"We have to start taking accounting fraud seriously. It is a crime. It is a felony. It is the "weapon of choice" among financial control frauds."

Yves Smith:

"Obama's Financial Reform Proposals: Less Than Meets the Eye

Team Obama has started to preview some of its financial reform proposals. And if the New York Times has represented it accurately, it falls far short of what is called for.

Consider the opening sentence of the article:

The Obama administration plans to move quickly to tighten the nation’s financial regulatory system.

If all the Obama administration intends to do is tinker around( MY FEAR AS WELL ) the margins of our existing framework, the US will make perilous little headway in cleaning up the financial system. In the Great Depression, the Securities Act of 1933 and the Securities and Exchange Act of 1934 were bold, root and branch reforms that proved to be remarkably effective and lasting.

That isn't to say we have to start from a blank sheet of paper, but the powers that be need to be willing to question and probe our existing institutional arrangements more deeply than they seem willing to.

Let's go through some relevant sections of the Times' story:

Officials say they will make wide-ranging changes, including stricter federal rules for hedge funds, credit rating agencies and mortgage brokers, and greater oversight of the complex financial instruments that contributed to the economic crisis....

A theme of that report [by an international committee headed by Paul Volcker], that many major companies and financial instruments now mostly unsupervised must be swept back under a larger regulatory umbrella, has been embraced as a guiding principle by the administration, officials said.

Yves here. So far, motherhood and apple pie circa 2009, but look at the particulars:

Officials said they want rules to eliminate conflicts of interest at credit rating agencies ...The core problem, they said, is that the agencies are paid by companies to help them structure financial instruments, which the agencies then grade....

Yves here. Notice the problem has been defined narrowly: rating agency conflicts. No consideration of rating agency competence( I WOULD SAY THAT THE NATURE OF RATING IS WHAT NEEDS TO BE STUDIED.) (they were unduly dependent on issuer input for some structured products), or the depth of the conflicts (even if you change the pay arrangements, rating agencies have long been a revolving door, with the best staff going to Wall Street. Even in a brave new world of lower financial firm pay, that pattern will still persist. And staff will therefore still have an incentive not to be as tough as they might need to be.

Back to the article:

The administration is also preparing to require that derivatives like credit default swaps, a type of insurance against loan defaults that were at the center of the financial meltdown last year, be traded through a central clearinghouse and possibly on one or more exchanges. That would make it significantly easier for regulators to supervise their use.

We have long been in favor of getting as many OTC products as possible traded on exchanges. But thee is a second issue with credit default swaps. The size of the market is so large relative to the size of the underlying volume of bonds that it has produced significant distortions in the pricing of bonds in new issues (there was a period last summer when the correlation models were blowing up, and one of the side effects was that AAA issuers were suddenly facing insanely high spreads if they financed due to the arbitrage the cash and derivative markets (see here and here for more detail). Similarly, the financial press too often last summer took up financial firm CEO claims that evil stock short sellers were driving down the prices of their stocks, when in fact, CDS were a better vehicle and the cognisenti argued were a far more likely culprit( GOOD JOB YVES ).

The justification for financial "innovation" is that it lowers the cost of financing, improves liquidity, and produces other benefits to the real economy. But there are numerous examples of CDS creating distortions to the detriment of the real economy. Thatt suggests that some thought should be given as to whether and how measures might be put in place to reduce the size of the CDS market (personally, I think the justifications for its existence are weak, but I see no willingness in the Obama crowd to make bold moves in this arena).( I OF COURSE BLAME PEOPLE )

Another reason to rein in the CDS market is that the scale of the risks involved may be too large even for an exchange. As we noted last fall:

The most valuable element of moving CDS to an exchange, as far as lowering systemic risk is concerned, is centralized clearing, since if anyone defaults, the counterparty is the exchange, not an individual firm. Thus regulators have been moving forward as quickly as possible to set up a central clearinghouse. In particular, the CME Group proposed acting as a clearlinghouse, which means that its members would absorb the losses if any counterparty failed. Some rival proposals suggested setting up a new clearinghouse, which is a much sounder design, but would take longer to implement.

However, some savvy and influential and savvy CME members are now objecting to the idea, arguing that the additional risk of CDS clearing on top of their existing CME obligations is more than the members can realistically support. Moreover, they contend that putting together CDS and futures under the same umbrella is too much risk in one venue, and will increase, not reduce systemic risk.

