Manually tuning MBS prepayment models is messy. In what amounts to an elaborate trial-and-error exercise, modelers must frequently resort to subjectively selecting sub-populations to calibrate, running back-testing to see where and how the model is off, and then tweaking knobs and re-running the back-test to see the impacts. Rinse and repeat.

RiskSpan’s Janet Jozwik and Steven Sun present an approach for running a set of back-tests on MBS pools that automatically solves for the right set of tuners to align model results to actuals. Learn how, by automatically covering every feasible combination of model knobs possible, you can visualize for every pool the impact each knob combination has on:

• Modeled prepay vs. actuals
• Model error
• Refi incentive and other pool features

Traditional statistical models apply a single set of coefficients by pooling a large dataset or for specific cohorts.

Hierarchical models learn from feature behavior across dimensions or timeframes.

Suhrud Dagli and Jing Liu host an informative workshop applying hierarchical models to a variety of mortgage and structured finance use cases, including:

• Changes in beta and covariance of portfolios across time
• Loan performance across geographies and history – e.g., combining credit performance data from 2008 with unemployment-driven credit issues in 2020.
• Issuer-level prepayment performance

Traders and investors rely on time series patterns generated by asset performance to inform and guide their trading and asset allocation decisions. Economists take advantage of analogous patterns in macroeconomic and market data to forecast recessions and other market events.

But you need to be able to spot these patterns in order to use them.

Catch the latest in RiskSpan’s series of machine learning and data workshops as Chirag Soni and Jing Liu, two of RiskSpan’s experts working at the intersection of data science and capital markets, demonstrate how advanced machine learning techniques such as Dynamic Time Warping and KShape can be applied to automate time series analysis and effectively detect patterns hiding in your data.

Chirag and Jing will discuss specific applications, explain popular algorithms, and walk through code examples.

Join us on Wednesday, January 13th! 

RiskSpan Director Steven Sun shares a procedural approach to tackling the difficulties of efficiently extracting structured data from images, scanned documents, and handwritten documents using Google’s latest Document AI Solution. This approach greatly improves:

• Effectiveness and accuracy of extracting data which will be otherwise difficult or impossible, and 
• Automating and streamlining the process of feeding extracted data into a data analytic framework

RiskSpan CIO Suhrud Dagli, who discussed how ML is being incorporated into model risk management during our Sep. 30 webinar: Machine Learning in Model Validation, demonstrates in greater detail how machine learning can be used:

• In input data validations,
• To measure feature impact, and
• To visualize how multiple features interact with each other

RiskSpan’s Bernadette Kogler led a panel of industry experts in a review of the U.S. economy and how mortgage companies can employ alternative data to responsibly extend mortgage credit more broadly to current and potential homeowners.

Participants include 

• Bernadette Kogler, Co-Founder & CEO, RiskSpan
• Amy Crews Cutts, President, AC Cutts and Associates
• Janet Jozwik, Managing Director, RiskSpan
• Laurie Goodman, Director, Housing Finance Policy Center, The Urban Institute