model_clo_insight.clo_internal_objects module¶

This module serves as the location for all of the internal model object classes used in the computational engines.

Setup() method:

used for assigning relationships based on other objects in the model

class ConcLimitException(number, percentage)¶

Bases: object

Concentration limitation exception

number: int¶: number of exceptions allowed at this level

percentage: float¶: concentration limitation level for a set number of exceptions

class CorpLoan(ident, name, prop_id, par, tenor=None, recovery_rate=None, custom_pd_curve=None)¶

Bases: object

Represents a Loan to a Corporation

amort_sched¶: amortization schedule for loan

contract_tenor¶

actual tenor of the Security, only used in the modeling if turned on in settings

Type: float

ident¶

unique identifier for the loan

Type: string

name¶

name identifier for loan

Type: string

par¶

par balance of the corporate loan

Type: float

parent_obligor¶

instance of the parent Obligor which secures this corporate loan

Type: Obligor

pd¶: default probability, based on the PD curve of the obligor, tenor of security

pd_normed¶: pd converted to standard normal

prop_id¶

identifier for the parent Obligor

Type: string

recovery_rate¶

asset specific recovery used for the Security, only used in the modeling if turned on in settings

Type: float

reset()¶

set_amort(raw_amort, settings)¶

Compute amortization schedule for this Corp Loan

Parameters

raw_amort – list[float]: array containing the remaining balance at the end of each year (optional)
settings – takes in objects.ModelSettings

Returns

amortization schedule

setup(obligors, transaction, settings)¶

Setup method for the CorpLoan class

CorpLoan needs access to the fields of its parent Obligor. This method also uses the Settings object to determine which tenor assumption to use. The security PD is normalized here so that the random normals in the simulation do not have to be converted to uniforms, which saves computation time

Parameters

obligors (list[clo_internal_objects.Obligor]) – all Properties in the pool
transaction – TransactionData: pool/deal data (custom PD curves)
settings – ModelSettings: contains settings for tenor

Returns

None

Notes

self.pd not used in the simulation, but is needed to compute pool metrics

tenor¶: float:

class HypoPoolRow(row, ind_code, numerical_rtg, pct, total_par, pd_curve)¶

Bases: object

Represents a single line item in a pool generated by the hypo pool generators

compute_pd(tenor, pd_lookup)¶

ind_code: int¶: DBRSM industry code

numerical_rtg: int¶: DBRSM rating in integer form

pct: float¶: percentage of total par for pool item

pd_curve: numpy.ndarray¶: cumulative pd curve (from IDT)

row: int¶: single line item of the pool

total_par: float¶: par amount for pool item

class MethodologyParamsDefaults(IDT, rating_list, rtg_regime_map, correl)¶

Bases: object

Contains the Methodology Parameters for Simulating Defaults in the Pooling Method

Notes

Typically these are stored in a json file inside the project

IDT¶

DBRS idealized default table

Type: list of list

correl_diff_reg_diff_ind¶: depricated in methodology, but functionality available in model

correl_diff_reg_same_ind¶: depricated in methodology, but functionality available in model

correl_same_reg_diff_ind¶

asset correlation between different industries

Type: float

correl_same_reg_same_ind¶

asset correlation within industry

Type: float

rating_list¶

list of DBRS ratings

Type: list

rtg_regime_map¶

each rating mapped to the whole rating

Type: dict

class ModelSettings(n_trials, seed, n_bins, is_stochastic_rec=False, is_asset_specific_rec=False, is_asset_specific_tenor=False, is_custom_pd=False, is_amort=False)¶

Bases: object

Contains the User-Specified Model Settings for the Pooling Method

is_amort¶

if true, build asset-specific amortization schedules in terms of remaining balance

Type: bool

is_asset_specific_rec¶

flag for using asset specific recovery assumptions

Type: bool

is_asset_specific_tenor¶

flag for using asset specific tenors

Type: bool

is_custom_pd¶

flag for using custom pd curves

Type: bool

is_stochastic_rec¶

flag for using stochastic recoveries (not yet implemented)

Type: bool

n_bins¶: number of bins to build the default/loss distribution

n_trials¶

number of trials for the simulation

Type: int

seed¶

seed for the random number generator

Type: int

class Obligor(name, ind_code, numerical_rtg, pd_curve_ref=None)¶

Bases: object

Represents the Obligor Securing a Corporate Loan

Data variables

_calc_total_par(securities)¶

Calculates total par across all securities belonging to this Obligor

Parameters: securities – all securities belonging to obligor
Returns: Total Par for all securities

custom_pd_curve¶

optional, used to lookup custom PD curve vs PD base on rating

Type: string

ind_code¶

DBRSM industry code #testine float

Type: int

name¶

unique identifier

Type: string

numerical_rtg¶: DBRSM numerical rating (fractional implies split public ratings)

pd_curve¶

vector of PD curve over time

Type: np.ndarray[float]

pd_curve_normed¶

normalized PD, used for time-to-default lookup

Type: np.ndarray[float]

reset()¶

Not applicable for Obligor

Returns:

setup(corp_loans, transaction, methodology)¶

Setup method for the Obligor class

Obligors need to know the Corp Loans that belong to them in order to calculate total_par. This method also sets up the PD curve.

Parameters

corp_loans (list[clo_internal_objects.CorpLoan]) – Corp Loans belonging to this Obligor (one Obligor can have many Corp Loans)
transaction (clo_internal_objects.TransactionData) – pool/deal data (custom PD curves)
methodology (clo_internal_objects.MethodologyParamsDefaults) – contains IDT for PD lookup from rating

Returns

None

total_par¶

total par across all securities belonging to this Obligor

Type: float

class PoolPosition(row, ob_name, sec_name, ind_code, rtg, par, rec_rate, tenor, pd_curve)¶

Bases: tuple

represents raw row of data for pooling inputs

_asdict()¶: Return a new dict which maps field names to their values.

_field_defaults = {}¶

_fields = ('row', 'ob_name', 'sec_name', 'ind_code', 'rtg', 'par', 'rec_rate', 'tenor', 'pd_curve')¶

_fields_defaults = {}¶

classmethod _make(iterable)¶: Make a new PoolPosition object from a sequence or iterable

_replace(**kwds)¶: Return a new PoolPosition object replacing specified fields with new values

ind_code¶

ob_name¶

par¶

pd_curve¶

rec_rate¶

row¶

rtg¶

sec_name¶

tenor¶

class TransactionData(pool_tenor, tranche_tenor_rolldown, tranche_tenor_level, tranche_tenor_rtg, pool_amort=None, custom_pd_dict=None)¶

Bases: object

Contains the Transaction Level Data

Pool and deal-level information. This object also contains transaction-specific lookup tables and settings

applicable_ind_codes¶

unique list of industries across the pool

Type: list

custom_pd_dict¶: dictionary to assign asset PD curves based on custom curve in the simulation method

pool_amort¶

pool amortization per rating regime

Type: dict of lists

setup(obligors)¶

Setup method for the Transaction Data

Transaction needs to know the Property in the pool in order to determine which property types & MSA Groups to shock in the simulation.

Parameters: obligors – list of Obligor: obligors in the pool

Returns:

tenors_agg¶

aggregate tenors across ratings stack

Type: list of float

tranche_tenor_level¶

expected final maturity of each tranche/rating level

Type: list of float

tranche_tenor_rtg¶

expected final maturity of each tranche/rating level

Type: list of int

transaction_tenor¶

tenor of the pool, generally weighted average or covenant based

Type: float

build_obs_and_loans_sp(raw_pool, pool_wal)¶: Used to build / debug hypo, so just assume some dummy parameters for setttings and transaction