Liquidity Driving Hedge Fund Returns
Submitted by: Andy Deutsch, Risk Product Development
Liquidity has been cited as a one of the main reasons for the systemic breakdown of the financial markets. Consequently, more investors are now looking into liquidity risk in addition to more typical risk measures. Liquidity risk, however, is not easily quantified.
Financial instruments that are not liquid have static pricing over long periods. Funds with a large percentage of their assets invested in these instruments can be categorized as illiquid, and tend to demonstrate relatively smooth monthly returns. We decided take a look at all the funds in the HFR universe, and categorized them as either liquid or illiquid by calculating their autocorrelation and thereby outing a value on how smooth their returns are. We calculated this value based upon 6 lags of autocorrelation and scaled so as to fit a chi square distribution. We then categorized a fund as illiquid if the probability that value exceeds the critical value associated with 5%. We considered a fund very illiquid if the probability that value exceeded the critical value associated with 1%.
We performed the analysis at two different times on the funds in the HFR database that reported returns for at least 24 months. The first analysis was performed in May 2007, with data through April 2007. The more recent analysis has data through January 2009. The initial analysis had a breakdown of 60/40 of liquid to illiquid funds. Now we're seeing a 50/50 split. On the highly illiquid front the breakdown was 76/24 liquid to highly illiquid, now it is 62/38 in favor of liquid funds. The analysis shows that funds are trending towards being more illiquid. This could be due to the fact that funds are holding their illiquid instruments and trying to realize the losses associated with them.
On a return analysis level, in the first study the illiquid funds had better 12 month returns than the liquid funds with the illiquid funds retuning 9.5% versus 7.8 for the more liquid funds. In the weaker market of the last year, the returns for funds on the whole have gone down tremendously and even more so for illiquid funds. The comparative returns for 12 month returns for illiquid vs. liquid funds is now roughly -22% to -7%. The conventional wisdom is that a liquidity premium exists and the return on illiquid instruments should be more than that of liquid instruments. In down markets, however, the opposite appears to be true.
To view the distribution of funds chart and returns by liquidity bucket chart, please visit here.
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Wednesday, January 7, 2009 |
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Value-at-Risk as an Altimeter
Submitted by: Gilles Zumbach, Senior Researcher, Risk Management
In a recent New York Times Magazine cover story, the efficacy of VaR, or Value-at-Risk, was debated. The reference by one risk manager to VaR as an altimeter was perfect and deserves further thought.
VaR as an Altimeter
An altimeter is a reliable but imperfect instrument: essentially it just measures the air's pressure. You can calibrate on the ground before take-off, land five hours later, and find the instrument 100 meters off while on the runway (I have seen it many times). A possible remedy would be just to throw away all altimeters as junk. In practice, every pilot "just looks outside," and, when needed, correct the possible discrepancy between the altimeter and the actual altitude. The point is not to believe blindly the instrument. And, despite the altimeter limitation, every plane in the world includes one at the center of the dashboard as it gives (albeit imperfectly) very important information during a flight.
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Friday, August 22, 2008 |
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Did VaR Forecast the U.S. Subprime Crisis?
Submitted by: Alan Laubsch, RiskMetrics Labs Asia, and Ron Papanek, Market Strategist
That depends on what VaR model was used. Most banks' models performed poorly which is not surprising given the popular use of historical simulation. While historical simulation provides stable VaR numbers, it has weak forecasting power and is entirely inappropriate during regime shifts (see Finger's "How Historical Simulation Made Me Lazy"). Responsive volatility estimators, such as EWMA and ARCH type models performed much better, and indeed provided early warning signals months before the full subprime meltdown in July 2007.
Download file Chart 1 illustrates the RM 2006 VaR forecast vs. realized log spread changes on the 2006-1 AAA ABX tranche. The first warning was a 300% vol increase from Dec 12 to 21 '06. The second was a 12 standard deviation / 350% vol spike on Feb 23 2007 (this was the day after HSBC announced that it fired the head of its US mortgage lending business as losses reached $10.5bn... the alarm bells were clearly ringing). Even though spreads almost tripled on that day from 11 to 30.8 bps, as seen in Download file Chart 2, it was not too late to hedge. In fact, spreads proceeded to tighten to a low of 14.08 bps on June 25 '07 before widening significantly in three major bear waves. In other words, risk managers had between two to six months lead time to execute hedges.
The main lessons are (1) pay attention to early warning signals, and (2) not all VaR models are created equally. To be useful, VaR should be dynamic and responsive to market conditions. After all, risk is dynamic. And while no model is perfect some models are certainly more useful than others.
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Thursday, February 14, 2008 |
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The Other Side of Stress Testing
Submitted by: Ran Fuchs, Head of Credit Business
Basel II endorses stress testing as one of the main risk assessment tools. While the definition of stress testing is open to interpretation, it is mostly taken as the change in the value of a portfolio resulting from external market conditions. In practice, the process is made up of four distinct steps:
1. External stress events (such as bird flu or default of a major financial institution) are ‘invented’.
2. These events are ‘translated’ and their economic impact on market factor (interest rate, GDP, …) is modeled
3. Running simulation on an organization’s portfolio based on the change in the market factors
4. Producing statistics to compare the result to the modeled portfolio in comparison with the same portfolio under ‘standard’ conditions
While the latest credit crises have demonstrated the necessity of stress testing, a complementary, yet just as important application of the same methodology has been mostly neglected: testing the stability of our measurements.
In most financial institutions, once a model has been verified and approved, it becomes part of the workflow and is rarely questioned or tested again. However, as all models have their limitations, understanding the limitations of a model is just as important as knowing its output.
One of the limitations of any risk model is its sensitivity to input parameters. In some cases models can become too insensitive, when big changes in market factors will only produce minute changes in the output. In other occasions, models may demonstrate the opposite behavior and become too sensitive, producing a ‘butterfly effect, that is, minute changes in the input parameters produces unjustifiably large changes in the output. Such behaviors could happen with any model, and don’t reflect on the quality of the model in general, but rather indicate that we are trying to use the model under conditions to which it is not calibrated.
Regularly using stress testing like functionality with marginal-change scenarios, rather than the ‘typical’ stress scenarios, will highlight the occasions when we cannot blindly rely on our model– a crucial piece of information when using any model for making decisions.
RiskMetrics Group will be holding a webcast, Iterative Basel II Implementation for European Banks: Credit simulation in low data coverage environments, on Wednesday, Feb. 20 at 14:00 GMT/15:00 CET. The webcast will cover: low data environments, the three pillars of Basel II, different approaches to missing data, the iterative approach to implementing Basel II, and the weakness of the linear approach to Basel II in a low data environment.
To register for the webcast, please visit here.
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