Bitcoin Volatility Forecasting with On-Chain Activity

Tue, 01 Sep 2026 00:00:00 +0000

Description

This research project investigates whether Bitcoin blockchain activity can improve forecasts of future realized volatility compared with models based only on historical market data.

The central research question is:

Can on-chain information improve one-week-ahead forecasts of Bitcoin realized volatility?

The project focuses on volatility forecasting rather than predicting Bitcoin returns, with potential applications in market-risk assessment, portfolio management, and hedging.

Data

The analysis uses cryptocurrency market and blockchain data obtained from Coin Metrics.

The dataset contains 3,189 daily observations covering the period from January 1, 2018 to September 24, 2026.

After calculating daily returns and retaining complete seven-day weeks, the final forecasting dataset contains 454 weekly observations from January 8, 2018 to September 20, 2026.

Variables

The main dependent variable is weekly Bitcoin realized volatility, calculated from daily Bitcoin returns within each week.

Blockchain activity is represented by two explanatory variables:

Weekly average active addresses
Weekly confirmed transactions

The resulting dataset is designed to test whether network activity contains predictive information beyond historical volatility alone.

Forecasting Methodology

The project will compare models with increasing levels of information:

A baseline model using historical realized volatility.
A time-series model such as HAR-RV (Heterogeneous Autoregressive Realized Volatility).
Extended models incorporating on-chain activity variables as explanatory features.

The primary forecasting horizon is one week ahead.

The final 30% of observations will be reserved for out-of-sample evaluation, while the remaining observations will be used for model estimation.

Model Evaluation

Model performance will be evaluated using both in-sample and out-of-sample criteria.

During the training period, model fit will be assessed using information criteria such as:

AIC
BIC

Out-of-sample predictive accuracy will be evaluated using:

MAE (Mean Absolute Error)
RMSE (Root Mean Squared Error)

The main objective is to determine whether incorporating blockchain activity improves forecast accuracy relative to models based exclusively on historical market information.

Research Motivation

Bitcoin volatility is highly time-dependent and can change substantially during periods of market stress.

Blockchain activity provides an additional source of information generated directly by the Bitcoin network. The project therefore examines whether variables such as active addresses and transaction activity contain useful predictive information about future realized volatility.

Rather than simply testing correlation, the study focuses on out-of-sample forecasting performance, which provides a more direct test of the practical predictive value of on-chain information.

Current Status

Ongoing Research Project

The current stage establishes the research question, dataset, variable construction, forecasting framework, and evaluation methodology. The empirical comparison of the forecasting models and the final out-of-sample results are part of the ongoing research process.

Tools & Technologies

R
Time Series Analysis
Financial Data Analysis
Bitcoin Blockchain / On-Chain Data
Forecasting & Statistical Modeling
Out-of-Sample Evaluation

Expected Outcome

The project aims to determine whether blockchain activity provides statistically and practically useful information for forecasting future Bitcoin volatility.

More broadly, it evaluates whether incorporating network-level data can improve traditional volatility forecasting approaches based on historical market dynamics.

Financial Time Series Analysis

Tue, 01 Sep 2026 00:00:00 +0000

Description

This project focuses on the analysis of financial time series with different temporal frequencies using R and data from the Federal Reserve Bank of St. Louis FRED database.

Three financial indicators were analyzed:

S&P 500 at daily frequency
CBOE Volatility Index (VIX) at weekly frequency
U.S. 10-Year Treasury Yield at monthly frequency

The analysis explores the temporal behaviour, transformations, volatility, persistence, and statistical characteristics of each series.

Technical Approach

S&P 500 returns: Daily continuously compounded returns were calculated using logarithmic price changes.
VIX transformation: Daily observations were converted to weekly frequency and week-to-week changes were calculated.
Treasury Yield transformation: Daily observations were aggregated to monthly frequency and monthly changes were computed.
Multi-frequency analysis: Each series was analysed at a frequency appropriate to its characteristics and intended forecasting horizon.

Analysis & Visualization

The project includes exploratory analysis and visualizations to identify:

Long-term trends
Volatility and large market movements
Persistence and temporal dependence
Structural changes across different market regimes

The analysis highlights notable periods such as the 2008–2009 financial crisis, the 2020 market shock, and subsequent changes in equity prices, volatility, and interest rates.

Descriptive Statistics

Descriptive statistics were calculated for the original time series, including:

Mean
Standard deviation
Minimum and maximum values
Quartiles

These statistics were used to compare the scale and historical variability of the three financial variables.

Forecasting Perspective

The project also evaluates potential applications of forecasting models for different financial decision-making scenarios.

The proposed forecasting horizons vary according to the temporal frequency and behaviour of each series:

S&P 500: 1–5 trading days for short-term portfolio monitoring and risk management.
VIX: Several weeks for volatility and hedging analysis.
10-Year Treasury Yield: Several months for interest-rate exposure and asset-allocation decisions.

Tools & Technologies

R
FRED API / FRED Database
Time Series Analysis
Statistical Analysis
Data Visualization
LaTeX

Outcome

The project provides a comparative analysis of three financial time series with substantially different dynamics and frequencies, demonstrating how transformations, temporal aggregation, visualization, and descriptive statistics can be used to prepare financial data for further forecasting analysis.

The complete analysis is reproducible through the accompanying R script, which handles data retrieval, cleaning, transformation, visualization, and statistical summaries.

R | Guillermo F. Parra

Bitcoin Volatility Forecasting with On-Chain Activity

Description

Data

Variables

Forecasting Methodology

Model Evaluation

Research Motivation

Current Status

Tools & Technologies

Expected Outcome

Financial Time Series Analysis

Description

Technical Approach

Analysis & Visualization

Descriptive Statistics

Forecasting Perspective

Tools & Technologies

Outcome