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Risk Pragmatics

December 17, 2011

Risk is deeply underappreciated.

Moreover, it is misunderstood—even by many who have smelled it up close personally via big trading loses on hedged positions. Aaron Brown’s most recent text, Red-Blooded Risk, explains why.

In doing so, it is simultaneously brilliant and flawed. For the former, Brown deserves credit; for the latter, the publisher presumably deserves most of the blame.

First, the brilliance; summarized in one word, with two intended meanings: pragmatics. Oh yeah, the book includes 漫画-style comic strips, helpfully provided as idiot self-detectors.

First, well-known meaning is the philosophical tradition linking practice and theory. Arguably unique for risk books, Brown builds deep intuition around the concept of risk and its manifestation from structural to marked positions to historical roots in tulips. Brown has clearly lived and breathed risk management for many years (self-proclaimed from its modern origin in the 1980s), and that wisdom shines through via first-person prose combining insight, intuition, arrogance, pride, greed, humility, regret, condescension, and insecurity. Perhaps the first book ever to make VaR sound geek-sexy—with nary a technical definition.

Second, lesser-known meaning is the subfield of linguistics which investigates ways in which context contributes to meaning. Unlike technical risk texts, Brown spends much of the book deep diving into context and letting meaning emanate from therein. As he states, “different aspects are easier to understand from different vantages” (p. 57). As a reader who never personally worked at a big bank, this is deeply informative for attuning mental models (akin to Harris for trading mechanics, Rebonato for derivatives, and Taleb for hedging). Explaining the lineage of quant hedge funds through the corresponding frequentist versus Bayesian disposition of their founders is fascinating, and indeed makes sense in retrospect. Slicing away the credibility mystique and exposing the raw underbelly of banks, down to explaining front, middle, and back office in depth. For readers familiar with disciplined metrics-driven tech companies, the apparent intellectual and technical sloppiness of big banks is simply jaw dropping.

One excerpt simply must be quoted, beginning of Chapter 4, as it is perhaps the most accurate and beautiful summary of self-entitlement believed by geeks with 4\sigma-IQ time immemorial:

The rocket scientists came together on Wall Street in the 1980s and began the process that eventually explained the modern concept of probability and reconstructed the global financial system. We were not individually ambitious. All we wanted was to make more money than any rational person could spend, without ever putting on a tie or being polite to anyone we didn’t like. We didn’t have any use for the money, except for maybe some books and cool computer equipment. We didn’t want to throw (or go to) fancy parties or buy political power—and we didn’t spend it on cars, jewelry, or places to live, and least of all on clothes. We’d probably give the money away, but until then, it would give us the power to say “f- you” to anyone, except that we were mostly pretty soft-spoken and civil in our expressions

Now, the flawed parts.

One reviewer caveat worth advance mention is 9 books out of 10 read by Quantivity have content dense with math, code, or both. On the positive side, reading of Brown’s book indicates positive noteworthiness due to its statistical abnormality (given it has neither); on the negative side, any review is biased through such lens.

First, lots of effort was expended by the publisher making this text appeal to a mass audience, clearly rushing to fill the void of perceived post-financial crisis publishing opportunity. From the ridiculous title (and cover) to the silly use of “secret history” meme to hilarious tongue-in-cheek back cover reviewer comments by Gatheral, Taleb, Wilmott, and Thorp. Parts of the book are prone to hyperbole, which read like they were edited in for sales effect. While these nuisances detract credibility, such can be ignored and arguably contributes the positive benefit of reducing its purchase price to mass market (i.e. under $25).

Second, the book lacks unifying organization. While Brown provides the following disclaimer for such, the editor equally deserves some blame (p. 57):

If I had all the theory worked out, I could write a textbook organized in logical sequence. Instead, I’m going to intersperse theoretical discussions with accounts of the development of the ideas.

While this makes sense, it is admittedly a bit jarring to see that disclaimer juxtaposed alongside supposition of having “explained the modern concept of probability”. Thus, the reader is left wondering whether perhaps either of the following may be true:

  • Brown has a theory of risk, but was refused in editing due to overabundance of equations
  • Brown has a new theory of probability, but could not muster a theory of risk

Either are intriguing, although perhaps the former seems more likely as Brown includes the following tongue-in-cheek disclaimer regarding use of a tiny bit of high school-level math included in Chapter 5 (p. 73):

Warning, this chapter contains a little math. It’s nothing intimidating, mostly multiplication and some simple algebra, but I know a lot of people don’t like it. If that describes you, I urge you to read the chapter anyway. It’s one of the most important in the book. You can skip the math and get the ideas anyway.

Having never met Brown and thus unfamiliar with his personality, cannot escape the sense that he is making gentle fun of readers which possess such bias. Either way, it’s amusing.

While modest disorganization is a textual flaw, astute readers may perhaps perceive it as subtle financial opportunity: if such theory was sufficiently well-defined to warrant standard textbook treatment, then there would undoubtedly be much less juice possible from doing it really well.

