Package {riskweightedassets}

Reproducible Risk-Weighted Asset Calculations

Description

The package implements deterministic, auditable risk-weighted-asset and capital calculations using canonical data frames or versioned workbooks. It contains synthetic reference data and source metadata, but no downloaded regulatory documents and no customer data.

Legal information

Copyright 2026 RiskDataScience GmbH. GPL-3. The installed LEGAL.md file contains the imprint, privacy policy and usage limitations. The designated package maintainer is Dr Dimitrios Geromichalos riskdatascience@web.de.

Author(s)

Maintainer: Dimitrios Geromichalos riskdatascience@web.de

Authors:

• RiskDataScience GmbH [copyright holder]

See Also

Useful links:

• https://github.com/rds0001/risk-weighted-assets-r

• Report bugs at https://github.com/rds0001/risk-weighted-assets-r/issues

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

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

Value

An rwa_domain_analysis.

Analyse Credit Risk

Description

Analyse Credit Risk

Usage

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

Arguments

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

Value

An rwa_domain_analysis.

Analyse IRRBB and CSRBB

Description

Analyse IRRBB and CSRBB

Usage

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

Arguments

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

Value

An rwa_domain_analysis.

Analyse Operational Risk

Description

Analyse Operational Risk

Usage

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

Arguments

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

Value

An rwa_domain_analysis.

Analyse Securitisation Risk

Description

Analyse Securitisation Risk

Usage

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

Arguments

Value

An rwa_domain_analysis.

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

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

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

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

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

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.

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

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

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

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

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

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

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)

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
)

Arguments

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)

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

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

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

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

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

Value

x, invisibly.

Print a Domain Analysis

Description

Print a Domain Analysis

Usage

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

Arguments

Value

x, invisibly.

Print a Validation Report

Description

Print a Validation Report

Usage

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

Arguments

Value

x, invisibly.

Print a Workspace

Description

Print a Workspace

Usage

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

Arguments

Value

x, invisibly.

Read One Regulatory Parameter

Description

Read One Regulatory Parameter

Usage

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

Arguments

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

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

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

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

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

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

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

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

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

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

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

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

Value

Modified canonical tables.

Examples

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

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

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

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))