What challenges are fund managers facing?

Written by Fares Boukrouh, Manager Risk Advisory

Why capital market regulators require a stress testing approach for Money Market Funds?

Before the last financial crisis, Money Market Funds were considered a quasi-risk free investment. This could have been due to the short duration of the assets held by such funds and the higher credit quality of the issuers covered by the Asset Managers in these markets. However, the 2007 credit crunch crisis reflected the fact that a liquidity shortage could cause a damaging impact on the Net Asset Values and could result in considerable losses, especially for less informed investors (retail investors). From a systemic perspective, a market liquidity crisis could be followed by credit spreads widening as some counterparties default and shocks on other macroeconomic variables could be observed at the same time.

Stress testing is a well-established practice in the Banking and Insurance Sectors and is used in order to assess the resilience of the financial system. The objective of the regulators is to expand this analysis to money market managers, hence helping to handle systemic risks.

What are the considered risk factors?

The asset allocation in Money Market Funds is usually chosen to reflect the views on short-term interest rates and the sectorial analysis, as is usually the case in a top down asset allocation process. The resulting exposure is a portfolio which is sensitive to short-term rates (sensitivity is measured by WAM² and WAL³), credit risk of the issuer and the cyclical sectors.

In addition to the volatility of theses economic variables, portfolio managers could face rapid changes in client behavior, especially in stressed market conditions.

To address these risks, the MMF regulation lists the risk factors that need to be distressed in a stressed market conditions:

• Credit Risk: Credit spreads widening, default from major counterparties and sectors, 
• Interest rate Risk: Shifts in interest rate levels,
• Currency risk: Currency rate volatility, 
• Liquidity risk on the assets side: Deterioration of market liquidity conditions (Bid/ask spreads widening and market depth reduction)⁴,
• Redemption risk on the liabilities side: Acceleration of redemption levels.

The objective of the stress test exercise is to assess the impact of a severe (but plausible) variation in the level of each factor on the Net Asset Values. It is computed for each risk factor on a standalone basis and in a combined scenario situation of adverse conditions. The ability of a fund to generate sufficient cash from selling-off assets to meet a higher level of redemption is also under the scope of analysis.

How to calibrate a stress on risk factors?

The shocks that need to be applied to the risk factors can be calibrated using quantitative methods and scenario analysis.

Retrospective analysis is the most straightforward approach among the quantitative methods. It is based on statistical analysis from a selected historical series of data and the detection of extreme values in variations of risk factors over a specified horizon. While the theory behind this approach is well established, the practical implementation raises some issues, such as the definition of the stressed period and the availability⁵ of data within these periods of stress.

The calibration of shocks could also be done based on prospective analysis through the simulation of future values that give a set of possible variations of risk factor levels. Based on a specified confidence interval and a fixed time horizon (1 week for example), the level of the shock to consider for stress testing is defined from the set of potential future variations of the risk factors.

Several issues need to be addressed when performing such quantitative analysis. The most important are:

• Linking risk factors to fair value variations (full repricing vs sensitivities approximation),
• Dealing with extreme values, 
• Fat tails of statistical distributions observed in stressed market conditions,
• Stationarity of historical series of data and their predictive power, 
• Parameter calibration before simulation running (implied volatilities, risk neutral vs real world probabilities …).

Scenario analysis can help to overcome some weaknesses from the statistical methods, especially the stationarity hypothesis and extreme unexpected shocks.

Finally, expert judgment can also be used to improve the results of these statistical methods and help to integrate the portfolio managers’ views.

How to model a stress on redemptions?

Redemptions from open-ended funds can be classified into normal and stressed redemptions. Normal redemptions occur under normal market conditions and are usually of small amounts relative to the assets being managed. They are generally offset by underwritings. During the periods of stress in capital markets, levels of redemptions accelerate and the fund manager would sell-off liquid and illiquid assets to meet cash withdrawals, which could be very costly for the shareholders.

Understanding the fund’s liabilities is the first step in modelling redemptions. This includes the determination of the type of investors (retail vs wholesale), their behavior (stable vs volatile) and the concentration risk (% of each shareholder). A second step is the construction of the historical data that will be used to calibrate statistical laws such as power and exponential laws. Finally, the Monte Carlo simulation method could be implemented to generate different redemption levels under normal and/or stressed market conditions over a specified horizon (for instance one-week period for a money market fund is often cited).

If the historical data available for calibration is gathered from a non-stressed period, the scenario to consider for the stress testing exercise must be an extreme value of the set of simulated redemptions; and underweighting might be ignored. In addition, hypothetical scenarios can help to understand the impact of the acceleration of the redemption on the Net Asset Values under normal and stressed market conditions.

Conclusion

The implementation of such regulation is challenging for most of the Asset Managers:

• Combined scenarios and correlation between risk factors
• Availability and cost of market data for liquidity stressed scenario calibration
• Calculation capacity and external tools
• Meaningful results to help assess and adjust the risk profile