I've recently neglected the Compact Model Portfolio, on this blog. This is probably due to my interest in the Dynamic Trading Risk Factor — which is work I've done more recently.

The history of the composition of this index, and it's daily return relative to the benchmark, have been available on the blog side panel for a while now.

This index has been short financials via a long position in SKF, which has reversed some of the exposure due to a long position in Bank of America (BAC). Today, this changed — as the SKF position was replaced with one in Wells Fargo.

In recent posts, we updated our data on the VIX-GARCH Spread and the VXN-GARCH Spread. I'm now in the spirit of Science 2.0, sharing that spread data via blog pages. The pages are available through the blog side panel and also, directly, for the VIX-GARCH spread data and the VXN-GARCH spread data.

In addition to forcasting the daily volatility of the S&P 500 Index, we also forecast and publish the daily volatility of the NASDAQ-100 Index. In a similar manner to that for the VIX, the CBOE also computes an implied forward volatility for this index — which is known as the VXN. This index is becoming better known, and is now carried on CNBC's upper data bar.

As can be seen from the above chart, the positive offset between the VXN Index and our GARCH forecast of volatility, very much resembles the patterns observed for the VIX Index itself.

In some of our posts from late last year, we discussed the comparison between the forward volatility as expressed by the CBOE's VIX Index and our daily forecasts of the volatility of the S&P 500 Share Index.

As can be seen from the above chart, the positive offset between the VIX Index and our GARCH forecast of volatility, after reversing pro articulus — indicating that the strategy of selling options on the S&P 500 Index and delta hedging them from an accurate volatility model would not be profitable — has recovered to its historical level, and now, sec. articulus, is richer than it was in recent history.

To the eye, this appears coincident, as we have discussed, with the recovery in profitability for hedge funds and trading firms, who's dynamic trading activityis mathematically equivalent to delta hedging synthetic options written on a risk factor and sold to their investors. We will follow up this chart with a more rigourous analysis, now that we are apparently back in the prior region of phase space.

In 1949, one of the oldest mutual funds, the company now known as Franklin Mutual Shares was established under the name Mutual Shares Corporation by Heine Securities Corporation. This fund recently celebrated its 50th. anniversary with the appearence of it's fund manager on the floor of the New York Stock Exchange, where he espoused the fund's philosophy as a long term large cap. value oriented stock picker.

I noticed this event, although in general I don't pay much attention to mutual funds as a class, because the fund's parent company, Franklin Resources, Inc. is a member of the Poor Man's Hedge Fund portfolio. That membership indicates that the fund manager's monthly returns regress strongly onto the Dynamic Trading Risk Factor. As the fund manager's revenues arise from the fund itself, albeit mostly from the assets under management rather than from incentive fees, as would be the case for a pure hedge fund, it is naturally interesting to ask whether the fund's monthly returns are also explained by the factor. The regression above indicates that this is indeed the case, and strongly so with an R² of 75%; α = (−0.7 ± 0.2) %/month; and, β =1.7 ± 0.1. This is a stronger regression than that we observed for Goldman Sachs, although with less leverage onto the factor and higher (relative) funding costs.

In a recent post, I discussed a simple model for the time series of the followers of our Twitter account, http://twitter.com/StatTraderCTA. A working paper discussing this topic is available on my Social Science Research Network Author Page. I was recently notified by SSRN that the paper is listed in the top ten downloads for the Economics Research Network — Estimation topic. The paper is available directly here.