Ray, Extreme Risk Management

Christina Ray has written an intriguing though somewhat frustrating book. Extreme Risk Management: Revolutionary Approaches to Evaluating and Measuring Risk (McGraw-Hill, 2010) draws in part on methodologies of the intelligence community and argues for their efficacy in managing financial risk. More specifically, she contends that causal models, coupled with expert knowledge, are better predictors of black swan events than statistical models that rely on correlation.

It would be a cheap shot to note that U.S. intelligence saddled the country with the costly results of false positives and missed deadly events. I am enough of a philosopher, a word I use loosely here, to analyze conflicting methodologies on their theoretical merits. Well, that’s not quite true. I am somewhat biased against statistical models perhaps because, statistically, I don’t think they work particularly well. So I was prepared to be convinced by an argument for a causal model, all the while holding my breath because causality is such a thorny concept. I can’t say that I became a true believer, but certain elements of the causal model as developed in this study are intellectually attractive.

Although Ray has written an actionable book for financial professionals and regulators who engage in varieties of stress testing and whose mission is to prevent their firms or the system from getting slapped around by fat tails, I’m going to focus in this post on a couple of topics I consider to be important to the individual trader or investor. First, road signs of regime change and, second, inferring causality from historical market behavior.

Ray criticizes stochastic volatility models such as GARCH because they assume reversion to a long-term mean and “implicitly assume system stability.” But what, she asks, if this assumption isn’t true? “What if instead the system evolves to an enduring new state in which risk is substantially higher?” (p. 139) Looking at historical VIX levels and bifurcating them into normal and extreme expressions of risk, she argues that “such very different patterns of volatility [illustrated with contour plots] provide some empirical evidence that risk regimes can shift and that stochastic volatility models may be ineffectual at the point of transition.” (p. 141)

As for inferring causality from historical market behavior, Ray outlines two approaches—the theory-driven approach and the data-driven approach. In the former “the analyst hypothesizes some model of a system and then attempts to determine whether observational data bear out or contradict that theory.” In the latter, “a modeler assumes no prior knowledge about systems behavior, and instead attempts to infer causality from empirical data alone.” The first approach “requires an iterative process, in which prior knowledge is refined to posterior knowledge as theory and experience merge.” The data-driven approach relies on inductive reasoning, much of it fleshed out by researchers in the field of artificial intelligence. It also differentiates among types of causality such as potential causes, genuine causes, spurious association, and genuine causation with temporal information. (pp. 226-27) By the way, here Ray cites the work of Judea Pearl from the 1990s; for those interested in his research, he has a website that offers its visitors two “gentle” introductions.

Ray continues, and here let me quote her at greater length: “In spite of the fact that the global financial system continually evolves in small ways and large, historical data can nevertheless provide evidence of causality. Although old cause-and-effect relationships may change and new ones form, there are still carryovers from state to state, not the least of which are behavioral effects. Human nature is one of the few constants, and human preferences—especially with respect to risk aversion—are one of the major drivers of market prices, perhaps even more important than any invisible hand.

“A firm that recognizes the causal capacity of the current state—and then first notices indicators that may trigger a chain reaction that uses that capacity—can beat the crowd by initiating changes in strategy that mitigate loss or generate profit.” (pp. 227-28)

By way of clarification, the author redefines the concept of causal capacity to mean “a measurement of a system’s capacity for change. In a complex system, causal capacity might be thought of as the spring loading of a fragile or robust system that makes it susceptible to shocks caused by the actions of free agents.” (p. 170)

Extreme Risk Management is not for the novice trader or investor or the quantitatively challenged, even though there is almost no math in the book. It is also not a book that could ever be described as a good read because the prose is often dense and the arguments truncated. Moreover, it draws from so many fields that almost by definition the reader will lose her way now and again. It assumes a familiarity with some notions I’ve written about in this blog, such as degrees of freedom and complex adaptive systems, and with a host of others better left to those more qualified than I.

I’m sure that most people who read this book will learn from it and move on to something else in their lives. I, however, came away from it with a “to-do” list—references to follow up on, ideas that are enticing but need more development. This may not be the litmus test of a good book, but I personally appreciate an author who can stimulate me to do some work on my own.

