Package 'riskweightedassets'

Title: Reproducible Risk-Weighted Asset Calculations
Description: Provides transparent, deterministic and auditable calculations of risk-weighted assets, own-funds requirements, interest-rate risk in the banking book and related capital metrics. It supports canonical in-memory tables and versioned spreadsheet datasets, strict validation, synthetic reference profiles, bitemporal snapshots, calculation controls and traceable regulatory source metadata. A granular analyst API exposes individual formulae, domain views, controls, schemas and auditable parameter overrides. The implementation is intended for analytical, educational and model-validation use and does not constitute legal or supervisory advice.
Authors: RiskDataScience GmbH [aut, cre, cph]
Maintainer: RiskDataScience GmbH <[email protected]>
License: GPL-3
Version: 1.2.3
Built: 2026-10-09 09:29:42 UTC
Source: https://github.com/rds0001/risk-weighted-assets-r

Help Index

Analyse Capital Adequacy, Leverage and MREL/TLAC

Description

Analyse Capital Adequacy, Leverage and MREL/TLAC

Usage

analyze_capital_adequacy(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Analyse Counterparty Credit Risk, CVA and Settlement Risk

Description

Analyse Counterparty Credit Risk, CVA and Settlement Risk

Usage

analyze_counterparty_risk(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Analyse Credit Risk

Description

Analyse Credit Risk

Usage

analyze_credit_risk(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis with relevant metrics, tables and controls.

See Also

calculate_tables()

Examples

analyze_credit_risk(generate_synthetic_tables(bank_profile = "KSA_BANK"))

Analyse ICAAP Economic and Normative Perspectives

Description

Analyse ICAAP Economic and Normative Perspectives

Usage

analyze_icaap(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Analyse IRRBB and CSRBB

Description

Analyse IRRBB and CSRBB

Usage

analyze_irrbb(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Analyse Market Risk and FRTB

Description

Analyse Market Risk and FRTB

Usage

analyze_market_risk(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Analyse Operational Risk

Description

Analyse Operational Risk

Usage

analyze_operational_risk(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Analyse the Output Floor

Description

Analyse the Output Floor

Usage

analyze_output_floor(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Analyse Securitisation Risk

Description

Analyse Securitisation Risk

Usage

analyze_securitisation(x, view = c("applied", "fully_loaded"))

Arguments

x

Canonical tables or an existing calculation result.
view

"applied" or "fully_loaded".

Value

An rwa_domain_analysis.

Apply Credit Supporting Factors

Description

Apply Credit Supporting Factors

Usage

apply_credit_supporting_factors(
  rwea,
  sme_factor = 1,
  infrastructure_factor = 1
)

Arguments

rwea

Non-negative risk-weighted exposure amount before supporting factors.
sme_factor, infrastructure_factor

Separate factors in (0, 1].

Details

Both factors may apply together if both sets of legal criteria are fulfilled. This arithmetic helper does not attest eligibility.

Value

RWEA after multiplying the factors once.

Examples

apply_credit_supporting_factors(100, 0.7619, 0.75)

Convert a Validation Report to a Data Frame

Description

Convert a Validation Report to a Data Frame

Usage

## S3 method for class 'rwa_validation_report'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x

An rwa_validation_report.
row.names

Unused compatibility argument.
optional

Unused compatibility argument.
...

Unused.

Value

A data frame with one row per structured validation message.

Available Rule Sets

Description

Available Rule Sets

Usage

available_rule_sets(tables = NULL)

Arguments

tables

Optional canonical tables; bundled reference tables are used otherwise.

Value

The rule-set data frame.

See Also

select_rule_set()

Examples

available_rule_sets()

Operational Risk, Output Floor, NPE and Tier-2 Formulae

Description

Operational Risk, Output Floor, NPE and Tier-2 Formulae

Usage

business_indicator_component(
  il,
  ie,
  assets,
  dividends,
  oi,
  oe,
  fi,
  fe,
  trading_pnl,
  banking_pnl,
  parameters = NULL
)

applicable_output_floor_factor(as_of, fully_loaded = FALSE, parameters = NULL)

apply_output_floor(
  u_trea,
  s_trea,
  factor,
  optional_cap = FALSE,
  cap_multiplier = 1
)

npe_unsecured_coverage_factor(year, parameters = NULL)

npe_secured_coverage_factor(year, property_security = TRUE, parameters = NULL)

tier2_eligible_amount(
  current_amount,
  first_day_amount,
  maturity,
  as_of,
  parameters = NULL
)

