Going Beyond Markowitz – One-Size-Fits-All is Not An Option for Fixed Income Portfolio Optimization

Why Flexibility is Key to Fixed Income Portfolio Optimization

In the current market, pressure on management fees and investor demand for returns are fuelling the growth of both passive and active fixed income strategies. However, fixed income investors are keenly aware of the twin specter of central banks phasing out quantitative easing while raising interest rates.

In response, many fixed income managers are turning to portfolio optimization tools to enhance their products and prepare for an increasingly challenging market. In doing so, they find that fixed income instruments pose unique challenges for portfolio optimization.  

The portfolio optimization techniques invented by Markowitz, which have become one of the cornerstones of modern portfolio management, were primarily developed for equities. Yet it’s clear that fixed income portfolios have characteristics that require a different optimization framework:

  • While trading volume is an adequate liquidity measure for equities, different liquidity scores need to be constructed for bonds - a traded bond is not necessary a liquid bond.
  • The larger minimum holding and trading sizes associated with fixed income securities contrasts with the small holdings and trades possible in equities, creating a constraint that is problematic for some optimizers.

These fundamental differences between equities and fixed income are often sand in the gears of portfolio optimizers, which were constructed with a different asset class in mind. Portfolio managers have already recognized this lesson in the field of risk management, and use specific fixed income risk models.

But knowing your fixed income risk is not enough: to be competitive, in either active or passive strategies, portfolios need to be optimized. After all, even in passive investing, tracking error needs to be reduced, made more challenging by the fact that fixed income indices may contain difficult-to-replicate illiquid securities.

Three key reasons why fund managers choose to combine portfolio optimization techniques with their fixed income risk model are:

  • REPLICATION of an index or fund is a useful application of optimization.
  • HEDGING, for example the hedging of FX risk from bonds that pay coupons in foreign currencies.
  • CUSTOM INDICES can be constructed using an optimizer. This is important for sell-side firms, as they receive order flows from clients and need to build a basket of bonds based on client preferences, such as sectors or credit quality.

Fixed income optimization is an intricate task that can be demanding, especially without the right tools. In addition to the reason above, there are additional reasons portfolio managers need to go beyond traditional Markowitz mean-variance optimization (MVO) and work with a flexible fixed income optimizer. They may want to:

  • Tilt towards desired exposures
  • Control (unwanted) exposures
  • Rebalance/construct portfolios
  • Quantify deviation from a benchmark
  • Implement manager strategies
  • Add hedging and overlay strategies
  • Control for liquidity and transaction costs

The need for flexibility in fixed income portfolio optimization cannot be overestimated: Given that fixed income investing has so many aspects to take into account, portfolio managers need the ability to set their own custom-defined parameters and constraints. One-size-fits-all won’t do when the aim is to achieve fixed income investment objectives - be they on a benchmark basis or an absolute one.

To learn more on this topic, read our Axioma In-Practice article, titled, “Fixed-Income Portfolio Optimization.”

Robert Stamicar

Before joining Axioma about four years ago, Robert worked as head of risk at Lighthouse Partners, a fund of hedge funds that specializes in managed account investments. Most recently, Robert has focused on applied research projects using Axioma Risk. 

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