git:20261104.ac964e2 to git:20261106.6f9670c

---

name: risk-tail

description: Calculate VaR and Expected Shortfall from P&L the user supplies, historical only or side by side under normal, Student-t, EVT and GARCH models, over the hosted Risk Desk MCP. Use for loss quantiles, tail severity and model comparison, not counterparty XVA and not for testing a forecast record.

---

# Risk Tail, hosted

Call `risk_desk_status` first to check availability, input limits and

reporting conventions. Then use `risk_tail` for historical VaR and ES, or

`risk_tail_models` for the historical result beside normal, Student-t,

extreme value, GARCH filtered historical, GARCH-EVT and GARCH-t Monte

Carlo estimates, on the connected hosted Risk Desk MCP. Discover its input

schema instead of guessing field names.

No local runtime or market-data feed is needed.

For a requested demonstration, omit `request` to use the shipped synthetic

example. Label the result as synthetic. For the user's own book, pass the

supplied data in `request`; never substitute the demo when input is

missing or invalid. Ask only for missing required fields.

Report degraded status, its reason, base currency, horizon and sign

convention before the figures. Preserve source and as-of information.

If the service is unavailable, report the failure without inventing results.

For supplied data, require currency, as-of date, source, data mode,

portfolio value, confidence and horizon. Each P&L observation must already cover that

horizon. Do not convert daily results to another horizon by silently

scaling them. For `risk_tail_models` the P&L must be in time order, oldest

first: the GARCH columns depend on order, the others do not.

Give VaR and Expected Shortfall as monetary losses; negative values mean gains and are not floored at zero. Include

the sample count and the tail probability mass. The small-tail warning is

a reporting rule, not a confidence test. Do not call ES the mean of all

losing observations. These are empirical results over one supplied

sample, not validated forecasts.

For `risk_tail_models`, report every entry in `unavailable` with its

reason; a missing column is not a zero. If the GARCH posterior did not

converge, the result is degraded and the three GARCH columns are

unreliable; say so. EVT with fewer than 50 exceedances is flagged; do not

present it as robust. Disagreement between columns is model risk, not an

error to average away. The normal, Student-t and unconditional EVT fits

ignore volatility clustering, and the GARCH columns look one sample period

ahead only. To test a forecast record, use `risk_backtest`.

Before sending user data, establish that the user is permitted to share

it with a hosted service. Existing permission in the conversation is

sufficient. `data_mode` records their declaration; it is not a

rights certificate and the service does not check it.

Counterparty XVA is not available here. It needs a trusted local ORE

project directory, which a remote endpoint cannot read. Point the user at

the local `risk-desk` plugin for that.