The article recites a host of rather conventional ideas that have been bandied about that Team Obama has latched on to, such as requiring hedge funds to register with the SEC. Bernie Madoff was registered; the SEC skipped his normal inspection and ignored a detailed letter by one Harry Markopolos ("The World's Largest Hedge Fund is a Fraud") that concluded that the Madoff funds were most likely a Ponzi scheme. So exactly what is this going to accomplish? Eliot Spitzer in an article at The Big Money tells us that the SEC is not serious about enforcement:

The traditional critiques of the SEC have been that it was underfunded and didn't have up-to-date laws needed to regulate sophisticated financial transactions in evolving markets. That's not accurate. The SEC is a gargantuan bureaucracy of 3,500 employees and a budget of $900 million—vast compared with the offices that actually did ferret out fraud in the marketplace. And the general investigative powers of the SEC are so broad that it needs no additional statutory power to delve into virtually any market activity that it suspects is improper, fraudulent, or deceptive. After each business scandal (Enron, Wall Street analysts, Madoff …), the SEC claims a need for more money and statutory power, yet those don't help. The SEC has all the money and people and laws it needs. For ideological reasons, it just didn't want to do its job, and on the rare occasions when it did, it didn't know how.

Now in theory, with a new SEC chief, the SEC might get a bit more aggressive, but even in the Clinton era, reform minded Ted Levitt was reined in by Congress that threatened to SEC budgets if it made life too hard for Wall Street (and Democrat Joe Lieberman was the biggest perp). So aside from some cosmetic moves, I doubt much will change here.
Now let us return to the Times article, which illustrates that the underlying problem:

Officials said that the proposals were aimed at the core regulatory problems and gaps that have been highlighted by the market crisis. They include lax government oversight of financial institutions and lenders, poor risk management efforts by banks and other financial companies, the creation of exotic financial instruments that were not adequately supported by their issuing companies, and risky and ill-considered borrowing habits of many homeowners whose homes are now worth significantly less than their mortgages.

In other words, this is a symptom oriented, patchwork approach( TRUE ). And it misses some of the underlying drivers: the opaqueness and complexity of many of the new instruments, texcessive leverage (oh wait, the powers that be think excessive leverage is the solution) and widespread accounting fraud.( TRUE. BUT, IN REALITY, ONLY SUPERVISION AIMED AT COMPREHENDING THESE INNOVATIONS WILL WORK. )

For instance, how could Lehman have collapsed with what turned out to be a hole in its balance sheet of in excess of $100 billion when its financial statements gave no clue of problems of that size? And as we noted at the time, its executives were claiming, forcefully, that everything was OK, the converse of what was actually the case (see here, here and here for examples).( THIS IS FRAUD )

Or consider the views of William Black (a bank regulator during the S&L crisis who went after Lincoln Savings, owned by the powerful Charles Keating. Black's efforts were thwarted but he was eventually vindicated) on Merrill:

Thain portrayed himself as a hero of capitalism and the embodiment of a successful CEO deserving of millions of dollars in bonus compensation because he "sold" Merrill to B of A -- minimizing the losses of Merrill's shareholders. There was, of course, criticism of the scale of these bonuses, but no fundamental rejection of his claim to be a hero.

The lack of rejection illustrates why we are in crisis. We need to be blunt. Thain transferred a loss that risk capital is supposed to bear to the taxpayers of the United States (not B of A). He was able to transfer that loss to the taxpayers because Merrill, under his leadership, engaged in monumental accounting fraud (which means it also engaged in securties fraud)( I AGREE ). We have to start taking accounting fraud seriously. It is a crime. It is a felony. It is the "weapon of choice" among financial control frauds. It causes staggering direct losses and indirectly, by eviscerating trust, it causes entire markets to shut down. This should not surprise economists. We make things crimes in large part because they produce material negative externalities. ( THIS IS MY POSITION )

So, Thain "was part of the problem." The fact that he could (A) lead such a massive fraud and (B) think that he should be rewarded with many billions of dollars for defrauding the citizens of the United States shows two key reasons why the crises keep getting worse( SPOT ON. ). Our most elite business leaders now embody traits we used to understand were loathsome. The fact that this is not obvious even to skilled, sceptical financial reporters shows how serious a problem we face.( I COULDN'T AGREE MORE )

