Simple text logging with a batch file

@echo off
Cls
set /p m=Task:
echo %DATE%^|%TIME%^|%m% >> log.txt

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Lifehacker - xls quicklogger

When the river runs dry

A special report on financial risk  
When the river runs dry
The perils of a sudden evaporation of liquidity

Feb 11th 2010 | From The Economist print edition

STAMPEDING crowds can generate pressures of up to 4,500 Newtons per square metre, enough to bend steel barriers. Rushes for the exit in financial markets can be just as damaging. Investors crowd into trades to get the highest risk-adjusted return in the same way that everyone wants tickets for the best concert. When someone shouts “fire”, their flight creates an “endogenous” risk of being trampled by falling prices, margin calls and vanishing capital—a “negative externality” that adds to overall risk, says Lasse Heje Pedersen of New York University.

This played out dramatically in 2008. Liquidity instantly drained from securities firms as clients abandoned anything with a whiff of risk. In three days in March Bear Stearns saw its pool of cash and liquid assets shrink by nearly 90%. After the collapse of Lehman Brothers, Morgan Stanley had $43 billion of withdrawals in a single day, mostly from hedge funds.

Bob McDowall of Tower Group, a consultancy, explains that liquidity poses “the most emotional of risks”. Its loss can prove just as fatal as insolvency. Many of those clobbered in the crisis—including Bear Stearns, Northern Rock and AIG—were struck down by a sudden lack of cash or funding sources, not because they ran out of capital.

Yet liquidity risk has been neglected. Over the past decade international regulators have paid more attention to capital. Banks ran liquidity stress tests and drew up contingency funding plans, but often half-heartedly. With markets awash with cash and hedge funds, private-equity firms and sovereign-wealth funds all keen to invest in assets, there seemed little prospect of a liquidity crisis. Academics such as Mr Pedersen, Lubos Pastor at Chicago’s Booth School of Business and others were doing solid work on liquidity shocks, but practitioners barely noticed.

What makes liquidity so important is its binary quality: one moment it is there in abundance, the next it is gone. This time its evaporation was particularly abrupt because markets had become so joined up. The panic to get out of levered mortgage investments spilled quickly into interbank loan markets, commercial paper, prime brokerage, securities lending (lending shares to short-sellers) and so on.

As confidence ebbed, mortgage-backed securities could no longer be used so easily as collateral in repurchase or “repo” agreements, in which financial firms borrow short-term from investors with excess cash, such as money-market funds. This was a big problem because securities firms had become heavily reliant on this market, tripling their repo borrowing in the five years to 2008. Bear Stearns had $98 billion on its books, compared with $72 billion of long-term debt.

Even the most liquid markets were affected. In August 2007 a wave of selling of blue-chip shares, forced by the need to cover losses on debt securities elsewhere, caused sudden drops of up to 30% for some computer-driven strategies popular with hedge funds.

Liquidity comes in two closely connected forms: asset liquidity, or the ability to sell holdings easily at a decent price; and funding liquidity, or the capacity to raise finance and roll over old debts when needed, without facing punitive “haircuts” on collateral posted to back this borrowing.

The years of excess saw a vast increase in the funding of long-term assets with short-term (and thus cheaper) debt. Short-term borrowing has a good side: the threat of lenders refusing to roll over can be a source of discipline. Once they expect losses, though, a run becomes inevitable: they rush for repayment to beat the crowd, setting off a panic that might hurt them even more. “Financial crises are almost always and everywhere about short-term debt,” says Douglas Diamond of the Booth School of Business.

Banks are founded on this “maturity mismatch” of long- and short-term debt, but they have deposit insurance which reduces the likelihood of runs. However, this time much of the mismatched borrowing took place in the uninsured “shadow” banking network of investment banks, structured off-balance-sheet vehicles and the like. It was supported by seemingly ingenious structures. Auction-rate securities, for instance, allowed the funding of stodgy municipal bonds to be rolled over monthly, with the interest rate reset each time.

The past two years are littered with stories of schools and hospitals that came a cropper after dramatically shortening the tenure of their funding, assuming that the savings in interest costs, small as they were, far outweighed the risk of market seizure. Securities firms became equally complacent as they watched asset values rise, boosting the value of their holdings as collateral for repos. Commercial banks increased their reliance on wholesale funding and on fickle “non-core” deposits, such as those bought from brokers.

