Module 6: Clearing & Settlement / Regulation with Chris Giancarlo, former chairman, CFTC

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Module 6 Description

At Goldman Sachs and other Wall Street firms, leadership primarily concentrates its attention on the front office, where traders committed the firm’s capital. It is no accident that SecDB arose in the front office, where market risk and counterparty credit risk -- the response of the firm’s portfolio of positions to the movement of underlying market variables, and to the creditworthiness of derivatives counterparties -- dominated all other risks. By evolving SecDB to the point where it became the definitive representation of all front-office business processes, Goldman Sachs was able to ask counterfactual questions in software, identifying and mitigating risks before they materialized -- rather than responding reactively to risks after they had materialized, when it was too late to do anything about them.

In the early days of SecDB, we poked fun at ourselves over the magnitude of our aspirations. We began in 1993-94 by capturing the foreign-exchange spot-and-forward front-office trading business in software, dreaming even in those early days of reflecting the commodities, interest rate, credit, and equities businesses in software. We realized those ambitions over the 1995-2007 period, in part by restricting our scope to the capital-commitment decisions of the front office. At the point where derivatives and securities trades settled into cash, we provided feeds to the downstream systems, and cared little for what happened after that point.

With perfect hindsight, it is clear that we were not nearly ambitious enough. In our emphasis on market-price and counterparty credit risk, we paid insufficient attention to other downstream risks, including the calculation and posting of variation margin in the trading business, and the funding lags and liquidity risk of the prime-brokerage business. Our comprehensive view of risk extended to the boundary of the front office, but our disaggregated approach to downstream software materialized in the form of difficult-to-mitigate liquidity, reputational, and client-satisfaction risk.

In the post-2008 period of the Dodd-Frank regulation, the Federal Reserve and other regulators demanded massive upgrades to the systems of the Global Systemically Important Banks (GSIBs). Goldman Sachs and its competitors overhauled their systems and processes to comply with Dodd-Frank, and in particular to implement the Federal Reserve’s new Comprehensive Capital Analysis and Review (CCAR) regime.

It is most useful to think of CCAR as the logical culmination of a decades-long journey to converge the software and the business of banking: Only by reflecting all processes in software can GSIBs conduct a comprehensive nine-quarter forward simulation of their firmwide balance sheet, income statement, and cash flows, demonstrating capital adequacy in the face of the Fed’s Severely Adverse scenario.

In this class, we consider some of those downstream risks, and the GSIBs’ approach to modeling and mitigating those risks. Specifically, we contemplate how the API journey -- so advanced in the front office -- might eventually one day (we hope) evolve in the back office, giving rise one day to the ‘Holy Grail’ of a shared industry back office.

Last, we have the opportunity to speak with former CFTC Chair Chris Giancarlo about his efforts surrounding post-crisis regulatory reform, shepherding the ascendance and regulatory acceptance of digital assets (accompanying digital asset futures), and his current work on the digital dollar.

Link to Slides

Recorded Lectures

Module 6, Chapter 1
Zoom with Transcript / Vimeo

Module 6, Chapter 3
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Module 6
(All Four Chapters)

Module 6, Chapter 2
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Module 6, Chapter 4
Zoom with Transcript / Vimeo

Explainer Videos

The Knight Capital Error 2012

Study Questions

Why have Wall Street firms embedded feeds and trade reconciliations across their businesses, rather than creating a single data lake with a uniform representation of ‘ground truth’? What are the costs and benefits of doing so?
Why have Wall Street firms failed to create a uniform software representation of the downstream processes for confirming trades, making payments, and moving collateral?
Each Wall Street firm performs the same back-office processes more or less effectively, replicating similar processes multiple times across businesses within banks, and then again across banks. What would be the benefits of an API-based unified producer of back-office services? And why does such a producer not exist?
Evaluate the consequences of CCAR and Dodd-Frank. Did the Federal Reserve, the SEC, and the CFTC make post-Financial Crisis regulations too stringent, or not stringent enough? Did these regulations spur or hinder innovation in risk management at large financial institutions?
Was Chairman Giancarlo’s Project KISS an inevitable response to US regulatory overreach in the global financial system? Is it too soon to tell whether he achieved his goals?
“How did the opacity of counterparty credit risk of global money center banks and financial institutions and the securitized debt they held on their balance sheets contribute to the 2008 Financial Crisis and how could emerging digital technology, like DLT, provide more precise quantitative analysis of credit risk?”
"How has the G-20 financial reforms since the 2008 Financial Crisis, including Dodd-Frank, increased operational and capital complexity and how might emerging financial technology help manage such complexity?"

Reading List

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Module 6 Description

Recorded Lectures

Explainer Videos

Study Questions

Reading List

Required

Optional

Deep Dive

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