100 Cayman Islands Hedge Funds Launch Despite Predictions

Despite dire predictions for the Cayman Islands hedge fund industry reported in the Financial Times this week, local figures paint a very different picture. As of 30 June there were 9,486 funds in this jurisdiction, an increase of over one hundred on the previous quarter. Other good news is that so far this year, authorizations have outstripped terminations each and every month. Ingrid Pierce, partner and head of Walkers' Hedge Fund Group in the Cayman Islands, said clients continue to have the utmost confidence in the Cayman Islands as the domicile of choice when it comes to hedge funds, and despite concerns to the contrary, very few funds have either redomiciled or determined to set up funds elsewhere.

With clients among the world's leading investment managers and financial institutions, Walkers reports a healthy flow of instructions for new funds to be established.

“Recent press commentary has pointed to a preference for new hedge funds to be domiciled in Ireland and Luxembourg at the expense of Cayman, however a more likely scenario is that these jurisdictions will co-exist with the Cayman Islands, each appealing to different if partially overlapping segments within the investor and investment manager communities,” Pierce added.

In a short article in the UK’s leading financial paper, The Financial Times, the future picture of the Cayman Islands hedge fund sector was painted as bleak, with some hedge fund managers stating that institutional investors are increasingly choosing to invest in funds that comply with Europe’s onshore Ucits regime rather than unregulated offshore hedge funds.

Mark Fleming, a partner at Tiburon Partners, an Asia specialist that runs both Newcits and Cayman funds, said that the Cayman Islands would “wither on the vine” and his firm would not open another Cayman fund.“If I was a Cayman lawyer with more than three or four years of career expectation I would wonder what I’m going to do with the rest of my working life,” he told the FT. Dan Mannix, head of sales at London-based RWC Partners, said his firm was also looking at redomiciling its Cayman funds into the EU. “Funds in offshore centres such as the Caymans are not favoured at the moment by the investment community,” he said.

However, Pierce pointed out that, while UCITS funds are suitable for particular strategies and investors with certain requirements, they are wholly unsuitable for a large number of other funds that continue to enjoy the benefits offered by funds established in Cayman.

“This shows that the Cayman model is incredibly resilient and this is borne out by the statistics on fund registrations as well as the experience of Walkers and other firms regarding instructions for the establishment of new funds,” she said.

Meanwhile, on Thursday morning Anthony Travers, Chairman of Cayman Finance, said he was bemused by the comments made to the FT. Based on CIMA’s figures, he predicted there would be in excess of 10,200 fund registrations by the financial year end. “This will exceed the all-time high watermark for the Cayman fund Industry,” he added.

Company incorporations were also growing again, he noted, with increases of over 14% for Q1 and 24% for Q2. “It is more helpful to report on the actual numbers rather than the wishful thinking on the part of competitor jurisdictions,” Travers observed.

“We take a statistical approach to these matters and will also look forward to comparing the performance metrics of our traditional and successful hedge fund product to the EU regulated ‘Newcits’ funds over the next couple of years. Monthly fund dissolutions in Cayman appear well within historical norms and do not evidence any trend towards redomiciliation. We would anticipate however that fund managers with a purely retail product will in the immediate term create a Eurocentric model but Cayman has not historically provided a UCITS product,” the Cayman Finance chair explained.

An article in Hedge Funds Review concerning Dalton Strategic Partnership’s doubling the investment risk of its Melchior European Fund in a bid to seek higher returns illustrated the point that Cayman offers a different kind of fund regime.

Magnus Spence, a partner in the firm, pointed out that traditional hedge fund investors are still prepared to take more risk in order to achieve higher returns outside of UCITS where investors are happy to sacrifice returns for a lower risk profile. Spence pointed out the need of the hedge fund sector to offer investors the choice. "If we want to be attractive to both types of hedge fund investors we need to be structured in the right products and we need to have the right risk return profile," Spence added.