Regulation did nothing to discourage this, treating banks that funded themselves with deposits and those borrowing overnight in wholesale markets exactly the same. Markets viewed the second category as more efficient. Northern Rock, which funded its mortgages largely in capital markets, had a higher stockmarket rating than HSBC, which relied more on conventional deposits. The prevailing view was that risk was inherent in the asset, not the manner in which it was financed.

At the same time financial firms built up a host of liquidity obligations, not all of which they fully understood. Banks were expected to support off-balance-sheet entities if clients wanted out; Citigroup had to take back $58 billion of short-term securities from structured vehicles it sponsored. AIG did not allow for the risk that the insurer would have to post more collateral against credit-default swaps if these fell in value or its rating was cut.

Now that the horse has bolted, financial firms are rushing to close the door, for instance by adding to liquidity buffers (see chart 4). British banks’ holdings of sterling liquid assets are at their highest for a decade. Capital-markets firms are courting deposits and shunning flighty wholesale funding. Deposits, equity and long-term debt now make up almost two-thirds of Morgan Stanley’s balance-sheet liabilities, compared with around 40% at the end of 2007. Spending on liquidity-management systems is rising sharply, with specialists “almost able to name their price”, says one banker. “Collateral management” has become a buzzword.
Message from Basel

Regulators, too, are trying to make up for lost time. In a first attempt to put numbers on a nebulous concept, in December the Basel Committee of central banks and supervisors from 27 countries proposed a global liquidity standard for internationally active banks. Tougher requirements would reverse a decades-long decline in banks’ liquidity cushions.

The new regime, which could be adopted as early as 2012, has two components: a “coverage” ratio, designed to ensure that banks have a big enough pool of high-quality, liquid assets to weather an “acute stress scenario” lasting for one month (including such inconveniences as a sharp ratings downgrade and a wave of collateral calls); and a “net stable funding” ratio, aimed at promoting longer-term financing of assets and thus limiting maturity mismatches. This will require a certain level of funding to be for a year or more.

It remains to be seen how closely national authorities follow the script. Some seem intent on going even further. In Switzerland, UBS and Credit Suisse face a tripling of the amount of cash and equivalents they need to hold, to 45% of deposits. Britain will require all domestic entities to have enough liquidity to stand alone, unsupported by their parent or other parts of the group. Also controversial is the composition of the proposed liquidity cushions. Some countries want to restrict these to government debt, deposits with central banks and the like. The Basel proposals allow high-grade corporate bonds too.

Banks have counter-attacked, arguing that “trapping” liquidity in subsidiaries would reduce their room for manoeuvre in a crisis and that the buffer rules are too restrictive; some, unsurprisingly, have called for bank debt to be eligible. Under the British rules, up to 8% of banks’ assets could be tied up in cash and gilts (British government bonds) that they are forced to hold, reckons Simon Hills of the British Bankers Association, which could have “a huge impact on business models”. That, some argue, is precisely the point of reform.

Much can be done to reduce market stresses without waiting for these reforms. In repo lending—a decades-old practice critical to the smooth functioning of markets—the Federal Reserve may soon toughen collateral requirements and force borrowers to draw up contingency plans in case of a sudden freeze. Banks that clear repos will be expected to monitor the size and quality of big borrowers’ positions more closely. The banks could live with that, but they worry about proposals to force secured short-term creditors to take an automatic loss if a bank fails.

Another concern is prime brokerage, banks’ financing of trading by hedge funds. When the market unravelled, hedge funds were unable to retrieve collateral that their brokers had “rehypothecated”, or used to fund transactions of their own; billions of such unsegregated money is still trapped in Lehman’s estate, reducing dozens of its former clients to the status of unsecured general creditors. Brokers suffered in turn as clients pulled whatever funds they could from those they viewed as vulnerable. Temporary bans on short-selling made things even worse, playing havoc with some hedge funds’ strategies and leaving them scrambling for cash. Regulators are moving towards imposing limits on rehypothecation.

Early reform could also come to the securities-lending market, in which institutional investors lend shares from their portfolios to short-sellers for a fee. Some lenders—including, notoriously, AIG—found they were unable to repay cash collateral posted by borrowers because they had invested it in instruments that had turned illiquid, such as asset-backed commercial paper. Some have doubled the share of their portfolios that they know they can sell overnight, to as much as 50%.