In either case, remedy for this shortcoming is to read the book in as few distinct sittings as possible. Having read it in two sittings, the wisdom was able to percolate together and expand personal mental models nicely. Well worth the read.

8 Comments leave one →
  1. December 20, 2011 2:49 am

    Unquestionably an excellent book but I do hope that Brown was not poking fun at people who do not have the math. You may need rigour to think clearly about mathematics, but you do not need maths to think rigorously – even in risk management. The more precise the mathematics becomes, the more it is giving answers to the number of angels dancing on the head of a pin. Yes we know (for example) that a distribution is not Normal, but rather than expending energy on some other distribution, forget the n-th decimal place and work on thinking about plausible scenarios.

    I think his distinction between ‘VAR’-type risk management and management ‘beyond the boundary’ is somewhat open to question, if only because the very notion of the boundary is never justified.

    Finally, Brown seems more like a thoughtful trader than a risk manager. His eclectic use of ‘tools that work’ rather than being married to a particular dogma is more akin to a tinkering trader than a data crunching risk manager. For him, risk management is about maximising opportunity through taking the optimum amount of risk for your series of ‘Kelly bets’. However, I do not think you need to be a ‘risk manager’ to do this; this is the domain of an intelligent trader with a good grasp of basic risk concepts. I would like Brown to help me win the game, but isn’t the idea of risk management to keep me in it?

    In any event, these approaches seem more appropriate to taking positions rather than running customer-led businesses; but don’t most banks care about the latter?

    • quantivity permalink*
      December 20, 2011 10:53 pm

      @Peter: thanks for your comments. Can you elaborate on your claim that “very notion of the boundary is never justified”?

      Perhaps part of the book’s appeal is indeed his blending of risk and trading, which speaks to a growing sense that risk drives trading (rather than vice versa); e.g. risk-based asset allocation models. Having performed risk management solely in the “intelligent trader” sense you mention (rather than full-time bank risk role), my personal experience is insufficient to effectively calibrate your distinction.

      Agree with your remaining remarks.

      • December 23, 2011 2:51 am

        Oops! Didn’t realise that somebody had replied so this may be a bit late.

        In terms of the boundary, Brown makes a number of distinctions between risk managing ‘within and without’ the VAR boundary, but I don’t think he ever explains what is a good/bad boundary.There’s a good reason – there isn’t one. Riccardo Rebonato (Plight of the Fortunetellers and his more recent book on stress testing) puts some technical clothing around a fundamental intuition held by non-mathematicians such as myself – calculating confidence intervals to some second or third decimal point is a meaningless exercise simply because of the uncertainty around the putative distribution.

        I would love Brown to trade a book for me, but I’m not sure that he cares about risk management in the dull sense of preventing rogue trading.

        Note that my own background is similarly ‘intelligent [I hope] trader’, (I was at JPM at the same time as Brown), but I have something of a bee in my bonnet around the concept of Risk management expertise being accumulated in some specialist area. When we used to trade interest-rate swaps at JPM we were called risk managers for good reason. I don’t think you need to know about GARCH in order to manage risk.

  2. December 20, 2011 3:36 am

    It is older and has fewer pictures, but see Kent Osband’s Iceberg Risk book too:

  3. March 30, 2012 7:54 pm

    Thanks for the great review, which I somehow missed until now.

    I can’t let the publisher take the blame for anything except the silly subtitle. I dislike subtitles in general, and stupid ones in particular. When I wrote “The Poker Face of Wall Street,” Wiley was pushing me for a subtitle. One of the sales guys said, “People will think it’s a novel.” The oldest sales guy grunted out, “If you can’t sell a book with ‘poker’ and ‘Wall Street’ in the title, you’re in the wrong business.” So I got to keep that one pure.

    However, you’re correct that compromises were made to broaden the potential audience (and, as you say, keep the price down). I write lots of technical stuff and speak at conferences, the book is an attempt to describe things to smart people who don’t get excited about money and math.

    The answer to your specific question is that I see the defects in the dominant academic theories of both risk and probability, and know the direction to look for a better theory. I also know some of the practical applications that a better theory will someday justify. What I don’t have is that theory. A grandiose comparison would be Kepler knowing the earth revolved around the sun and being able to use that computation to make useful predictions and observe regularities, but not having a theory of universal gravitation.

    In reply to Mr. Fraser, the distinction between inside and outside the VaR boundary is simple. Inside you have so much data that prior beliefs and opinions don’t matter much, all quants should agree. Outside you need to rely on something other than data. Of course, there is a wide gray area between those regimes. Given that, it doesn’t seem all that useful to specify a precise boundary definition, and devote a lot of effort to estimating it. I report as an empirical observation something that was a big surprise to everyone at the time: defining and estimating VaR is extremely useful. I still don’t know why it works, but it works.


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