JOST A MON

  I attended the latest useR! conference this week, which lasted three days (Aug 12-14) at Dortmund. We've been using R at work for about a year or so now, and there's much to learn about this incredible statistical language and its ancillary 'packages', tools developed by academicians and practitioners in a variety of disciplines, all available free and online, supported by a vast cast of enthusiasts and gurus. So here I was, toting my little bag of goodies, and ambling from room to room to listen to some very interesting presentations across the R user spectrum.

The conference was held at the Statistics department of the Technical University of Dortmund. Only two weeks earlier, torrential rains had flooded the large auditorium of the department. Stains were still visible, but a massive cleanup before our arrival meant that everything appeared as it should - ready for four hundred visitors.

Unlike Curving Normality who blogged regularly and live from the various sessions he attended, I am doing this from back at home. I did take notes during the talks on a little pad that was given to us courtesy Google, but my handwriting these days is much worse than it used to be, and I doubt even a pharmacist would be able to make much of it. Still, here goes - summaries of three of the more interesting talks I attended.

The first talk I attended was on Loss Functions by a professor of actuarial studies, Vincent Goulet. Actuaries are interested in such things as ruin, the distribution of insurance claims, and the probabilistic properties of insurance payouts. To model these events, Goulet introduced an R package named 'actuar', in which he proposed several families of distributions, including those called censored. These, in particular, are interesting to insurance specialists. A client with a deductible is not going to make a claim if he thinks his damages cost less than the deductible to make good. In such a situation, the distribution of claims would be left-censored. Likewise, insurance payouts are usually capped by the size of the cover. Payouts, therefore, follow a right-censored distribution.

This is of relevance to us in finance as well, especially if we want to model the effects of management fees on a portfolio. Because irrespective of how well a manager performs, he always gets his management fee, the returns to a client from the manager are always that much less than the manager's overall performance. For the manager's revenue stream, on the other hand, there's an effective floor - and that can be modelled by a left-censored distribution.

An example of the multifarious uses of statistical tools came from Miriam Marusiakova, of the Charles University of Prague, who presented an R package 'forensic' to help in DNA fingerprinting. It is well-known that the DNA composition of any two human beings (other than identical twins) are distinct. Since it is unfeasible to compare DNA strands in their entirety from various possible sources,  forensic scientists have isolated certain markers that can serve as witnesses for distinction. Unfortunately, these markers themselves do not provide sufficiency of difference, and so a statistical analysis is required to determine how likely it is that the DNA found at a location came from one person or many.

Naturally, this is important! Let's say that a certain amount of DNA was found at a crime scene. There is a victim V and a suspect S. There are three possibilities: a portion of the DNA is known to be of the victim; there's only one type of DNA, suspected to be that of the offender; there's several sources of DNA found. The prosecution's hypothesis is that some of the DNA came from S. The defence's hypothesis is that the remaining DNA came from persons unknown U. How to determine which of the hypotheses is the correct one?

Miriam explained that external information needs to feed into the statistics. For instance, certain genetic factors are present to a greater or smaller degree in various populations. Not incorporating these factors into the statistics leads to overstating the case against the defendant. The classical probability law, named the Hardy-Weinberg law, is inapplicable in this case.

Another twist is that the offender and/or the victim, and the defendant are related! Usually, there is an assumption that the offender and the victim are independent. Miriam's package enables the analysis of all the above possibilities.

As a concrete example, she showed the widely different conclusions that could be drawn from the O.J. Simpson murder trial of the early 1990s in California. If the various factors and match probabilities were not estimated correctly, it was as easy to prove that the DNA found at the crime site was Simpson's as it was not. Lesson - estimate accurately and account for all possible variations.

The effervescent Janet Rosenbaum of Harvard University produced one of the most entertaining examples of research I've ever come across. She dealt with the notorious abstinence (virginity) pledge in the USA, and examined whether the sexual behaviour of teenagers who took the pledge was any different from those who didn't. (See here for some of her work, and this news report at the Washington Post.) I can do no better than quote from her very thorough abstract at the conference:

Objective: The US government spends over $200 million annually on abstinence-promotion programs, including virginity pledges, and measures abstinence program effectiveness as the proportion of participants who take a virginity pledge. Past research used non-robust regression methods. This paper examines whether adolescents who take virginity pledges are less sexually active than matched non-pledgers.

Previous researchers had compared the sexual behaviour of pledging teenagers against the general population of teenagers, and concluded that, indeed, the former were less likely to have had sex, and had a lower incidence of sexually-transmitted disease. For the US conservatives, this was brilliant news, meaning they could cut federal funding for contraception and women's sexual health, and provide instead abstinence coaching and use the numbers of pledgers as a metric of success.

But, of course, the comparison is not fair. The correct thing to do would be to match pledging teens with non-pledging teens who have similar backgrounds and ideologies. After all, the people who take the pledge are not average US teens. Many of them are from evangelical families, deeply religious, often born-again. When this matching is done, the results are quite clear:

Five years post-pledge, 84% of pledgers denied having ever pledged. Pledgers and matched non-pledgers did not differ in premarital sex, STDs, anal, and oral sex. Pledgers had 0.1 fewer past year partners, but the same number of lifetime sexual partners and age of first sex. Pledgers were 10 percentage-points less likely than matched non-pledgers to use condoms in the last year, and also less likely to use birth control in the past year and at last sex.