Regulators might consider asking them to go further. Bond markets, unlike stockmarkets, revolve around quotes from dealers. This creates a structural impediment to the free flow of liquidity in strained times, argues Ken Froot of Harvard Business School, because when dealers pull in their horns they are unable to function properly as market-makers. He suggests opening up access to trade data and competition to quote prices. Some senior figures at the Fed like the idea, as do money managers, though predictably dealers are resisting.
Twin realities

The other brutal lesson of the crisis concerns the way liquidity can affect solvency. In a world of mark-to-market accounting, a small price movement on a large, illiquid portfolio can quickly turn into crippling paper losses that eat into capital. Highly rated but hard-to-shift debt instruments can finish you off before losses on the underlying loans have even begun to hurt your cash flows. If markets expect fire sales, potential buyers will hold off for a better price, exacerbating fair-value losses.

In future banks will be more alert to these dangers. “We were looking at the bonds we held, focusing on the credit fundamentals. We lost sight of the capital hit from illiquidity and marking to market that can seriously hurt you in the meantime,” says Koos Timmermans, chief risk officer at ING, a large Dutch banking and insurance group. “We now know that you have to treat the accounting reality as economic reality.”

Another lesson is the “opportunity value” of staying liquid in good times, says Aaron Brown, a risk manager with AQR, a hedge fund. In an efficient market dollar bills are not left lying around. But in the dislocated markets of late 2008 there were lots of bargains to be had for the small minority of investors with dry powder.

For some, though, bigger liquidity problems may yet lie ahead. Some $5.1 trillion of bank debt rated by Moody’s is due to mature by 2012. This will have to be refinanced at higher rates. The rates could also be pushed up by an erosion of sovereign credit quality, given implicit state guarantees of bank liabilities. And, at some point, banks face a reduction of cut-price liquidity support from central banks—offered in return for often dodgy collateral—which has buoyed their profit margins. Mortgage borrowers on teaser rates are vulnerable to payment shock. So too are their lenders.

Economist article

Number-crunchers crunched

A special report on financial risk
Number-crunchers crunched
The uses and abuses of mathematical models

Feb 11th 2010 | From The Economist print edition

IT PUT noses out of joint, but it changed markets for good. In the mid-1970s a few progressive occupants of Chicago’s options pits started trading with the aid of sheets of theoretical prices derived from a model and sold by an economist called Fisher Black. Rivals, used to relying on their wits, were unimpressed. One model-based trader complained of having his papers snatched away and being told to “trade like a man”. But the strings of numbers caught on, and soon derivatives exchanges hailed the Black-Scholes model, which used share and bond prices to calculate the value of derivatives, for helping to legitimise a market that had been derided as a gambling den.

Thanks to Black-Scholes, options pricing no longer had to rely on educated guesses. Derivatives trading got a huge boost and quants poured into the industry. By 2005 they accounted for 5% of all finance jobs, against 1.2% in 1980, says Thomas Philippon of New York University—and probably a much higher proportion of pay. By 2007 finance was attracting a quarter of all graduates from the California Institute of Technology.

These eggheads are now in the dock, along with their probabilistic models. In America a congressional panel is investigating the models’ role in the crash. Wired, a publication that can hardly be accused of technophobia, has described default-probability models as “the formula that killed Wall Street”. Long-standing critics of risk-modelling, such as Nassim Nicholas Taleb, author of “The Black Swan”, and Paul Wilmott, a mathematician turned financial educator, are now hailed as seers. Models “increased risk exposure instead of limiting it”, says Mr Taleb. “They can be worse than nothing, the equivalent of a dangerous operation on a patient who would stand a better chance if left untreated.”

Not all models were useless. Those for interest rates and foreign exchange performed roughly as they were meant to. However, in debt markets they failed abjectly to take account of low-probability but high-impact events such as the gut-wrenching fall in house prices.

The models went particularly awry when clusters of mortgage-backed securities were further packaged into collateralised debt obligations (CDOs). In traditional products such as corporate debt, rating agencies employ basic credit analysis and judgment. CDOs were so complex that they had to be assessed using specially designed models, which had various faults. Each CDO is a unique mix of assets, but the assumptions about future defaults and mortgage rates were not closely tailored to that mix, nor did they factor in the tendency of assets to move together in a crisis.

