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How to Manage Diverse Investments with Unified Analytics
By Jonathan Rosen PhD | January 3, 2018

With financial investments, there are two ends of the spectrum—tangible assets and arbitrary contractual claims. Various well-known products live on the spectrum between them, and over its life-cycle, a trade can move from one end to the other.

Very different modeling and analytics are needed for these two worlds, and modern regulatory requirements add additional complexity and the need to handle portfolio-level analytics of combined asset+derivative portfolios over future time horizons. To be well positioned to handle this complexity and span the spectrum to produce the needed results requires powerful enterprise valuation and risk technology.

Financial investments, including everything from the very simple trades to exotics, are on a spectrum in terms of pricing complexity. At one end are the relatively simple investments, for which we can easily look up the price on any given day from the market. Two examples are liquidly traded bonds or spot-starting market swaps. For these relatively simple situations, pricing does not involve any deeper theoretical aspects since prices can be directly observed.

In contrast, at the other end of the spectrum we find derivatives, from vanilla to even exotics and OTC, which require complex analytics and mathematics involved in pricing from theoretical considerations of risk-neutral agency and absence of arbitrage. In these cases, we seek to find an equivalent trading strategy to replicate the payoff of the derivative, and use this as the basis of pricing from theoretical grounds.

If pricing individual instruments were the whole story, then perhaps we could reconcile the opposite ends of the spectrum, by simply separating the market-driven instruments from those to be priced on theoretical grounds and treating them individually. However the current outlook is more complex - in particular the portfolio analytics requirements are much more complex, since it is often required to assess the aggregate risk on various time horizons of investments for reporting and regulatory requirements. A few examples are:

  1. CVA – Here we factor in the market’s view of counterparty credit risk exposure into our risk-neutral portfolio valuation.
  2. Historical VaR – This is a standard risk measure for current portfolio holdings based on historical observations of market risk factors.
  3. PFE – Here we apply scenarios to simulate the market conditions on future time horizons. At each future time we analyze the returns distribution to obtain exposure estimated at given confidence levels.

What these three pricing and risk measures have in common is that they are non-linear portfolio-level calculations, which means there is no way to split them up across different instruments in the spectrum and combine the results later. Instead, we need a way to treat the entire portfolio of instruments in a consistent way. This might not itself be terribly onerous either, as long as the analytics library is capable of pricing all instruments appropriately in the portfolio and combining the results.

However, an important complexity factor is a need to account for the entire trade-lifecycle, past, present, and future within the portfolio. This needs to maintain connection to trade management systems and external book of record, while also dynamically instructing the analytics library how to describe the trade state to the pricing machinery. At this stage, we are touching many more points in the technology stack, which presents new challenges in vertical integration of the horizontal layers of quantitative analytics and applications. This demonstrates that, in order to do this correctly across a sizeable organization, it will very quickly require a more tightly connected technology stack, and a software design concept of the vertically integrated enterprise solution in order to build out successfully.

To finish let’s return to the product complexity spectrum, and consider if in our portfolio we hold underlying securities simultaneously with future obligations and rights to those securities - now the situation is suddenly becoming very complex. The need to consistently price trades that evolve into securities or other securities, spanning every aspect of market-calibrated pricing from simple supply and demand to purely theoretically derived fair value - in this new world it becomes an expert challenge to the end-users to figure out how to price everything consistently and coherently, unless they already have a fully coherent portfolio pricing and risk technology that is vertically integrated into an enterprise solution.

For more information on managing the pricing complexity and other challenges that accompany multi-asset investing, check out our eBook: The Buy-Side Move to Multi-Asset Trading

About the author
Jonathan Rosen PhD
Jonathan Rosen PhD
Product Manager Quantitative Analytics | FINCAD

Jonathan oversees analytics development for FINCAD’s products and solutions. Before joining FINCAD's product management team in 2016, he worked as a senior quant solving a wide range of problems in the financial tech industry. Jonathan holds a PhD in Physics from the University of British Columbia.