Arguments

il, ie, assets, dividends, oi, oe, fi, fe, trading_pnl, banking_pnl

Numeric vectors for the business-indicator components.
parameters

Optional parameter data frame or store.
as_of

Reporting date.
fully_loaded

Whether to return the fully-loaded floor factor.
u_trea

Unfloored TREA.
s_trea

Standardised TREA.
factor

Output-floor factor.
optional_cap

Whether an optional cap applies.
cap_multiplier

Cap multiple of unfloored TREA.
year

NPE vintage year.
property_security

Whether secured by property.
current_amount, first_day_amount

Tier-2 instrument amounts.
maturity

Tier-2 maturity date.

Value

Numeric, named numeric vector, or output-floor result list.

Examples

apply_output_floor(100, 180, 0.725, FALSE, 1)
applicable_output_floor_factor(as.Date("2026-12-31"), FALSE)

Calculate a Canonical Workbook Dataset

Description

Validates a dataset, creates an immutable deterministic run directory and writes six structured output workbooks plus a JSON run manifest.

Usage

calculate_dataset(dataset)

Arguments

dataset

Dataset directory containing an inputs subdirectory.

Details

Output is written below ⁠dataset/outputs/<run-id>⁠. The run ID fingerprints the input files, code and package version.

Value

An rwa_calculation_result object with output paths.

See Also

validate_dataset(), generate_synthetic_dataset()

Examples

root <- file.path(tempdir(), "rwa-calculation-example")
dataset <- generate_synthetic_dataset(root, bank_profile = "KSA_BANK")
calculate_dataset(dataset)

Calculate Canonical In-Memory Tables

Description

Validates and calculates canonical tables without producing spreadsheet output. A parallel fully-loaded result is calculated from the same snapshot.

Usage

calculate_tables(
  tables,
  run_id = NULL,
  project_root = NULL,
  parameter_overrides = NULL,
  override_reason = NULL,
  override_approved_by = NULL
)

Arguments

tables

Named list of canonical data frames.
run_id

Optional caller-defined run identifier.
project_root

Optional root used to resolve local source documents.
parameter_overrides

Optional data frame of controlled parameter overrides prepared as described in override_regulatory_parameters().
override_reason, override_approved_by

Required non-empty governance information when parameter_overrides is supplied.

Details

The return includes applied and fully-loaded result tables. All calculations use one official bitemporal snapshot. The function does not write files and does not change the global random-number stream. Exposure default_flag values are normalised once on the selected snapshot. Accepted inputs are logicals, numeric 0/1, and case-insensitive strings TRUE/FALSE, 1/0, 1.0/0.0, YES/NO or JA/NEIN (whitespace is trimmed). Missing or unknown values fail closed. Both SA and IRB detail tables expose the effective status in defaulted, shared by both reporting views.

Value

An rwa_calculation_result object.

See Also

generate_synthetic_tables(), calculate_dataset()

Examples

tables <- generate_synthetic_tables(bank_profile = "KSA_BANK")
result <- calculate_tables(tables)
result$metrics[c("RWEA_KSA", "TREA")]

Compare Applied and Fully-loaded Metrics

Description

Compare Applied and Fully-loaded Metrics

Usage

compare_calculation_views(result)

Arguments

result

An rwa_calculation_result.

Value

A data frame with both values and their difference.

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
compare_calculation_views(r)

Create a Writable RWA Workspace

Description

Exports the package's synthetic reference environment to a caller-controlled directory. Existing non-empty directories require explicit overwrite.

Usage

create_workspace(path, overwrite = FALSE)

Arguments

path

Destination directory.
overwrite

Whether existing files may be replaced.

Value

An rwa_workspace object.

Examples

workspace <- create_workspace(file.path(tempdir(), "rwa-example"), overwrite = TRUE)
print(workspace)

Default Writable Workspace

Description

Uses RWA_WORKSPACE when it is set, otherwise a rwa-workspace directory below the current working directory. The function only describes the path; it does not create or modify it.

Usage

default_workspace()

Value

An rwa_workspace object.