Pierce pointed out that Cayman's leadership of the investment funds industry as the domicile of choice for hedge funds has developed over time as a result of its pro-business, cost efficient environment within a strong system of regulation that adheres to international standards. Even though Cayman was catering to higher risk investors, she said, it was still likely to be able to maintain access to the European market.

“Looking at any of the likely conditions that non-EU countries would be required to meet in order for their funds to gain access to the European market, there is nothing to suggest that the Cayman Islands will not maintain its leading position and continue to play a valuable role in the international financial system.”

Over the medium to long term, Pierce indicated that it was unlikely that the AIFM Directive will adversely impact Cayman’s position. “Government has already demonstrated its commitment to do everything within its power, acting with propriety and integrity, to safeguard its place among the world's leading international financial centres, including, where necessary, making amendments to its regulatory and legislative regime,” Pierce told CNS.

The statistics from CIMA reveal that despite the economic recession and the international criticism that Cayman and other offshore jurisdictions have faced in recent times, the hedge fund business here remains solid.

At 30 June 2010 there were 8929 registered funds in Cayman, 427 administered and 130 licensed. In January 158 funds were domiciled and 74 terminated, while in February there were 104 started and only 32 terminated; in March 100 came and 35 went; and again in April 96 new funds arrived compared to 28 which ended; In May 70 funds were gained compared to 36 terminations; and in June 57 were registered while 46 terminated.

The legislation which has caused some concern for the local fund industry, however, is the proposed EU Alternative Investment Fund Managers directive which would block funds from outside the region from being marketed to Europeans countries unless the jurisdiction adopted “equivalent” legislation, a restrictive barrier few countries are expected to meet.

The controversial proposal provoked a storm of opposition, not just from offshore jurisdictions such as Cayman but also the US and Europe’s governments. As a result the EU is now engaged in an effort to reconcile the now three separate suggested versions of the AIFMD text produced by the European Commission.

Instead of an “equivalence” stipulation, it is expected that non-EU fund jurisdictions will need to meet four criteria, such as having co-operation agreements between their regulators and those in the EU, not being blacklisted over money laundering or terrorist financing failures, having tax treaties with Europe and allowing reciprocal access to their market for European products. This means the Cayman Islands would have much less difficulty in complying with the AIFMD and therefore the industry is far less likely to suffer any adverse effects.

Source: www.caymannewsservice.com

Data Commoditization in the Securitization Markets (Part 3)

2. Loan Data Vendors – these are data vendors who specialize in the collateral (loan) information. This seems to overlap with component #1 item C (in blog entry 2 of this series), and to some degree it DOES overlap, however, Intex has not been known for providing the level of loan detail and in particular, ongoing historical monthly payment information that a proper Loan Data Vendor provides. Intex does provide monthly collateral information but it can be quite difficult to see and/or analyze it from an historical perspective. This thereby creates a market “niche” that various companies have stepped into in order to capture this need. Examples include Loan Performance (this is the largest and most well-known of the loan data vendors) , Black Box, ABSNet (Lewtan), Lender Processing Services, S&P and others. Most importantly this component includes at least the following:

a. Loan Data: this includes many fields of information relating to the actual loan itself including such things as original balance, current balance, purchase price of the property, zip code, state, MSA (Metropolitan Statistical Area), loan purpose (purchase, refinance or cash out), occupancy status (primary residence, investment property); documentation of income or assets, sale price, loan to value ratio, first or second lien, interest rate, loan type (fixed rate or adjustable rate mortgage), if an ARM loan, then what index does the loan reference, Interest Only period (if any), any prepayment penalties and for how long, loan modification details and so forth.

There are over 20 million loans within non-agency securitized deals so you can see that this is a fairly large data set.

b. Historical monthly payment records. These records tell you each month whether the borrow has paid and if so, how much; if the borrower is delinquent and how many days (30, 60, 90+); whether the loan is in foreclosure proceedings and, if it has already been foreclosed, how long it has been in REO. Also, when the property has been liquidated and whether there have been any losses and so forth.

