The Reality Volatility Index (RVI) is a live, data-driven signal that reflects the instability, unpredictability, and narrative tension in the world at any given time.
It is calculated using real-time data from Adjacent News API which aggregates prediction markets data across multiple platforms (Kalshi, Polymarket, Metaculus, etc.)
The RVI will act as a VIX of belief - instead of tracking financial instrument volatility, it tracks future expectation volatility.
It has one primary purpose:
- Public Signal — a macro measure of how "weird," tense, or chaotic the world currently feels.
This can be turned into a speculative instrument, a synthetic token like $RVI, that can be traded on chain
The RVI is constructed from a set of active binary prediction markets. Each market contributes to the final index based on a combination of:
- Impact(Significance),
- Urgency (time horizon),
- Volatility (probability standard deviation),
- Engagement (liquidity or forecaster count),
- Consensus Breadth (platform diversity)
- and the Probability of the Event Occurring
These are aggregated first into a market-level score, and then into category-level sub-indices before forming the final Reality Volatility Index.
The RVI is calculated at two levels:
- Market-level contribution:
Market RVI Contribution = Probability × Geometric Mean of 5 Signal Scores
- Index-level aggregation using category-weighted averaging:
RVI = Σ [ Category RVI × (Category Share of Total Markets) ]
This ensures that each thematic risk category (e.g. Geopolitics, Tech, Climate) contributes proportionally to its footprint in the dataset — no single category dominates unless it is both large and volatile.
Each category’s RVI is computed and then weighted based on how many markets fall into that category. The final RVI is the market-weighted average of all category RVIs.
This ensures:
- Interpretability over time
- Comparability across updates
- Proper reflection of issue salience
Each market is scored across five axes. These scores are combined into a single composite weight for that market:
| Dimension | Description |
|---|---|
| Impact Severity | How consequential the event is if it occurs |
| Time Horizon | How soon the event will resolve |
| Volatility | How much the market’s probability has fluctuated recently |
| Liquidity | How actively traded the market is |
| Platform Diversity | Whether the market is mirrored across platforms (consensus) |
Note: All dimension scores are individually normalized to [0, 1], and combined using a geometric mean. This ensures that no single factor can dominate the composite score, and markets with weak signals in any dimension are appropriately down-weighted.
The most subjective component, and one calculated by heuristics, which reflects how disruptive or significant an event would be if it happened.
Each market starts with a base score based on its core theme (inferred from tags or keywords):
| Topic Area | Base Score |
|---|---|
| Existential / Global Catastrophe (nuclear, AGI, pandemic, collapse) | 1.0 |
| Geopolitical / Systemic Risk (war, recession, default, elections) | 0.8 |
| Institutional / Regulatory Change (Fed, policy, Meta breakup, crypto) | 0.6 |
| Cultural / Symbolic / Media Events (Oscars, TikTok, TV, Elon tweets) | 0.4 |
| Trivial / Ephemeral Topics (memes, streaming, internet celebrities) | 0.3 |
This adjusts the score based on how widely the outcome would be felt:
| Scope | Modifier |
|---|---|
| Global (e.g. UN, Humanity, Multinational) | +0.15 |
| National (e.g. USA, China, EU, Fed) | +0.10 |
| Subnational / Company-specific (Meta, Texas) | +0.05 |
| Individual / Local (Elon, one state) | +0.00 |
Example:
“Will the US default on its debt?” → national → +0.10
“Will Meta be forced to spin off Instagram?” → company → +0.05
Some wording in a market title implies higher consequence or system stress. We add a small boost if it signals systemic disruption.
| Trigger Phrases Found in Title | Boost |
|---|---|
| collapse, invade, nuclear, emergency, shutdown, ban, break up, default | +0.10 |
| step down, resign, investigate, fine, indict | +0.05 |
| tweet, appear, nominate, stream, interview | 0.00 |
Total Score = Base + Scope + Phrasing Boost
Clamped between 0.3 and 1.0
“Will China invade Taiwan before 2026?”
Base: war, geopolitics → 0.8
Scope: national/global → +0.1
Phrasing: “invade” → +0.1
→ Final Score = 1.0
“Will Meta be forced to sell Instagram or WhatsApp in 2025?”
Base: tech, regulation → 0.6
Scope: company-level → +0.05
Phrasing: “forced to sell” → +0.1
→ Final Score = 0.75
“Will another MSNBC show be cancelled before July?”