The problem was exacerbated by the credit raters’ incentive to accommodate the issuers who paid them. Most financial firms happily relied on the models, even though the expected return on AAA-rated tranches was suspiciously high for such apparently safe securities. At some banks, risk managers who questioned the rating agencies’ models were given short shrift. Moody’s and Standard & Poor’s were assumed to know best. For people paid according to that year’s revenue, this was understandable. “A lifetime of wealth was only one model away,” sneers an American regulator.

Moreover, heavy use of models may have changed the markets they were supposed to map, thus undermining the validity of their own predictions, says Donald MacKenzie, an economic sociologist at the University of Edinburgh. This feedback process is known as counter-performativity and had been noted before, for instance with Black-Scholes. With CDOs the models’ popularity boosted demand, which lowered the quality of the asset-backed securities that formed the pools’ raw material and widened the gap between expected and actual defaults (see chart 3).

A related problem was the similarity of risk models. Banks thought they were diversified, only to find that many others held comparable positions, based on similar models that had been built to comply with the Basel 2 standards, and everyone was trying to unwind the same positions at the same time. The breakdown of the models, which had been the only basis for pricing the more exotic types of security, turned risk into full-blown uncertainty (and thus extreme volatility).

For some, the crisis has shattered faith in the precision of models and their inputs. They failed Keynes’s test that it is better to be roughly right than exactly wrong. One number coming under renewed scrutiny is “value-at-risk” (VAR), used by banks to measure the risk of loss in a portfolio of financial assets, and by regulators to calculate banks’ capital buffers. Invented by eggheads at JPMorgan in the late 1980s, VAR has grown steadily in popularity. It is the subject of more than 200 books. What makes it so appealing is that its complex formulae distil the range of potential daily profits or losses into a single dollar figure.
Only so far with VAR

Frustratingly, banks introduce their own quirks into VAR calculations, making comparison difficult. For example, Morgan Stanley’s VAR for the first quarter of 2009 by its own reckoning was $115m, but using Goldman Sachs’s method it would have been $158m. The bigger problem, though, is that VAR works only for liquid securities over short periods in “normal” markets, and it does not cover catastrophic outcomes. If you have $30m of two-week 1% VAR, for instance, that means there is a 99% chance that you will not lose more than that amount over the next fortnight. But there may be a huge and unacknowledged threat lurking in that 1% tail.

So chief executives would be foolish to rely solely, or even primarily, on VAR to manage risk. Yet many managers and boards continue to pay close attention to it without fully understanding the caveats—the equivalent of someone who cannot swim feeling confident of crossing a river having been told that it is, on average, four feet deep, says Jaidev Iyer of the Global Association of Risk Professionals.

Regulators are encouraging banks to look beyond VAR. One way is to use CoVAR (Conditional VAR), a measure that aims to capture spillover effects in troubled markets, such as losses due to the distress of others. This greatly increases some banks’ value at risk. Banks are developing their own enhancements. Morgan Stanley, for instance, uses “stress” VAR, which factors in very tight liquidity constraints.

Like its peers, Morgan Stanley is also reviewing its stress testing, which is used to consider extreme situations. The worst scenario envisaged by the firm turned out to be less than half as bad as what actually happened in the markets. JPMorgan Chase’s debt-market stress tests foresaw a 40% increase in corporate spreads, but high-yield spreads in 2007-09 increased many times over. Others fell similarly short. Most banks’ tests were based on historical crises, but this assumes that the future will be similar to the past. “A repeat of any specific market event, such as 1987 or 1998, is unlikely to be the way that a future crisis will unfold,” says Ken deRegt, Morgan Stanley’s chief risk officer.

Faced with either random (and therefore not very believable) scenarios or simplistic models that neglect fat-tail risks, many find themselves in a “no-man’s-land” between the two, says Andrew Freeman of Deloitte (and formerly a journalist at The Economist). Nevertheless, he views scenario planning as a useful tool. A firm that had thought about, say, the mutation of default risk into liquidity risk would have had a head start over its competitors in 2008, even if it had not predicted precisely how this would happen.

