Case Studies

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  • Client #1

    International Bank

    Fixed Income Risk Management

    Pre-payment Models

    The problem

    Following its acquisition of independent fixed-income broker dealer, the Global Systemically Important Financial Institution (referred to as “the Bank”) necessitated the integration of the broker dealer’s third-party non-agency mortgage prepayment model.

    The solution

    • Reviewed documentation of the third-party model conceptual design.
    • Performed Back Testing with breakouts along various dimensions such as loan credit rating (Prime, Alt-A, Sub-prime) and loan characteristics (LTV, FICO, Age, WAC, origination date).
    • Conducted Sensitivity Testing that covered a broad range of tests of the model response (CPR, CRR, CDR, Severity) to changes in independent parameters and inputs.
    • Performed Historical stress testing applying market and economic model inputs for a historical stress period (e.g., 2008 Financial Crisis and/or 2020 Covid) as a projection from the current date and observing the model response.
    • Performed comparison of the non-agency mortgage prepayment model to a separate, independently developed, model that was desirable for this type of validation.

    The result

    • Achieved compliance with the requirements of the Bank’s model risk management framework.
    • Enabled the broker-dealer group to continue using a familiar and effective market risk system and process based on the results.
    • Leveraged the prepayment model and experience gained from the broker-dealer across the Bank’s broader risk processes, leading to improved oversight and cost savings.

  • Client #2

    Utility Company

    Energy and Natural Resources

    Power Purchase Agreements

    The problem

    The utility company had the imperative to accurately assess and proactively manage the valuation and risks associated with their renewable Power Purchase Agreements (PPAs), encompassing both fixed-shape and ‘as generated.

    The solution

    • Designed and developed a comprehensive model for valuation and risk analysis.
    • The model used Monte Carlo method to simulate the joint behavior of prices (Price Model) and generation quantities (Generation Model).
    • Price Model is a multi-factor algorithm for evolving various electricity forward curves while matching market inputs and generating prices with required granularity: monthly, daily, hourly.
    • Generation Model is a statistical model used to determine the solar and wind energy output.

    The result

    • Provided a model for a better understanding of the risks associated with investments in the Power Purchase Agreements (PPAs).
    • Developed a comprehensive valuation and risk analysis model enabling senior management to make more informed decisions about investing in PPAs.

  • Client #3

    Credit Rating Agency

    Model Risk Management

    CMBS Models

    The problem

    A prominent credit rating agency sought assistance in validating CMBS models.

    The solution

    • Validated CMBS models.
    • Provided full documentation of validation process.

    The result

    • Strengthened the quantitative and qualitative aspects of model validation:
      • Implemented a collaborative oversight approach involving modeling experts and senior management.
      • Increased emphasis on in-sample parameter quantification and on out-of-sample forecasting, significantly improving quantitative validation.

  • Client #4

    International Bank

    Quantitative Risk Management

    FRTB

    The problem

    International Bank required a comprehensive Risk Factor Inventory and FRTB Implementation.

    The solution

    • Built a complete inventory of Risk Factors.
    • Implemented FRTB SA.
    • Assisted with the implementation of FRTB internal model.

    The result

    • Provided the key component in the bank’s FRTB project.

  • Client #5

    Asset management firm

    Model Risk Management

    Algo Trading Models

    The problem

    Asset Management necessitated the validation of two distinct algo-trading models: a dark pool model and an advanced model utilizing extreme gradient boosted trees.

    The solution

    • Assessed the conceptual soundness of models, ensuring the accuracy and appropriateness of model parameters.
    • Executed comprehensive model testing to validate its functionality and performance.
    • Devised and implemented robust Model Performance Monitoring system to ensure that Algo Trading models operate according to their intended specifications.
    • Designed and implemented a Model Risk Control process specifically tailored for Algo Trading models.
    • Effectively communicated and presented the outcomes and findings to clients.

    The result

    • Provided senior management and the board of directors’ analysis and validation of the accuracy and reliability of the algo-trading models.

  • Client #6

    Utility Company

    Market Risk Management

    Energy Risk Analytics

    The problem

    Utility company wanted to enhance its Market Risk Management capabilities due to changes of their commercial strategy.

    The solution

    • Assessed capabilities and use of the existing Market Risk Management system.
    • Corrected and enhanced existing sensitivities to improve hedging activity.
    • Investigated the stability and accuracy of hedging strategies.
    • Identified existing issues in existing Market Risk Model, suggested improvements in their use, and enhanced documentation.
    • Trained the client’s team.

    The result

    • Identified opportunities to enhance the current configuration and modeling approaches within the risk analytics engine.
    • Developed a more efficient risk engine and metric for commercial strategies, improved reporting, and enhanced calibration results.

  • Client #7

    International Bank

    Quantitative Risk Management

    Market Risk Analytics

    The problem

    Bank’s Market Risk Management Department needed a simulation engine that would allow to model correlated fat-tailed random variables.

    The solution

    • Expended calibration information from 1 year to multiyear dataset.
    • Performed advanced PCA for FX and interest rate factor simulation.
    • Designed, built, and oversaw implementation of simulation engine.

    The result

    • Improved significantly with new VaR model results compared to a rudimentary Gaussian VaR model.
    • Experienced far fewer VaR exceedances, leading to the regulator’s subsequent acceptance of the model/system.

  • Client #8

    International Bank

    Quantitative Risk Management

    Credit Risk Analytics

    The problem

    Bank’s Credit Risk Management Department wanted to assess credit risk rating and default rating migration and mark-to-market risks in various lending/trading books.

    The solution

    • Built fundamental credit models (e.g., advanced Merton model) to work alongside credit. ratings and to identify mispriced risk from equity and equity derivatives markets.
    • Developed metrics to understand and measure risk drivers of the aggregate results.
    • Detected and improved overexposures in certain sectors within trading books.
    • Designed strategies for risk optimization.

    The result

    • Identified and reduced single-name exposures arising from complex credit derivative positions.
    • Identified and mitigated exposures resulting from overconcentration in various sectors.
    • Developed a model that was responsive to the market environment, showing higher risk in times of higher volatility, unlike a static credit transition model.
    • Suggested strategies for optimization and specific actionable trades.