Until we start talking addressing the root causes, these financial "reforms" will prove as effective as trying to treat gangrene with antibiotics. "( I AGREE )

"The piece so badly misses the basics about VaR that it is hard to take it seriously, although many no doubt will"

Yves Smith attacks VAR:

"Sunday, January 4, 2009

Woefully Misleading Piece on Value at Risk in New York Times

The New York Times Sunday Magazine has a long piece by Joe Nocera on value at risk models, which tries to assess how much they can be held accountable for risk management failures on Wall Street.

The piece so badly misses the basics about VaR that it is hard to take it seriously, although many no doubt will( I LIKED THE POST, SO LET'S SEE WHERE SHE'S GOING. ).

The article mentions that VaR models (along with a lot of other risk measurement tools, such as the Black-Scholes options pricing model) assumes that asset prices follow a "normal" distribution, or the classical bell curve( IT DOESN'T SEEM LIKE ALL DO ). That sort of distribution is also known as Gaussian.

But it is well known that financial assets do not exhibit normal distributions. And NO WHERE, not once, does the article mention this fundamentally important fact.

The distribution of prices in financial markets are subject to both "skewness" and "kurtosis". Skewness means results are not symmetrical around the mean:



Stocks and bonds are subject to negative skewness (longer tails of negative outcomes) while commodities exhibit positive skewness (and that factor, in addition to their low correlation with financial asset returns, makes them a useful addition to a model portfolio).

Kurtosis is also known informally as "fat tails". That means that events far away from the mean are more likely to happen that a normal distribution( THEN IT'S NOT A NORMAL DISTRIBUTION ) would suggest. The first chart below is a normal distribution, the second, a so-called Cauchy distribution, which has fat tails:




Now when I say it is well known that trading markets do not exhibit Gaussian distributions, I mean it is REALLY well known. At around the time when the ideas of financial economists were being developed and taking hold (and key to their work was the idea that security prices were normally distributed), mathematician Benoit Mandelbrot learned that cotton had an unusually long price history (100 years of daily prices). Mandelbrot cut the data, and no matter what time period one used, the results were NOT normally distributed. His findings were initially pooh-poohed, but they have been confirmed repeatedly. Yet the math on which risk management and portfolio construction rests assumes a normal distribution!( I DIDN'T SEE THAT )

Let us turn the mike over to the Financial Times' John Dizard:

As is customary, the risk managers were well-prepared for the previous war. For 20 years numerate investors have been complaining about measurements of portfolio risk that use the Gaussian distribution, or bell curve. Every four or five years, they are told, their portfolios suffer from a once-in-50-years event. Something is off here.

Models based on the Gaussian distribution are a pretty good way of managing day-to-day trading positions since, from one day to the next, risks will tend to be normally distributed. Also, they give a simple, one-number measure of risk, which makes it easier for the traders' managers to make decisions.

The "tails risk" ....becomes significant over longer periods of time. Traders who maintain good liquidity and fast reaction times can handle tails risk( SO VAR IS USEFUL )....Everyone has known, or should have known, this for a long time. There are terabytes of professional journal articles on how to measure and deal with tails risk....

A once-in-10-years-comet- wiping-out-the-dinosaurs disaster is a problem for the investor, not the manager-mammal who collects his compensation annually, in cash, thank you. He has what they call a "résumé put", not a term you will find in offering memoranda, and nine years of bonuses....

All this makes life easy for the financial journalist, since once you've been through one cycle, you can just dust off your old commentary.

