Briefly summarized: the researchers construct their own
VaR (value at risk) model for four different kinds of financial intermediaries
– commercial banks; investment banks; insurance companies and hedge funds. Using “daily data”, they derive VaR estimates
for each sector (and a control group of REITs, commodities and non-financial
stocks) over 2,000 trading days from 2003-2010, which they further divide into
periods of tranquility, normality and financial stress. They then run regressions showing
correlations, etc., among the various sectors during these periods, to see
whether risk appears to spill over in a persistent pattern from any of the
sectors to any of the other ones. They
conclude that, during periods of “tranquility” or “normality,” increases in VaR
of the HF universe produce very small (8-9 bps) changes in the VaR of
investment banks; otherwise, not much changes.
But in periods of financial stress, they find much greater (71 bps)
correlation and impact running from HFs to IB’s and also that the spillover
tends to run its course over approximately a two-week period and thus is not
detectable when measurements are done using less-than-daily data.
Conversely, they perceive that insurance companies play
little role in transmitting risk, as their returns are found to be negatively
correlated with the returns of other financial institutions. Finally, they find very little spillover
between commercial and investment banks. The conclusion that hedge funds are
the largest amplifier of risk has
implications in relation to the scope of regulation for hedge funds, which are
relatively lightly regulated compared to the other sectors.
Their conclusion has an intuitive appeal, in that hedge
funds are commonly seen to be more risky and active that the larger
institutions and conversely, insurance companies are seen to be the most
conservative. As Gropp explains,
Why
are the spillovers from hedge funds during financial crises so much bigger, and
why do they seem to increase more than those from other financial institutions?
Hedge funds are opaque and highly leveraged. If highly leveraged hedge funds
are forced to liquidate assets at fire-sale prices, these asset classes may sustain
heavy losses. This can lead to further defaults or threaten systemically
important institutions not only directly as counterparties or creditors, but
also indirectly through asset price adjustments (Bernanke 2006). One channel
for this risk is the so-called loss and margin spiral. In this scenario, a
hedge fund is forced to liquidate assets to raise cash to meet margin calls.
The sale of those assets increases the supply on the market, which drives
prices lower, especially when market liquidity is low. This in turn leads to
more margin calls on other financial institutions, creating a downward spiral.
Another example is investment banks that hedge their corporate bond holdings
using credit default swaps. If hedge funds take the other side of the swap and
fund the investment by borrowing from the same bank, the spillover risk from
the hedge fund to the bank increases. These types of interconnectedness may
underlie some of the spillover effects in our study.
The paper appears to have crunched on a very sophisticated
level through massive amounts of data, producing an analysis that would take a
reader a very long time to investigate thoroughly[1]. But I also have two huge reservations. The quotation above carries the seeds of one
of them. Most of the statements in it
are actually wrong to the extent they purport to describe characteristics that
are unique to hedge funds; that is, they are not true “if and only if” the
subject is a HF. For example, when the
paper states “hedge funds are opaque and highly leveraged”, that is only a
partly true statement. Opaque – yes, but
not that much more so than the other institutions, and not as much as you think
(and also, as I realized while I wrote this post, it cannot be true of the HFs
whose data they rely on – that is, the HFs they analyze cannot be opaque to the
extent the paper relies on information about them!
Sure, to most observers and probably regulators, most
individual hedge fund trades are opaque, yes.
But there are reporting requirements concerning equity stakes in public
companies that provide disclosure on the largest equity positions, and various
other ways in which hedge funds’ positions become disclosed, such as
shareholder activism, being a member of an ad hoc committee in a bankruptcy, or
talking up a position in some conference.
