Uses of Package
com.quantfinlib.risk
Packages that use com.quantfinlib.risk
Package
Description
Cross-sectional alpha research: signal generation, evaluation,
validation, execution-aware backtesting, portfolio construction and
reporting, each a separate step.
Risk measurement, decomposition, limits and model validation.
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Classes in com.quantfinlib.risk used by com.quantfinlib.alphaClassDescriptionFundamental equity factor risk model in the Barra USE4 style: a market factor, one dummy factor per sector and standardised style factors, estimated by weighted cross-sectional regression each period.Estimation settings.
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Classes in com.quantfinlib.risk used by com.quantfinlib.riskClassDescriptionCounterparty credit exposure modeling with netting: Current exposure — max(0, net mark-to-market) per netting set. Potential future exposure — notional add-ons by tenor bucket using the FX/gold factors of the Basel Current Exposure Method (1988 Accord Annex 3 as amended in 1995: <1y 1%, 1–5y 5%, >5y 7.5%), applied to every trade whatever its
product.A fitted POT tail model.A maximum-likelihood GPD fit with asymptotic standard errorsVar ξ̂ = (1+ξ)²/m,Var β̂ = 2β²(1+ξ)/m(Smith 1987).Fundamental equity factor risk model in the Barra USE4 style: a market factor, one dummy factor per sector and standardised style factors, estimated by weighted cross-sectional regression each period.Ex-ante risk of one portfolio under the model.Estimation settings.Default risk weights by credit quality (MAR22.24 Table 2, verified).A net JTD per obligor (signed) and its risk weight.The 11 asset classes of MAR22.31(2)(a), plus the corporates bucket of MAR22.31(1).Long-term rating grades of SEC-ERBA Tables 2 and 4 (CRE42.4, CRE42.13).Short-term rating grades of SEC-ERBA Tables 1 and 3 (CRE42.2, CRE42.12).A net JTD of one tranche (signed, positive long) and its DRC risk weight per unit of JTD.A non-CTP bucket (MAR22.31, MAR22.32).A net tranche JTD tagged with its bucket key (aFrtbDrcSecuritisation.NonCtpBucket, or an index name for the CTP).The four regions of MAR22.31(2)(b).The Basel backtesting traffic light over 250 days of 99% VaR exceptions: GREEN ≤ 4 (model fine), AMBER 5-9 (capital multiplier rises), RED ≥ 10 (model presumed wrong).One bucket: weighted sensitivities and the medium-scenario intra-bucket correlation matrix (diagonal ignored).Bucket-level result.One risk class: delta, vega and curvature inputs (any may be empty).Correlation scenarios of MAR21.The PLAT verdict for one desk over one window.Multi-asset portfolio of positions with live price updates.Full risk report.Pre-trade risk gate: validates every order against configured limits before it reaches the market — order size, notional, resulting position, price collar versus a reference mid, restricted symbols, and counterparty credit headroom.Eigenvalues (descending) and matching unit eigenvectorsvectors[k].Pluggable risk metric over a periodic return series.Custom Risk Metrics Framework: registry of built-in and user-defined risk metrics, evaluated together over a return series.The five SA-CCR asset classes.The four commodity hedging sets (para 161).Credit rating buckets and their supervisory factors (Table 2).Hedging-set kind of a trade (paras 162-163) and its multiplier on the supervisory factor.Margin agreement terms for a margined netting set.SA-CCR outputs for one netting set.One part of a netting set forSaCcr.subNettingSets(List): the trades under one margin agreement (or the unmargined trades whenmarginis null) and the haircut collateral that belongs to them.Collateral kinds of the supervisory haircut table.Issue rating buckets for debt collateral.Debt classes of the CRE22.50 table (jurisdictions without external ratings).Which supervisory haircut table to use.One derivative as the SA-CCR sees it (BCBS 279, March 2014, Annex 4): the asset class, the hedging-set key, the inputs to the trade-level adjusted notional and the supervisory delta.One settlement instruction pair: we pay one leg and receive the other.The reverse-stress answer: the most-probable shock vector and its distance.VaR and ES of one sample, both in loss units.Result of the Basel traffic-light test.Backtesting zones of the Basel internal models approach (MAR32.8).Revalues the book under one scenario's factor moves.VaR and ES from Gaussian Monte Carlo factor scenarios.