Analisis Dampak Diferensial Aktivitas Whale terhadap Volatilitas Ethereum: Perbandingan Exchange Flow dan Liquid Staking

Nugraha, Farrel Akmalazmi (2026) Analisis Dampak Diferensial Aktivitas Whale terhadap Volatilitas Ethereum: Perbandingan Exchange Flow dan Liquid Staking. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Ethereum (ETH) merupakan aset kripto dengan volatilitas tinggi yang diduga berkaitan dengan aktivitas pelaku pasar berskala besar atau whale. Penelitian ini menganalisis perbedaan hubungan aktivitas whale melalui centralized exchange dan liquid staking dengan volatilitas Ethereum. Data yang digunakan berupa data per jam pada periode 1 Desember 2023 hingga 19 November 2025 dengan 17.256 observasi setelah pembentukan log-return. Aktivitas whale didefinisikan sebagai transaksi atau deposit individual minimal 100 ETH, sedangkan ambang 500 dan 1.000 ETH digunakan sebagai analisis sensitivitas. Data exchange flow dibatasi pada transaksi native ETH. Volatilitas diestimasi menggunakan GARCH(1,1) berdistribusi Student-t, sedangkan hubungan antarvariabel dianalisis menggunakan Kausalitas Granger, Vector Autoregression (VAR), Impulse Response Function (IRF), dan Forecast Error Variance Decomposition (FEVD). Hasil menunjukkan bahwa net exchange flow memberikan informasi prediktif terhadap volatilitas pada ambang 100 ETH setelah koreksi FDR pada lag 1–24 dan Bonferroni pada lag 2–24. Pada ambang 500 dan 1.000 ETH, hubungan tersebut signifikan pada lag 6–24 setelah kedua koreksi. Deposit Lido tidak menunjukkan hubungan prediktif terhadap volatilitas. Hasil IRF menunjukkan respons volatilitas terhadap guncangan net flow hanya berbeda dari nol pada horizon satu dan dua jam untuk ambang 100 ETH. Pada horizon 24 jam, kontribusi total aktivitas whale hanya sebesar 0,1874%, 0,0357%, dan 0,0511% untuk ambang 100, 500, dan 1.000 ETH. Dengan demikian, exchange flow memiliki informasi prediktif jangka pendek yang lebih kuat dibandingkan deposit Lido, tetapi kontribusinya dalam menjelaskan volatilitas tetap sangat kecil.
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Ethereum (ETH) is a highly volatile crypto asset whose volatility may be associated with the activities of large market participants, commonly referred to as whales. This study examines differences in the relationship between whale activity through centralized exchanges and liquid staking and Ethereum volatility. Hourly data from December 1, 2023, to November 19, 2025, were used, comprising 17,256 observations after log-return construction. Whale activity was defined as individual transactions or deposits of at least 100 ETH, while thresholds of 500 and 1,000 ETH were used for sensitivity analysis. Exchange-flow data were restricted to native ETH transactions. Volatility was estimated using a GARCH(1,1) model with a Student-t distribution, while the relationships among variables were analyzed using Granger causality, Vector Autoregression (VAR), Impulse Response Function (IRF), and Forecast Error Variance Decomposition (FEVD). The results show that net exchange flow provides predictive information for volatility at the 100 ETH threshold at lags 1–24 after the FDR correction and lags 2–24 after the Bonferroni correction. At the 500 and 1,000 ETH thresholds, the relationship remains significant at lags 6–24 after both corrections. Lido deposits do not provide predictive information for volatility. The IRF results show that the volatility response to a net-flow shock differs from zero only at horizons one and two hours for the 100 ETH threshold. At the 24-hour horizon, total whale activity contributes only 0.1874%, 0.0357%, and 0.0511% at the 100, 500, and 1,000 ETH thresholds, respectively. These findings indicate that exchange flow provides stronger short-term predictive information than Lido deposits, although its contribution to explaining volatility remains very small.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Aktivitas whale, Ethereum, exchange flow, GARCH, Kausalitas Granger, liquid staking, VAR, volatilitas. Ethereum, exchange flow, GARCH, Granger causality, liquid staking, VAR, volatility, whale activity.
Subjects:	H Social Sciences > HA Statistics > HA30.3 Time-series analysis H Social Sciences > HG Finance > HG4529 Investment analysis H Social Sciences > HG Finance > HG4910 Investments Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	FARREL AKMALAZMI NUGRAHA
Date Deposited:	22 Jul 2026 09:11
Last Modified:	22 Jul 2026 09:11
URI:	http://repository.its.ac.id/id/eprint/136297

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