model_clo_insight.engines.hypo_pool module

This Module Contains the Primary Methods for Constructing Stressed Modeling Pools (Hypo Pools)

class HypoResultMonitor(is_solve, riskscore_solution, riskscore_realized, credit_dispersion, adj_bdr, bdr, sdr, sdr_diag, obligors)

Bases: object

Object which stores the result fields for the hypo_pool_formula_constraints() method Descriptions can be found below in model_clo_insight.engines.hypo_pool.hypo_pool_formula_constraints

adj_bdr: float
bdr: float
credit_dispersion: float
is_solve: bool
obligors: list
riskscore_realized: float
riskscore_solution: float
sdr: float
sdr_diag: float
hypo_pool_discrete_constraints(pool_par, conc_limit_basis, dscore_min, ob_max, ind_max, riskscore_max, ob_exceptions, ind_exceptions, ob_floor, ind_floor, risk_score_tbl, tables, force_high=None, force_low=None, is_simple_industry=False, n_simple_industry=40)

Main entry point for constructing the hypothetical modeling pool where the CQT constraints are an explicit Dscore and risk score (CQT matrix)

General Algorithm

Solver Goal => Pool actual Dscore ~ Dscore constraint (slightly higher)

2 stage solver: - The outer layer (this method) iterates over the concentration limitation exceptions - The inner layer (the solvers.XXX methods) use numerical optimization on the base concentration limitations

Note: the solver performs the rating overlay once the pool percentages are computed

General steps of the algorithm:

  1. Calculate scaling factor for cases where the conc limit basis is different from actual pool par, and apply

  2. Run solver and check if solution is found (the solvers spin down the base ob/ind concentration limitation, then rating overlay)

  3. If no solution is found, iteratively remove the last (smallest) obligor conc limit exception

    1. If Dscore in compliance, solve for partial last (smallest) obligor percentage s.t. Dscore_act approx Dscore_trigger

    2. If Dscore not in compliance, go to next obligor exception

  4. If no solution is found and all obligor conc limits are removed, iteratively remove the last (smallest) industry conc limit exception until a solution is found

Parameters

  • pool_par (float) – Aggregate par balance of the current portfolio

  • conc_limit_basis (float) – Basis for concentration limitations; typically defined the same as rvst target par

  • dscore_min (float) – maximum diversity score limit for the pool

  • ob_max (float) – base max obligor concentration limitation

  • ind_max (float) – base max industry concentration limitation

  • riskscore_max (float) – max DBRSM risk score of the pool

  • ob_exceptions (list[clo_internal_objects.ConcLimitException]) – obligor concentration limitations

  • ind_exceptions (list[clo_internal_objects.ConcLimitException]) – industry concentration limitations

  • ob_floor (float) – lowest level that the base obligor concentration limitation is allowed to spin down to

  • ind_floor (float) – lowest level that the base industry concentration limitation is allowed to spin down to

  • risk_score_tbl (dict[int, float]) – lookup table for DBRSM Risk Score

  • tables (dict[str, np.ndarray]) – Lookup tables for Dscore and industry codes

  • force_high (tuple) – high rating (rating integer, percentage) to lock prior to solving for belly ratings

  • force_low (tuple) – low rating (rating integer, percentage) to lock prior to solving for belly ratings

  • is_simple_industry (bool) – if true, industry allocation is done on a repeating sequence. Hypo only iterates on obligor in this case.

  • n_simple_industry (int) – if is_simple_industry is True, this is the number of industries to repeat in sequence

Returns

results dictionary

Return type

dict

Results Dictionary Keys:

  • is_solve: True if solver found solution, False otherwise

  • dscore: diversity score calculated on the actual pool

  • riskscore: DBRSM risk score calculated on the actual pool

  • ob_base: base obligor concentration limitation that the solver found

  • ind_base: base industry concentration limitation that the solver found

  • ob_exceptions: obligor concentration limit exceptions actually used

  • ind_exceptions: industry concentration limit exceptions actually used

  • obligors: OrderedDict[objects.Obligor] pool of Obligors.

hypo_pool_formula_constraints(hypo_pool_diversity, run_mode, rs_abs_max, coef_bdr, coef_sdr, risk_score_tbl, IDT_sp, WARF_sp, rvst_target_par, collat_prin_amt, was, warr, wal, rs_bound_lower, rs_bound_higher, monitor_cushion, force_high=None, force_low=None)

Main entry point for constructing the hypothetical modeling pool where a continuous formula is the constraint

General Algorithm

Solver Goal => Solve for the worst rating mixture such that the Monitor BDR ~ Monitor SDR

Notes: - Diversity should be solved for prior to running this method; this method solves for rating mixture

General steps of the algorithm:

  1. Check edge case to see if using the max allowable risk score causes BDR < SDR

  2. Check edge case to see if using the min allowable risk score causes BDR > SDR

  3. Run a bisection optimizer to solver for a rating mixture (via a risk score) such that BDR ~ SDR

  4. Iterate the resulting risk score locally in small chunks until BDR > SDR

Parameters

  • hypo_pool_diversity (list[clo_internal_objects.Obligor]) – pool where obligor & industry percentages have been previously solved for a given Dscore

  • run_mode (str) – Expected portfolio default rate or WA Rating Factor

  • rs_abs_max (float) – absolute max risk score as per deal (monitor solver won’t create pools any worse than this)

  • coef_bdr (list) – Break even default rate (BDR) coefficients for the transaction

  • coef_sdr (list) – Scenario even default rate (SDR) coefficients for the transaction

  • risk_score_tbl (ndarray) – DBRSM risk score 1-D table

  • IDT_sp (ndarray) – Idealized default 2-D table; sourced from transaction documents

  • WARF_sp (ndarray) – WARF-equivalent 1-D table; sourced from transaction documents

  • was (float) – Target porfolio weighted average spread

  • warr (float) – Target portfolio weighted average recovery

  • wal (float) – Target portfolio weighted average life

  • rs_bound_lower (float) – max risk score boundary (low), required for optimizer

  • rs_bound_higher (float) – max risk score boundary (high), required for optimizer

  • monitor_cushion (float) – optional; can be used to keep a constant cushion between BDR and SDR when solving

  • force_high (tuple) – (rating_number, pct) for higher rtg prior to solving for risk score, force this on pool

  • force_low (tuple) – (rating_number, pct) for lower rtg prior to solving for risk score, force this on pool

Returns

instance results data class object

Return type

HypoResultMonitor