As well, other informal disclosures occur: I often found that traders
had a good sense of which hedge funds had been taking positions in a distressed
situation. A prime broker would normally
know reasonably thoroughly the positions of its client HFs. Similarly, when a company goes out for a
“drive-by” bond offering, or an equity raise via a private placement, the
investment bank(s) running the deal invariably have a very good idea who is
interested and who is not, because they have teams of sales people calling on
asset managers all the time and staying up to date on what their interests may
lie. Finally, to take a position in a financial asset outside of the securities
exchanges, an investor must often enter into a contract in which its identity
is necessarily divulged: in loan
trading, for instance, when a loan passes by assignment, the admin agent will
have to sign off, and the borrower in a non-default context will also, and the
identity of the buyer and seller must be disclosed. So too in derivatives, the counterparty,
often a financial intermediary, knows who it is contracting with on an ISDA
form. And, just to reinforce the point,
is the “opacity” of an HF portfolio all that different from the opacity of the
portfolio and trading books of the largest commercial and investment
banks? From a public investor’s
perspective, I don’t think it’s all that different. An observer of the markets may well be able
to name a greater proportion of the positions held by, say, Bill Ackman’s hedge
fund, or Dan Loeb’s, than those held by Goldman Sachs. At the regulatory level, there is a
difference, I admit, although I question how much actual or practical insight
the regulators truly have over those institutions’ books, given their failure
to apprehend any of the insolvencies in 2007-09. As I said, the statement “hedge funds are
opaque…” is indeed partly true, but just partly.
Moving on, what does the statement “hedge funds are …
highly leveraged” mean, especially in comparison to the other kinds of
institutions Gropp studies? Although I
do recall one memorable anecdote to the contrary[2],
many of the hedge funds that I have worked with did not have any permanent
leverage at all, because they held leveraged loans, HY bonds, distressed
securities, ABS or other debt securities as to which the risk of illiquidity
was too high to get into a margin situation in the first place. But even assuming there are a lot of hedge
funds with leverage, what makes them “highly” leveraged compared to commercial
banks and investment banks from 2003-2010?
I doubt there was any hedge fund that had a leverage ratio higher than
the commercial and investments banks in that period. I would be surprised if any hedge fund had
more than a 4:1 debt/equity ratio, and I would expect the average among the
levered funds is less than 2:1, whereas the largest commercial and investment
banks have leverage ratios in the 10:1 or higher range, depending on how one
counts trust preferreds and other hybrids.
Especially for HFs that are mainly taking positions in equities, Reg U
and other rules make it very difficult to do so on a basis as leveraged as a
money center bank’s balance sheet is leveraged.
Tying this back to their research: the hedge fund universe
that Gropp works with in his paper consists, the 2013 paper says, of 47 of the largest and most liquid such funds
which comprise a “Hedge Fund Equally Weighted Index” which is one of the few
sources for daily data on hedge fund performance. But the researchers do not seem to know, and
probably it is not disclosed, which, if any of those, are levered and to what
extent and did it differ from day to day.
So I think the description of HFs relevant to the paper as “highly
leveraged” is not supported in a scientific manner.
Moving on through Gropp’s explanation of his research, the
two examples he gives of ways in which hedge funds might amplify risk are
loss-and-margin consequences, and hedging credit risk via CDS’s. But note that, again, these are not at all
unique to hedge funds. As I recall, when
the subprime mortgage started, a lot of hedge funds weren’t long that asset
class on borrowed money, they were short subprime MBS and indices tied thereto. This is important because the 2013 paper
states unequivocally “The subprime and financial crisis of 2007-2009 spread
from mortgage-backed securities and CDOs to commercial banks and on to hedge
funds and investment banks.” Think of “The Big Short”, or John Paulson being
short the ABACUS vehicle in the Fabrice Tourre lawsuit. The institutions that were long subprime were
investment banks (think Merrill); the GSE’s; commercial or investment banks at
home and especially abroad; andinsurance companies (AIG, the various bond
insurers like MBIA and FGIC, etc). And
most of all the dozens of originators themselves, like AHM and so on. There were certainly some mortgage funds,
like the Bear Stearns’ funds, that were long subprime, but was the HF universe
net long subprime? I would be skeptical
(it’s also an interesting taxonomy question relative to the research, how one
should classify a HF managed by an investment bank). So, both generally and specifically with
respect to the financial crisis of 07 onwards, I doubt “loss-and-margin” consequences
are particularly unique to hedge funds, especially in reference to
subprime-mortgage-related assets.
The other example Gropp gives, hedging credit risk through
CDS, is again, not unique to hedge funds; for example one of the biggest
individual players in CDS is the mega-billion PIMCO Total Return fund. Further, the idea of transmitting risk
through CDS raises the question of how matched the intermediary’s book is – it
may be the case that the value or risk of one side of a CDS position goes up in
value, but whether that intermediary’s overall CDS book loses value or has
increased risk exposure depends on whether there is an offsetting position,
among other things. I am not even sure how accurate it is relative to the HF
sample the paper studies, because the hedge fund index they study appears, as I
discuss below, to be heavily weighted toward investments relating to public
equity markets, not corporate credit strategies, which are a clear minority of
the strategies encompassed by that index.
