, O. E., Kinnebrock, S., Shephard, N. (2010). Measuring downside risk – realized semivariance. In T. Bollerslev, J. Russell and M. Watson. Volatility and time series econometrics: Essays in honor of Robert F. Engle. Oxford University Press. doi.org/10.1093/acprof:oso/9780199549498.003.0007
Barndorff-Nielson, O. E., Shephard, N. (2004). Power and bipower variation with stochastic volatility and jumps. Journal of Financial Econometrics, 2, 1-37. https://doi.org/10.1093/jjfinec/nbh001
Bashiri, M; Pariab, S. H. (2019). Bitcoin price prediction using machine learning algorithms. Applied Economics, (34)10,1-13.[In Persian]. 10.30495/JAE.2020.18114
Begusic, S., Kostanjˇcar, Z., Stanley, H. E., & Podobnik, B. (2018). Scaling properties of extreme price fluctuations in Bitcoin markets. Physica A: Statistical Mechanics and its Applications, 510, 400–406. https://doi.org/10.1016/j.physa.2018.06.131
Bollerslev, T., Law, T. H., & Tauchen, G. (2008). Risk, jumps, and diversification. Journal of Econometrics, 144(1), 234-256. https://doi.org/10.1016/j.jeconom.2008.01.006
Bollerslev, T., Patton, A. J., & Quaedvlieg, R. (2016). Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics, 192(1), 1-18. https://doi.org/10.1016/j.jeconom.2015.10.007
Chaim, P., Laurini, M. P. (2018). Volatility and return jumps in bitcoin. Economics Letters, 173, 158-163. https://doi.org/10.1016/j.econlet.2018.10.011
Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17.‌ https://doi.org/10.3390/jrfm10040017
Corsi, F. (2004). "A Simple Approximation for the Estimation of Volatility." International Journal of Forecasting.
Corsi, F. (2009). "A simple approximate long memory model of realized volatility." Journal of Financial Econometrics.
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.‌ https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
Gronwald, M. (2019). Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply. Journal of International Money and Finance, 97, 86-92. https://doi.org/10.1016/j.jimonfin.2019.06.006
Habibirad, A., & Panahi, A. (2021). Explaining the Relationship Between Bitcoin Price in Business Financial Transactions and Search Volume in Order to Identify its Behavioral Pattern: A Comparative Study Between Countries. Business Intelligence Management Studies, 10(37), 347-372. .[In Persian]doi: 10.22054/ims.2021.61455.1982
Huang, X., Tauchen, G. (2005). The relative price contribution of jumps to total price variance. Journal of Financial Econometrics, 3, 456-499. https://doi.org/10.1093/jjfinec/nbi025
Kajtazi, A., Moro, A. (2019). The role of Bitcoin in well diversified portfolios: A comparative global study. International Review of Financial Analysis, 61, 143-157. http://dx.doi.org/10.2139/ssrn.3261266
Kalyvas, A., Papakyriakou, P., Sakkas, A., & Urquhart, A. (2020). What drives Bitcoin’s price crash risk?. Economics Letters, 191, 108777. https://doi.org/10.1016/j.econlet.2019.108777
Karanasos, M., & Paya, I. (2004). "Forecasting Volatility: The Role of Time-Varying Conditional Variance Models." Journal of Forecasting.
Khaldi, R., El Afia, A., & Chiheb, R. (2019). Forecasting of BTC volatility: comparative study between parametric and nonparametric models. Progress in Artificial Intelligence, 8, 511-523.‌ https://link.springer.com/article/10.1007/s13748-019-00196-w
Kim, J. M., Jun, C., & Lee, J. (2021). Forecasting the volatility of the cryptocurrency market by GARCH and Stochastic Volatility. Mathematics, 9(14), 1614. https://doi.org/10.3390/math9141614
Köchling, G., Schmidtke, P., & Posch, P. N. (2020). Volatility forecasting accuracy for Bitcoin. Economics Letters, 191, 108836.‌ DOI: 10.1016/j.econlet.2019.108836
Koopman, S. J., Jungbacker, B., Hol, E. (2005). Forecasting daily variability of the S&P100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12, 445-475. https://doi.org/10.1016/j.jempfin.2004.04.009
Ma, F., Liang, C., Ma, Y., & Wahab, M. I. M. (2020). Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach. Journal of Forecasting, 39(8), 1277-1290.‌ https://doi.org/10.1002/for.2691
mohammadsharifi, A., Kahlili-Damghani, K., abdi, F., & sardar, S. (2021). Predicting the Price of Bitcoin Using Hybrid ARIMA and Deep Learning. Industrial Management Studies, 19(61), 125-146 .[In Persian]. doi: 10.22054/jims.2021.52374.2488
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Available at https://bitcoin.org/bitcoin.pdf
Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. Journal of Econometrics 160(1), 246–256.
Qadimpour, M. R, (1401). Bitcoin price forecasting using deep learning networks: an approach from the gated recurrent network (GRU) model, the 3rd International Conference on New Challenges and Solutions in Industrial Engineering, Management and Accounting, Chabahar, https:/ /civilica.com/doc/1564768.
Shen, D., Urquhart, A. & Wang, P. (2019). Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks. European Financial Management. 26(5), 1294-1323. https://doi.org/10.1111/eufm.12254
Todorov, V., & Tauchen, G. (2011). Volatility jumps. Journal of Business & Economic Statistics, 29(3), 356-371. DOI: 10.1198/jbes.2010.08342
White, H. (2000). A reality check for data snooping. Econometrica 68(5), 1097–1126. https://doi.org/10.1111/1468-0262.00152
Yu, M. (2019). Forecasting Bitcoin volatility: The role of leverage effect and uncertainty. Physica A: Statistical Mechanics and Its Applications, 533, 120707.‌ https://doi.org/10.1016/j.physa.2019.03.072