In addition to portfolio and market risk managers, the comprehensive library of credit spread and yield curve data can be used across your organization by trading, research, valuation, counterparty credit and treasury teams. Derived from a proprietary methodology for fitting full term structure issuer spread level and return curves, we outline a few use cases for the standalone content available as a standalone, flat file format.
Market Risk Calibration & Limit Checking
Who Benefits?
Market Risk, Counterparty Credit, Internal Model Teams
The Need
Whether you are at a bank looking at the market risk of your own portfolio or at an asset manager responsible for client assets, you will need risk models and tools to understand and define risk limits.
One of the data sets powering the models should be a set of credit spread curve data, which needs to be clean and have a consistent entity master.
The Challenge
CDS curves are often used to build these models which can lead to inaccurate risk measurement. CDS liquidity and depth of trading across names have dropped significantly which restricts the use of CDS as a core driver for corporate credit risk and pricing. In addition many issuers do not have traded CDS and the CDS-cash bond basis leads to different spread dynamics which can affect risk estimates.
Even if using credit spread curves data, there is still the issue of artificial volatility. For example, rating spread curves can exhibit spurious volatility if rating migrations are not treated appropriately. Rating transitions of large issuers can lead to large jumps in the spreads of adjacent ratings, which do not actually reflect true market volatility.
The Solution
Axioma Credit Spread Curves deploy a sophisticated curve building methodology that addresses spurious volatility in spread curves, and provides coverage across all issuers.
THE QONTIGO ADVANTAGE
Surface construction
Our curves deal with the ratings migration problem inherent in most rules-based construction methodologies
Sophisticated methodology
The Level Reverting Noise Reduction (LRNR) algorithm is applied to smooth the time series history of curves and reduce noise from poor or inconsistent pricing, bad liquidity and general market noise
Cost Reduction
Many institutions need to build curves for multiple uses. Some build in code, some in Excel or other tools. Qontigo has built a scalable curve-building framework from the ground-up leveraging modern cloud technology, complete with data normalization and quality assurance.
Reduce operational risk from bespoke solutions, support overhead and headcount by consuming a core data requirement and not reinventing the wheel.
Front Office Signal Tool
Who Benefits?
Algo Traders, Research, Trading Oversight, Risk
The use of spreads to imply ratings relative to peer-derived surfaces can also provide flags to challenge on potential future downgrades or to provide a challenge and query the strength of conviction in a position.
The Need
You are a trader focused on getting ahead of the market by being the first to uncover potential arbitrage or other trading opportunities. You will be keeping an eye on numerous trading signal tools which monitor your watchlist in order to make buy/hold/sell decisions. Access to clear intelligence will give you the edge you need.
The Challenge
Most signal generators and relative value tools only measure current spreads versus historical levels of the same instrument or the relative difference to another reference – like a government or credit index. That means they are not getting the full picture of the bond.
The Solution
Axioma Credit Spread Curves allow you to view not only the implied rating of a company but also to compare that relative value versus an automated, relevant and flexible set of references. Setting up this trading signal means you can immediately see when there is a bond, name or sector move – and what those moves are.
THE QONTIGO ADVANTAGE
Accurate data to ensure the clearest signal
The Level Reverting Noise Reduction (LRNR) algorithm
Smooth the time series history of curves and reduce noise from poor or inconsistent pricing, bad liquidity and general market noise
Axioma Risk Entity framework
Legal entities within the corporate hierarchy are grouped together to define issuers with different credit risk and return profiles
Peer influence
Shape of the spread term structures is informed by a credit surface built from comparable issuers
Outlier removal
Automatic detection and downweighting of outlying instruments plus customizable signal generation
In-House Limit Modelling
Who Benefits?
Risk (Credit Risk) Teams
The Need
As a bank, you need to evaluate your own entity credit risk as explained by the market and to do this, you turn to building out curves. Ultimately, banks need access to a clean set of curves driven by a methodology that accurately addresses risk.
The Challenge
Building quality curves is a complex undertaking with many looking to an off-the shelf curves solution to provide answers. However, that approach raises its own challenges as much of the curve data available from vendors is still fragmented and inconsistent.
The Solution
A set of robust curves with a consistent entity master focused specifically on risk. The Axioma Credit Spread Curves enable more accurate detection of increasing risk concentrations.
THE QONTIGO ADVANTAGE
Risk-focused entity master
Takes into consideration actual of country of risk, ultimate parent company as well as country of issue in order help you truly understand risk concentrations
Meaningful entity mapping
Instead of grouping together companies by similar ratings or legal entities, we ensure only correlated entities are. Legal entities within the corporate hierarchy are grouped together to define issuers and provide consistency across asset classes and within complex issuer hierarchies
Joint-estimation technique
Aligns the term structure with liquid market comparables creating a meaningful and robust shape
Approximate Valuation of Illiquid, Unpriced & Borrowed Assets
Who Benefits?
Valuations; Pricing; Trading Operations; Treasury
The Need
If you are part of a valuation team, you need to be able to price assets in your own portfolios at market value at any given time. Inclusion of instruments such as CDS, structured products and bonds – even when on-exchange – may provide challenges if your team is looking to find more than just a theoretical price for your assets. This is particularly important for if you’re a pension fund or other asset owner and need to get accurate pricing in order to build meaningful discount curves. What you need is either a Mark-to-Market model or a precise estimate driven by curves.
The Challenge
For a lot of credit-related financial markets, like loans or private placements, there is little public pricing data available for these instruments. Where they do exist, the data is either incomplete or unreliable.
The Solution
The bond markets can provide a proxy for pricing, especially where allowance for illiquidity premia is applied. By using Axioma Credit Spread Curves, you get access to robust credit curves underpinned by an innovative methodology to produce accurate results.
THE QONTIGO ADVANTAGE
More granular curves
Approximately 300 clusters fitted with a complete surface of 21 ratings
Increased cost efficiencies
Reduce time, cost and human resource with an off-the-shelf product which doesn’t require you to purchase additional data, hardware or to recode quirement and not reinventing the wheel.