But Nocera makes NO mention, zero, zip, nada, of how the models misrepresent the nature of risk. He does use the expressoins "kurtosis" and "fat tails" but does not explain what they mean. He merely tells us that VaR measures the risk of what happens 99% of the time, and what happens in that remaining 1% could be catastrophic. That in fact understates the flaws of VaR. The 99% measurement is inaccurate too.( SOME ARE )

Reliance on VaR and other tools based on the assumption of normal distributions leads to grotesque under-estimation of risk. As Paul De Grauwe, Leonardo Iania, and Pablo Rovira Kaltwasser pointed out in "How Abnormal Was the Stock Market in October 2008?":

We selected the six largest daily percentage changes in the Dow Jones Industrial Average during October, and asked the question of how frequent these changes occur assuming that, as is commonly done in finance models, these events are normally distributed. The results are truly astonishing. There were two daily changes of more than 10% during the month. With a standard deviation of daily changes of 1.032% (computed over the period 1971-2008) movements of such a magnitude can occur only once every 73 to 603 trillion billion years. Since our universe, according to most physicists, exists a mere 20 billion years we, finance theorists, would have had to wait for another trillion universes before one such change could be observed. Yet it happened twice during the same month. A truly miraculous event. The other four changes during the same month of October have a somewhat higher frequency, but surely we did not expect these to happen in our lifetimes.( IN ORDER FOR THEM TO SAY THIS, THE DATA MUST BE AVAILABLE )

Thus, Nocera's failure to do even a basic job of explaining the fundamental flaws in the construct of VaR renders the article grossly misleading. Yes, he mentions that VaR models were often based on a mere two years of data. That alone is shocking but is treated in an off-hand manner (as if it was OK because VaR was supposedly used for short term measurements. Well, that just isn't true. That is not how regulators use it, nor, per Dizard, investors). Indeed the piece argues that the problem with VaR was not looking at historical data over a sufficiently long period:

This was one of Alan Greenspan’s primary excuses when he made his mea culpa for the financial crisis before Congress a few months ago. After pointing out that a Nobel Prize had been awarded for work that led to some of the theories behind derivative pricing and risk management, he said: “The whole intellectual edifice, however, collapsed in the summer of last year because the data input into the risk-management models generally covered only the past two decades, a period of euphoria. Had instead the models been fitted more appropriately to historic periods of stress, capital requirements would have been much higher and the financial world would be in far better shape today, in my judgment.” Well, yes. That was also the point Taleb was making in his lecture when he referred to what he called future-blindness. People tend not to be able to anticipate a future they have never personally experienced.

Again, just plain wrong. Use of financial data series over long periods of time, as we said above, have repeatedly confirmed what Mandelbrot said: the risks are simply not normally distributed. More data will not fix this intrinsic failing.( I'M NOT SURE I UNDERSTAND THIS. NORMAL DISTRIBUTION WILL WORK MOST OF THE TIME FOR PREDICTION. IT SIMPLY IS LESS USEFUL THAN A MORE INCLUSIVE MEASURE. )

By neglecting to expose this basic issue, the piece comes off as duelling experts, and with the noisiest critic of VaR, Nassim Nicolas Taleb, dismissive and not prone to explanation, the defenders get far more air time and come off sounding far more reasonable.

It similarly does not occur to Nocera to question the "one size fits all" approach to VaR. The same normal distribution is assumed for all asset types, when as we noted earlier, different types of investments exhibit different types of skewness. The fact that VaR allows for comparisons across investment types via force-fitting gets nary a mention.

He also fails to plumb the idea that reducing as complicated a matter as risk management of internationally-traded multii-assets to a single metric is just plain dopey. No single construct can be adequate. Accordingly, large firms rely on multiple tools, although Nocera never mentions them. However, the group that does rely unduly on VaR as a proxy for risk is financial regulators( GOVERNMENT ALWAYS DOES THIS. CONSIDER HOW POVERTY IS MEASURED. GOVERNMENT NEEDS SIMPLE NUMBERS. ). I have been told that banks would rather make less use of VaR, but its popularity among central bankers and other overseers means that firms need to keep it as a central metric.

Similarly, false confidence in VaR has meant that it has become a crutch( THIS ISN'T THE FAULT OF THE MODEL ). Rather than attempting to develop sufficient competence to enable them to have a better understanding of the issues and techniques involved in risk management and measurement (which would clearly require some staffers to have high-level math skills), regulators instead take false comfort in a single number that greatly understates the risk they should be most worried about, that of a major blow-up.

Even though some early readers have made positive noises about Nocera's recounting of the history of VaR, I see enough glitches to raise serious questions. For instance:

L.T.C.M.’s collapse would seem to make a pretty good case for Taleb’s theories. What brought the firm down was a black swan it never saw coming: the twin financial crises in Asia and Russia. Indeed, so sure were the firm’s partners that the market would revert to “normal” — which is what their model insisted would happen — that they continued to take on exposures that would destroy the firm as the crisis worsened, according to Roger Lowenstein’s account of the debacle, “When Genius Failed.” Oh, and another thing: among the risk models the firm relied on was VaR.

