Structured Finance and Mortgage Consulting

With over five decades of combined expertise in structured finance, we can provide sophisticated advice on all aspects of mortgage and structured finance, including analysis and modeling of cash flows, risk management and mitigation, model validation, and financial derivatives and hedging.

 
 
 

Structured Products Analysis and Modeling

Mortgage backed and asset backed securities have embedded complexities stemming from their cash flow structures, sensitivity to interest rates, prepayments, credit, and market structure complexities, including agency issues. We provide expertise on a variety of models for prepayment, default, origination, hedging, servicing, interest rates, and credit.

 

Financial Derivatives

Whether you use derivatives for hedging, risk management, or for return enhancement, they can add a layer of complexity to an already difficult asset class. We bring decades of experience in modeling, trading, and risk management of interest rate, volatility, correlation, portfolio, index, and structured finance derivatives to bear to help you sharpen  your portfolio performance and risk management processes.

Risk Management

Structured Finance presents unique risk management challenges. Our decades long expertise, including the 2008 Global Financial Crisis, in managing billions of dollars in structured product risk helps our clients understand and address market and credit risk as well as scenario analysis, tail risk, counterparty risks and their interaction with business process and operational risk.

 

Model Validation and Risk Mitigation

When managing Structured Products, models are unavoidable, and yet fallible. The new generation of AI/ML models adds further  layers of opacity and complexity. Our deep knowledge of structured finance models, including their prior failures and weaknesses can help you pinpoint the  underlying strengths, assumptions, and fragilities in your modeling infrastructure, to make your business and financial outcomes more robust, and to satisfy regulatory requirements.  


 

 

Structured Products Analysis and Modeling

  • Mortgage backed models for prepayment and option adjusted spread models

  • Stochastic models for non-agency mortgages incorporating credit and prepayment optionality costs 

  • Originator / servicer evaluation models based on performance after controlling for stated collateral characteristics.

  • Innovative structured product models incorporating collateral performance and cash flow waterfalls

    • Collateral stratification-based default models, non-linear aggregation of risks on defaults.  

    • Roll-rate transition matrix-based models to project defaults and cash flows 

    • Default behavior models incorporating willingness to pay based on mortgage payment versus rental alternatives, extent of negative equity, and payment history

  • Interest rate and credit default models

  • Other expertise include risk neutral modeling, principal component modeling, and modeling of embedded options


 

 

Risk Management

  • Market risk management models for portfolios 

  • Mortgage Prepayment and default risk

  • Stress testing and scenario analysis

  • Originator and servicer risk analysis

  • Macro scenario based risk analysis

  • Tail risk evaluation

  • Counterparty risk

  • VaR and Risk based capital 


 

 

Financial Derivatives

  • Swaps, Futures, and Forwards on interest rates and currencies

  • Options in equities, currencies, rates and credit

  • Mortgages and mortgage derivatives in portfolios

  • Credit Indexes 

  • CDOs

  • Tranches, credit options, and other structured credit derivatives

  • ETFs

  • Structured finance derivatives and waterfalls 

  • Margins, cleared products, and collateral management

  • Leverage, repo, encumbered capital, and prime brokerage


 

 

Model Validation and Risk Mitigation

  • Stress testing

  • How models failed during prior financial crises

  • How to make investment firms and portfolios less vulnerable to model risks

  • Fragility in risk modeling assumptions

  • Fragility in alpha generation models, and good trading practices

  • Back testing and over-fitting of data

  • The perils of alternative data 

  • Vulnerabilities of AI/ML techniques when applied to investing