Interest Rate Swap Primer: Origins and Overview

Interest rate swaps have become an essential tool for interest rate risk management and speculation. With over 300 trillion dollars in outstanding notional and more than 10 trillion dollars traded each week, it is a staple of institutional investing: multi-national corporations, municipalities, sell-side firms, investment managers, and central banks all rely on interest rate swaps to manage interest rate risk.


Total Weekly Transaction Volume Type.png

*Source: US Commodity Futures Trading Commission’s ‘Weekly Swaps Report’ (

**Type of swaps depicted: Fixed-Float Swap; FRA (Forward Rate Agreement); OIS (Overnight Index Swap); Other ( Basis, Cap/Floor, Debt Option, Exotic, Fixed-Fixed, Inflation, and Swaption)



With IRS such an established player in the world of finance, it’s easy to forget how young these financial products really are. In order to understand the future direction of the market, it’s important to understand how the swaps market evolved into the behemoth we know today.

Swaps trading became an institutional instrument starting in 1981. At that time, the United States had an interest rate of 17%, West Germany’s rate was 11%, and Switzerland had a rate of 8%. The World Bank had reached its borrowing limits on German marks and Swiss Francs. IBM, on the other hand, had large reserves of Swiss francs and German marks, with debt payments owed in both currencies. The two parties realized they could reach a deal to leave both parties better off; together with Salomon Brothers, they engineered a specially tailored contract. Thus, the first swap contract was born.

In the early days immediately following the landmark IBM-World Bank deal, swaps were almost entirely used for hedging rather than speculation, being an attractive option for large companies seeking to protect themselves from rate hikes. As such, the swaps market was entirely made up of bilateral contracts, each with its own custom terms and conditions.

However, in the 1990’s swaps began to become increasingly popular with those looking to speculate on interest rates. Suddenly, IRS was no longer just for corporations hoping to hedge rate risk—it became a bona fide trading product, with volumes exploding through the end of the 20th century into the early aughts. While trading dwindled in the aftermath of the Global Financial Crisis, we’ve seen a steady recovery in IRS trading volumes in the post-crisis markets after 2013.

Interest Rate Derivatives.png

Source: ISDA SwapsInfo


As the IRS market grew, the products on offer became increasingly sophisticated. Instead of custom-tailored contracts for each set of counterparties, vanilla swaps emerged as a feasible standardized interest rate swap that helped to commoditize the market. This continues today with increasingly-popular MAC swaps, and the standards that have emerged for benchmarking (like the 3-month LIBOR in the US or the 6-month in Europe). At the same time, financial institutions began to customize swaps contracts in every way imaginable, creating cross-currency basis swaps, overnight index swaps, and FRAs—even leading to the rise of credit default swaps. 

IRD 2.png

Some of the popular ways swaps contracts are customized


Regulators caught on to the explosion of the swaps market and have followed suit, putting in place stringent requirements that have made swaps trading a major operational undertaking. Limited regulation started in the late 1980’s when it became clear that these wholly unregulated products carried significant risk. Most famously, this happened in the UK, where a small municipal government lost millions of pounds betting on declining interest rates. Since then, regulators have remained in lockstep with traders’ increasingly exotic swaps contracts. Consequently, IRS regulations are robust, most recently boosted by a bevy of requirements under Dodd-Frank including clearing, SEF execution, and minimum sizing standards.

With its size and regulatory oversight, it’s easy to view the swaps market similarly to any other financial marketplace. However, there are some major differences--the first is the asymmetrical standardization of swaps trading between the dealer-to-dealer market and the dealer-to-client market. Why is the market set up to favor dealers, and how will this imbalance change in the future?

We’ll explore that in our next post: ‘The Tradeoffs and Challenges of Buy-Side IRS Trading”

Takeaways from the Fixed Income Leaders Summit, Boston

Last week I attended FILS, an excellent event organized by Henry Wallis, Oliver Kirkbright, & Co. bringing together the good (buy & sell side + liquidity venues), the bad and the ugly (incumbent vendors :) ). It was a welcomed reprieve for embattled Fixed Income traders, enabling them to forget about market volatility for a few days.

There were a number of interesting trends and themes discussed – I’ll describe what I heard and opine on them a bit below.

