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Industries · Banking

What if markets move together?

Examine how risks change when rates, spreads and defaults turn at once: every loan, deposit and trade revalued on every path, exposures read by inner simulation instead of a shortcut.

01

One question, start to finish.

Ask what if; Henselian runs the model on every record and every path; you read what then.

  1. What if

    What if rates move by the six supervisory shocks?

  2. Run

    Rate risk on the balance sheet, loan by loan

    Every loan and deposit valued under each prescribed shock and on stochastic curves, not a duration shortcut.

  3. What then

    Economic value of equity in each shock: which way the balance sheet leans, and by how much.

02

Start from a model that already asks.

Each is a working model in the app's Model builder, written in Hensel and run on the GPU. They start from a synthetic book; your data replaces it, record by record.

6 models in the Model builder

Treasury1

  • Rate risk on the balance sheet, loan by loan

    Economic value of equity under the six supervisory shocks and stochastic curves, every loan and deposit valued.

Credit risk1

  • Credit losses, borrower by borrower

    Expected loss and 99.9% credit VaR from a multi-factor model, every borrower's default probability conditional on the year.

Counterparty risk2

  • Counterparty exposure and CVA, nested

    A swap and a floor with one counterparty, revalued at every future quarter on their own inner paths: CVA, expected exposure and the 95% peak exposure.

  • CVA on every trade

    CVA and expected exposure, every swap revalued every quarter on every path, netted and collateralised by netting set.

Model engineering1

  • Calibrating the prepayment model on every loan

    Maximum likelihood on every loan's quarterly history, slice by slice in parallel, then a backtest.

Group risk1

  • Group economic capital, every exposure simulated

    Economic capital for the group and each silo from a t-copula, and how much diversification saves.

03

Fast enough to ask again.

41 s

for 1.5 trillion member-paths: a 2.5-million-member UK buy-in, end to end on one graphics card

137×

faster than hand-optimised C on all 32 threads of the same machine

1

engine, one language, one app, for insurance, banking and energy alike

How these were measured →

These were measured on an insurance run, a UK buy-in. The banking models above run on the same engine and language; a benchmark of their own is next.

04

One invitation

Come and challenge us.

If you work in insurance, banking, energy, or anywhere uncertainty drives important decisions, bring us the simulation problem your organisation wishes it could solve faster. Let's find out what's possible.