Volatility Assurance Shield (VAS)

A proprietary framework that scores digital asset volatility in real time — empowering institutions to assess, compare, and manage risk with confidence.

OUR METHODOLOGY

Volatility, Scored and Explained

The Volatility Assurance Shield™ is designed to meet the needs of capital allocators, fund managers, and sovereign investment teams — with structured scoring that fits seamlessly into institutional workflows and governance processes.

Unlike traditional ESG reporting that relies on periodic disclosures, our ESGA certification provides continuous, on-chain verification of environmental and governance metrics. This real-time approach ensures permanent, tamper-proof records that meet institutional audit standards.

Our system continuously monitors digital asset movements, price trends, and liquidity conditions – delivering insights that reflect the current market environment with precision and speed.

Each volatility score is backed by clear data inputs and explainable methodology — enabling informed decisions, compliance alignment, and confident communication with stakeholders.

DESIGNED FOR DECISION-MAKERS

Clarity in Every Market Condition

In digital markets, volatility is constant but confusion doesn’t have to be. The Volatility Assurance Shield delivers real-time scoring to help institutions assess risk, act decisively, and invest with confidence.

Our Process

How Institutions Engage. With the Volatility Assurance Shield

01

Understanding Your Risk

Framework We meet to understand your current portfolio structure, governance requirements, and how you define volatility.

02

Scoring Methodology

Review We walk through our rating logic, data inputs, and reporting formats to align with your internal models.

03

Asset Set Scoring & Diagnostics

We assess your selected digital assets using our Volatility Assurance Shield™ — generating structured scores and insights.

04

Integration & Reporting

We deliver dashboards, documentation, and API-ready outputs — with ongoing support and model transparency.

Why us?

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