Volatility-Based Classification of Bitcoin and Ethereum Investment Risk Using Quantile and Multi-Scale Volatility Approaches

Prapatoni, Velian (2026) Volatility-Based Classification of Bitcoin and Ethereum Investment Risk Using Quantile and Multi-Scale Volatility Approaches. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

This study developed a method of investment risk classification of Bitcoin (BTC) and Ethereum (ETH) using a multi-scale volatility approach (short-, medium-, and long-term volatility of price log-return) which is adaptively classified with quantile estimation, supported by technical indicators (MA, RSI, ATR, Fibonacci Retracement) as additional interpretations. The results of statistical testing showed that the success rate of the model reached 77.73% in the Spearman correlation test between volatility and risk score (ρ = 0.7773; p ≈ 0.000000), showing a strong and significant positive relationship according to the theory that volatility is the main proxy of risk; the Chi-Square test on the uniformity of the quantile distribution resulted in a 100% degree of conformity with the theory (χ² = 0.00000; p = 1.000000), proving that the ten deciles of risk are evenly divided according to the principle of quantile division; and the Mann-Whitney U test of the risk scores of BTC (average 6.29) and ETH (6.30) yielded p = 0.953508, indicating a consistent and unbiased model towards either asset. These three results prove that the multi-scale volatility and quantitative-based risk classification system works consistently, objectively, and in accordance with the theoretical framework used, so it can be relied upon as a support for cryptocurrency investment decision-making, especially for novice investors. Keywords : Cryptocurrency, Bitcoin, Ethereum, Volatility, Multi-Scale Volatility, Quantil, Risk Classification.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Parlika., Rizky	NIDN0718058401	rizkyparlika.if@upnjatim.ac.id
Thesis advisor	Prima Aditiawan, Firza	NIDN0023058605	firzaprima.if@upnjatim.ac.id

Subjects:

Q Science > QA Mathematics > QA76 Computer software
Q Science > QA Mathematics > QA76.6 Computer Programming
Q Science > QA Mathematics > QA76.625 Internet Programming

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

velix Velian Prapatoni Velian

Date Deposited:

07 Sep 2026 02:39

Last Modified:

10 Sep 2026 03:01

URI:

https://repository.upnjatim.ac.id/id/eprint/59978