Examples

default_workspace()

Failed Calculation Controls

Description

Failed Calculation Controls

Usage

failed_controls(result, view = c("applied", "fully_loaded"))

Arguments

result

An rwa_calculation_result.
view

"applied" or "fully_loaded".

Value

A data frame containing only failed controls.

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
failed_controls(r)

Formula Catalogue

Description

Formula Catalogue

Usage

formula_catalog(tables = NULL)

Arguments

tables

Optional canonical tables; bundled reference tables are used otherwise.

Value

The versioned formula-definition data frame.

See Also

regulatory_parameters()

Examples

head(formula_catalog())

Generate a Synthetic Workbook Dataset

Description

Generate a Synthetic Workbook Dataset

Usage

generate_synthetic_dataset(
  root,
  as_of = NULL,
  version = NULL,
  seed = NULL,
  bank_profile = "MID_SIZE_UNIVERSAL",
  config_root = NULL,
  overwrite = FALSE
)

Arguments

root

Parent directory for dated datasets.
as_of

Reporting date.
version

Dataset version.
seed

Integer lineage seed.
bank_profile

Profile identifier.
config_root

Optional profile configuration directory.
overwrite

Whether an existing dataset may be replaced.

Details

The function writes 16 canonical workbooks plus a dataset manifest. An existing non-empty target requires overwrite = TRUE.

Value

The generated dataset directory.

See Also

validate_dataset(), calculate_dataset()

Examples

generate_synthetic_dataset(file.path(tempdir(), "rwa-runs"),
                           bank_profile = "KSA_BANK")

Generate Synthetic Canonical Tables

Description

Materializes a complete synthetic banking profile from immutable package fixtures. The seed is recorded for lineage; no business distributions are generated at runtime.

Usage

generate_synthetic_tables(
  as_of = NULL,
  seed = NULL,
  bank_profile = "MID_SIZE_UNIVERSAL",
  config_root = NULL
)

Arguments

as_of

Reporting date. Defaults to the profile base date.
seed

Integer lineage seed.
bank_profile

Profile identifier.
config_root

Optional directory containing profile YAML files.

Value

A named list of canonical data frames.

See Also

calculate_tables(), generate_synthetic_dataset()

Examples

tables <- generate_synthetic_tables(bank_profile = "KSA_BANK")
length(tables)

Infrastructure Supporting Factor under Article 501a CRR

Description

Infrastructure Supporting Factor under Article 501a CRR

Usage

infrastructure_supporting_factor(parameters = NULL)

Arguments

parameters

Optional governed parameter table or parameter store.

Details

Eligibility under all Article 501a criteria must be established separately. This function returns the configured factor, not an approval.

Value

Numeric infrastructure supporting factor.

Examples

infrastructure_supporting_factor()

Internal Ratings Based Formulae

Description

Direct access to IRB asset correlations, maturity adjustment and capital requirement. Probability, LGD and correlation inputs are decimal rates.

Usage

irb_asset_correlation(
  pd,
  annual_sales_million = NULL,
  financial_multiplier = FALSE,
  parameters = NULL
)

irb_retail_correlation(pd, subclass, parameters = NULL)

irb_maturity_coefficient(pd, parameters = NULL)

irb_maturity_factor(pd, maturity, parameters = NULL)

irb_capital_requirement(
  pd,
  lgd,
  correlation,
  maturity,
  apply_maturity_adjustment = TRUE,
  defaulted = FALSE,
  elbe = 0,
  parameters = NULL,
  lgd_treatment = NULL
)

Arguments

pd

Probability of default.
annual_sales_million

Optional annual sales in millions.
financial_multiplier

Whether to apply the financial-sector multiplier.
parameters

Optional parameter data frame or store.
subclass

Retail exposure subclass.
maturity

Effective maturity in years.
lgd

Loss given default.
correlation

Asset correlation.
apply_maturity_adjustment

Whether the maturity adjustment applies.
defaulted

Whether the exposure is defaulted.
elbe

Best estimate of expected loss for a defaulted exposure.
lgd_treatment

Required for defaults: "SUPERVISORY" (K = 0, ELBE ignored) or "OWN_ESTIMATES" (K = max(LGD - ELBE, 0)). Default normalises PD to 1; PD = 1 without default is rejected.

Value

One numeric coefficient or capital-requirement rate.

