Bloomberg: Morgan Stanley was burned by a wager on U.S. inflation expectations in the second quarter, three people informed of the dealings said.

Traders at the bank bet that inflation expectations for the next five years would rise in Treasury markets, while forecasts for the next 30 years would fall, according to two of the people. Such wagers on so-called breakeven rates involve paired purchases and short sales of Treasuries and Treasury Inflation Protected Securities, or TIPS, in both maturities.

The TIPS market was roiled last week by the combination of a slump in the price of crude oil and a stronger-than-expected auction of new 30-year TIPS. Source

There has been some talk about the high level of the Credit Suisse Fear Barometer lately (e.g. SurlyTrader).

Bloomberg: The CS Fear Barometer measures investor sentiment for 3-month investment horizons by pricing a zero-cost collar. The collar is implemented by the selling of a 10% OTM SPX call option and using the proceeds to buy an OTM put. The CSFB level represents how far out-of-the-money that SPX put is. The higher the level, the greater the fear.

With the proceeds of selling a 10% OTM call you can currently only buy a 28% OTM put:

{Click to enlarge}

But what does that mean? When comparing the CS Fear Barometer with the level of the S&P 500 over the last 15 years you will notice that

1. Fear had a very low reading at the time of the dot-com crash and it stayed pretty low during the whole 2000-2003 equity market downturn
2. Fear trended upwards during the equity market recovery
3. Fear fell off a cliff quite some time before the global financial market meltdown (false sense of security?)
4. Fear was close to its all-time low at a time where many bankers where talking about buying farmland
5. Fear has been rising since the markets turned in March 2009

So it seems like the indicator could measure anything but fear. In 2009, when Credit Suisse introduced the index Reuters wrote: "The index would rise when there is excess investor demand for portfolio insurance or lack of demand for call options."

Obviously, it could also be a glut in the market. For example in case fund mangers think that there is not much upside in the current market they would start writing covered calls and reduce implied call volatility in the process. That shouldn't count as "fear".

Anyway, since for the last couple of years the CS Fear Barometer was just a blurred version of the inverse VIX I wonder whether anybody can distill some additional information:

This post explains the Vasicek/Merton single factor model which is part of the Basel framework (IRB approach) and has been used to evaluate CDOs.

Imagine you loan money to a friend who will default with a probability of 1%. When it comes to paying back the loan you will either receive 100% (plus any interest) or 0%. That's pretty risky. So you figure you can improve your situation by making same size loans to n friends. (NB: You will need a license for doing so). In case they all default individually with a probability of p = 1%, the law of large numbers tells you that the more loans you make (increase n), the closer the average default rate (= portfolio default rate) will be to 1%. "The" central limit theorem states that the portfolio default rate will be normally distributed with a mean of 1% and a variance that goes to zero as n increases.

Now here is the result for n = 5000 loans and 100,000 simulations (portfolios):

Unfortunately, this is only true when default solely happens due to idiosyncratic reason such as illness or a divorce, i.e. when the default of one friend is not related to the default of another friend. But in case you make loans to colleagues from work this assumption won't be correct since bankruptcy of the company would turn most of your loans sour at the same time no matter how large (number of obligors) your loan portfolio actually is. In other words: Certain systemic risk can't be diversified away.

Assuming you really have lots of "similar" friends (p = 1%) and they are all pretty evenly distributed across the sectors the economy has to offer one could argue that defaults are actually only a function of this idiosyncratic risk (as before) and a single systemic risk factor reflecting the overall state of the economy. In case the economy does very well, hardly anybody will default (even an expensive divorce is not an issue) and in case the economy enters into a deep recession, the default rate goes north. The sensitivity of each obligor to this systemic factor and the correlation among the obligors is given by √ρ and ρ, respectively.

Result for n = 5000 loans and 100,000 simulations (portfolios):

The average portfolio default rate is not affected by an increase in correlation, but the higher the correlation the more likely extreme portfolio default rates become (good or bad). In case the correlation is one, we are back to a single obligor. Either nobody (0%) or everybody (100%) defaults.

The model (see comment section for details) is a useful starting point but since both systemic and idiosyncratic risk is assumed to be normally distributed and are connected via an uncertain correlation coefficient you could easily be on the wrong end of the trade. NB: That doesn't mean the Basel guys did a bad job. They had two parameters (correlation and systemic shock size) for calibrating the model.

CDS Curve (white line) vs. Z-Spread (+)

Read: Citi sees free lunch in Greek basis (almost), FT Alphaville

Deutsche Bank Research: Existing evidence related to the impact of HFT on certain market quality and efficiency indicators is inconclusive. Some studies (e.g. Hendershott and Riordan, 2009; Jovanovic and Menkveld, 2010) suggest that HFT using market making and arbitrage strategies has added liquidity to the market, reduced spreads and helped align prices across markets. While there is no proof of a negative liquidity impact in the academic literature, certain issues still remain:

• HFs are under no affirmative market making obligation, i.e. they are not obliged to provide liquidity by consistently displaying high-quality, two-sided quotes. This may translate into a lack of available liquidity, in particular during volatile market conditions.
• HFTs contribute little to market depth due to the marginal size of their quotes. This may result in larger orders having to transact with many small orders and may affect overall transaction costs
• HFT quotes are barely accessible due to the short duration for which the liquidity is available when orders are cancelled within milliseconds.

