Beware liquidity risk

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Sean Ryan, Senior Analyst, Research, at MPI (Markov Processes International) asks if there is more danger in High Yield Bond Funds, warning that investors reaping rewards from high yield returns should beware the dangers of liquidity risk.

The high yield bond market is rising strongly– up 12 per cent year-to-date according to the Barclays US High Yield Index. Furthermore, high yield has outperformed investment grade corporate debt by 13 per cent from February 11 through July 29, 2016 again according to Barclays. With approximately USD26 billion of new debt issued in the past four weeks, high yield corporate debt has attracted lots of buyers and sellers. 
 
In the winter of 2015, an almost unheard of situation happened. A mutual fund, which is normally required to guarantee daily liquidity, blocked its clients from withdrawing money. The Third Ave Focused Credit Fund (TFCIX), citing losses and a lack of liquidity in the high yield bond market, put some of its assets into a trust to be sold over time.

For mutual funds that invest in illiquid assets, such as high yield bond funds, liquidity is a major concern. If too many investors try to cash out at the same time, to provide liquidity, a fund may be forced to sell its holdings at fire sale prices – if it can find a buyer at all. Fitch has forecast that USD90 billion of high yield debt could default by year end, while S&P Global has forecast that the U.S. speculative grade default rate will climb to 5.3 per cent by the end of 2017’s first quarter, up from 3.8 per cent 12 months earlier. Furthermore, high yield debt’s recovery rate – or portion of principal and interest recovered in bankruptcy – was down to 34 per cent in 2015, well below its historical average of 46 per cent according to Lehmann Livian Fridson Advisors. More defaults with less recovered could set off panic bells for investors in high yield funds. Last September, the SEC even proposed permitting funds to adjust a fund’s redemption price to pass on the costs of immediate liquidity to redeeming shareholders. High yield bond investors could face a rocky ride ahead. If a fund is not able to sell investments quickly without taking a large loss, the ability of the fund to provide daily liquidity is compromised.

Measuring the liquidity of a fixed income portfolio is a challenging exercise.  One must be aware of the size and date of issuance of a bond. Large new issues tend to be very liquid in contrast to the often scant liquidity of smaller, older issues. Instrument type matters greatly; liquidity for structured products and credit default swaps has plummeted since the financial crisis. And while on the one hand, stricter capital rules and increased risk aversion have decreased investors’ appetite for corporate and high yield bonds; low interest rates globally have had the opposite effect. Measuring liquidity risk marries this qualitative information with quantitative data that are not easy-to-obtain nor evaluate such as bid-ask spreads, transaction slippage, and dealer inventories. Measuring liquidity for a particular fund should also consider a manager’s historical trade sizes, data that are usually not publicly available.

Looking at a fund’s return history can reveal much about its liquidity profile. Low liquidity or less frequent trading activity, means that actively traded prices are recorded at sparse time intervals. When an illiquid bond’s NAV is recorded, its option adjusted spread (OAS)3 is based on either an actively traded price or a dealer quote. If neither are available for the day in question, its OAS is typically carried or extrapolated forward, while the bond’s price may or may not reflect changes due to changes in time to maturity, interest rates or other pricing factors. This induces auto-correlation in the return time series of that bond, and as a result, the entire portfolio. Hence, the level of auto-correlation is related to the level of liquidity in a fund.

It is important here to clarify that low liquidity is not the sole source of auto-correlation, although the literature has shown it to be the largest factor in the auto-correlation of hedge fund returns. Auto-correlated returns can also appear due to difficulties in  pricing over the counter securities, window dressing, performance smoothing, marking to model, non-synchronous trading, fraudulent accounting, momentum, unexploited market opportunities, time varying expected returns and time varying leverage.

Assessing fund liquidity using the Durbin-Watson Statistic
 
Fund investors can use the Durbin-Watson statistic to measure auto-correlation.1,2 Using the Durbin-Watson statistic to assess investment liquidity is both valuable and simple to calculate and can help investors assess if any of their funds potentially stand out as being highly illiquid.

With the asset class achieving such high returns, from a portfolio diversification aspect, allocation to high yield makes sense. However, with the liquidity in the market at its most fragile, investors need to understand the investment that their managers make into illiquid securities. By using the Durbin-Watson Statistic, MPI believes that investors in high yield bond funds can avoid significant losses and lengthy lock ups. 

1. MPI measures the unconditional auto-correlation. Typically, managers will only smooth out the negative returns of their portfolio performance which means that auto-correlation will mostly exist when past returns are negative. Since MPI focuses on the illiquid nature of a fund, not on a manager’s intent, we only report the unconditional auto-correlation
2. MPI realizes that the Durbin Watson statistic is biased when lagged dependent variables are part of the regression, as is the case here. A better test would be to calculate the beta of the regression between a fund and its lag and check if that beta is positive and significant via the t statistic. When calculating both the Durbin Watson and t statistic on the high yield funds of this blog MPI found both tests to be quite consistent to each other, and both highlighted the Third Avenue Fund as an extreme outlier.
3. OAS, which stands for option adjusted spread, is a measure of the credit riskiness of a bond and moves inversely to its price
 
 

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