To some, stress testing will always seem maddeningly fuzzy. “It has so far been seen as the acupuncture-and-herbal-remedies corner of risk management, though perceptions are changing,” says Riccardo Rebonato of Royal Bank of Scotland, who is writing a book on the subject. It is not meant to be a predictive tool but a means of considering possible outcomes to allow firms to react more nimbly to unexpected developments, he argues. Hedge funds are better at this than banks. Some had thought about the possibility of a large broker-dealer going bust. At least one, AQR, had asked its lawyers to grill the fund’s prime brokers about the fate of its assets in the event of their demise.

Some of the blame lies with bank regulators, who were just as blind to the dangers ahead as the firms they oversaw. Sometimes even more so: after the rescue of Bear Stearns in March 2008 but before Lehman’s collapse, Morgan Stanley was reportedly told by supervisors at the Federal Reserve that its doomsday scenario was too bearish.

The regulators have since become tougher. In America, for instance, banks have been told to run stress tests with scenarios that include a huge leap in interest rates. A supervisors’ report last October fingered some banks for “window-dressing” their tests. Officials are now asking for “reverse” stress testing, in which a firm imagines it has failed and works backwards to determine which vulnerabilities caused the hypothetical collapse. Britain has made this mandatory. Bankers are divided over its usefulness.
Slicing the Emmental

These changes point towards greater use of judgment and less reliance on numbers in future. But it would be unfair to tar all models with the same brush. The CDO fiasco was an egregious and relatively rare case of an instrument getting way ahead of the ability to map it mathematically. Models were “an accessory to the crime, not the perpetrator”, says Michael Mauboussin of Legg Mason, a money manager.

As for VAR, it may be hopeless at signalling rare severe losses, but the process by which it is produced adds enormously to the understanding of everyday risk, which can be just as deadly as tail risk, says Aaron Brown, a risk manager at AQR. Craig Broderick, chief risk officer at Goldman Sachs, sees it as one of several measures which, although of limited use individually, together can provide a helpful picture. Like a slice of Swiss cheese, each number has holes, but put several of them together and you get something solid.

Modelling is not going away; indeed, number-crunchers who are devising new ways to protect investors from outlying fat-tail risks are gaining influence. Pimco, for instance, offers fat-tail hedging programmes for mutual-fund clients, using cocktails of options and other instruments. These are built on specific risk factors rather than on the broader and increasingly fluid division of assets between equities, currencies, commodities and so on. The relationships between asset classes “have become less stable”, says Mohamed El-Erian, Pimco’s chief executive. “Asset-class diversification remains desirable but is not sufficient.”

Not surprisingly, more investors are now willing to give up some upside for the promise of protection against catastrophic losses. Pimco’s clients are paying up to 1% of the value of managed assets for the hedging—even though, as the recent crisis showed, there is a risk that insurers will not be able to pay out. Lisa Goldberg of MSCI Barra reports keen interest in the analytics firm’s extreme-risk model from hedge funds, investment banks and pension plans.

In some areas the need may be for more computing power, not less. Financial firms already spend more than any other industry on information technology (IT): some $500 billion in 2009, according to Gartner, a consultancy. Yet the quality of information filtering through to senior managers is often inadequate.

A report by bank supervisors last October pointed to poor risk “aggregation”: many large banks simply do not have the systems to present an up-to-date picture of their firm-wide links to borrowers and trading partners. Two-thirds of the banks surveyed said they were only “partially” able (in other words, unable) to aggregate their credit risks. The Federal Reserve, leading stress tests on American banks last spring, was shocked to find that some of them needed days to calculate their exposure to derivatives counterparties.

To be fair, totting up counterparty risk is not easy. For each trading partner the calculations can involve many different types of contract and hundreds of legal entities. But banks will have to learn fast: under new international proposals, they will for the first time face capital charges on the creditworthiness of swap counterparties.

The banks with the most dysfunctional systems are generally those, such as Citigroup, that have been through multiple marriages and ended up with dozens of “legacy” systems that cannot easily communicate with each other. That may explain why some Citi units continued to pile into subprime mortgages even as others pulled back.

In the depths of the crisis some banks were unaware that different business units were marking the same assets at different prices. The industry is working to sort this out. Banks are coming under pressure to appoint chief data officers who can police the integrity of the numbers, separate from chief information officers who concentrate on system design and output.