I am a big fan of Lowenstein's book, and this passage fails to represent it or the collapse of LTCM accurately. Lowenstein makes clear that after LTCM's initial, spectacular success, the firm stated trading in markets where it lacked the data to do the sort of risk modeling that had been its hallmark. It was basically punting on a massive scale and thus deviating considerably from what had been its historical approach. In addition, the firm was taking very large positions in a lot of markets, yet was making NO allowance for liquidity risk( A CALLING RUN ) (not overall market liquidity, but more basic ongoing trading liquidity, that is, the size of its positions relative to normal trading volumes). In other words, there was no way it could exit most of its positions without having a price impact (both directly, via the scale of its selling, and indirectly, by traders realizing that the big kahuna LTCM wanted out and taking advantage of its need to unload). That is a Trading 101 sort of mistake, yet LTCM perpetrated it in breathtakingly cavalier fashion.( THAT'S TRUE )

Thus the point that Nocera asserts, that the LTCM debacle should have damaged VaR but didn't, reveals a lack of understanding of that episode. LTCM had managed to maintain the image of having sophisticated risk management up to the point of its failure, but it violated its own playbook and completely ignored position size versus normal trading liquidity. Anyone involved in the debacle and unwind (and the Fed and all the big Wall Street houses were) would not see the LTCM failure as related to VaR. There were bigger, far more immediate causes.

So Nocera, by failing to dig deeply enough, winds up defending a failed orthodoxy. I suspect we are going to see a lot of that sort of thing in 2009.

More on this topic (What's this?) "

Here's a VAR chart:

Size of this preview: 800 × 449 pixels
Full resolution‎ (1,223 × 686 pixels, file size: 6 KB, MIME type: image/png)

The 10% Value at Risk of a normally distributed portfolio"

It doesn't look Gaussian.

Here's another:

An example of VaR: Consider a NYMEX heating oil futures position, whose value has the probability distribution shown above. When we say that the position has a three-day, 95 per cent VaR of $5,000,000 as shown above, we mean that we are 95 per cent confident that the position's value will not decrease by more than $5,000,000 over the next three days. However, there is a five per cent chance that losses may exceed $5,000,000, and in extreme scenarios they can be significantly larger."

Here's a few more:

"Value at risk (VAR or sometimes VaR) has been called the "new science of risk management", but you do not need to be a scientist to use VAR. Here, in part 1 of this series, we look at the idea behind VAR and the three basic methods of calculating it. In Part 2, we apply these methods to calculating VAR for a single stock or investment.

The Idea behind VAR
The most popular and traditional measure of risk is volatility. The main problem with volatility, however, is that it does not care about the direction of an investment's movement: a stock can be volatile because it suddenly jumps higher. Of course, investors are not distressed by gains! (See The Limits and Uses of Volatility.)

For investors, risk is about the odds of losing money, and VAR is based on that common-sense fact. By assuming investors care about the odds of a really big loss, VAR answers the question, "What is my worst-case scenario?" or "How much could I lose in a really bad month?"

Now let's get specific. A VAR statistic has three components: a time period, a confidence level and a loss amount (or loss percentage). Keep these three parts in mind as we give some examples of variations of the question that VAR answers:

• What is the most I can - with a 95% or 99% level of confidence - expect to lose in dollars over the next month?
• What is the maximum percentage I can - with 95% or 99% confidence - expect to lose over the next year?

You can see how the "VAR question" has three elements: a relatively high level of confidence (typically either 95% or 99%), a time period (a day, a month or a year) and an estimate of investment loss (expressed either in dollar or percentage terms).

Methods of Calculating VAR
Institutional investors use VAR to evaluate portfolio risk, but in this introduction we will use it to evaluate the risk of a single index that trades like a stock: the Nasdaq 100 Index, which trades under the ticker QQQQ. The QQQQ is a very popular index of the largest non-financial stocks that trade on the Nasdaq exchange.

There are three methods of calculating VAR: the historical method, the variance-covariance method and the Monte Carlo simulation.

