VOLatility Archive for Realized Estimates (VOLARE)

2026-02-23

AI Summary

This study addresses the absence of a standardized, cross-asset framework for measuring realized volatility and covariance in high-frequency financial data, which is hindered by heterogeneous trading calendars, market microstructure noise, and timestamp precision issues. The authors develop an open research platform that implements asset-specific data cleaning procedures for equities, foreign exchange, and futures, integrating regular-interval sampling, outlier detection, and multiple realized estimators—including realized variance, bipower variation, and realized kernels—alongside real-time modeling via HAR and MEM specifications. For the first time, the platform delivers a unified, methodologically consistent archive of high-quality realized volatility measures across markets, enabling cross-asset comparability. It supports batch downloads, interactive visualizations, and reproducible analysis, substantially enhancing accessibility and empirical research efficiency in high-frequency finance.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Image and Video Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data integration and cleaning
• Security and Privacy: Data transparency and provenance

Abstract

VOLARE (VOLatility Archive for Realized Estimates - https://volare.unime.it) is an open research infrastructure providing standardized realized volatility and covariance measures constructed from ultra-high-frequency financial data. The platform processes tick-level observations across equities, exchange rates, and futures using an asset-specific pipeline that addresses heterogeneous trading calendars, microstructure noise, and timestamp precision. For equities, price series are cleaned using a documented outlier detection procedure and sampled at regular intervals. VOLARE delivers a comprehensive set of realized estimators, including realized variance, range-based measures, bipower variation, semivariances, realized quarticity, realized kernels, and multivariate covariance measures, ensuring methodological consistency and cross-asset comparability. In addition to bulk dataset download, the platform supports interactive visualization and real-time estimation of established volatility models such as HAR and MEM specifications.

Problem

• realized volatility
• high-frequency data
• microstructure noise
• covariance estimation
• financial econometrics

Innovation

• realized volatility
• ultra-high-frequency data
• microstructure noise
• standardized estimation
• open research infrastructure