So that is the first reservation I have about the study,
to what extent are HFs that unique in relation to the risks and characteristics
identified by the researchers as compared to the other kinds of financial
intermediaries studied.
The other major reservation I have is that the study is a
construct of constructs with potential for measurement errors or questionable
assumptions and choices at each level.
That is, value at risk, as calculated by the authors is, obviously, a
construct or a model, as it is for everyone who assays such a calculation. But on top of that, the underlying data sets
their VaR model is analyzing are themselves not the actual assets and
liabilities of the subcategories but proxies for those assets and liabilities
and thus potentially inaccurate reporters of the underlying value at risk,
especially on a daily basis. As well,
there are a variety of financial market sectors that don’t appear to be
analyzed in the paper that might have been relevant.
For example, as noted above, the set of “hedge fund” data comes from a Hedge Fund
Equally Weighted Index maintained by Hedge Fund Research. Its methodology is described here: http://www.hedgefundresearch.com/pdf/HFRX_formulaic_methodology.pdf. Fwiw, the “strategies” that are “equally
weighted” in the index are Equity Hedge;
Event-Driven; Macro/CTA; and Relative Value Arbitrage; HFR maintains indices in
each of those strategies and the HFRX is just the sum of the NAV of the four
individual indices. Each of the
underlying indices is comprised, HFR says, of funds that, in the aggregate,
have the highest statistical correlation the aggregate performance of all funds
with that strategy. So the index is
itself a statistical representation.
I am not going to go into a lot of detail about the
underlying index, as the scope of this post is just some high-level
observations; plus, I am not pursuing tenure as a professor of finance, nor
billing by the hour as an advocate for HFs so someone else is welcome to push
the analysis deeper. The keeper of the
index does indeed report it on a daily basis, which I find a bit curious as I
don’t know of any HFs that disclose daily NAVs.
I searched the index manager’s website a few times to see if I could
confirm it was receiving daily NAV data and not making its own estimates, but
could not find any statement one way or the other on the subject. I have to take them at their word, but
this is a cool article from professors at the University of Maryland who
tried to create daily VaR measurements for HFs using the same index that Gropp
appear to be working with; they have mixed results although their conclusion
that intra-month volatility is much higher than month-to-month volatility is
similar to the Gropp conclusion.
Mesirow Advanced Strategies put out a paper in 2011
entitled “Understanding Hedge Fund Indices” that contains a short, user-friendly discussion of
some of the issues with hedge fund indices, including the one used in Gropp’s
paper. It also has some eye-popping
charts that show wide variances in performance among the various indices that
amply illustrate the caution needed in drawing conclusions from them. A venerable alternative investment firm
called Pictet also has a paper available on the Web entitled “Hedge Fund
Indices: How Representative Are They?” from which I culled this little quote:
“less than 1 per cent of the hedge fund industry reports to all databases,
highlighting the unrepresentative nature of hedge fund databases.” I am sure the keepers of the HFRX would
disagree, but the point is, there are intelligent voices suggesting that all HF
databases be taken with at least a small grain of salt.
A further complicating factor is that a lot of HF assets
are not valued on a Level I basis, but may be Level II or Level III valuations
that contain greater human guesswork (link for an explanation of these terms: http://www.iasplus.com/en/standards/ifrs/ifrs13
) which introduces further potential for measurement errors in the data the
authors study.
Finally, the identity of the components of the index are
not disclosed in the Gropp paper or on the HFR website; they are only available
to subscribers. The Gropp paper
references an appendix that supposedly goes into more detail, but every time I
pasted the link into my web browser, I just got a “server error” message, so I
could not investigate further. But the
main point is I can’t tell how US-centric they are, which seems to be
reasonably important vis a vis the paper’s overarching topic of the regulation
of US financial intermediaries. . .