Overarching Themes:


o   Tighter liquidity from the sell side due to new capital requirements

o   Market transparency – TRACE (Trade reporting & compliance engine)

Evolving marketplace

o   “Electronification” of Fixed Income trading

o   Emerging execution patterns (all-to-all, pre-trade indication)

o   Success factors for SEF / ATSs to survive and thrive

o   Liquidity fragmentation and the logistical challenges it implies


Re: Regulation - Like bad-tasting medicine…

-        Nobody enjoys its first order consequences (added cost and complexity for doing business)

-        But speakers and attendees did attach to its longer term goals (market stability, less systemic risk, investors confident in “the system”) as long the implementation path was collaborative and not overly prescriptive. 

-         There were differing opinions on TRACE’s impact on liquidity. I could have done without the excessive repetition of hollow and obvious mantras like 'equitification of fixed income' and 'Equity is different than fixed income'


At Adroit, we love microstructure transparency. 

-        TRACE is far from that but, in exposing reference prices from the recent past, represents a step in the right direction.  

-        Naïve players mistaking it for something more will get burned. 

-        TRACE will also hurt players whose business models rely on inefficient markets (think of 'relationships', soft-dollars, with investors directly or in-directly paying the cost for market opacity).

-        There is no substitute for understanding the market micro-structure in a first principles way to come up with target/acceptable execution pricing levels.


Re: FI Trade Execution - The buy-side perspective: “walking up a down escalator”:

-        Despite a maturing market with increasing transparency, the buy side can’t easily capitalize on these advances to improve trade execution

-        It is becoming more of a logistical challenge to find liquidity in Fixed Income markets (shrinking sell side balance sheets, fragmentation of liquidity channels)

-        Vendor technology hasn’t kept pace with the evolution of the market; as a result, complexity gets propagated to end users (investment managers) and they either live with sub-optimal execution or spend a lot of time and energy solving for the added complexity.

-        We expect more liquidity models will emerge. For example, one can envision liquidity provided in Dv01 terms, almost like CDS where 'any eligible bond' will do.


Implications of the evolving marketplace for the buy side:

  • Traders (or their tools) will need to engage in instrument selection and ‘smart order routing’ to venues.

o      Either execution traders will need to relay market conditions to PMs that will influence instrument selection (impractical and burdens PMs with logistical details they’d generally prefer to delegate)

o     Or they will need to be provided target exposures (e.g. in DV01 terms and/or key rate durations) and afforded some creative license and degrees of freedom to find the best execution strategy (instruments, venues, etc) to implement the target exposures at lowest cost.

o     Trading strategies will quickly evolve towards 'exposure trading' as opposed to looking for specific bonds/instruments.

  • Need for advanced Liquidity aggregation

o     Normalize, aggregate and act on liquidity across various providers and models (RFQ (voice & electronic), Orderbook, All-to-all, published dealer inventory, axes)

  • Need to continuously evaluate related markets

o     To ensure there is no 'standards arbitrage' (e.g. par vs. MAC swaps) and ensuring dealers are not charging excessively for customization

  • Need for decomposing desired exposures into standard instruments (lower execution & maintenance costs) and custom instruments for any residual target exposure
  • Need to continuously evaluate and take advantage of package trading opportunities

o     Using bond lists, rolls, compression services and comparing these opportunities against other options (outright, stripping a package into standard and odd residual exposure)

  • TCA is evolving beyond bid-ask spread and two-way quotes from multiple dealers

o      Dealer scorecards must go beyond simple transaction costs and firmness of quotes in the primary execution channel (e.g. bigger picture view of different trading and liquidity models)

o     Anticipating and decomposing the sell-side pricing model: Understanding the motivation, hedging and related markets used by sell-side to quote a price. This pricing model reverse engineering will allow for rational tradeoffs between using standardized OTC products and highly customized products (and weighing this vs. any operational efficiency gains/losses).

o     Interpreting more and more data from diverse sources.  Data will increasingly be transparent and available, liquidity providers and venues will compete and differentiate themselves by providing that data.


Some additional highlights that made my trip to Boston worthwhile:

Keynote speakers:

  • Christopher Voss on how the skills he developed as the FBI’s Lead International Hostage Negotiator are relevant to Fixed Income trading and regulation – I never doubted the relevance :)
  • Paul Hamill presented Citadel Securities’ vision that I believe will have them eating everybody else's lunch (as they often do)
  • Billy Hult on how Tradeweb plans to stay ahead of the competition.  As the leading incumbent, it will be interesting to see how it plays out and whether they can avoid the perils of the “Innovator’s Dilemma”

Notable panelists:

  • Hicham : Always insightful.
  • Zack : I wholeheartedly agreed with his assertion that, 'You need to hire smart people to figure out intelligent trading process'.
  • Susan Estes :  Her command of Treasury markets and its data was impressive.

