by Arnab Banerjee, PhD
Portfolio and risk managers must be able to track exposures to a variety of systematic factors that can impact the risk and performance of their portfolios. In a more interconnected and dynamic world, many of those factors are macroeconomic, such as interest rates or commodity prices, not just traditional price or balance sheet factors. To help with this challenge, we recently released a macroeconomic factor library that provides asset-level time series sensitivities for global securities to fixed income, commodity, and break-even inflation factors.
Clients can use this data for many use cases:
- Portfolio managers can analyze sensitivities to macroeconomic factors for their portfolios. For example, they can find out if they are exposed to oil, gold, interest rate or inflation factors or hedge exposures to these macro factors.
- Risk managers can use this data as a building block to generate custom risk models by adding factors from the library.
- Quantitative managers can construct/back-test strategies tilted to factors in the library.
- Systematic equity managers can use asset-level macro sensitivities as an input in their alpha construction process.
Let’s look at a practical scenario where the factor library can be useful to portfolio and risk managers.
Effect of rising oil prices on US airline stocks
The primary drivers of cost for the airline industry are jet fuel and labor. With rising oil prices, there is a concern that airline securities may experience profit pressure and underperform the market on a relative basis. Some airlines companies such as Southwest buy hedging contracts to lock in fuel costs to reduce volatility in their earnings. Other airline companies such as American and United Continental don’t hedge oil prices, and they are more at risk when oil prices start to climb. Delta Airlines owns an oil refinery to manage its fuel costs. In order to cope with rising oil prices, airline companies try to pass on the higher cost to consumers by raising their fares over time (typically it takes about six months to fully transfer the cost).
A portfolio manager who holds a concentrated position in airlines stocks in her portfolio may want to understand and manage her exposure to oil prices. Using the Axioma Macroeconomic Factor Library, she will be able to find out how sensitive her portfolio is to oil prices. As shown in Figure 1, the average oil sensitivity of the airline industry is negative while that of the energy, oil and metals & mining industries are positive.
If she has a view on oil prices, she can position the portfolio to reflect those views. For example, if she thinks there is a possibility that oil prices may go up further, she may want to control exposure to rising oil prices at the portfolio level. This can be achieved in few different ways.
One simple approach could be an overlay hedging strategy that will hedge out oil prices above a certain oil price level while keeping the portfolio holdings the same. She would incur some hedging costs to implement this overlay.
Another option is to construct a portfolio by tweaking the original portfolio so it is neutral to changes in oil prices (this can be accomplished by creating a tracking portfolio using Axioma’s Medium-Horizon US Risk Model and constraining the oil sensitivity factor in the Axioma Portfolio Optimizer). By controlling the turnover of the portfolio, she can neutralize the effect of changes in oil prices without deviating too much from her original portfolio and keep her transaction costs low.
She can also create a custom risk model using Axioma’s Risk Model Machine by adding oil sensitivity as a custom factor in the model. One advantage of creating a custom model is the ability to estimate the risk/return trade-off for the oil sensitivity factor within a multivariate regression framework and the correlation of the oil sensitivity factor with other factors in the model. By following the same procedure as mentioned earlier, she can use this custom model to neutralize the effect of changes in oil prices. She can also conduct stress tests by simulating different oil shocks (scenarios) to understand how her portfolio would have performed before and after the hedge.
Would you like more insight into your unique portfolio management challenges? If so, let us know.
