Most buy and sell side institutions realize the importance of having a robust system for creating cool new models, gaining support for complex payoffs, and performing advanced scenario analysis—that is, the more complicated tasks involved in managing a trade portfolio.
However, over the years in my work with numerous financial firms, I have found that while institutions may initially be drawn to a solution because it is able to support the complexities of trading, a system that handles “the basics” well is just as important. Therefore, in today’s blog post, I’ll discuss a few different examples of, perhaps less exciting, but very essential functionality that leading derivatives firms are relying on in their investment businesses.
Example 1: Validating Curves
Curve building is an integral part of any derivatives business. It is critical that curves accurately reflect the nature of markets they are intended to describe. If they don’t, you’re probably losing opportunities by lagging the market, trading on bad information, misreporting P&L or misunderstanding risk in your book.
But equally important to constructing accurate curves is having the tools to validate existing ones. Such functionality will help you calibrate existing models, and in turn, proactively manage model risk, a task growing more important given the increasing complexity of firms’ financial transactions. Furthermore, accurate curves can result in competitive advantage. They enable you to avoid lag time of assessing multiple dealer quotes, produce independent prices that you can rely on, report the most accurate P&L and get a holistic view of risk across your organization.
Example 2: Pricing the Simple Stuff
A strong selling point of many risk and valuation platforms is their ability to price really exotic instruments. This functionality is certainly important to have, however, for most firms pricing the simpler, vanilla trades is also a key part of business. Specifically performing, what I think of, as basic actions such as modelling simple equity derivatives is important to a lot of firms.
Various assumptions go into the valuation of derivatives, e.g. the use of discrete/continuous dividends or the use of various discounting/accruing/repo curves. As opposed to continuous dividends (which are more analytically tractable), where you assume you will earn interest right away and continuously on your trade, with discrete dividends, you only assume dividends will happen at discrete points in time. I have found that many valuation platforms in the market today do not offer coverage for all combinations of these assumptions, so this is a point to keep in mind if you are in the process of evaluating solutions.
Example 3: Pricing Portfolios with Different Instrument Types
Recently we had one client from an advisory firm who expressed his satisfaction over the fact that our solution allows him to pass client portfolios comprised of assets and hedges through the systems as one cohesive portfolio. He explained that with prior systems in use, he was forced to separate the contents of diverse portfolios and submit them separately by instrument. This workflow proved inefficient and neglected to acknowledge the relationships that existed between the various trades. But now, the client can price and analyze portfolios with assets and hedges together in a single platform, where he can control the curves being valued against. From there, consolidated risk reports can be generated.
Additionally, for many firms the process of aggregating cash flow is difficult when systems do not allow for portfolios to be submitted as a whole. This is because performing an aggregation often requires information about the risk factors shared by multiple positions within a given portfolio.
Example 4: Asset/ Liability Modeling (ALM)
ALM functionality is particularly useful for firms like pension funds that have a steady stream of payments coming out of their fund over a certain period of time. To offset the outgoing payments, pensions will often purchase instruments like bonds or swaps in a cashflow matching exercise. This is where strong analytical support for ALM becomes necessary.
But many financial institutions are overall challenged by ALM decision-making. They struggle with managing a balance sheet comprised of inter-related assets and liabilities. And, beyond that, regulators are imposing their own limits on ALM. An example is the liquidity coverage ratio (LCR) being phased in by the Basel Committee on Banking Supervision, which is requiring banks to maintain a stock of high-quality liquid assets (HQLA) sufficient for 30 calendar days under stressed scenarios.
To optimize ALM matching and hedging decisions, firms should look for a risk and valuation platform that offers the abilities to perform cash flow projections, flexible modeling, on-demand sensitivity analysis and agile scenario analysis.
The promising news is that there are sophisticated solutions available in the marketplace today that are equally as good at offering you coverage for your more complicated risk and valuation challenges as they are at providing basic, but essential functionality for items like pricing plain, vanilla instruments or validating curves.
For information on additional advantages you can gain from using a modern enterprise valuation and risk platform, please view our on-demand webinar, Valuation and Risk Best Practices.