The Gropp paper compares the VaR of the HF index to three
baskets of equities of various large, publicly traded US-centric commercial
banks, investment banks and insurance companies. To a certain extent, I
question a VaR comparison between the NAVs
of HF’s and the equity prices of these other kinds of institutions, as
equity prices of financial stocks are not equal to their respective NAVs, but
are determined by secondary trading. As
well, all these other types of institutions have substantial operating, income-generating
businesses in addition to holding portfolios of financial assets. So, there is something of an apples and
oranges comparison here, although I don’t think too much needs to be made of
it; the geographical issue I mentioned above is perhaps more worth
pondering.
The Gropp paper states that its rosters of commercial
banks and insurance companies is taken from a list compiled by Viral Acharya
and others in a paper called “A Tax on Systemic Risk”, but, when I checked that
paper for the list, I found the description in the Gropp paper did not quite
match the Acharya paper (Gropp: 26 commercial banks and 31 insurance companies;
Acharya: 29 commercial banks and 36 insurance companies). I have no idea what changes were made, or
whether they were explained by the link that did not work.
Turning to the commercial bank subset, assuming it is the
Acharya set, it contains the large money center banks which had substantial
capital market businesses, like JPM, Citi, B of A (Gropp acknowledges that
their classification as “commercial banks” is imperfect). But this set also has numerous regional banks
with no capital market business as well.
The insurance sector
list oddly contains Countrywide Financial. That oddity is compounded by
the fact that none of the largest mortgage insurers – Fannie, Freddie, MGIC –
show up on the list (MBIA, AMBAC and AIG do, though). So how accurate a list is that?
In contrast to the large number of constituents in the
insurance and commercial bank sets, it is noteworthy that the investment bank
category in the Gropp paper is composed of only 8 institutions (not all of
which I can identify; compare the Acharya paper which lists 10 but those 10
include NYMex, Schwab, T. Rowe Price and ETrade, yet omit Jefferies, so I just
don’t know how accurate these categories are).
Further, at least two of the investment banks in the Acharya list
collapsed (Bear Stearns and Lehman) and one (ML) was merged out of existence,
during the period studied. It jumped out
at me that the IB sample, as best as one can understand it, seems to be a little
small to confidently draw conclusions from; appears to be much more tied to equity markets than the
other sets; and also a large proportion of the constituents seem to have been
the subject of one-time events during the period of study, making a very noisy
sample as well.
Another aspect of “noise” in this data is how much was
unknown in real time, but came out later, in the form of fines, penalties,
damages, settlements etc, about the amount of contingent liabilities that
various financial institutions other than HFs had during the period in
question, which leads one to wonder, how accurate were the equity prices of the
underlying assets and liabilities of those institutions?
A lot of other financial entities don’t appear in any of
the sets Gropp studies, to the extent the paper tracks the Acharya paper’s
list. Fannie, Freddie, MGIC, as
mentioned, and also Amex, Annaly, Blackrock, Capital One, CIT, Franklin
Resources, Legg Mason, TD Ameritrade.
The exclusion of the mortgage –related entities utterly baffles me since
the researchers state “The subprime and financial crisis of 2007-2009 spread
from mortgage-backed securities and CDOs to commercial banks and on to hedge
funds and investment banks.” I would have thought it would have been essential
in the context of that thesis to study correlations between mortgage-centric
entities and financial institutions, but no.
Also on the subject of things not studied for correlation, it struck me
as odd that whole sectors of the capital markets, like HY indices, leveraged
loan indices & MBS indices that are at least as credit-driven as anything
the Gropp paper studies were not examined for correlations.
A last observation on the underlying data relates to what
sounds like an overstatement in regard to the period studied. The paper covers 7 years roughly. Recall that one of its self-described
principal innovations, compared to prior analyses, is to break that period up
into periods of “tranquility”, “normality” and “distress”. While I cannot get the link to the backup
data to work, so I cannot be sure of what I am about to say, I gleaned from the
paper that its repeated references to “periods of financial distress” are
really just references to the 2007-09 period, taken as a whole. As I said before, that was a very noisy
period with all sorts of things going on – collapses, bailouts, shotgun
weddings -- that had never happened
before. And, in any event, it’s just one
period! So I wind up skeptical about the
prescriptive significance of finding some correlations between HF NAV changes
and a small set of financial intermediaries in a single period that was
complicated by many one-time events. I
don’t see how anyone could ever determine whether this was a phenomenon capable
of recurring, or just a one-time confluence of factors, or an artifact of the
assumptions and choices the authors made in generating the paper. It seems impossible to replicate the
conditions of the period to test the hypothesis..