See Also

regulatory_parameters()

Examples

irb_asset_correlation(0.01)
irb_capital_requirement(0.01, 0.45, 0.20, 2.5, TRUE, FALSE, 0)

IRB Risk-Weighted Exposure Amount including Supporting Factors

Description

IRB Risk-Weighted Exposure Amount including Supporting Factors

Usage

irb_risk_weighted_assets(
  ead,
  capital_requirement,
  sme_factor = 1,
  infrastructure_factor = 1,
  parameters = NULL
)

Arguments

ead

Non-negative exposure at default.
capital_requirement

Non-negative unadjusted K rate, for example from irb_capital_requirement().
sme_factor, infrastructure_factor

Separate factors in (0, 1].
parameters

Optional governed parameter table or parameter store.

Details

This helper does not change K, PD, LGD or expected loss. Its caller establishes eligibility; the table calculation API requires supporting evidence.

Value

EAD times configured RWA multiplier times K times both factors.

Examples

irb_risk_weighted_assets(1000000, 0.08, infrastructure_factor = 0.75)

IRRBB, FRTB and Economic-capital Formulae

Description

IRRBB, FRTB and Economic-capital Formulae

Usage

irrbb_scenario_shock(scenario, t, parallel, short, long, parameters = NULL)

irrbb_shocked_zero_rate(base, shock, t, parameters = NULL)

present_value_discount_factor(continuous_zero_rate, t)

aggregate_correlated_capital(capitals, correlation)

frtb_scenario_correlation(base, scenario, parameters = NULL)

frtb_quadratic_charge(values, correlation, curvature = FALSE)

Arguments

scenario

IRRBB or FRTB correlation scenario.
t

Time in years.
parallel, short, long

Scenario shocks as decimal rates.
parameters

Optional parameter data frame or store.
base

Base continuously compounded zero rate or base correlation.
shock

Rate shock.
continuous_zero_rate

Continuously compounded zero rate.
capitals

Vector of standalone capital amounts.
correlation

Correlation matrix or scalar intra-bucket correlation.
values

Weighted sensitivities.
curvature

Whether curvature aggregation applies.

Value

Numeric shock, rate, discount factor or aggregated capital.

Examples

present_value_discount_factor(0.03, 5)
aggregate_correlated_capital(c(10, 20), matrix(c(1, .25, .25, 1), 2))

List Bundled Reference Datasets

Description

List Bundled Reference Datasets

Usage

list_reference_datasets()

Value

A data frame with dataset identifiers and versions.

Examples

list_reference_datasets()

List Bundled Reference Profiles

Description

Returns metadata for every synthetic reference profile included with the package. No customer or production data is included.

Usage

list_reference_profiles()

Value

A data frame with profile identifiers, versions, base dates, seeds, default dataset versions and descriptions.

Examples

list_reference_profiles()

Select the Official Bitemporal Snapshot

Description

Select the Official Bitemporal Snapshot

Usage

official_snapshot(tables, as_of_date = NULL, knowledge_time = NULL)

Arguments

tables

Canonical table list.
as_of_date

Optional business date; defaults to run_config.
knowledge_time

Optional knowledge timestamp; defaults to run_config. Accepts a POSIXt instant or a UTC string with a T or space separator, optional fractional seconds and optional trailing Z. Date-only values denote midnight UTC. Missing, malformed or non-scalar values are rejected.

Details

The time-of-day is preserved, including when read from run_config. The same timestamp parser is used by calculate_tables().

Value

Canonical tables reduced to the effective official records.

Examples

t <- generate_synthetic_tables(bank_profile = "KSA_BANK")
s <- official_snapshot(t)

Apply Auditable Regulatory Parameter Overrides

Description

Creates a modified copy of canonical tables. Existing parameter rows are replaced only when key and both dimensions identify exactly one row. The original input is not mutated and an audit record is attached.

Usage

override_regulatory_parameters(tables, overrides, reason, approved_by)

Arguments

tables

Canonical input tables.
overrides

Data frame with parameter_key, parameter_value and optional dimension_1, dimension_2 columns.
reason

Non-empty business rationale.
approved_by

Non-empty approver or governance reference.

Value

A copied table list carrying an rwa_parameter_overrides audit attribute.