Notable moderators:

  • Kevin McPartland : Intelligent, witty and quick – is he available to moderate the Clinton / Trump debates?
  • Chris White : did a wonderful job picking out the most germane threads from the panelists’ ruminations and used them to drive the conversation in the most interesting directions.

I am sure I missed many notable speakers and sessions.  Some focused on areas beyond my pay grade (e.g. 'Global interest rate trends') :)


Plug: We at Adroit are well on our way to solving the buy side’s predicament in Fixed Income trading (and beyond).

Putting the IBOR hype to the test

  The Truth is Out There

The Truth is Out There

In my last post, I laid out a data, ability, and asset class framework that helps investment managers understand their level of IBOR requirements. In this post I’ll examine categories of IBOR solutions and their applicability.

What options are out there?

  • In-house IBOR – fit for purpose, typically organically grown, rarely referred to as an IBOR.
  • Vendor solution based on accounting system – Currently has the most market share in IBOR. We’ll examine them without naming them, but you’ll know. :)
  • Vendor solution based on OMS – Every OMS vendor claims to have an IBOR. We’ll look at the leading vendor in big, customized deployments. Again, I will not name names but they are “in charge” like Scott Baio and they would love to make you one of their tributaries.


In-house IBOR:

Consultants might not have noticed (despite their deep industry knowledge) that every reasonably sized investment manager has/had an IBOR and uses it for investment decisions every day.

I was first assigned the task of building one over a decade ago for a diverse multi-strategy fund and have built several since. At the time, I was not an expert on the topic but I had a few things going for me:

  • My firm’s front-office and back-office teams worked very closely together. There were no silos and no turf wars.
  • There was a common technology platform: the front and back office leveraged the same data, valuation models, and other functions.
  • My boss understood the domain and problem better than anyone I have ever encountered (or ever will). He devised ingenious solutions to eliminate data consistency and accuracy problems (especially for swaps).

Our design leveraged the strengths of existing components:

  • The leading vendor Portfolio Accounting system -- By utilizing its bi-temporal views and a bit of query language magic, we were able to get exactly the data we needed at an acceptable frequency for front-office needs. This data was reasonably accurate as the accounting and middle-office functions needed it to be correct, so we were able to free-ride on the back-office data quality to serve front-office needs.
  • Valuation – We used a firm-wide valuation model library to calculate investment returns and risk metrics.
  • Visualizations – We reused the visualizations from our bespoke Portfolio Management System.
  • Current Day's Trading Activity – We utilized real-time interfaces to our Order Management Systems.

It is hard to generalize the in-house solutions (as they are custom by definition) but we have seen a few consistent traits in the many such solutions I have encountered:

  • Typically very functional, handle a lot of deep corner cases well. Each solution will invariably be narrow (to reflect the investment style and asset class mix of the firm).
  • Usually built without regard to the boundary between front and back office. Hence it doesn’t work when the back office is outsourced.
  • Organically grown: very rarely do we see firms take a step back and build completely new position management systems. This eventually leads to high maintenance costs and operational issues.

There are also cases of In-House IBORs where Portfolio Managers (PMs) have cobbled a solution using Excel. In these instances, PMs create their own front-office data "island" that doesn’t reflect the actual back-office positions (ABOR), causing issues with accuracy that can result in wasted time and sub-optimal trading decisions.

In-house IBOR Strengths:

Usual benefits of custom software – Can perform very specific functions catered to the authoring firm’s business.

In-house IBOR Weaknesses:

Usual problems with custom software – Expensive to build and maintain. Organic growth stretches the architecture beyond its original mandate, making it difficult to pivot to new investment styles and operating models (e.g. outsourcing Back Office).

Bottom line:

Sadly, this is currently the only game in town if you fall into the intermediate or advanced use-cases or have medium to high complexity asset classes (these categories are outlined in my part 1 blog post). Shameless plug trigger warning: My company, Adroit Trading Technologies, aims to provide the first vendor platform capable of delivering IBOR for investment managers at the high end of the complexity spectrum.


Vendor IBOR based on Accounting System

The particular accounting system vendor we reference does not have a front-office offering aside from a simplistic trade capture solution for Europe. They do have robust functionality in the middle and back-office space. We’ll examine it as if a fund were to use it as a standalone IBOR (i.e. assuming you didn't already have their accounting system) and also as an extension of the accounting system. 

