ERCOT Credit Loss Model Mark Ruane Credit Work Group / Market Credit Work Group ERCOT Public April 22, 2015

Former ERCOT Credit Loss Model ERCOT Public ERCOT received Potential Future Exposure (PFE) estimates from Oliver Wyman in February In 2009, subsequent to the adoption of the Market Credit Risk Standard, PFEs were re-estimated quarterly using the Wyman model. Estimates were derived from two models: –Credit scoring model Uses Counter-Party financial data to generate synthetic Counter-Party credit ratings and probabilities of default, which was an input to: –Credit loss model Generates probability distribution of credit losses Excel front end with VB code Versions for zonal and nodal markets

Credit Scoring Model ERCOT Public The credit scoring model estimated proxy ratings based on Counter- Party financial ratios and qualitative assessment. Quantitative Inputs Working Capital / Sales Cash / Assets Current Ratio EBITDA / Interest Expense FCF / Debt Total Debt / Total Capital Equity / Assets EBITDA / Sales Net Income / Assets Total Assets Sales / Assets Qualitative Inputs Policies Management Quality ERCOT Relationship Performance / Strategy Industry Characteristics

Credit Scoring Model ERCOT Public –Allowance for warning signal adjustments –Ratings of subsidiaries tied to parents with “group logic” scoring –Default probabilities for ratings calibrated based on ERCOT historical average loss rate and assumed worst case default probability. Group Logic Determinants QSE and parent have same primary line of business QSE covers a key geographical area for the parent QSE covers a key customer segment for the parent QSE fulfills an essential activity for the parent within a service line QSE is clearly controlled by the parent through influence on management QSE and parent share the same name The parent has a minimum 45% ownership in the subsidiary

Credit Loss Model ERCOT Public High-level model schematic

Credit Loss Model ERCOT Public Price module schematic

Credit Loss Model ERCOT Public Default module schematic

Credit Loss Model ERCOT Public Price module schematic

Credit Loss Model ERCOT Public Exposure module schematic

Credit Loss Model ERCOT Public Key Assumptions – Default Module Default probabilities from credit scoring model Default correlations among market segments QSE default sensitivity to market events Default occurrence is a stochastic variable Key Assumptions – Price Module Mean reversion and jump parameters Implied ERCOT market forward prices (based on NG forwards) Shape factors reflecting differences between simple average and load-weighted prices

Credit Loss Model ERCOT Public Key Assumptions – Exposure Module Distinguishes between market price events and non-market (random) shocks Volume escalation, depending on nature of Counter-Party Volume escalation and likelihood of default near a high-priced day are stochastic variables Mass transition days Key Assumptions – Collateral Module No excess cash collateral in base case Illiquid collateral, eg guarantees, can be haircut Collateral recomputed based on simplified EAL

Credit Loss Model ERCOT Public Model Base Case –Did not include current (excess) collateral held by ERCOT –Represented what was enforceable by ERCOT under Protocols Model “Current Case” –Used actual levels and types of collateral –Assumed some degree of excess collateral would be maintained until a default event

ERCOT Public Historic outcomes – Base Case Credit Loss Model Note 1-year mean and median losses are higher than actual experience to date in nodal market. Simulations using FYE-2009 and Q Financials FYE-2009 Q3-2009

FYE-2009 Q Simulations using FYE-2009 and Q Financials ERCOT Public Former ERCOT Potential Future Exposure Model Historic outcomes – Current Case ERCOT Public

Credit Loss Model ERCOT Public Key Assumptions – Nodal Model Price simulation in key locations only DAM price locations considered as additional hubs with different parameters Forward values for CRRs based upon implied ERCOT market forward prices CRRs envisioned as one-month obligations only, settling in DAM Exposure calculation similar to zonal, with additions of DALE, DAM invoices and Average Invoice Liability (AIL) A model for the nodal market was prototyped but apparently never validated or run.

Former ERCOT Potential Future Exposure Model ERCOT Public In 2011 ERCOT solicited bids to update the credit loss model for the nodal market. With a cost estimated at $400k - $800k, F&A elected to not update the model. The credit scoring model could be updated internally with Market input.

Questions ERCOT Public Credit Updates