Some loan data vendors provide web-based tools that you can use to query their loan data but many firms also license to routinely receive the data from the loan data vendor onto their own computer systems because they have mortgage research groups who want to be able to analyze the data in depth in order to assist them to predict the future performance of the loans based on what the historical data shows. This provides only a partial picture of the borrower and the property serving as collateral. See the next section for an extremely important additional piece of the puzzle.

Source: www.theticasystems.com

Data Commoditization in the Securitization Markets (Part 2)

Today we continue our discussion of the RMBS data industry with a brief section on “Deal Data Vendors”.

1. Deal Information Data Vendors – by this we mean those data vendors that provide information about deals which includes:

*Overall deal information (Deal name, issuer, investment banker, servicer, trustee, whether the deal is Prime, AltA, Subprime, Original and Current Deal Balance, Cleanup-Call details, etc)
*Tranches (cusips, tranche names, original and current balances, coupon information, writedowns, ratings, credit support, etc)
*Collateral data (loan-level details, loan originator, collateral group related information and historical payments, etc)
*Triggers (primarily cumulative loss and delinquency triggers)
*Hedge Information (interest rate swaps, monoline bond guarantees, etc)
*Historical performance (especially useful for deal surveillance purposes)
*and so forth

A notable example of a data vendor who has all of the above is Intex. This is the most familiar and most widely used vendor of deal information in the industry. There are others including ABSNet (Lewtan) and Markit that also provide this information, but none to the degree that Intex does. Intex has been the longest and most firmly entrenched player in this space and consequently, the most costly.

Whereas it’s true that Intex provides deal information inside deal files and that these files are routinely delivered to a properly licensed client’s own file server, these files are made up of “one deal per file” therefore it becomes quite difficult to compare deals or query across all subprime deals and so forth.

What is really necessary is for a client to have software that reads all of the data about the deals (as enumerated above) into a proper database which can then be queried and analyzed with the purposes of spotting trade ideas or opportunities or generating various reports.

Note that this component does NOT include Bond Analytics – see the 5th component for more data about Analytics Providers)

Source: www.theticasystems.com

Data Commoditization in the Securitization Markets (Part 1)

Introduction

As has occurred within many industries throughout history, the data vendors that provide various information about securitization have been undergoing huge changes causing large price decreases and company consolidation. Additionally, we are seeing many new partnerships springing up concurrent with the increasing need for coherent complete systems to manage all information relating to securitized deals. Without a complete picture of your ABS bonds, how can you accurately assess risks and price volatility?

In this article, we will be reviewing what the key components of a securitization system should be and take a look at some of the vendors participating in each of those components and then describe some of the market forces which we think are creating more and more of a “commoditization” of securitization information.

This trend, we believe, is leading to substantial price compression for securitization data products, causing data to become cheaper and more accessible. This trend is continuing into the future and will result in better deals for industry participants.

In this paper, we describe all the major components of an RMBS information system so as to provide a broad overview of the data industry and how it relates to trading activities. We then go on to talk about some new initiatives in the industry and what their potential impacts will be on the various players in the market place and what this means for your firm.


Data Components of an ABS System

There are five primary components making up the key data needs relating to securitizations. Without these, a firm engaged in trading bonds backed by mortgages is going to be “picked off” by other firms and might as well not be trading as it’ll be just too risky.

These five components are:

1.Deal Information Data
2.Loan Data
3.Enhanced Loan Data
4.Predictive Model
5.Bond Analytics

Historically, trading firms have emphasized one or more of the above components, mostly due to lack of investment in technology and data. It is no longer enough to be “two guys and a Bloomberg” in this industry. What is needed are comprehensive yet flexible systems.

Source: www.theticasystems.com

Credit IO’s – Don’t Be a Slave to Your Data

In this blog we’re going to address a common complaint that we’ve seen users have: that when they’re putting together systems, too much automation creates a “black box” which then doesn’t permit the user to adjust the data in the manner in which they see fit.

Let’s face it, traders are on the front lines evaluating complex securities such as ABS bonds and the more you can permit users to take the data and create useful models that don’t “lock them into a particular view” of what’s being traded, the better it will be. Most often, traders build their own spreadsheets and, in general, do a great job. However, the lack of ability to dynamically communicate with a database of securities information can cause a great deal of trouble in the ABS market, if only when the next month’s data set comes out from trustees and they find themselves scrambling to manually update their spreadsheets.