How it works:
You integrate it with trading system(s) and feed it:

  • Trades or post trade allocations that you send to back-office like a ‘drop copy’

  • New Securities
  • Market data, valuation, corporate actions (unless you already use their accounting system)

Accounting Vendor IBOR graded by…

Data Sets:



Pretty accurate, handles corporate actions. Has historic positions. Positions cannot be assigned a custom hierarchy or managed at the sub-tax-lot-level. Position updates occur in batches, not real-time.
NAV is fairly accurate. No risk measures.
Attributed by static security terms (region, etc.) No factor based or granular attribution.
Cash ladder works. No swap, margin, or cash buffer support.




Fairly good. HTML5. Can extract data for analysis. No dynamic calculation, pattern discovery.
Front-office Interface
Very little to write home about :) Lots of integration and maintenance work as mentioned above. Can not handle global trading (as described in last post).
Back-office Interface
Fairly good if you use their accounting system. A huge problem if you have another accounting system or have outsourced back-office. No way to reconcile this IBOR with back-office.

Security Types:



Works well for Equities. Issues in settlement (cash vs physical) for Futures and Options.
Can handle FX and basic bonds well. Very spotty functionality for Repos, Syndicated Loans, Converts, and ABS/MBS.
OTC Swaps, Structured
TRS, CFD mostly works. IR Swaps, Variance/Correlation swaps, CLO/CDO/CMO not supported.

Bottom line:

Accounting vendor IBOR solutions are worth considering if you haven’t already outsourced your back office, you already utilize their accounting platform, your data & ability needs are basic, and you can augment their IBOR solution with some custom development.


Vendor IBOR based on OMS:

This OMS we have in mind offers the ability to make customizations; hence, it is popular with bigger asset managers and hedge funds.

How it works:

You load positions and NAV at the start of trading day. Intraday trades are added to the held positions and you have your IBOR.

Data Sets:



Near real-time position updates for simple asset classes.
  • Not a position management system, falls painfully short if you attempt to use it in this way.
  • Cannot support historic positions (doesn't even have a field for position date).
  • Does not handle corporate actions. Relies solely on external update of positions.
  • Position cannot be at a custom hierarchy or sub-lot level.
Simple exchange published prices supported. No risk measures. Cannot even value many types of bonds. No swap valuation.
Attributed by static security terms (region, etc.) No time based attribution (as it doesn't maintain history), no attribution breakdown for FX or dividends (vs price movements). No factor based or granular attribution.
Very simple cash ladder works. No swap, margin, or cash buffer support. Settlement proceeds not calculated correctly.




Just ok. The PDF reports with charts in them are a poor excuse for visualization. No dynamic calculation, pattern discovery.
Front-office Interface
Fairly good as OMS automatically feeds intraday trades. Integrating with internal reference data is hard but doable. Cannot handle global trading (as described in last post).
Back-office Interface
OK interfaces to export allocations to standard accounting systems. No way to reconcile this IBOR with back-office.

Security Types:



Works ok for Equities. Issues in settlement (cash vs physical) for Futures and Options. Equity corporate actions not handled.
Can handle FX and basic bonds well. Very spotty functionality for Repos, Syndicated Loans, Converts, and ABS/MBS.
OTC Swaps, Structured
Not much to report. IR Swaps, Variance/Correlation swaps, CLO/CDO/CMO don't work in practice.

Bottom line:

OMS Vendor IBOR solutions provide a narrow set of IBOR capabilities for a limited set of asset classes, netting very limited value for sophisticated investment managers.


In conclusion:

Front-Office and Back-Office vendors have each tried to stretch their platforms to bridge the chasm separating their worlds and capture the market for IBOR solutions. However, it's hard to retrofit a FO system to handle BO data, accuracy, and update cycles; and, similarly it's hard to retrofit a BO system to handle FO needs.

For investment managers on the medium to high end of the investment complexity spectrum, you need an IBOR solution that has been constructed from the ground up that considers the requirements of both FO and BO realms and keeps them in sync. For a refreshing new approach take a look at Adroit’s offering.


What your IBOR salesman won’t tell you...

He won't tell you that "IBOR" (Investment Book of Record) is just a buzzword coined to help his offering through its midlife crisis — a fresh coat of paint for the old Studebaker on his lot. Also, that he has no firsthand experience with IBOR, or with the investment process in general (but hey, he has a winning personality). 

IBOR is a hot topic for the vendor-consultant industrial complex. Not to be left behind, the ‘industry experts’ have also lapped it up. I couldn’t tolerate the cacophony of bloviating pundits any longer, so I decided to improve the signal to noise ratio on the subject.