See Also

parameter_overrides(), calculate_tables()

Examples

t <- generate_synthetic_tables(bank_profile = "KSA_BANK")
o <- data.frame(parameter_key = "RWA_MULTIPLIER", dimension_1 = "PILLAR1",
                dimension_2 = "", parameter_value = 12.5)
t2 <- override_regulatory_parameters(t, o, "Sensitivity", "Model Risk")
parameter_overrides(t2)

Parameter Override Audit Trail

Description

Parameter Override Audit Trail

Usage

parameter_overrides(x)

Arguments

x

Canonical tables or an rwa_calculation_result.

Value

A data frame of old/new values and governance information.

See Also

override_regulatory_parameters()

Examples

parameter_overrides(generate_synthetic_tables(bank_profile = "KSA_BANK"))

Print a Calculation Result

Description

Print a Calculation Result

Usage

## S3 method for class 'rwa_calculation_result'
print(x, ...)

Arguments

x

An rwa_calculation_result.
...

Unused.

Value

x, invisibly.

Print a Domain Analysis

Description

Print a Domain Analysis

Usage

## S3 method for class 'rwa_domain_analysis'
print(x, ...)

Arguments

x

An rwa_domain_analysis.
...

Unused.

Value

x, invisibly.

Print a Validation Report

Description

Print a Validation Report

Usage

## S3 method for class 'rwa_validation_report'
print(x, ...)

Arguments

x

An rwa_validation_report.
...

Unused.

Value

x, invisibly.

Print a Workspace

Description

Print a Workspace

Usage

## S3 method for class 'rwa_workspace'
print(x, ...)

Arguments

x

An rwa_workspace.
...

Unused.

Value

x, invisibly.

Read One Regulatory Parameter

Description

Read One Regulatory Parameter

Usage

regulatory_parameter(
  key,
  dimension_1 = "",
  dimension_2 = "",
  parameters = NULL
)

Arguments

key

Regulatory parameter key.
dimension_1, dimension_2

Optional parameter dimensions.
parameters

A parameter-store object or parameter data frame. Bundled regulatory defaults are used when omitted.

Value

One numeric parameter value.

See Also

regulatory_parameters()

Examples

regulatory_parameter("RWA_MULTIPLIER", "PILLAR1")

Regulatory Parameter Inventory

Description

Returns the effective parameter table used by granular formulas. Analysts can inspect every weight and coefficient before calculation.

Usage

regulatory_parameters(tables = NULL)

Arguments

tables

Optional canonical table list. If omitted, bundled defaults are used.

Value

A data frame containing parameter keys, dimensions and values.

See Also

regulatory_parameter(), override_regulatory_parameters()

Examples

head(regulatory_parameters())

Regulatory Source Metadata

Description

Returns links and archived checksums for official sources used to design the engine. The source documents themselves are deliberately not redistributed.

Usage

regulatory_sources()

Value

A data frame. The redistributed column is always false for the bundled metadata.

Examples

regulatory_sources()

Calculation Controls

Description

Calculation Controls

Usage

rwa_controls(result, view = c("applied", "fully_loaded"))

Arguments

result

An rwa_calculation_result.
view

"applied" or "fully_loaded".

Value

A data frame containing reconciliation and governance controls.

See Also

failed_controls()

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
rwa_controls(r)

Read One Calculation Metric

Description

Read One Calculation Metric

Usage

rwa_metric(result, metric, view = c("applied", "fully_loaded"))

Arguments

result

An rwa_calculation_result.
metric

Metric identifier.
view

"applied" or "fully_loaded".

Value

One numeric value.

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
rwa_metric(r, "CET1_RATIO")

Calculation Metrics

Description

Calculation Metrics

Usage

rwa_metrics(result, view = c("applied", "fully_loaded"))

Arguments

result

An rwa_calculation_result.
view

"applied" or "fully_loaded".

Value

A tidy data frame with metric, value, unit and view.

See Also

rwa_metric(), compare_calculation_views()

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
rwa_metrics(r)

Read One Result Table

Description

Read One Result Table

Usage

rwa_result_table(result, table, view = c("applied", "fully_loaded"))

Arguments

result

An rwa_calculation_result.
table

Result-table name.
view

"applied" or "fully_loaded".

Value

A result data frame.

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
rwa_result_table(r, "Capital_Stack")

Result Tables

Description

Result Tables

Usage

rwa_result_tables(result, view = c("applied", "fully_loaded"))

Arguments

result

An rwa_calculation_result.
view

"applied" or "fully_loaded".

Value

A named list of result data frames.