Rittereiser and Kochard, Top Hedge Fund Investors

Most hedge fund books focus on managers and their strategies. Top Hedge Fund Investors: Stories, Strategies, and Advice by Cathleen M. Rittereiser and Lawrence E. Kochard (Wiley, 2010) is written from a different perspective. The authors profile nine successful investors in hedge funds—“the pioneers and next-generation, high-net-worth individuals, funds of hedge funds, and institutions.”

According to industry metrics investment in hedge funds is concentrated: most hedge fund investors commit 85-90% of their assets to the same 170 to 200 large hedge funds. (p. 183) But some of the investors interviewed for this book have seeded new funds or have invested in early-stage funds. For instance, Frank Meyer seeded Ken Griffin of Citadel fame.

Many of the interviewees describe in great detail the qualities they are looking for in a fund manager. I particularly enjoyed Mark Anson’s cautionary top ten quotations from hedge fund managers he has spoken to over the course of his career. One was “Basically I look at screens all day and go with my gut.” Anson’s response: “While he may be a gutsy investor, there is simply too much process risk associated with this hedge fund manager to make a credible investment.” (p. 92)

At Investcorp Deepak Gurnani proceeded more quantitatively through his Alpha Project. A group of five quants and tech people dissected the primary hedge fund strategies and “determined the generic trade that defines each strategy.” They then constructed a database of historical prices for securities in that strategy. Moving strategy by strategy, they calculated proprietary indexes of generic hedge fund returns. On the basis of these indexes they will either look for managers who are adding alpha or use more cost-effective ways to generate the same return.

Some strategies, Guarnani says, are easy to replicate and can be executed passively; convertible arbitrage is the best example. By stark contrast, macro discretionary “is so skill-based” that he hasn’t even started the Alpha Project on it. “It is totally skill, there is no natural return.” (p. 146)

Top Hedge Fund Investors is written for those who invest in hedge funds and, by extension, for hedge fund managers. (It’s imperative to know what your potential clients are thinking.) Ma and Pa Kettle won’t learn much about where to put their nest egg.

Dodd-Frank: A fine alphabet soup appetizer, but where’s the beef?

Today I am turning over the blog to a guest poster, Steve Boyko, whose book I reviewed some time back. Although we are not cut from the same political cloth and aren’t impassioned by the same issues, we share an interest in analyzing the weaknesses of standard risk models. Steve looks at these weaknesses at the regulatory level; I go at them from the perspective of the trader. But enough blathering; here’s his contribution. He welcomes comments.

Consider the way in which the politics of capital market governance provide for interesting legislative “calls to action.” The Dodd-Frank Wall Street Reform and Consumer Protection Act went from lofty legislative intentions at the inception of the Financial Crisis Commission when Senator Dodd stated that it was his

“hope that this commission will provide valuable insights that will help us to continue our efforts to ensure economic security for American families.”

Then later to:

"We can’t legislate wisdom or passion. We can’t legislate competency.”

Like the World War II French Generals who built the Maginot Line’s fixed fortifications in reaction to World War I tactics, Dodd and Frank proposed legislation in response to the most recent crash. Their response focuses on scale, Too-Big-To-Fail (“TBTF”), rather than the economic randomness of predictable, probabilistic, and uncertain valuations which is Too-Random-To-Regulate (“TRTR”). Our “financial generals” have unfortunately conflated risk and uncertainty in deterministic, one-size-fits-all governance metric that almost certainly will result in a dysfunctional price discovery mechanism leading to increasingly more frequent and larger economic dislocations.

The perfect financial storm

The subprime housing crash has been called the perfect financial storm. It, like most recent crashes, was the perfect blend of financial innovation and congressional interference that enabled financial opportunism to take place when low-hanging opportunities were exhausted. There were no innocents—not unqualified home-buyers, not banks, not Government Sponsored Enterprises (“GSEs”), not regulatory agencies. Financial alchemists marketed the hole-in-the-donut to naive opportunists whose faulty due diligence suggested that cheap was well-bought and that well-bought would soon become profitably well-sold.