In this two-part blog series, I will debunk the hype around IBOR. In this first part, I will introduce a framework that helps customers understand their level of requirements and benchmark vendor offerings against those needs. I will do this by breaking IBOR down into its components: data, abilities, and asset-class considerations and stratify requirement levels within each component (high/medium/low). 

Practitioners (the buy side) are encouraged to use and extend this framework. Consultants however, are forbidden from plagiarizing or bastardizing it.  

What is IBOR and why do we need it

IBOR – Investment Book of Records can be roughly described as ‘real-time positions for the front office’ (FO). Accurate, timely positions are essential for the front-office to perform its functions. All good investment managers have been using IBOR concepts since the dawn of time.

This topic is gaining importance as investment managers are increasingly outsourcing their middle and back offices. As more back office functions are handled outside of a fund's walls, investment managers are finding that the outsourced providers don't offer the same functionality, accuracy, and timeliness that their internal back offices once did. With IBOR, they look to regain some of the functionality they used to enjoy with their in-house solutions of yore.

The Framework

In simplistic terms, FO needs positions, returns, and risk measures for analysis and portfolio management. The IBOR can be described along 3 dimensions -- namely datasets, functionality and asset specific needs. 

Not all front offices require the same degree of complexity from their IBOR. For that reason, I have segmented the datasets & functionality category into minimal, intermediate, and advanced usage patterns.  Factors that influence how complex an IBOR a FO requires include:

  • Strategy: are they a basic mutual fund or benchmark tracking portfolio, or are they a quantitative, global macro investor?
  • Trading Frequency: do they simply rebalance monthly, or do they trade with high frequency from multiple global trading desks?
  • Investment Types: do they trade just basic equities, or more complex instruments like OTC interest rate swaps, collateralized loan obligations, or asset backed securities?


The typical datasets needed for a functional IBOR are:





Accurate view of held and in-flight trades.

Note: Often the front-office positions are more granular than back-office.

Historical positions

Hypothetical trades, scenario analysis.



Greeks, key rate durations.

● Advanced risk metrics: VaR, position covariance (both historical and forward-looking).

● Scenario analysis: curve shocking, event simulation.


Attribute daily, MTD, QTD, YTD, inception to date risk, returns to

● Book/ sub-book, strategy

● Static security terms (Industry, geography, ratings etc.).

● FX, dividends, interest accrual and price breakdown

● Attribute at sub-taxlot level

● Attribute based on dynamic properties of security (like OAS Duration buckets, correlations to other assets, conceptual markets)

Attribute to ‘factors’ (for example using MSCI Barra models).


Cash ladder for each currency that accounts for

● Settlement proceeds for open trades.

● Dividends, accrued interest.

● Financially settling futures, options.

● Swap fixed and floating leg cashflows.

● Initial and variation margins on OTC trades.

● Handle REPO roll, inception, maturity

Cash buffers; projected cashflows based on probabalistic factors.


The features that use the datasets:





● Allow for easy slice and dice of data. Linked charts and tables (like a standard BI tool)

● All data is available to programs, Excel.

● Dynamic calculated KPIs (like Sharpe Ratio, betas to markets) for selected slice(s) of data.

● Views that can be used for investor reporting.

Discover hidden patterns in data (correlations, regressions)

Front-office Interface

Effortlessly consume

● In-flight, historic trades

● State change on orders, corrections on trades.

● Market Data, Security master


● Internal reference data

● Internal valuations

Handle global trading. For example, loading tomorrow’s Asian market targets while US is still trading the current day.

Keeping positions consistent across day-roll, corporate actions while ADRs are actively trading requires pretty intelligent systems.

Back-office interface

Reconcile position, trades, cash with:

● Outsourced back office

● Internal Accounting system

● Act as data hub for back-office (export allocations, cancel/corrects)

● Data enrichments for back-office (commissions, fees, margins etc.)

● Intelligent reconciliation, auto correction of data issues

● Data quality metrics


We have categorized assets based on the typical complexity in position management and valuation.





Equities, ETF, Indexes

Futures, Options


FX Spot/Fwd/SWAP, Govt/Corp Bonds

Repos, Syndicated Loans, IL bonds, Emerging Mkt Debt (sinking, capitalizing)

Converts, ABS/MBS

OTC Swaps/Structured

Total Return Swap, CFD


Interest Rate Swaps, Variance/Correl Swaps, CDOs, CLOs

In the next post we’ll examine the following options using the above framework:

  • In-house IBOR
  • Vendor IBOR based on an Accounting System
  • Vendor IBOR based on an OMS