See Also

rwa_result_table(), rwa_table_names()

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
names(rwa_result_tables(r))

Compact Calculation Summary

Description

Compact Calculation Summary

Usage

rwa_summary(result)

Arguments

result

An rwa_calculation_result.

Value

A list with identity, core metrics, controls and overrides.

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
rwa_summary(r)

List Result-Table Names

Description

List Result-Table Names

Usage

rwa_table_names(result, view = c("applied", "fully_loaded"))

Arguments

result

An rwa_calculation_result.
view

"applied" or "fully_loaded".

Value

Character vector of result-table names.

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
rwa_table_names(r)

Calculation Validation Report

Description

Calculation Validation Report

Usage

rwa_validation(result)

Arguments

result

An rwa_calculation_result.

Value

The rwa_validation_report attached to the result.

Examples

r <- calculate_tables(generate_synthetic_tables(bank_profile = "KSA_BANK"))
rwa_validation(r)

Counterparty, SFT, Securitisation, CVA and Settlement Formulae

Description

Granular formula interface for SA-CCR, SFT, securitisation approaches, BA-CVA and settlement risk. Rates are decimal values.

Usage

sa_ccr_multiplier_value(V, C, addon, parameters = NULL)

sa_ccr_exposure_value(V, C, addon, alpha, parameters = NULL)

sft_exposure_value(cash_leg, security_value, security_haircut, fx_haircut)

securitisation_irb_pool_capital(
  rwea_pool_irb_ul,
  el_pool_irb,
  pool_ead,
  parameters = NULL
)

securitisation_sa_pool_capital(rwea_pool_sa, pool_ead, parameters = NULL)

securitisation_ssfa_coefficient(ka, attachment, detachment, p)

securitisation_ssfa_risk_weight(
  pool_k,
  attachment,
  detachment,
  p,
  floor,
  parameters = NULL
)

securitisation_irba_p(
  pool_type,
  senior,
  effective_number,
  pool_k,
  average_lgd,
  tranche_maturity,
  sts,
  parameters = NULL
)

securitisation_erba_risk_weight(
  cqs,
  maturity,
  senior,
  sts,
  attachment,
  detachment,
  parameters = NULL
)

securitisation_risk_weight(
  approach,
  pool_k,
  attachment,
  detachment,
  p,
  sts = FALSE,
  senior = FALSE,
  resecuritisation = FALSE,
  cqs = 0,
  parameters = NULL
)

cva_basic_approach_capital(items, parameters = NULL)

settlement_risk_factor(days_late, parameters = NULL)

Arguments

V, C

Current value and collateral under SA-CCR.
addon

Aggregate SA-CCR add-on.
parameters

Optional parameter data frame or store.
alpha

SA-CCR regulatory alpha.
cash_leg, security_value

Monetary SFT legs.
security_haircut, fx_haircut

Haircuts.
rwea_pool_irb_ul, el_pool_irb, rwea_pool_sa, pool_ead

Pool inputs.
ka, pool_k

Pool capital ratio.
attachment, detachment

Tranche attachment and detachment points.
p

Supervisory SSFA parameter.
floor

Minimum risk weight.
pool_type

Pool category.
senior, sts, resecuritisation

Logical tranche flags.
effective_number

Effective number of pool exposures.
average_lgd

Average pool LGD.
tranche_maturity, maturity

Maturity in years.
cqs

Credit-quality step.
approach

Securitisation approach.
items

BA-CVA rows, each with the documented nine numeric fields.
days_late

Settlement delay in days.

Value

Numeric formula result or named vector.

See Also

analyze_counterparty_risk(), analyze_securitisation()

Examples

sa_ccr_exposure_value(100, 30, 20, 1.4)
sft_exposure_value(100, 90, 0.1, 0.05)

Standardised Credit-risk and CRM Formulae

Description

Granular, side-effect-free formulae for exposure value, risk weights, real estate treatment and credit-risk mitigation. Rates and weights are decimals. Monetary inputs and outputs use one caller-selected currency consistently.

