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These comments on the LBMA Liquidity Survey come from FOFOA's blog. I've assembled them here for my future reference convenience and are probably of interest to about four people. See also this post for earlier comments on the survey.
To the extent that the clearing banks have the same mix of
clients there would probably never be any sustained/ongoing movement of
physical from one to another as over time a bank's flows would net.
However if the mix of clients is not even (eg one bank predominantly has
mining/selling clients and another has jewellers/buying clients) then there
would be ongoing movement of physical from one clearer to another.
This was a debate I was having with FOFOA around the LBMA survey imbalance as
if the mix of clients between banks is not symmetric then you will have an
imbalance in the trading reported (yes, I have not forgotten, it is still on my
to-do FOFOA). FOFOA doesn't think so, I do, but maybe I need more time to get
to where he is.
Fwiw, that survey was actual reported volume (divided by two in the table so as
not to double count). Any additional banks reporting would only have added to
the total. Even if the rest did the smaller volume, that would not have reduced
those totals given. So that is why it can be taken as a minimum.
That's right MF, but the question FOFOA (and I) had was whether LBMA in
aggregate sold more unallocated ('gold') than they purchased. The net
difference between purchases and sales was some 7500t over the surveyed
quarter, and this is too much to be hedged in non-LBMA 'gold' instruments such
as COMEX, TOCOM etc.
So if they indeed sold more than they bought, this would indicate that they are
not flat 'gold', but that they rather delta-hedge their net exposure to 'gold'
using correlated instruments, say AUDUSD, CADUSD etc.
The question that got us there was the question of why the 'gold' price goes up
after a GLD puke. The puke is just a small amount of physical, say about 13
tonnes, which is a lot less than the 2700 tonnes of paper that are traded every
day. So how can that little puke move the price over the subsequence 2-6 weeks?
If LBMA in aggregate does delta-hedging, this might be the explanation: after
the puke they shift their hedge from 'gold' correlated instruments to proper
'gold', thereby running up the 'gold' price. (They would lose a bit due to
friction if they do this, but, hey, you are the market maker and if you fleece
everyone by a fraction of a percentage point, you should still come out even).
If Bron is right, and LBMA in aggregate is flat 'gold', then the GLD puke
indicator still awaits an explanation.
Bron SucheckiDecember 3, 2012 9:43 PM
My suggestion as to why the GLD puke indicator may work is
that in the fact of net selling the APs and others will use all and every means
to arbitrage GLD to spot except redeeming (as that has a slight cost). They
would build up "stock" of GLD shares in the hope they can resell it
later (and avoid redemption and subsequent creation costs).
If the selling continues they build up too much GLD shares (long) and too much
leased unallocated (short) so at some breakeven point it is less costly to
redeem GLD and collapse the hedge. These capitulations by the APs signal their
assessment that the selling is "real" (ie not temporary).
On a sentiment basis this represents a low point, from which on probabilities,
the price has more of a chance of rising. Just a theory.
I'm glad to see that you brought this up again, because I
was just explaining my take on our debate to Joe Yasinski and Dan Flynn of GBI
the other day. Did you happen to see that they mentioned the discrepancy in the
LBMA survey in their latest post?
The way I explained it is basically this. We have three proposed explanations
for the 7,575 tonne (aka $337B) discrepancy between purchases and sales in the
1Q11 LBMA member survey. As you correctly pointed out, it is likely a
combination of these explanations, because as I recall you finally conceded
that the discrepancy is far too large to be solely explained by the survey
methodology. But even still, that "halving of interbank transactions"
is our first of three explanations.
The second explanation is yours, which we called "asymmetric
reporting". And the third is mine which is basically that demand shocks
are being contained/absorbed by expanding or contracting paper supply rather
than by price. Three competing/complementary explanations for the reported
discrepancy:
1. Statistical methodology
2. Asymmetric reporting
3. More paper gold sold than bought
As Victor just mentioned, we have an explanation for the hedging requirement of
the price exposure implied by my explanation which is similar to what they do
for FOREX exposure. So having given this brief synopsis of our debate, here's
an excerpt from one of my emails to Joe the other day:
Bron's explanation is that there is a difference in clients between the LBMA
members who chose to report on the survey and those who chose not to report,
and that this difference informed their decision of to report or not to report.