Some worry that the good work will be cast aside. As markets recover, the biggest temptation will be to abandon or scale back IT projects, allowing product development to get ahead of the supporting technology infrastructure, just as it did in the last boom.

The way forward is not to reject high-tech finance but to be honest about its limitations, says Emanuel Derman, a professor at New York’s Columbia University and a former quant at Goldman Sachs. Models should be seen as metaphors that can enlighten but do not describe the world perfectly. Messrs Derman and Wilmott have drawn up a modeller’s Hippocratic oath which pledges, among other things: “I will remember that I didn’t make the world, and it doesn’t satisfy my equations,” and “I will never sacrifice reality for elegance without explaining why I have done so.” Often the problem is not complex finance but the people who practise it, says Mr Wilmott. Because of their love of puzzles, quants lean towards technically brilliant rather than sensible solutions and tend to over-engineer: “You may need a plumber but you get a professor of fluid dynamics.”

One way to deal with that problem is to self-insure. JPMorgan Chase holds $3 billion of “model-uncertainty reserves” to cover mishaps caused by quants who have been too clever by half. If you can make provisions for bad loans, why not bad maths too?

Economist article

Debt sustainability - Not so risk-free

Debt sustainability

Not so risk-free
Feb 11th 2010
From The Economist print edition

Which countries have the biggest problems?

MARKETS have suddenly woken up to the idea that not all government debt is risk-free. There is a long and not very honourable history of sovereign default, either explicitly or implicitly via inflation and currency depreciation.

So which countries are in the biggest trouble? The ability of a government to honour its debt depends on a number of factors, in particular the size of the debt burden relative to GDP, the interest rate paid on that debt relative to the economy’s growth rate and the size of the government’s primary budget balance—the surplus, or deficit, before interest costs.

If the interest rate paid on public debt is higher than the economy’s growth rate, the stock of government debt will rise as a share of GDP unless governments run a primary budget surplus. The bigger the stock of debt, the bigger that surplus needs to be. This arithmetic suggests that countries with big primary deficits, big debt stocks and a big gap between interest rates and growth are most vulnerable.

This can be a self-fulfilling process. Investors will worry about governments’ ability to service their debt and will push up yields, making debt servicing even harder. The shorter the maturity of the debt, the quicker this problem will arise. And if the debt is denominated in a foreign currency or held largely by foreign creditors, then a debt crisis can be compounded by a currency crisis.

The table shows the main sources of vulnerability for a range of OECD countries. The first column shows each country’s primary deficit or surplus adjusted for the economic cycle. The second column shows the OECD’s forecast for each country’s net debt-to-GDP ratio in 2010. The third column measures the gap between bond yields on debt of average maturity for each country and the OECD’s forecasts for growth in 2010 and 2011. The bigger the negative number, the bigger the problem (although longer-dated debt tends to pay higher yields, so this measure may disadvantage countries which have less refinancing risk). The countries are ranked by adding together their relative league-table positions on these three measures, a rough gauge of the scale of their debt problems.

The fourth column adds another source of risk—the average time to maturity of outstanding government debt. Countries with shorter maturities are more likely to face refinancing problems than those with longer ones. Two big borrowers stand out on this measure: America, for its short debt maturities, and Britain, which can draw some comfort from the lengthy duration of its debt.

Countries that come out badly from the tables may not default, of course. Japan looks worrying on many measures, for example, but has long been able to fund itself by issuing government debt to domestic investors. America’s debt remains the sanctuary of choice when risk aversion rises. Some, like Ireland, have already taken tough decisions to get their finances under control. But as Greece and others are finding out, they will all face severe pressure from the markets to bring their deficits under control. And that may cause a political as well as a fiscal crisis.

Economist article

Appending a date to files

7z.exe a "c:\temp\onenote_%date:/=%.zip" "C:\Users\\AppData\Local\Microsoft\OneNote\12.0\Backup\Personal Notebook"

A simple way of appending a date to a backup file. It works by stripping out the '/' symbol from the date, which may not seem seem like much but always had me stumped. I found a few date scripts on the web but those were just too complicated for me.  This will be very useful for backups of Onenote, great program it might be but could really use an export data function. The program backup data automatically by default to the hidden application data folder. How difficult would it be to add that option and let folks backup their data easily? 

Another way would be to use Namedate , a program that appends dates to a specified program.