FFF has three major risks: Market, credit and operational risk. Regarding market risk, we have built a two-factor parametric portfolio value-at-risk (VaR) model to illustrate the actual risk of the fund. We have no minimum VaR as every year we have periods where trading is halted; thus, VaR drops to 0. The actual VaR of our fund is shown below.
|Year||Avg. daily (95%)||Max|
The FFF has credit risk exposure to both JPMorgan Chase and FXCM. JPMorgan holds approximately $6,000 of resting capital and FXCM holds about $6,000 of risk capital. If either one of these institutions were to fail, we still would be able to manage our program, although leverage would increase.
We still have a long way to go in controlling operational risk. Operational errors for the student-managed fund remain numerous (see “True cost of learning: model implementation,” September 2011).
The FFF uses a benchmark as a point of reference to measure performance. This benchmark is the return of the active model as if it were traded continuously and executed automatically at the beginning of the four-hour candlestick. As the model has evolved, performance with respect to the benchmark has improved considerably.
There are three factors that can cause a difference between actual and benchmark returns. These are slippage, operational errors and downtime (surpasses the other two factors). The FFF has had downtime in the following areas: Stopping trading to upgrade a model, university holidays when students refrain from trading and the faculty manager halting trading because of excessive operational errors. In only one instance did the FFF team outperform the benchmark: Summer 2011 when the faculty manager guided trading to test a market theory that resulted in a model upgrade.
The alpha of the FFF is in its model and the students who successfully build and manage the model, its upgrades and its risk. During the spring term, an algorithmic modeling class will teach advanced Excel and Visual Basic for Application skills and will work to provide a new upgrade to the existing model. This class is limited to FFF members and is invitation only.
This past summer, an internship program produced a complete quantitative overhaul of the existing model (to find validation errors), built a new upgrade to the model, built the trading benchmark, built the-two factor parametric VaR model, built the volatility/girth overlay, produced trading returns in an acceptable format and validated all work processes with an advisory board member.
Research support for this article was contributed by Christine E. Muench (’13), William M. Schaid (’13), Christopher J. Waldock (’14), Samuel C. Fister (’14) and Christopher E. Bell (’15).
Leslie K. McNew is a clinical professor of finance and director of the Hanley Trading Center, Department of Economics and Finance at the University of Dayton Business School. E-mail her at firstname.lastname@example.org.