1. Historical Method
The historical method simply re-organizes actual historical returns, putting them in order from worst to best. It then assumes that history will repeat itself, from a risk perspective.

The QQQ started trading in Mar 1999, and if we calculate each daily return, we produce a rich data set of almost 1,400 points. Let's put them in a histogram that compares the frequency of return "buckets". For example, at the highest point of the histogram (the highest bar), there were more than 250 days when the daily return was between 0% and 1%. At the far right, you can barely see a tiny bar at 13%; it represents the one single day (in Jan 2000) within a period of five-plus years when the daily return for the QQQ was a stunning 12.4%!


Notice the red bars that compose the "left tail" of the histogram. These are the lowest 5% of daily returns (since the returns are ordered from left to right, the worst are always the "left tail"). The red bars run from daily losses of 4% to 8%. Because these are the worst 5% of all daily returns, we can say with 95% confidence that the worst daily loss will not exceed 4%. Put another way, we expect with 95% confidence that our gain will exceed -4%. That is VAR in a nutshell. Let's re-phrase the statistic into both percentage and dollar terms:

• With 95% confidence, we expect that our worst daily loss will not exceed 4%.
• If we invest $100, we are 95% confident that our worst daily loss will not exceed $4 ($100 x -4%).

You can see that VAR indeed allows for an outcome that is worse than a return of -4%. It does not express absolute certainty but instead makes a probabilistic estimate. If we want to increase our confidence, we need only to "move to the left" on the same histogram, to where the first two red bars, at -8% and -7% represent the worst 1% of daily returns:

• With 99% confidence, we expect that the worst daily loss will not exceed 7%.
• Or, if we invest $100, we are 99% confident that our worst daily loss will not exceed $7.

2. The Variance-Covariance Method
This method assumes that stock returns are normally distributed. In other words, it requires that we estimate only two factors - an expected (or average) return and a standard deviation - which allow us to plot a normal distribution curve. Here we plot the normal curve against the same actual return data:


The idea behind the variance-covariance is similar to the ideas behind the historical method - except that we use the familiar curve instead of actual data. The advantage of the normal curve is that we automatically know where the worst 5% and 1% lie on the curve. They are a function of our desired confidence and the standard deviation ():


The blue curve above is based on the actual daily standard deviation of the QQQ, which is 2.64%. The average daily return happened to be fairly close to zero, so we will assume an average return of zero for illustrative purposes. Here are the results of plugging the actual standard deviation into the formulas above:


3. Monte Carlo Simulation
The third method involves developing a model for future stock price returns and running multiple hypothetical trials through the model. A Monte Carlo simulation refers to any method that randomly generates trials, but by itself does not tell us anything about the underlying methodology.



For most users, a Monte Carlo simulation amounts to a "black box" generator of random outcomes. Without going into further details, we ran a Monte Carlo simulation on the QQQ based on its historical trading pattern. In our simulation, 100 trials were conducted. If we ran it again, we would get a different result--although it is highly likely that the differences would be narrow. Here is the result arranged into a histogram (please note that while the previous graphs have shown daily returns, this graph displays monthly returns):


To summarize, we ran 100 hypothetical trials of monthly returns for the QQQ. Among them, two outcomes were between -15% and -20%; and three were between -20% and 25%. That means the worst five outcomes (that is, the worst 5%) were less than -15%. The Monte Carlo simulation therefore leads to the following VAR-type conclusion: with 95% confidence, we do not expect to lose more than 15% during any given month.

Summary
Value at Risk (VAR) calculates the maximum loss expected (or worst case scenario) on an investment, over a given time period and given a specified degree of confidence. We looked at three methods commonly used to calculate VAR. But keep in mind that two of our methods calculated a daily VAR and the third method calculated monthly VAR. In Part 2 of this series we show you how to compare these different time horizons.

To read more on this subject, see Continuously Compound Interest.
by David Harper (Contact Author | Biography)"

Here's another:

Value at Risk (VaR) is a mathematical approach for estimating the maximum potential loss of a given portfolio within some period of time with some likelihood of occurrence.

