BTC– – ETH– – SOL– – KRP VaR 1D– BTC funding– Stablecoins– Updated –

Treasury‑grade risk analytics for digital asset portfolios

Kintechs measures what standard tools miss in digital assets: Value at Risk under shifting volatility regimes, what a position would cost to exit in a thin market, and how exposure concentrates across exchanges, custodians and stablecoin issuers. Every figure is recomputed daily from public market data.

Open the Risk Monitor Methodology
Kintechs Reference Portfolio · VaR 99% 50 BTC / 30 ETH / 20 USD stables
Historical VaR · 1D
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EWMA VaR · 1D
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Breaches, 365 days
– vs – expected
HistoricalEWMABreach Source: Coinbase; Kintechs calculations

3assets modelled 4spot order books 3perpetual contracts –exchange reserve sets 20+risk metrics Dailyrecalculation

Digital assets break the assumptions behind standard risk tools

Classic market risk models expect deep, continuous markets. Kintechs keeps the language risk committees already use and adapts the measurement to how these markets actually behave.

  • Volatility
    Regimes shift within daysBTC 30-day realised volatility is – today against – over 90 days. A VaR built on stale volatility misprices risk, so Kintechs runs EWMA alongside the historical window.
  • Liquidity
    Exit cost depends on the book, not the priceSelling $10m of BTC on a single venue can cost several times more than across the consolidated book. Kintechs walks live order books instead of assuming a spread.
  • Exposure
    Risk concentrates in a few placesOne issuer holds – of USD stablecoin supply, and the largest exchange holds over half of reported CEX reserves. Each is a counterparty risk in its own right.

One risk engine, five views of the portfolio

Each module reads the same daily dataset, so a VaR spike, a liquidity squeeze and a concentration flag can be read side by side.

Reference portfolio drawdown from peak 365 days
Max drawdown –Source: Coinbase; Kintechs calculations

This week in risk

Every week the engine writes a short brief from the numbers: where VaR stands, how liquidity and funding moved, and where concentration is building.

Read the full brief

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Metric library

Every figure on the site traces back to one of these definitions. Formulas and assumptions are in the methodology.

MetricCategoryFrequencyMethod
Historical VaR, 99%var.histMarket riskDaily1st percentile of portfolio returns over a rolling 250-day window; 7-day by square-root-of-time
EWMA VaR, 99%var.ewmaMarket riskDailyRiskMetrics exponentially weighted volatility, λ = 0.94, z = 2.326
VaR breachesvar.backtestMarket riskDailyDays when the realised loss exceeded the prior-day forecast, against the 1% expected rate
Drawdown from peakdd.peakMarket riskDailyReference portfolio NAV against its running maximum
Realised volatilityvol.realMarket riskDailyStandard deviation of daily log returns, 30 and 90 days, annualised with √365
Return correlationcorr.90dMarket riskDailyPearson correlation of daily log returns over 90 days
Market sell slippageliq.slipLiquidityDailyVolume-weighted fill against mid when walking the bid side, $250k to $20m
Consolidated slippageliq.consLiquidityDailyBids from all venues merged relative to each venue's mid, then walked as one book
Depth within 1%liq.depthLiquidityDailyUSD value of bids and asks within 1% of mid, per venue
Order book imbalanceliq.obiLiquidityDailyBid depth as a share of total depth within 1% of mid
Quoted spreadliq.spreadLiquidityDailyBest ask minus best bid, in basis points of mid
Perpetual fundingdrv.fundDerivativesHourly → dailyHourly relative funding averaged per day and annualised
Perpetual basisdrv.basisDerivativesDailyMark price against index price, in basis points
Open interestdrv.oiDerivativesDailyOpen contracts valued at mark price
Stablecoin supplystb.supplyStablecoinsDailyCirculating USD-pegged supply, 365-day history
Issuer concentrationstb.hhiStablecoinsDailyHerfindahl-Hirschman index of supply shares by issuer
Chain distributionstb.chainStablecoinsDailyShare of USD stablecoin supply by blockchain
Peg deviationstb.pegStablecoinsDailyMarket price against $1.00, in basis points
CEX reserve sharecpt.shareCounterpartyDailyShare of reported exchange reserves by venue
CEX concentrationcpt.hhiCounterpartyDailyHerfindahl-Hirschman index across reporting exchanges
Reserve change, 7 dayscpt.chgCounterpartyDailyWeekly change in reported reserves per exchange

From raw market data to a committee report

An automated pipeline pulls public market data every day, recalculates every metric and publishes the results here. Sources that fail keep their last good values, and the status page shows when each one last updated.

Market sources

Spot, derivatives and on-chain supply data from public APIs.

  • Coinbase · Kraken
  • OKX · Binance
  • Kraken Futures
  • DefiLlama

Daily dataset

Prices, order books, funding and supply aligned into one snapshot.

  • 615 days of prices
  • 4 order books

Risk engine

VaR, liquidity, derivatives, stablecoin and concentration models.

  • 20+ metrics
  • backtested VaR

Monitor and brief

Live dashboard and a weekly written risk brief.

  • daily refresh
  • weekly archive

Team

Kintechs is founder-led and research-driven, and open to collaborators working on models and data.

Georgii Mishchenko

Founder · Head of Research

Georgii leads research and product at Kintechs, from the risk engine and data pipelines to the methodology behind every published metric. His work centres on market and liquidity risk: Value at Risk, stress testing and the measurement of exit costs and concentration in digital asset markets.

He previously worked as a financial and risk analyst at a private investment office, building cash-flow models, scenario analysis and stress tests for real estate investments, and earlier analysed corporate financial documentation in commercial disputes. He holds an MSc in Financial Technology with Distinction from Middlesex University, a BSc in Finance from the University of Reading (Henley Business School) and a degree in International Finance and Investment Management from MGIMO.

mishchenko.co.uk

Collaborate with Kintechs

  • Quantitative researcherVolatility models, tail risk and backtesting for digital assets.
  • Data engineerMarket data pipelines, order book capture and storage.
  • Risk analystStablecoin, counterparty and exchange risk research.

Interested in contributing research or code? Get in touch.

Contact

For research enquiries, data questions or collaboration, write to the team.

contact@kintechs.org