Recall the S&L meltdown, where the indeterminate “asset” on the books of many insolvent S&Ls was “regulatory goodwill” – the regulator’s reward for acquiring an even more insolvent thrift. Who could have foreseen the Resolution Trust Corporation’s (RTC) liquidations that occurred when minimum reserve requirements became illusory in a setting where capital consisted of vapor assets?

Likewise with the subprime crash, Dr. Stanley Liebowitz of the University of Texas posited that the single most important factor in home foreclosures was negative equity. No-money-down, liar loans effectively gave property rights to renters. This created a moral hazard where investor rights exceeded their responsibilities. Liebowitz demonstrated that the presence of such loans also misdirected policymakers’ focus toward the wrong variables for controlling the adverse consequences of the subprime crash.

Governing randomness

In a world of financial innovation, it is uncertainty — not risk — that should be the randomness component of focus. Uncertainty is different from, rather than a higher degree of, risk. This distinction was made famous by economist Frank H. Knight in his seminal book, “Risk, Uncertainty, and Profit” (1921). Risk refers to situations in which the outcome of an event is unknown, but the decision maker knows the range of possible outcomes and the probabilities of each. Uncertainty, by contrast, characterizes situations in which the range of possible outcomes, let alone the relevant probabilities, is unknown.

If there is complexity, there is uncertainty. For example, software iteration 2.1 was written because of unforeseen circumstances experienced with version 2.0. As there are innate complexities in the capital markets, the element of uncertainty always will be a part of complex adaptive systems.

Difficulties arise when risk is conflated with uncertainty under deterministic, one-size-fits-all governance metrics that frustrate the market’s price discovery function. We can insure (put options) and hedge (Ford and Exxon) risk. We can insure (natural disaster insurance) uncertainty, but we cannot hedge (Ford and pork bellies) uncertainty. If we cannot hedge, we cannot regulate risk and uncertainty in a one-size-fits-all regime due to non-correlative information. It is similar to having a single thermostat regulate temperature for H2O conditions of ice, water, and steam.

Investments that lack cash flow and are valued on a mark-to-model basis are uncertain. Credit default swaps (CDSs) are uncertain derivatives that required constant hedging by dealers. This activity resulted in cost with very little benefit. Absent randomness segmentation, indeterminate information cannot be processed effectively and efficiently by determinate metrics.

The Dodd-Frank financial reform was brought about by a troubling trend of more frequent and larger economic crashes. In undertaking a complex reform, sequence and timing are critical. Policymakers had to determine whether the capital market needed reform by amending the existing deterministic, one-size-fits-all system; or, whether the capital market system was broken and required fundamental structural repair.

• If the former, what is new or innovative in the 2,000-plus pages of Dodd-Frank?
• If the latter, which proposals introduce REAL structural change?

The solution for Dodd-Frank-type legislation with one-size-fits-all deterministic regulatory metrics is to segment randomness into: predictable (money market investments), probable (positive cash low, earnings-driven, NYSE and NASDAQ issuers that are marked-to-the-market) and uncertain (negative cash low, event-driven issuers that are marked-to-the-model) regimes.

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Stephen A. Boyko is the Chairman and CEO of N2K Ecosystems, Inc. — a business development consultancy providing market-based solutions for entrepreneurial wealthfare. He has over forty years of financial services industry experience that include formulating regulatory policy for the National Association of Securities Dealers ("NASD"), managing regional brokerage operations for retail, institutional, and corporate clients, and providing a practitioner's perspective for the privatization of the former Soviet Union in the areas of corporate governance and regulatory development of the Ukrainian Capital Market. Mr. Boyko holds a BA in history from Bates College and an MBA in finance from American University. He is conversant in French, Russian, and Ukrainian and serves on the Advisory Board of Yorktown University. Mr. Boyko is the Author of “We’re All Screwed: How Toxic Regulation Will Crush the Free Market System” http://www.traderspress.com/detail.php?PKey=671. He can be reached at n2keco@bellsouth.net.