Usage

sa_exposure_value(
  gross_carrying_amount,
  specific_adjustments = 0,
  additional_valuation_adjustments = 0,
  other_own_funds_reductions = 0,
  committed_undrawn = 0,
  annex_i_class,
  data_path = "GROSS_COMPONENTS",
  net_carrying_amount_article_111 = NULL,
  parameters = NULL
)

sa_risk_weight(
  exposure_class,
  cqs = NULL,
  short_term = FALSE,
  transactor = FALSE,
  retail_eligible = FALSE,
  defaulted = FALSE,
  default_coverage_ratio = 0,
  specialised_lending_type = "",
  parameters = NULL
)

real_estate_risk_weight(
  ead,
  property_value,
  property_type,
  ipre,
  counterparty_rw,
  senior_liens = 0,
  adc = FALSE,
  parameters = NULL
)

crm_maturity_factor(t_protection, t_exposure, minimum_t = 0.25, maximum_T = 5)

crm_adjusted_exposure(exposure, collateral, he, hc, hfx)

Arguments

gross_carrying_amount, specific_adjustments, additional_valuation_adjustments, other_own_funds_reductions

Monetary amounts.
committed_undrawn

Undrawn commitment amount.
annex_i_class

CRR Annex I conversion-factor class.
data_path

"GROSS_COMPONENTS" or "NET_ARTICLE_111".
net_carrying_amount_article_111

Optional net carrying amount.
parameters

Optional parameter data frame or rwa_parameter_store; bundled regulatory parameters are used by default.
exposure_class, cqs

Exposure class and credit-quality step.
short_term, transactor, retail_eligible, defaulted

Logical classification flags.
default_coverage_ratio

Coverage ratio for defaulted exposures.
specialised_lending_type

Optional specialised-lending category.
ead, property_value, senior_liens

Monetary real-estate inputs.
property_type

Property category.
ipre, adc

Logical real-estate flags.
counterparty_rw

Counterparty risk weight.
t_protection, t_exposure, minimum_t, maximum_T

Maturity-mismatch inputs in years.
exposure, collateral

Exposure and collateral values.
he, hc, hfx

Exposure, collateral and currency haircuts.

Value

Numeric value, named numeric vector, or documented list depending on the formula.

See Also

regulatory_parameters(), override_regulatory_parameters()

Examples

sa_exposure_value(100, 2, 0, 0, 40, "CLASS_2", "GROSS_COMPONENTS")
crm_adjusted_exposure(100, 60, 0.05, 0.10, 0.08)

Select a Rule Set

Description

Returns a modified copy of canonical tables with the requested rule set in run_config; it never mutates the caller's object.

Usage

select_rule_set(tables, rule_set_id)

Arguments

tables

Canonical input tables.
rule_set_id

Identifier listed by available_rule_sets().

Value

Modified canonical tables.

Examples

t <- generate_synthetic_tables(bank_profile = "KSA_BANK")
t <- select_rule_set(t, "CRR3-EU-FL")

SME Supporting Factor under Article 501 CRR

Description

SME Supporting Factor under Article 501 CRR

Usage

sme_supporting_factor(total_amount_owed_eur, parameters = NULL)

Arguments

total_amount_owed_eur

Positive Article 501 E* amount in EUR, including the prescribed connected-client/institution-group aggregation and residential collateral exclusion/fallback. This is not necessarily the individual EAD.
parameters

Optional governed parameter table or parameter store.

Details

This pure numeric function does not certify legal eligibility. The threshold and lower/upper rates are read from SUPPORTING_FACTOR parameters.

Value

Numeric weighted SME supporting factor.

Examples

sme_supporting_factor(5000000)

Canonical Table Dictionary

Description

Canonical Table Dictionary

Usage

table_dictionary()

Value

A data frame describing all logical tables, workbooks and sheets.

See Also

table_schema()

Examples

head(table_dictionary())

Canonical Table Schema

Description

Canonical Table Schema

Usage

table_schema(table)

Arguments

table

Logical table name from table_dictionary().

Value

A list with workbook, sheet, Excel table, required flag and columns.

Examples

table_schema("exposure_lot")

Validate a Canonical Dataset

Description

Reads and validates all canonical input workbooks without calculating or writing any output files.

Usage

validate_dataset(dataset)

Arguments

dataset

Dataset directory containing an inputs subdirectory.

Value

An rwa_validation_report object.

See Also

calculate_dataset(), generate_synthetic_dataset()

Examples

root <- file.path(tempdir(), "rwa-validation-example")
dataset <- generate_synthetic_dataset(root, bank_profile = "KSA_BANK")
as.data.frame(validate_dataset(dataset))