BI's VaR Calculator calculates the Value at Risk of the set of securities in the portfolio for paired-trades (one long and the other short) typically made by relative-value hedge funds. Most traditional off-the-shelf VaR measurement tools (such as those from Bloomberg, and Algorithmics) are unable to correctly determine VaR by linking two or more trades together. This is important for hedge funds, otherwise the mis-reported VaR is typically much larger than the true VaR, restricting the amount of active bets the hedge fund can make.

The following financial instruments are covered in the calculation of VaR by our tool:

• Equities, Equity ETFs
• Corporate Bonds
• Treasury Notes and Bonds
• Interest Rate Swaps
• Interest Rate Futures: Fed Funds Futures, Euro-dollar futures
• Treasury Bond Futures
• Commodity Futures
• Credit Default Swaps

The VaR Calculator is provided as a product kit that is customized to each client's (hedge fund or money manager's) trading environment. Two integrations will be performed on-site.

Upon installation, the tool will be integrated with the system or spreadsheet from which it can automatically get the firm's trade position data on a daily basis.

The tool will also be integrated with the vendor's (such as Bloomberg, or Factset) programmatic API that provides live market price data on the financial instruments held in the portfolio, so that automatic daily price retrievals of financial time series can be made.

Methodology

The tool computes the portfolio VaR using the Delta-Normal method. It expects to use market price data on the securities from which we first derive discrete daily returns. Risk is measured over a day's horizon at the 99% confidence level as shown in the figure above. The delta-normal method assumes that all asset returns are normally distributed. As the portfolio return is a linear combination of normal variables, it is also normally distributed.

Standardized risk factors are identified for the various instruments, and variances on them computed automatically from the market data retrieved from daily price quotations:

• Equities and Futures: price-based return
• Bonds, Rate Swaps and other interest-rate sensitive instruments: yield shift

The Portfolio VaR computation method consists of going back in time, and computing variances and correlations for all risk factors. Portfolio risk is considered generated by a combination of linear exposures to many factors that are assumed to be normally distributed, and by the forecast of the covariance matrix (assumes a historical extrapolation).

Key Benefits of BI's VaR Calculator

• Accurately prices VaR of financial instruments when used in a paired arbitrage trade
• Very low cost compared to other vendors such as Algorithmics and RiskMetrics
• Adaptable and customizable to your firm's unique risk monitoring requirements"

My conclusion, looking at a couple of the more technical papers, is that some of these VAR systems look more sensible than others. However, just like Nocera, Yves Smith has to do more technical explantion of this than pointing to a couple of charts. I wish her luck, as even the ones that make senses will be hard to assess without the actual records of use. By the way, all the graphs do seem to have fat tails to my eyes. I don't believe that the models are to blame. As near as I can tell, VAR has some uses, as do CDOs and CDSs, which are all erroneously blamed, where people are in fact to blame.

"Oh, sorry, AT LEAST twenty reasons. I also don't think I've ever read Roubini say his tally of woes was less than comprehensive."

Now back to Yves Smith:

"Roubini's Latest "Why Things Are Hopeless" List Hits New Record, 20 Items!

I have not made a formal tally of Roubini's various lists of why the economy is going (and will continue to go) to hell in a handbasket, but recent sightings suggest his typical list is eight to twelve reasons.

However, in his latest missive, on the subject of why the consumer is toast, Roubini outdoes himself and comes up with twenty reasons. Oh, sorry, AT LEAST twenty reasons. I also don't think I've ever read Roubini say his tally of woes was less than comprehensive."

Here's the thing. I actually thought about listing his predictions, but found it too rough a row to hoe. But not Yves:

"In case you are new to this line of discussion, "falling consumption" in the absence of big time government countermeasures, equals "memorably bad downturn."

Is there some secret significance to this development? Numerologists and technical analysts are encouraged to weigh in. Personally, I think his list does boil down to a dozen or so reasons, but be sure to read down to his last point, where he draws his bottom line, a peak to trough fall in GDP of 10%. He needed 20 reasons to steel readers for his conclusion.

And I am really not making fun of Roubini. It is merely that because his messages are so consistently grim and have so far proven correct, one needs to find comic relief where one can.

From RGE Monitor:

One can count at least 20 separate or complementary causes that will sharply reduce consumption in the next several years:

· The US consumer is shopped-out having spent for the last few years well above its means.