Additionally, IT departments blanche at the thought of those overly flexible, manipulable spreadsheets that defy “systemization”. In this article we will discuss a specific example and how to satisfy the needs of both areas: IT and the Trading Desk.

Let’s take up the subject of “Credit IO’s”.

Definition: A Credit IO is an ABS bond which is sufficiently far down in the Capital Structure of an ABS deal that, based on the level of collateral defaults and loss severities that the market is currently experiencing, cause an investor to NOT expect any payment of principal.

Assumption: the bond’s principal WILL be written down to zero at some point. The investor expects NOT to get any principal back. However, until that point, the bond can earn interest cash flows therefore it’s an “Interest Only” bond.

Key Factor: Loss timing. Between now and precisely WHEN the bond is fully written down, the bond will be earning interest. Those monthly cash flows are worth something. The faster the bond will be written down, the less interest cash will be received. The longer the bond exists, the longer the bond will receive cash flows. The trick is to figure out when the losses will hit the bond. The timing of the losses will therefore have a dramatic effect on the price that an investor should be willing to pay for the bond. Less time until the fully-written down point = lower price.

So let’s take a look at some of the elements relating to the data side of this. Here are some of the relevant points:

1. Delinquencies

2. Foreclosure and REO timelines

3. Loss Severities to be used in determining how much of each loan will be lost due to defaults.

4. Credit Enhancements levels – primarily overcollateralization (OC) and each tranche’s current level of credit support (how much of the capital structure is supporting the particular tranche(s) we are evaluating).

On a Bloomberg you can bring up a simplistic method of evaluating this by typing an ABS cusip followed by the Mortgage key (F3) and then typing “MTCS” . This gives you the ability to take the deal’s current level of 60 day and 90 day delinquencies and apply a particular percentage of each that you expect to go through to default. The amounts of loans in Foreclosure (FC) and Real Estate Owned (REO) are assumed to be 100% in default. So we have as an example:

	%	% that will default	default amt
% of Deal 60+ Day Delinq	8%	60%	4.8%
% of Deal 90+ Day Delinq	5%	70%	3.5%
% of Deal in FC	3.5%	100%	3.5%
% of Deal in REO	2.5%	100%	2.5%

For a total of 14.3% that we expect to end up in full default and thereby experience a loss.

Sum those figures up (14.3%) and multiply by a single loss severity input and you will have the approx amount of the deal that you will experience as a loss. Let’s say we use 50% Loss Severity. That will give us 7.15% of the outstanding collateral balance in the deal that we expect to impact the deal’s capital structure in the form of losses. Compare that amount versus the particular bond’s credit support that you’re evaluating and if you have a ratio (called the “Coverage Ratio” on Bloomberg), that is less than 1.00, then that bond is likely to disappear completely because there is simply not enough support for the bond to survive. Anyone with access to a Bloomberg can do the above. The above doesn’t actually try to predict WHEN the losses will occur – only that they are expected to occur at some point in the future. It also does not let you consider future loans that are current on their mortgage payments or are 30 days delinquent that will come down the “pipeline” into the more severely delinquent states and finally into realized losses. It also doesn’t try to tell you what it all means in terms of a “price” that you might be willing to pay for the bond.

So let’s kick this up a notch.

Loan-Level Delinquency information

First of all, let’s assume that we have access to loan-level information and that we know, not only the current delinquency status of each loan but exactly when the loan entered that status. Intex provides good loan level data for deals from about 2006 and onwards. Loan Performance provides loan-level information for all deals – loan level information is generally what Loan Performance is known for (but they don’t have very good data about the capital structures nor can they do really good cash flows on the bonds as Intex does). The point is that loan-level delinquency information is available.

So let’s retrieve all the loans from a particular deal into a spreadsheet from our database of loan-level information. Ideally, this should be automated from within the spreadsheet so we can always refresh the data whenever we need to ensure that it is representative of the most current data in our database.

