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The Secret to Superior Portfolio Management and Hedging

In a world where markets — and risk — move at a rapid pace, traditional risk measurement methods are no longer cutting it. Commonly used finite difference methods, otherwise known as “bumping,” provide a slow and very limited view of risk. That is why over the last several years many leading quant teams have implemented Algorithmic Differentiation (AD), a technique that has enabled them to accelerate risk calculations by up to 1000x.

So, you may be wondering, what is AD and where did it come from? Well, it is a mathematical technique that helps firms rapidly solve complex pricing and analytics problems. While AD has only been popularized in the Finance space recently, for several decades it has been successfully applied to a variety of fields including Oceanography, Physics, Geology, Meteorology, Engineering, and many others. The fact is that today most of the industry is (or should be) moving to AD. Here are three key reasons why:

1. AD Enables Pre-Trade Risk Management

With the increased speed and accuracy that AD affords in calculating greeks and sensitivities, managing exposure is no longer an overnight activity, but a pre-trade one. For teams with complex multi-asset, multi-currency portfolios that need to bump, corners are often cut for overnight runs to reduce run-time, including not bumping every quote or bumping curves altogether with a parallel shift, twist, or other aggregate bump.

If firms cannot afford to bump every quote, they are left making the potentially risky decision of what to bump. From a risk management perspective, this is like trying to navigate a dark and dangerous landscape with a small flashlight. With AD, you don’t have to determine the quote for which you want to calculate portfolio sensitivity. Everything is clear and visible. Essentially, you trade in your flashlight for a floodlight, yielding a complete view of the risk landscape. Sensitivities to every relevant quote – including intermediate ones – are available for a fraction of the cost.

2. AD Enables Better Hedging  

AD is a highly beneficial tool for improved hedging, as it helps you re-project risk to form an alternative view of your exposure. With risk re-projection, you can transform sensitivities from one set of instruments into equivalent sensitivities for another set of instruments.

Imagine you’re managing a long-only fund and you want to control your rate exposure, but your mandate prohibits the use of contingent liabilities like swaps. You can measure your rates exposure with a model built from the swap market (Libor, OIS, etc.), but what you really need is to calculate exposure with respect to the medium-term notes that you’re actually allowed to trade. By transforming your swap sensitivities to sensitivities to medium-term note yields, you can build an effective interest rate hedge.

3. AD Enables You to Seize Profitable Trading Opportunities

In a fast-paced market, you need the ability to move quickly on any and all profitable opportunities. AD can help tremendously on this front, giving you access to real-time measurement of the sensitivities of your portfolio, trading book or fund. This information helps you to rapidly assess the impact of a new trade’s exposure on your portfolio, and therefore quickly take advantage of good trading opportunities.

At FINCAD, we have integrated our own powerful implementation of AD, which we call Universal Algorithm Differentiation (UAD)®, into our flagship F3 solution. UAD delivers unrivaled calculation speed for greeks, hedge factors, DV01, marginal xVA, and other sensitivities for any model, trade or portfolio, and method of valuation. What all this means is that you will be empowered to proactively hedge and manage exposures—resulting in more robust market risk management, better deployment of capital and increased profit potential. 

Learn more about the many benefits of AD by downloading our educational eBook: The Case for Algorithmic Differentiation

About the author

Russell Goyder PhD

Director of Quantitative Research and Development, FINCAD

Russell Goyder, PhD, is the Director of Quantitative Research and Development at FINCAD. Before joining FINCAD’s quant team in 2006, he worked as a consultant at The MathWorks, solving a wide range of problems in various industries, particularly in the financial industry. In his current role, Russell manages FINCAD’s quant team and oversees the delivery of analytics functionality in FINCAD’s products, from initial research to the deployment of production code. Russell holds a PhD in Physics from the University of Cambridge.


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