Another interesting issue is whether HFT contributes to the price formation process on equities markets. In this context, Brogaard (2010) examines a large data set of HFT firms trading on Nasdaq and finds that, firstly, HFTs add substantially to the price formation process as they tend to follow a price reversal strategy (irrespective of whether they are supplying liquidity or demanding it), driven by order imbalances, and so tend to stabilise prices. Secondly, HFTs do not seem to systematically front-run non-HFTs. They provide the best bid and offer quotes for a significant portion of the trading day, but only around a quarter of the book depth (as do non-HFTs) and reduce their supply of liquidity only moderately as volatility increases. Thirdly, HFTs engage in a less diverse variety of strategies than non-HFTs, which may exacerbate market movements if HFTs use similar trading strategies. Fourthly, while in principle high cancellation rates could impact the smoothness of execution in markets where HFTs are present, prevailing narrow spreads seem to suggest that cancelled quotes are quickly replaced by other market participants. Hendershott and Riordan (2009) find that algorithmic traders’ quotes play a larger role in the price formation process than human quotes. Summing up, on the one hand, price discovery benefits from market participants who quickly detect anomalies in market prices and correct them. On the other hand, HFT may distort price formation if it creates an incentive for natural liquidity to shift into dark pools as a way of avoiding transacting with ever-decreasing order sizes. In terms of market volatility, neither Hendershott and Riordan (2009) nor Brogaard (2010) find any evidence for a detrimental impact of either AT [algorithmic trading] or HFT. Economic perspective and potential regulatory aspects.

High-frequency trading? Better than its reputation?, DB Research, Feb 2011

Fallacies, Irrelevant Facts, and Myths in the Discussion of Capital Regulation: Why Bank Equity is Not Expensive
by Admati, DeMarzo, Hellwig, Pfleiderer

I've just returned from the OeNB where Admati & Hellwig presented the paper. The take-home message: It's often argued that with higher capital requirements / restrictions on leverage bank's funding cost will increase. But the argument that equity is expensive is wrong because with more equity and less leverage banks will become less risky which reduces the required ROE/funding costs. And banks don't have to cut back lending when they can substitute debt with cheap equity. Unfortunately, 1. most tax systems allow interest to be deducted as an expense, which creates a debt tax shield and 2. often there is an implicit state guarantee on bank debt. This effectively penalizes equity financing.

tag : Modigliani-Miller

Banks in ‘Downward Spiral' Buying Capital in Discredited CDOs
Bloomberg News

Bloomberg: David Siniapkin, a postal worker in York, Pennsylvania, uses some of his retirement money to trade options. After three years and being down as much as $10,000, he’s broken even.

“I’ll do the iron condors, I’ll do calendars, I like double diagonals,” said Siniapkin, 46, who said he has had “mixed success” with these strategies, known as multi-leg transactions, which involve buying or selling multiple contracts on the same underlying security.

“Trading options is one of the all-time suckers’ bets,” said Whitney Tilson, founder of hedge fund T2 Partners LLC, based in New York.

Regulators permit trading options using retirement accounts, said Herb Perone, spokesman for the Financial Industry Regulatory Authority. Certain trading may violate Internal Revenue Service rules, which is why firms including Schwab, Fidelity, TD Ameritrade, E*Trade, Interactive Brokers Group Inc. and OptionsXpress Holdings Inc. prevent investors from executing strategies that may cause an IRA to go into debt, according to the companies. Full Story

related items:
LIFFE Options - a guide to trading strategies

Banks are exposed to credit risk in all areas of business: loans, letters of credit, guarantees and foreign exchange settlement to name a few. The arrival of credit default swaps in the mid 1990s transformed bank credit portfolio management by allowing banks to lay off the credit risk that, in its raw form, was virtually unsellable.

The very nature of illiquid non-standard credit instruments (and most credit risk fits this definition) means that the instrument itself often cannot be hedged or even sold: it is the credit risk of the instrument that is being hedged. The “liquidity” that is brought by credit derivatives is risk liquidity, not cash liquidity.

Banks tend to specialise in regions, industries and products, and the bulk of their business is often done with a small percentage of their clients. Over time this can produce very lumpy credit portfolios that, before the arrival of credit derivatives, could only be managed (not hedged) by imposing limits and by striving for diversification.

To ban CDS altogether would virtually remove a bank’s ability to manage credit risk; to ban naked shorts would completely remove the ability to find proxy hedges for the huge array of credit risk that cannot be sold or directly hedged. More

Ban will restrict ability to manage credit risk, Financial Times, Letter, Robert Reoch, New College Capital, London