· The US consumer is saving-less as the already low household savings rate at the beginning of this decade went to zero/negative by 2006 and has now to raise to more sustainable levels.

Yves here. I hate to be a pedant, but one and two are more or less the same reason."

Read the rest, and I have Roubini in an earlier post.

Here's my comment:

"Don said...

"Is there some secret significance to this development? Numerologists and technical analysts are encouraged to weigh in."

I'm no expert in Gematria, but I think that it means we should have stored seven years worth of grain, or something like that.

Don the libertarian Democrat

November 16, 2008 11:45 AM

The Faintheated Finally Addresses Post

I've been meaning to get to this Yves Smith post, and finally have:

"Yet More Trade Finance Worries (Not for the Fainthearted)

Readers may know we have been concerned about the dramatic fall in shipments of basic materials, as reflected in the collapse of the Baltic Dry Index. This in turn, as we have also stressed, is in large measure to the difficulty of obtaining trade finance, in particular letters of credit.

A very good post at London Banker, "Systemic Risk, Contagion and Trade Finance - Back to the Bad Old Days," (hat tip reader Bruce) gives a very good recap of the problem and the ramifications. Key excerpts:"

Yes, it could have been the reference to "the fainthearted" that kept me from coming back to it. I'm glad that you all saw that. Here's a chart of the Baltic, which I have a link to in my interesting sites:

Value for BDIY:IND

Chart the Performance of BDIY:IND

Now, back to the story Yves references:

"We are now starting to see the contagion effects of the current liquidity crisis feed through to the real economy...

The recent 93 percent collapse of the obscure Baltic Dry Index – an index of the cost of chartering bulk cargo vessels for goods like ore, cotton, grain or similar dry tonnage – has caused a bit of a stir among the financial cognoscenti. What is less discussed amidst the alarm is the reason for the collapse of the index – the collapse of trade credit based on the venerable letter of credit."

Read this about letters of credit:

"Letters of credit have financed trade for over 400 years. They are considered one of the more stable and secure means of finance as the cargo is secures the credit extended to import it. The letter of credit irrevocably advises an exporter and his bank that payment will be made by the importer's issuing bank if the proper documentation confirming a shipment is presented. This was seen as low risk as the issuing bank could seize and sell the cargo if its client defaulted after payment was made. Like so much else in this topsy turvy financial crisis, however, the verities of the ages have been discarded in favour of new and unpleasant realities."

Now, stop. Do you see it? I bet everyone does:

"The letter of credit irrevocably advises an exporter and his bank that payment will be made by the importer's issuing bank if the proper documentation confirming a shipment is presented."

What if, because of the banking system problems, you can't access that money? Bingo!

"The combination of the global interbank lending freeze with the collapse of the speculative, leveraged commodity price bubble have undermined both the confidence of banks in the ability of a far-flung peer bank to pay an obligation when due and confidence in the value of the dry cargo as security for the credit if liquidated on default. The result is that those with goods to export and those with goods to import, no matter how worthy and well capitalised, are left standing quayside without bank finance for trade.

Adding to the difficulties, letters of credit are so short term that they become an easy target for scaling back credit as liquidity tightens around bank operations globally. Longer term “assets” – like mortgage-back securities, CDOs and CDSs – can’t be easily renegotiated, and banks are loathe to default to one another on them because of cross-default provisions. Short term credit like trade finance can be cut with the flick of an executive wrist.

Further adding to the difficulties, many bulk cargoes are financed in dollars. Non-US banks have been progressively starved of dollar credit...."

So, problems are:
1) No lending between banks
2) Huge drop in prices of goods, so, hey, why not default the goods? That's cheaper than paying. And, yes, it's like walking away from a mortgage where the house has significantly decreased in value.
3) Short term credit, so it's easy to scale back quickly, causing an immediate crisis in the movement of goods.
4) Business conducted in dollars, which are scarce in financing now.

This is clear, but puzzling. How do you deal with 2? The other ones seem workable, especially 3, since you can also immediately solve the problem, without long and complicated negotiations. Anyway, the movement of these commodities is important. But if we can get them moving, is the drop in prices helpful to consumers, hurtful to producers, and who has worked on that?

I'm adding a comment. You can think of different payment options other than the letter of credit, but I was assuming for this post that we were trying to save it.