Schwartz et al., Mastering the Art of Equity Trading through Simulation

Mastering the Art of Equity Trading through Simulation: The TraderEx Course by Robert A. Schwartz, Gregory M. Sipress, and Bruce W. Weber (Wiley, 2010) is in effect a manual for some nifty web-based software that is freely accessible to anyone who has purchased either this book or MicroMarkets, which I reviewed earlier. So let’s look at what this software does.

First of all, the software offers four kinds of markets: a continuous order book market, a call auction, a dealer market, and a block trading facility. It then generates order flow drawn from various statistical distributions outlined in some detail in the book. You interact with this order flow, placing either market or limit orders in a size you choose which in turn influences the market. You can, of course, configure the simulator’s parameters—for instance, the length of the simulation (one day is the default), the tick size, initial price, and daily return volatility. You can also set how often the machine generates orders and how often information arrives that indicates the stock is over- or undervalued. And you can set the speed of the simulation. Once you have finished an exercise you can save order and trade data to an Excel file.

(click to enlarge)

A trader will probably not profit much from simply hacking away at the software, though admittedly it’s fun. But the second half of Mastering the Art of Equity Trading through Simulation offers a series of exercises that turn play time into enjoyable structured learning. For instance, the exercises allow you to compare the results of various limit order and market order tactics. They challenge you to buy and sell a large position in prescribed chunks, deal with the effects of heightened volatility, and assume the role of the ax.

TraderEx, it is important to recognize, offers something very different from the market replay scenarios available on many trading platforms. For instance, it is not designed to improve a trader’s chart reading skills; there are no charts. It focuses instead on tape reading and how to work orders to get the best price, reduce transaction costs, and minimize risk. In brief, it addresses something that many traders ignore at their peril.

I rarely consider books bargains, but considering that you get a mini-course in order execution plus access to simulation software which you can use to your heart’s content for around $50 I think this paperback qualifies. Now back to honing my skills and having some fun!

Some definitions to guide traders and investors--a foray into humor

Occasionally we all need a break from the seriousness of life. Here, from way back in the past, though still available online, is an abbreviated version of one of Philip M. Halperin’s “Scribblings,” originally entitled "Devil's Dictionary for Options Trading."

FUNDAMENTAL ANALYSIS

A Method by which the practitioner can, after much study, predict the impact of news the market has already discounted in its pricing.

ECONOMETRIC FORECASTING MODEL

A Learned System for predicting the immediate past.

TECHNICAL ANALYSIS

A Learned System for predicting that what has happened before might happen again.

TECHNICAL ANALYSIS SYSTEM

A Learned System for mindlessly predicting that what has happened before might happen again.

DELTA NEUTRAL

A mathematical Method for losing money on long options, regardless of which way the market trades.

DIRECTIONAL TRADING

A Technique to lose money on options volatility.

VOLATILITY TRADING

A Technique to lose money on market direction.

RISK-REWARD RATIO

A Learned Method to compare what you will lose to what you might make, assuming you don't change your position and assuming the market goes your way.

HISTORICAL VOLATILITY

An estimate of how the average ratio of market prices in some underlying actually differed from some series of ratios of market prices, for some period in the past, often used to estimate what that ratio deviation might be now.

Indispensable for historically pricing options that have already expired.

IMPLIED VOLATILITY

An estimate of the market's best guess as to what historical volatility might ultimately turn out to be. This is computed on the basis of observed options premiums that are, in turn, based on the market's estimates as to what implied volatility might be now.

CORRELATION

The cop of the quantitative world. Never around when you need it.

NUMBER (Usage: Trade Number, Inflation Number, GNP Number)

A macroeconomic figure that provides the impetus for you to profit from your best guess as to what the consensus of everybody else's best guess about what the market's best guess of that same macroeconomic figure might have been, assuming your guess about the analysts' guess consensus about the market's guess was correct.

Who was Adam Smith?