Base: media, culture → 0.4
Scope: national-ish (but limited impact) → +0.05
Phrasing: “cancelled” → 0
→ Final Score = 0.45
Shorter-term markets are weighted more heavily because:
- They reflect current tension or resolution cycles
- They contribute more to immediate volatility
| Time Until Resolution | Score |
|---|---|
| < 30 days | 1.0 |
| 1–3 months | 0.8 |
| 3–6 months | 0.6 |
| 6–12 months | 0.4 |
| > 1 year | 0.2 |
This measures how “twitchy” a market is — the standard deviation of the market’s probability over the past 48–72 hours. Higher volatility leads to a higher contribution to RVI.
| Std Deviation (in % points) | Score |
|---|---|
| ≥ 12 | 1.0 |
| 8–11 | 0.85 |
| 4–7 | 0.7 |
| < 4 | 0.5 |
Reflects how much money or activity is behind the market — more liquidity = more signal.
| Volume Rank (platform-relative) | Score |
|---|---|
| Top 10% | 1.0 |
| Top 25% | 0.8 |
| Top 50% | 0.6 |
| Bottom 50% | 0.5 |
We give a small multiplier to markets that appear on multiple platforms, as they reflect shared belief across communities (e.g. Kalshi + Polymarket + Metaculus).
| Platforms Hosting the Market | Score |
|---|---|
| 3+ platforms | 1.2 |
| 2 platforms | 1.1 |
| 1 platform | 1.0 |
Vibes — but principled.
Each scoring dimension in the RVI system uses thresholds and ranges that are tuned for signal quality, interpretability, and narrative sensitivity. They are inspired by real world forecasting, narrative theory, and financial volatility modelling. As opposed to being mathematically pure - we want to reflect peoples belief, and how they experience uncertainty and tension.
-
Why not 0?
If we allowed a score of 0, a market could be completely excluded or misinterpreted due one low dimension, which can be risky as probability ≠ value. A soft floor of 0.3 means that something can matter, but not much. -
Why these tiers?
They reflect symbolic → systemic importance:- 0.3 = trivial/meme
- 0.4–0.6 = cultural/institutional
- 0.8–1.0 = systemic, existential
-
Inspired by:
-
DHS threat modelling - Gives impact scale from 1-5 and avoids binary risk (threat / no threat)
-
Gartner-style rating frameworks - Subjective but repeatable labels (low / medium / high)
-
Insurance risk scales - Risk = probability x severity (where severity cannot = 0)
-
Why shorter = higher?
Short term resolutions means more tension, we care about what is coming soon. -
Why these buckets?
| Time | Score | Why |
|---|---|---|
| <30 days | 1.0 | Maximum urgency and newsworthiness |
| 1–3 months | 0.8 | Actively monitored |
| 3–6 months | 0.6 | Still visible |
| 6–12 months | 0.4 | Low ambient tension |
| >1 year | 0.2 | Too distant to matter yet |
- Inspired by:
- Options pricing - Short-term volume = premium
- Newsroom calendars
- Election cycle pacing - More tension in media as we get closer to election day
-
Why standard deviation?
It’s the cleanest, most direct measure of uncertainty, mirroring real swings in belief. -
Why these thresholds?
| Std Dev (%) | Score | Why |
|---|---|---|
| ≥12 | 1.0 | Major churn, narrative in flux |
| 8–11 | 0.85 | Active contestation |
| 4–7 | 0.7 | Noticeable twitch |
| <4 | 0.5 | Flat, settled |
- Inspired by:
- VIX methodology - Use volatility as core market sentiment signal
- Metaculus forecast volatility - They track forecast swings over time
-
Why use percentiles?
Absolute volume is misleading — we care about relative engagement on a given platform. -
Why this structure?
| Percentile | Score | Why |
|---|---|---|
| Top 10% | 1.0 | High signal |
| Top 25% | 0.8 | Strong interest |
| Top 50% | 0.6 | Moderately relevant |
| Bottom 50% | 0.5 | Weak signal, but still counts |
- Inspired by:
- Token metrics - Percentile ranks are common in crypto
- Behavioral finance models - Attention as a signal for strength - more volume - more people care - more relevance in RVI
This is a multiplier, and boosts markets that are mirrored across communities — evidence of shared salience.
- Why 1.1 and 1.2?
| Platforms | Score | Why |
|---|---|---|
| 1 | 1.0 | No alignment bonus |
| 2 | 1.1 | Some narrative overlap |
| 3+ | 1.2 | Broad consensus or shared concern |
- Inspired by:
- Cross-source corroboration in journalism
- Multi-exchange market spreads
This design is intentional. The RVI doesn’t just track risk — it tracks narrative weight and the belief of events occurring.
Markets are grouped into thematic categories for sub-indexing and narrative insights.
| Category | Example Tags |
|---|---|
| Geopolitics | geopolitics, war, nuclear, conflict |
| Governance Risk | elections, shutdown, fed, policy |
| Tech Disruption | ai, openai, automation, privacy |
| Climate Risk | climate, carbon, environment |
| Health/Bio Risk | pandemic, virus, disease |
| Financial Instability | recession, banking, inflation |
| Info/Culture Chaos | misinformation, deepfakes, media, tiktok |
These categories are inferred from Adjacent API market tags and are used for breakdowns and visualizations.
Each market is mapped into a risk category such as Geopolitics, Tech Disruption, Climate Risk, etc. This allows us to create sub-indices and track which domains are contributing most to global narrative tension.
Each sub-index uses the same geometric mean formula and normalisation, and their weighted average forms the final RVI.
This design enables:
- Thematic dashboards (e.g. RVI_Tech, RVI_Geo)
- Narrative analysis over time
- Tokenization of sub-sectors (e.g. $RVITech, $RVIClimate)