دانش سرمایه‌گذاری

دانش سرمایه‌گذاری

پیش بینی و مقایسه روند بازدهی قیمتی با استفاده از روش مارکوف پنهان و روش گارچ- مارکوف مطالعه موردی بیت کوین و اتریوم و سولانا و بی ان بی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گرو ه مدیریت مالی، واحد تهران مرکزی، دانشگا ه آزاد اسلامی ، تهران، ایران
2 گرو ه مدیریت مالی، واحد تهران مرکزی، دانشگا ه آزاد اسلامی ، تهران، ایران.
10.30495/jik.2024.77948.4598
چکیده
این پژوهش برای چهار رمز ارز بیت کوین، اتریوم، بی ان بی و سولانا پیش بینی روند حرکتی بازدهی با استفاده از دو روش مارکوف پنهان و گارچ مارکوف پنهان در طی بازه یکسال 2022 تا 2023 صورت گرفت. براین اساس برای پیش بینی دقیق تر روند بازدهی، در ابتدا با استفاده از مدل های معروف گارچ نوسانات بازدهی مدل سازی شد سپس با بکاربردن نوسانات بازدهی به عنوان ورودی، مدل مارکوف پنهان تخمین زده شده است و در نهایت برای مقایسه و نتیجه گیری در مورد دقت پیش بینی روند بازدهی از معیار DAP استفاده شد.
براساس نتایج، برای رمز ارز بیت کوین دقت مدل با روش مارکوف پنهان ساده 76 درصد است و با روش ترکیبی 82 درصد شده است. برای رمزارز اتریوم مارکوف پنهان 65 درصد و روش ترکیبی 91 درصد است. همچنین برای رمز ارز بی ان بی مدل مارکوف پنهان ساده 66 درصد است و روش ترکیبی به 74 درصد می رسد. درمورد رمز ارز سولانا روش مارکوف پنهان ساده 55 درصد و روش ترکیبی به 74 درصدرسیده است. بنابراین و به طور کلی استفاده از روش ترکیبی گارچ- مارکوف پنهان برای هر چهار رمز ارز باعث بهبود تشخیص روند حرکتی به طور معنی داری می شود.

عنوان مقاله English

Forecasting and comparing the trend of price return using hidden Markov method and Garch-Markov method, case study of Bitcoin, Ethereum, Solana and BNB

نویسندگان English

maryam bagherzadeh sohrabi 1
Hossein Mombeini 1
Safieh Mehrinejad 2
1 Financial Management Group, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Financial Management Group, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده English

This research was carried out for the four cryptocurrencies Bitcoin, Ethereum, BNB and Solana by predicting the dynamic trend of returns using hidden Markov and hidden Garch Markov methods during the one year period of 2022 to 2023. Therefore, in order to predict the efficiency trend more accurately, at first, the efficiency fluctuations were modeled using the famous Garch models, then by using the efficiency fluctuations as input, the hidden Markov model was estimated, and finally, to compare and draw conclusions about The DAP criterion was used to predict the efficiency trend.
Based on the results, the accuracy of the model with the simple hidden Markov method is 76% for the Bitcoin cryptocurrency and 82% with the combined method. For Ethereum Cryptocurrency Hidden Markov is 65% and hybrid method is 91%. Also, for the BNB currency, the simple hidden Markov model is 66%, and the combined method reaches 74%. Regarding the Solana currency, the simple hidden Markov method has reached 55% and the combined method has reached 74%. Therefore, in general, the use of the Garch-Hidden Markov combination method for all four currencies significantly improves the detection of movement trends.

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