PARTNER CONTENT
By Duncan Coutts
Principal, Axioma Client Experience, SimCorp
When you think about all the available technology out there for hedge funds – execution algos, AI and the like – factor risk models are probably not where your mind goes. But hear me out: Perhaps you’re a portfolio manager who wants to focus more energy on strategic research or you’re trying to hedge or neutralize risk. Below, I outline four different use cases – ways we’ve seen our hedge fund clients use the humble risk model.
1: For accurate decision-support that reflects a non-traditional market perspective
I often speak with hedge fund managers who extract value because they perceive the market landscape in specific ways. It’s generally accepted that each country or region has some specificity but this principle extends to any definition of the ‘market’ one selects. For example, sectors and themes are equally valid ways to assign commonality for a group of assets. From these alternative perspectives, the definition of ‘average exposure’ (and the dispersion) for characteristics such as size, momentum or value, and their returns and volatilities, can be markedly different from the baseline given by a standard risk factor model.
This means for a new risk number and decomposition – which are statistically more accurate for portfolios investing in the theme of interest – the specific investor can use the gap analysis or ‘risk spread’ versus a standard risk factor model to identify opportunities, corroborate decision-making, and enable fairer comparisons of portfolios and assets from distinct categories. Importantly for managers, global coverage can be retained, and it is just the focus of estimation that changes.
Hedge funds can build their own unique risk models using a tool like Axioma Risk Model Machine to complement their off-the shelf ones. By covering alternate perspectives, this can prevent otherwise hidden risk-taking, help refine the research process and ensure a more specific allocation of risk to the premia that the hedge fund manager believes will outperform.
2: For advising portfolio managers more proactively and to increase collaboration
Traditionally, risk management has been reactive, flagging risk limit breaches and non-compliant trades. But I’ve seen that quantitative risk managers have a collaborative relationship with portfolio managers, providing them with detailed guidance that adds support to the good decisions as well as preventing problems.
This form of proactive risk management occurs when both portfolio managers and risk mangers are singing from the same hymn sheet. Regardless of asset class type – from equities, fixed income or somewhere in between – the Axioma risk models enable a dialogue between risk managers and portfolio managers in which the common factors of the model act as a framework for monitoring, understanding, and discussing how the market is (or could) influence the portfolio.
Portfolio managers like it because the conclusions of a factor analysis are immediate and actionable and can be interpreted and sliced at all levels of a portfolio from single name assets, upward. The risk managers like it because the factors facilitate more than risk measurement; it is also performance attribution, scenario generation and example hedge construction, all of which complement their guidance. Having a ‘nowcast’ of the portfolio in full context of the market it exists within is a win-win for all concerned. Decision making becomes more agile, and with a robust and proactive oversight, the portfolio manager can focus more energy on strategic research.
3: For a new perspective on the risk and return of fixed income strategies
Fixed income data has always been notoriously difficult to work with and has stumped many who have tried building curves and fixed income risk models for their credit strategies, in-house. Traditionally, vendors relied on ‘granular models’ where the issuer spread returns alone drive a vast covariance matrix. The issue with this is that it is unwieldy and assumes a lot about the accuracy of individual issuer correlations. In contrast, the equity world has long since imposed systematic structure (i.e., factors) onto asset returns. This helps to identify statistically testable commonalities (of countries, industries, styles) and isolate idiosyncratic risk and return and at the same time reduce risk to a manageable dimension and gain more flexibility over the model’s reactivity.
Some hedge funds saw the step-change potential but knew the quality of curves required to support such a model, together with the style factor development would be years of painstaking effort – work that the research team for Axioma solutions had already undertaken. We have been able to help existing and new hedge fund clients when their existing fixed income models lacked the detail or flexibility to decompose credit risk into significant factors like market, sectors, quality, and fundamentally (no pun intended!) revitalize their perspective on how the market is influencing their strategies.
4: For hedging and neutralizing risk within a multi-asset portfolio
With the introduction of fewer, but more intuitive and explanatory factors to explain the total market, targeted exposure and risk hedging becomes feasible. The combination of a suitable model and optimization strategy enable this kind of decision process.
Dealing with cross-asset risks is a great example and we often speak with managers seeking to remove risks that confound their investment theses. For example, if one wants equity exposure to quality banking and energy earnings but without the underlying interest rate and oil price volatility impacts on these sectors, it would be natural to ask how many (and which) bond and oil futures are needed to negate the broader macroeconomic environment implicit in the equity book. Managers do not want to take directional bets on oil and interest rates because that may not be their area of expertise.
The cross-asset risk factor model answers the question of how interest rates and oil are correlated to the factors that drive the risk in these companies. By using a multi-asset class risk model in conjunction with Axioma Portfolio Optimizer, the manager can effectively reduce the risk of their portfolios to oil and interest rates.
Learn more about the solutions we’ve built for hedge funds.
Duncan Coutts, Principal, Axioma Client Experience, SimCorp – Duncan is a product specialist, helping portfolio and risk managers extract the most value from the Axioma suite of portfolio analytics solutions. Prior to joining Axioma (now part of SimCorp) in 2015, Duncan has had a career spanning biomedical science, accountancy, and travel. Duncan bridges the gap between the R&D effort, and the real-world use cases that demand Axioma tools. He holds an MSci in Chemistry and Medicinal Chemistry from the University of Glasgow, an MSc in Financial Mathematics from Kings College London and is CIMA chartered.
