Claims, exposure and the XVA engine

Exposure measures the value of a trade or portfolio at future dates. To calculate it, QuantSupport converts supported positions into scheduled ContingentClaim payments and simulates the market observations needed to value them. At each exposure date, the remaining payments are valued under the simulated market state.

The resulting path values can be inspected as exposure profiles or combined under netting and collateral agreements to calculate valuation adjustments.

Claim-level valuation

A claim retains payment/fixing/accrual dates, currencies, notional, side and stable trade/leg identifiers. Its strategy describes deterministic amounts, rate coupons, optional rates, spot/path-dependent payoffs, exercise-contingent payments or a scripted payment.

For a native claim, the implemented contribution is

\[ v_{j,p,k} =a_{j,p,k}\,P_{j,p}(t_k,T_j)\, X_{j,p,k}\,s_jN_j. \]

The strategy determines the raw amount \(a\); discount, currency conversion, sign and notional supply the remaining factors. A scripted claim obtains its payment value from the compiled script response block instead. Claim construction must use the strategy’s amount/notional convention consistently to avoid double scaling.

Before simulation, FixingPreprocessor installs realized or partially realized observations. Compression can merge compatible deterministic claims to reduce repeated evaluation. The claim converter checks supported contract features, including overnight observation rules, before these steps.

Cubes and pointwise profiles

NpvCube stores npvs[path][date] for a trade. For values \(V_{p,k}\),

\[ EE_k=\frac1n\sum_pV_{p,k},\qquad EPE_k=\frac1n\sum_p\max(V_{p,k},0),\qquad ENE_k=\frac1n\sum_p\min(V_{p,k},0). \]

ENE is signed and nonpositive. These are pointwise date profiles; the methods do not calculate a time-average EPE or a PFE quantile. XvaAggregator reduces each path into an adjustment contribution, with implementations for CVA, DVA and FVA. Contributions are normalized by the total path count and summed across paths.

Returned cubes and these profiles remain raw, pre-margin values. Adjustment aggregators can consume netted, post-margin exposure instead. A sum of trade-level EPE is generally not the EPE of the netted sum:

\[ \max\left(\sum_jV_j,0\right) \neq\sum_j\max(V_j,0). \]

Calculate netted profiles from the summed path values before taking their positive and negative parts.

High-level orchestration

XvaEngine receives a market provider and XvaEngineConfig. Rate factors identify mean reversion/parameter sources or reuse driver factors; FX configuration adds foreign currencies, volatilities and supported correlations. The configured frequency determines the simulation/exposure grid.

At a high level, run preprocesses claims, determines required curves and factors, builds the date grid, prepares system discount factors and per-netting set factories, then evaluates shared model paths. Foreign currencies and policy-selected discount identities need corresponding supported models. The engine reports an error if a required factor is missing.

The following fragment assumes prepared context, configuration and netting_sets:

let engine = XvaEngine::new(&context, configuration)?;
let result = engine.run(&mut netting_sets)?;
for cube in &result.cubes {
    println!("{}: {:?}", cube.trade_id, cube.epe());
}
for adjustment in result.xva_values.iter().flatten() {
    println!("{} {} {}", adjustment.netting_set, adjustment.measure, adjustment.value);
}

One run returns trade-level cubes, optional adjustments by netting set and optional aggregated labeled sensitivities. High-level construction supplies CVA and FVA factories. DVA exists through lower-level aggregators with explicit own-credit inputs, not an automatic third output of run.

Numerical and modeling boundaries

Shared paths make netting internally coherent, but finite grids and samples remain approximations. LGM path construction, calibration targets and correlations determine the future-state distribution. Optional payoffs may require numerical conditioning or smoothing. Comparisons with native pricers need matching payoff, model and payment-selection assumptions, together with tolerances for numerical and sampling error.

The engine’s system discount sequence is stored numerically and held fixed during AD. Credit/funding factories also introduce particular independent leaves rather than replay every original calibration. The adjustment chapter explains the resulting derivative meaning; collateral explains which exposures those adjustments actually consume.