In order for Bron's explanation to explain the entire discrepancy, there
needs to be a flow of physical gold twice the size of the discrepancy, or
15,150 tonnes (7,575 X 2) from the non-reporting members to the reporting
members and then on to their (the reporting members') clients. It must be
double because of the halving of the interbank transactions in the survey. This
is simply a ridiculous notion given of the sheer size of physical gold flow
(15K tonnes of physical in one quarter) required to explain the entire discrepancy
between purchases and sales given the methodology of the survey.
But Bron may have a point that, on some level, certain types of LBMA members
might have been more prone to report than others. His theory is that the BBs
selling to Giants and industrial users are reporting, and the mints and
refineries buying from mines and scrap dealers are the non-reporting members.
Something like that anyway. So that phenomenon might contribute to some
of the discrepancy. So how much?
As you point out in your post, new mining supply in one quarter is only about
625 tonnes. Add some reasonable number for scrap supply and then halve the
total, and that's the very most that Bron's explanation could
account for in the discrepancy. So let's say (generously) that 500 tonnes could
be accounted for by asymmetric reporting.
So now we have three explanations for the discrepancy, and we can assign
reasonable numbers to two of them. Statistical methodology could possibly
account for around 750 tonnes of the 7,575 tonne discrepancy. And asymmetric
reporting could maybe account for another 500 tonnes of the discrepancy. So…
7,575 – 750 – 500 = 6,325 tonnes discrepancy unaccounted for
And that leaves my explanation as the winner for the remainder, IMHO.
(Note: The 750 tonnes potentially attributable to the "halving
methodology" is just my guess as an amateur statistician. But I think it's
a generous and close guess. It could be a little more, but not more than twice
that number, and it's probably less. I mentioned p-value
in one of the comments before our debate moved to email. It is a way of
distinguishing what is statistically insignificant from what is significant. In
this case, up to the limit of statistical insignificance is possibly
attributable to the "halving of interbank transactions without all banks
reporting", in my amateur opinion of course. ;)
I saw his article, but this comment:
"Based on the survey, we deduce that in 1Q11 excess demand for gold was
243,560,000 ounces which translates into approximately 7,575 metric tons. In a
typical year, quarterly physical production (new mining supply) is
approximately 625 tons. One would imagine that with a traditional commodity,
physical demand outstripping new supply in a given quarter by a factor of 10
would cause a significant increase in price!!"
Is really not a correct way to look at it, because the 7575t is not all
physical demand and 625t is not the only physical supply in that quarter.
I disagree with this statement:
"...there needs to be a flow of physical gold twice the size of the
discrepancy, or 15,150 tonnes (7,575 X 2) from the non-reporting members to the
reporting members and then on to their (the reporting members') clients."
The survey was of bullion bank trades/turnover, which would not relate to flow
of physical gold.
The reason I'm not so keen to jump to a conclusion is I have an idea of how
trades (paper and physical) are recorded in trading systems and the errors that
are likely to inflect the data based on how the database was interrogated.
For example, head of precious metals passes on LBAM request to IS guy who has a
lot on their plate, they run a query which they think is OK, but they haven't
thought about whether the two legs of a swap should be treated as two trades or
one, or if they have excluded reversing (error) trades out etc etc.
Then assuming no errors I need to think through typical transctions and how
those may be reported once summarised into the LBMA requested data and whether
they will result in the correct result, which is what I'm trying to do with my
spreadsheet.
And I think you are underplaying the importance of a correct/representative
sample size in determining statistical significance. That sort of stuff relies
on normal distribution and representative samples. If a certain group bullion
banks of the same type have not reported for strategic reasons then you can't
rely on statistical assessments but need to go back to understanding the
underlying data.
If asymmetric reporting is to account for the discrepancy,
then "there needs to be a flow of gold twice the size of the discrepancy,
or 15,150 tonnes (7,575 X 2) from the non-reporting members to the reporting
members and then on to their (the reporting members') clients."
I removed the word physical. Do you still disagree with the statement given
this change?
"I removed the word physical. Do you still disagree
with the statement given this change?"
Taking physical out helps.
"there needs to be a flow of gold twice the size of the discrepancy, or
15,150 tonnes (7,575 X 2) from the non-reporting members to the reporting
members"
Don't agree. From my model of the 56 banks, random spread of trades between
them all, this is how one of the random runs plays out:
Reporting Banks Buys 5780t
Reporting Banks Sells 5627t
Reporting Banks Discrepancy 153t long
This is a 2.71% compared to 4.35% in the actual LBMA survey. Good enough for an
example, I don't have time to run through all iterations to find a 4%
discrepancy. For those "reported" figures above, the model shows:
Non-Reporting Banks Buys 3096t
Non-Reporting Banks Sells 3232t
Non-Reporting Banks Discrepancy 136t short
All Banks Buys 8876t
All Banks Sells 8859t
All Banks Discrepancy 17t long
So from a simple model of 56 banks summed buys and summed sells with each other
bank (a total 3136 bank-to-bank data point relationships) with random data we
get a overall 56 bank universe trading position of 17t over 17734t of turnover.