We now have our hands on which loans are in which delinquency condition. Now, if we simplistically project out maximum timelines that all the loans will experience in FC and REO before they hit their loss point, we can derive a table of months going forward and WHEN those losses will be experienced.

For example, we can state the following:

A. Let’s say that a loan has been in FC for two months already: Let’s permit 6 months for the total “normal” amount of time that a loan is going to be in FC so that there are expected to be 4 months more of FC time for this particular loan. Then permit 6 months more for the full REO process. This means that month 10 is WHEN we expect the loss to hit.

B. Let’s say that a loan is currently in REO and has been so for 4 months. Permitting 6 months of complete REO time suggests that we have 2 more months to go. So 2 months from now is when we think we will realize a loss on this loan.

C. Let’s say that a loan has just become 90 days delinquent for the first time. They’re probably going to be in FC real soon, but maybe we feel that we should allow an additional month of being 90 days delinq. So we would have 1 more month of 90 days delinquency. A full 6 months of FC and 6 months of REO so that we expect the loss to hit in month 13.

We can continue to do the above for 60 days delinq loans and 30 day delinq loans. And possibly take some current loans based on the idea that some of these will also hit the skids.

Let’s assume an overall “Loss Severity” of 60%. According to some market participants 60% is getting more and more real. This means that, given a loan amount of $100,000 you are expecting to lose $60,000. Apply the loss severity input to each of the loan balances and sum those loss amounts up into each of the months you have projected into the future.

The result is that you end up with a table of months into the future within which losses can be summed up – month by month. At that point we have a relatively simplistic table giving us WHEN we expect the losses to hit. These losses will be applied to the bond’s outstanding balance and will eventually “amortize” the bond’s principal, via write-downs, down to zero. At each month, you calculate what amount of interest the bond should receive. Then we apply the loss amount for that month and decrease the bond’s outstanding principal balance so that in the next month there will, of course, be less interest earned. We keep doing this until the bond’s balance has been written down to zero, at which point you’re not earning any more interest on the bond. At that point, the bond has disappeared. Then sum up the interest payments that you received during the time when the bond was still “alive” and you have the amount of cash you’re going to receive on this bond. Divide that by the principal currently outstanding on the bond and you have the price that might be indicative of what you would be willing to pay. Notice that this last sentence is disregarding the time value of money. It can be an enhancement to “present value” (PV) those interest cash flows and then sum up the PV-ed cash flows to get a more accurate price.

It should be noted that if there is any “OC” remaining at the bottom of the capital structure in the deal, you have to allocate the loss amounts to the OC first before they start to impact the bond you’re evaluating. Likewise if there are any bonds BELOW the one you’re evaluating, because of the fact that losses are allocated from the bottom of the capital structure upwards, then each of those bonds below your bond each have to be written down to zero before the loss amounts start to impact your particular bond. The point being that your spreadsheet application must retrieve all of the bonds and any OC BELOW your bond and apply the loss amounts to EACH of their principal outstanding amounts BEFORE the losses start to impact your particular bond. Of course, this means that ALL of the bonds below the one you’re evaluating are also, each one, a “Credit IO” bond.

A few other observations

I want to emphasize that decreasing the FC and REO timelines in the model will have the impact of decreasing the amount of time that the bonds will survive thereby decreasing the length of time that the bonds will earn interest resulting in a lower price that one would be willing to pay for the bond. Obviously, if you’re buying you want to pay as low as possible so underestimating time lines will help you. If you’re selling, you’ll probably want to consider that the time lines are longer so that you can sell it for a higher price. These are the normal competitive sort of interests in the market place.

The above represents a simplistic model but one which gives a much greater degree of flexibility than the Bloomberg MTCS function. Done correctly, it also permits the user to adjust the time lines and severities to ones which they feel comfortable with when evaluating “Credit IO’s”.

Also, by keeping all of the above factors in mind, the user/trader can still perform the analysis in the way that they see fits best for the environment they’re in. They’re not “locked” into a “black box” which they can’t see inside of.

