INVESTIGATION OF LONG MEMORY AND STOCHASTIC PROPERTIES OF HIGH FREQUENCY CRYPTO ASSET VOLATİLİTY BY FIGARCH MODELING

Abstract

In this study, it is aimed to comparatively examine the volatility models and long memory properties of high frequency intraday asset returns of selected crypto assets. Bitcoin (BTC), Ethereum (ETH), Cardano (ADA) and Binance Coin (BNB) 4 different crypto assets, 1-day, 12-hour, 8-hour, 6-hour, 4-hour, 2-hour, 1-hour, 30 minute and 15 minute frequency levels are the subject of the study. The 36 data sets examined are discussed in the FIGARCH (Fractional Integrated-GARCH) model. As a result of the analysis, it has been determined that all series have long memory feature, except for the ETH 30-minute return series. It was determined that the errors were not independently and randomly distributed with the increase in the sampling frequency. It was concluded that the long memory parameters of different sampling frequencies were similar to each other on average. however, there are findings that various frequencies of some assets can create an advantageous investment strategy. By providing all the conditions and constraints with the FIGARCH model, it was determined that 35 of 36 datasets were successful in modeling as meaningful and well-defined.

Keywords

• Long Memory
• Crytptocurrency Market
• High Frequency Data
• Figarch

YÜKSEK FREKANSLI KRİPTO VARLIK OYNAKLIĞININ UZUN HAFIZA VE STOKASTİK ÖZELLİKLERİNİN FIGARCH MODELİ İLE İNCELENMESİ

Abstract

Bu çalışmada, seçilmiş kripto varlıkların yüksek frekanslı gün içi varlık getirilerinin oynaklık (volatility) modelleri ve uzun hafıza özelliklerinin karşılaştırılmalı olarak incelenmesi amaçlanmıştır. Bitcoin (BTC), Ethereum (ETH), Cardano (ADA) ve Binance Coin (BNB) olmak üzere, 4 farklı kripto varlığın, 1 günlük, 12 saatlik, 8 saatlik, 6 saatlik, 4 saatlik, 2 saatlik, 1 saatlik, 30 dakikalık ve 15 dakikalık frekans düzeylerinde gerçekleşen 36 getiri serisi FIGARCH (Fractional Integrated- Kesirli Bütünleşik/Entegre edilmiş GARCH) modeli özelinde ele alınmıştır. Yapılan analizler sonucunda, ETH 30 dakikalık getiri serisi dışında, tüm serilerde uzun hafıza özelliğinin mevcut olduğu belirlenmiştir. Örneklem frekansının artması ile hataların bağımsız ve rassal dağılmakta güçlük çektiği, farklı örneklem frekanslarının uzun hafıza parametrelerinin ortalama olarak birbirine benzer olduğu, ancak bazı varlıkların çeşitli frekanslarının avantajlı bir yatırım stratejisi oluşturabileceği yönünde bulgular elde edilmiştir. FIGARCH modeli ile tüm koşul ve kısıtlar sağlanarak, 36 veri kümesinin 35’inin anlamlı ve iyi tanımlanmış olarak modellemede başarılı olduğu belirlenmiştir.

Keywords

• Figarch
• Uzun Hafıza
• Kripto Para Piyasası
• Yüksek Frekanslı Veri
• Long Memory
• Crytptocurrency Market
• High Frequency Data

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