Often a correlation exists
between two data sets because both are displaying the influence of a third
variable. For example, if Mary and Herb
live in Scarsdale and work at banks in Manhattan, and Mary leaves her house
every morning to catch the 7:16 from Scarsdale, and, Herb leaves his house
every morning to catch the 7:34, there is a high degree of correlation between
their schedules, but no causation even though Mary consistently precedes Herb. Their schedules are determined by exogenous
variables, namely the schedule people who work in the banks and take Metro
North to get there have to keep.
Here for example, the correlation
between the VaR metrics for HF and IB in times of financial distress could
simply show, not that one caused the other, but that both categories held
assets that were more similar than they were to the portfolios of commercial
banks and insurance companies. That is,
IB’s assets may have been more HF-like than CB’s or insurance companies assets
were – for instance, it jumps to mind that they may have had more HY and
equities as a proportion of total assets than CB’s and insurers did. The HY part of that conjecture would help
explain why the authors found the correlation greater on the downside – being a
debt instrument, HY can only go up so much, so starting from a non-distress
point, (which is where the Gropp study starts, in 2003, a bull year), HY tends
not to provide much return beyond the coupon, while in a distress environment
it can fall several multiples of the coupon.
When the authors find that
negative changes in HF VaR appeared to lead changes in the VaR of a portfolio
of IB equities, that could just be because the HF VaR reflects daily marking to
market of the underlying assets (or the index manager’s estimation thereof),
undiluted by other factors that may affect the stock prices of IB’s, such as
secondary market technical factors, or the market’s evaluation of the advisory
and other operating businesses of the IB’s.
Another possibility could be that
hedge funds, as they saw a broad financial deterioration sweeping the developed
world, looked to hedge their exposure by finding shorts, and the hugely
overleveraged balance sheets of certain brokerage firms were prime candidates,
better than insurance companies or FDIC-insured banks . They are hedge funds after all. I know I got many calls from HF clients in
2007 asking for an explanation of how SIPC receiverships worked in
reference to a generic or hypothetical
brokerage firm where they had repo’d their cash balances or otherwise had
exposure from other balances. And everyone remembers David
Einhorn’s very public short bet against Lehman Brothers throughout
2008. So there was definitely worry
among them about the health of some brokerage firms. And recall that the investment banking sector
of the Gropp study is only 8 firms, so it is very susceptible to one or two
members of the group driving the results in a certain direction.
All of the above seem plausible
to me, yet none of the above would justify any sort of heightened regulation of
HFs. I tend to think that most HFs tend
to carry less leverage than money center intermediaries do. As always, the most appropriate
financial-sector-specific regulation that needs to be in place is having enough
equity capital in the system to buffer it against the level of loss that might
arise from a specified level of financial distress, and to apply the capital
requirements broadly throughout the financial markets so they cannot be evaded
. But again I suspect the HF universe
would be broadly in compliance with the kind of capital adequacy requirements
now imposed on banks and the like. I'm not necessarily averse to the concept, as I don't work in or for HFs anymore. All I am trying to do is use my experience to pose some hopefully intelligent questions on the topic, fwiw.
[1] In part
because the authors kept much of the data crunching out of the paper itself,
and in a statistical abstract, the link to which unfortunately did not work the
three times I tried it.
[2] In
early 2004, I had drinks with a small group that included the number 2 guy at a
well-known hedge fund that was focused on below investment grade bonds. I asked
him how his fund had done in 03, and he said, proudly, “up 81%” which was
incredible for the billions of AUM they had.
My next question, in all innocence, was “did you have any leverage?” and
he replied, “yes, 2.7 to 1”. When I got
into the office the next day, I looked up the performance of the relevant HY
index, I think it was Lehman, for 03, and, amazingly, it was exactly 30%. Of course that was unlevered, so what that
meant was the brilliant hedge fund had, as far as asset class and security
selection go, been just a market performer, and its entire outperformance was
due to the leverage ratio. Of course
that is only one anecdote and I have others about guys running even larger
amounts of AUM in the same sector with zero leverage, fwiw (but of course they
weren’t getting 81% returns).
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