I have an advance reader’s copy of Nicholas Phillipson’s Adam Smith: An Enlightened Life (Yale University Press, October 2010). For those unfamiliar with the book publishing business, this means a paperbound copy of uncorrected page proofs, with illustrations merely empty boxes marked FPO, for position only, some key production data described as TK, to come, and some elements—most notably, the index—not yet typeset. It also means that my Amazon link will have no image.

This book is the intellectual biography of a man who wanted to have a not quite invisible hand in shaping his intellectual biography. His first serious biographer, Dugald Stewart, whose work was published in 1794, wrote that “he seems to have wished that no materials should remain for his biographers, but what were furnished by the lasting monuments of his genius, and the exemplary worth of his private life.” (p. 3) For instance, he saw to it that all his letters and lecture notes were destroyed.

Naturally, no one of Adam Smith’s renown could micromanage his legacy to the extent that he wished. We know that he was an eccentric who never married and who lived with his mother for many years. When she died at the age of ninety he wrote to his publisher that “the final separation from a person who certainly loved me more than any other person ever did or ever will love me; and whom I certainly loved and respected more than I shall ever either love or respect any other person, I cannot help feeling, even at this hour, as a very heavy stroke upon me.” (p. 11)

He had a few close friends, David Hume being the most famous, but did not shine in society. Samuel Johnson considered him “as dull a dog as he had ever met with.” (p. 210) His students didn’t agree. After a stint teaching at Edinburgh, he moved on to Glasgow where he lectured for more than a decade and became something of a cult figure. Students could buy his portrait bust at local bookshops. And what did he teach? At Edinburgh he gave lectures on rhetoric and jurisprudence; at Glasgow he lectured on moral philosophy.

Adam Smith was first and foremost a philosopher whose ambition it was to develop a “science of man” that would incorporate law, history, aesthetics, economics, and ethics. The Wealth of Nations and The Theory of Moral Sentiments were meant to be part of this overarching project, never completed.

He wrote most of The Wealth of Nations in Kirkcaldy, where he was born and where his mother still lived. His business in Kirkcaldy, he wrote to Hume, “is Study in which I have been very deeply engaged for about a Month past. My amusements are long, solitary walks by the Sea side. You may judge how I spend my time. I feel myself, however, extremely happy, comfortable and contented. I never was, perhaps, more so in all my life.” (p. 201)

Although Adam Smith spent most of his life learning, teaching, and writing and was renowned for his formidable erudition, he also demonstrated management skills. He was an academic administrator and a customs commissioner. He advised the Duke of Buccleuch on running his estates and when the Ayr Bank collapsed in 1772, three years after its founding, he spent a great deal of time trying to extricate some of his friends, including Buccleuch, from the fallout.

It is impossible to do justice to Phillipson’s biography in this brief space. I have touched on only Adam Smith the man, not Smith’s ideas, the primary focus of the book. In closing, let me correct this bias ever so slightly.

In The Wealth of Nations Smith pays tribute to Quesnay, whom he met in Paris when traveling with the young Buccleuch and who influenced his thinking on economics. But Smith’s empiricist roots led him to criticize some of Quesnay’s ideas. As Phillipson writes, according to Smith “...questions of price and value were regulated by ‘higgling and bargaining’, not mathematical necessity. …whereas Quesnay believed that economics could be turned into an exact, mathematically based science, Smith remained firmly committed to the Humean view that systems of philosophy could only appeal to the understanding, and that their credibility in the eyes of their readers would depend on the philosopher’s ability to illustrate his principles with examples drawn from common life and history.” (p. 206) Smith famously demonstrated this ability in his claim that “it is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest.”

Phillipson interweaves Adam Smith’s philosophy, the intellectual and commercial setting in which he lived, and his personal story. It’s a fascinating tale.

A heads up

Just to give you an idea of what’s coming down the pike, I have the following books here for review:

Gastineau, The Exchange Traded Funds Manual
Gregoriou, The Handbook of Trading
Labuszewski et al. The CME Group Risk Management Handbook
Phillipson, Adam Smith
Ray, Extreme Risk Management
Rittereiser and Kochard, Top Hedge Fund Investors
Schwartz et al. Mastering the Art of Equity Trading through Simulation