The behaviour of pledging and non-pledging teens is statistically identical! Worse, one to five years after having taken the pledge, 84% of those teens denied having pledged. Egregiously, many who had sex before taking the pledge declared themselves virgins shortly thereafter. To add insult to injury, pledgers were often more ignorant of contraception when they did succumb and have sex, and were thus less likely to protect themselves from disease or pregnancy before marriage.

Rosenbaum concluded that federal funds would be better spent in teaching effective birth and STD control than on abstinence measures.

Other interesting presentations:

1. Tomoaki Nakatani, ccgarch: An R package for modelling multivariate GARCH with conditional correlations.
2. Rory Winston, Real-Time Market Data Interfaces in R. (How to connect to Reuters from R)
3. Susana Barbosa, ArDec: Autoregressive-based time series decomposition in R.
4. Ray Brownrigg, Tricks and Traps for Young Players.
5. Wei-Han Liu, A Closer Examination of Extreme Value Theory Modelling in Value-at-Risk Estimation.
6. R. Ferstl, J. Hayden, Hedging Interest-Rate Risk with the Dynamic Nelson-Siegel Model.

There are several benefits to attending a conference. One overhears some interesting gossip. One supposedly learns a thing or two about one's field. One may, if one is overwhelmed by the awesome weather, sleep through a particularly boring presentation with no qualms. And one may also have the opportunity to inveigh against those who have the temerity to not attend the conference after their paper has been accepted.

As you can perceive, I had the opportunity to participate in each of these delicious activities. [As mentioned earlier in the week, I was in Aix-en-Provence, attending the Forecasting Financial Markets 2008 conference.] Chief among them was the gossip. Academics are like old people in this regard. They trade titillation and amusement and raised eyebrows. Unfortunately, I missed out on all these. At my table during lunch, there were, instead, hagiographies of the worthies of the world of financial econometrics. "What a fellow," said one professor admiringly of Mark Taylor.

Mark Taylor, of course, is well-known in our field. He read for a Politics, Philosophy and Economics degree as an undergraduate at Oxford (you can't study any of these in isolation, it appears) and worked in the financial industry for a bit. He then got himself a PhD from Birkbeck College and obtained a faculty position at University College, Oxford. Here, because his doctorate was not Oxon. or Cantab., he was addressed by his colleagues as Mister. The snobbery extended to virtually all his dealings with them, despite his steady progress to the heights of achievement in his field. Eventually, he left them and joined the University of Warwick, where he remains to this day.

Of course, he missed the perks of the high table and the elaborate system designed to support an academic at Oxford for conferences and things. Warwick, in comparison, is a bit of a backwater. But in the interim, he managed - while raising a family, publishing solid research and editing several journals - to obtain an MA in English Literature. And, tiring of paying expert repairers of antique clocks (the family hobby) through the nose, he apprenticed himself to one of the master clockwrights, and learned the trade.

Where the devil does he find the time?

Anyway. The FFM conference is bedevilled by one serious problem - that of presenters not showing up to present their papers after acceptance. For one thing, this completely screws up the scheduling. For another, many of them don't bother to pay the conference fee either, which results in the financial difficulties for the organisers. And finally it is incredibly frustrating for attendees who were looking forward to learning something new on a topic of interest.

I'm not sure what the deal is in the other disciplines, but this lack of attendance appears to be a serious canker among financial conferences.

The main reason for this sad lack of professionalism appears to be that it suffices for a paper to be accepted at a conference to appear on an academic's resume. We mulled over several solutions to this problem. One possibility is not to publish the accepted list of papers until the authors pay up. Another is not to do so unless they present (but why would they present unless they were told their paper was accepted?)

The FFM used to be held in London for years, and provided at one time a good mix of presentations from professionals in the finance industry and academics. These days, though, it is skewed more and more towards the ivory tower. People such as I turn up hoping to learn some new techniques in financial modelling, but some of the academic work is so removed from the day-to-day grind that it would take a person with considerably higher IQ than me to make it applicable to our grotty world of money-making.

I presented a piece of work done jointly with the head of my group. From a statistical standpoint, it was probably not completely rigorous, and I had hoped to obtain some feedback on how to strengthen the work (which I did get). For anyone who is interested, the work dealt with the extraction of currency positioning information by the use of peer-group benchmarks. For currency managers, this is interesting, because it's known that when positions get stretched (i.e., say, there's considerable selling of the US dollar and lots of buying of the Japanese yen), the positions tend to unwind rapidly, and there's usually short-term turmoil in the markets. This happens fairly regularly - so can one forecast it? A peer-group benchmark is an index that tracks the daily aggregated performance of a bunch of currency managers, and there are several published by various sources. If we, by means of our simple approach, can determine that our competitors in other banks are positioned in one particular way, and we have an indication thereby that those positions are stretched, we could take the opposite position, for example, and make a profit when the market snaps back.

Somewhat surprisingly, the simplicity of the approach won us some favourable comment, and the one or two enthusiastic questioners were inclined to suggest more rigorous statistical methods to make our results robust. They also mooted a possible collaboration, and this is something we'll have to take seriously, although I did point out that our market priorities change so often that it was unclear how much time or effort we could spend on further developing the work.

Aix is a lovely little town, and the local council provides some amount of funding for the conference. But the FFM has been held here for four years in a row now, and the organisers are thinking of alternative venues.

Luxembourg, anyone?