There are, of course, much more extensive features that can be built into such a model which are not within the scope of this blog.

Jack Broad is the CEO of Thetica, LLC. Thetica's ABS Trader Tools allows you to price bonds faster and easier. You can analyze data across many deals at once, from multiple vendors and run many scenarios at the same time with rapid results. Integrated into your own systems for easy and fast customizable access to the data you need.

Source: www.theticasystems.com

SEC Proposes New Disclosure Rules for Target-Date Funds

Securities and Exchange Commission

On the heels of the turmoil caused by the financial crisis, regulators are proposing new rules governing how target-date funds are named and marketed.

The rules, announced late Wednesday, are part of an effort to provide transparency for investors and guide investment decisions, the Securities and Exchange Commission said.

The SEC's proposals would require an allocation tagline next to a fund's name, stating the proportion of cash, stocks, and bonds in the portfolio, and more detailed disclosure on the types of investments made throughout the life of the fund.

The market turmoil in 2008 revealed many investors' misunderstanding of how target-date funds managed their money. In some cases, investors in funds close to maturity were shocked to discover the high proportion of stocks in their portfolios. For example, Oppenheimer Transition 2010 /quotes/comstock/10r!ottnx (OTTNX 7.47, +0.02, +0.27%) lost 41% in 2008.

The market meltdown showed that target-date funds could lose money and perform in ways investors did not expect, SEC Chairman Mary Shapiro told The Wall Street Journal.

The SEC worried that the retirement year, which is a part of the fund name, led investors to believe the fund's performance depended solely on this factor. While there is one date, risk levels vary widely depending on asset allocation.

The proposals would see target-date fund marketing materials provide more detail on investment mixes through the course of the fund. Both print and electronic brochures would need to include graphics that depict the asset allocation and clearly noting that the mix changes over time.

Disclaimer text would also be revised to realign investor expectations. Marketing materials would have to encourage investors to consider their own risk tolerance and the fact that the fund may be in the red, even at the target date.

"Together these rule amendments are designed to foster investor understanding of target-date funds and reduce the possibility that investor will be confused or misled," Shapiro said in a statement.

The SEC will seek public comment on the amendments for a 60-day period following publication in the Federal Register.

Created in the mid-1990s, target-date funds offer an all-in-one investment choice, letting holders choose an approximate retirement date as the fund automatically changes the mix of cash, stocks, and bonds to more conservative balances as the date approaches.

Since their inception, target-date funds have grown quickly and are a popular choices in 401(k) plans. According to the SEC, assets held in target-date funds total about $270 billion.

Source: www.marketwatch.com

Reverse Mortgage Pools Keep AAA Rating says Fitch

The Ratings Outlook of HECM Trusts backed by reverse mortgage loans continue to hold their AAA rating said a statement from Fitch Ratings. The classes confirmed consist of a pool of reverse mortgages mortgages (HECM) insured by the Federal Housing Administration and their ratings outlook remains stable.

During the review Fitch compared the actual pay-down performance of the classes to its own cash flow projections and found that there have been no trigger events.

“Taken as a whole, reverse mortgage securitizations are definitely performing better than most other non-agency residential mortgage securitizations of the same vintage,” said Joe Kelly, Partner at New View Advisors, a capital markets and investment bank specializing in the reverse mortgage sector. “The bonds secured by reverse mortgage securitizations have not experienced the downgrades and write-downs so widespread in other sectors of the securitization market.”

However, Kelly said that with any HECM investment vehicle, the issue of taxes and insurance defaults affect the deals. “Some of the HECM loans in these securitizations have defaulted and some of these trusts now own REO properties. Of course, the role of the servicer is very important here, so it is significant that Fitch recently upgraded Financial Freedom’s servicer rating.”

Even with the T&I issues, Kelly added, “generally speaking the reverse mortgage securitization sector is performing well under difficult economic conditions, and Fitch’s recent ratings affirmations attest to that.”

Source: www.reversemortgagedaily.com