However, that minimal net position of 17t "appears" when we look only
at the reporting banks to be a 153t position over 11407 of turnover. The key
point is the non-reporting banks are net short 136t.
The mathematics don't work out as you think.
The very idea of your asymmetric reporting explanation for
the discrepancy (as you are standing it in opposition to my explanation)
represents a net flow from LBMA members more heavily weighted in one kind of
client (selling clients) to BBs more heavily weighted in a different kind of
client (buying clients).
We are both operating on the premise that the banks are not exposing themselves
to a large net-position. Your explanation opposes mine in that the banks'
"net-neutrality" comes from the non-reporting members as opposed to
derivative hedges in other markets as described above by Victor. This
neutrality would obviously be reflected in a net-neutral position, aka an
"All Banks Discrepancy" that is zero or much smaller just as your
model puts forth.
So I think that in your example above you are conflating statistical
insignificance and the asymmetric reporting explanations. Let's stick, for the
moment, to the question of my statement about the flow required for your
asymmetric explanation to fully suffice.
Here's your model, except I reversed the buys and sells to make it
directionally the same as the real LBMA survey (easier to discuss that way
IMO):
Reporting Banks Sells 5780t
Reporting Banks Buys 5627t
Reporting Banks Discrepancy 153t short
Non-Reporting Banks Sells 3096t
Non-Reporting Banks Buys 3232t
Non-Reporting Banks Discrepancy 136t long
All Banks Sells 8876t
All Banks Buys 8859t
All Banks Discrepancy 17t short
In your model here, the top portion (reporting banks) represents all that we
can see in the LBMA survey (magnitude doesn't matter in this discussion because
we are discussing the flow issue related to the asymmetric reporting
explanation at any magnitude). So this is our "toy LBMA survey":
Reporting Banks Sells 5780t
Reporting Banks Buys 5627t
Reporting Banks Discrepancy 153t short
My statement would therefore be adjusted to read: "there needs to be a
flow of gold twice the size of the discrepancy, or 306 tonnes (153 X 2) from
the non-reporting members to the reporting members"
We are both assuming that the individual banks (as well as in aggregate) have
no (or little) net position/exposure. So what we are seeing in this toy LBMA
survey is that the reporting banks are short vis-à-vis their clients but long
vis-à-vis the non-reporting banks in nearly equal amounts so as to be
net-neutral. And the non-reporting banks (because they are more heavily
weighted with selling clients like mines and the Perth Mint) are long vis-à-vis
their clients and short vis-à-vis the reporting banks (which have more buying
clients). All in all, the banks themselves are net-neutral (or at least close
to that; your model shows them with a real net position almost an order of
magnitude smaller than the reported discrepancy).
The point is that your reporting members in aggregate hold the same (or
similar) net position in magnitude, but opposite in direction, vis-à-vis their
clients and vis-à-vis the non-reporting members as the non-reporting members hold
in aggregate. This is the asymmetric reporting explanation!
So if the banks are essentially net-neutral, why are we seeing any discrepancy
at all? We are seeing it because only the interbank transactions were halved,
not the client transactions. So the reporting members are net short vis-à-vis
their clients and net long vis-à-vis other banks in equal/similar amounts, yet
because we halved only the trades with other banks, we see a discrepancy of
153t. So the discrepancy is the product of the halving the directional net
flow, therefore the magnitude of directional net flow must be twice the
discrepancy.
This applies in all of your models, from your simplest to your most complex, if we assume
asymmetric reporting.
So let's adjust my statement one more time and see if you still disagree with
it…
"For whatever portion of the discrepancy asymmetric reporting is to account
for, there must be a flow of gold twice the size of that portion of the
discrepancy from the non-reporting members to the reporting members and then on
to their (the reporting members') clients."
Do you still disagree with the statement given these changes?
Bron, your model is describing asymmetric reporting, intentional
or not, so you really should consider this flow issue and whether it makes any
sense at larger magnitudes like we saw in the actual survey. In your model we
can peek at the non-reporting banks and thereby know the "all banks
discrepancy" which your model is keeping low through asymmetric reporting.
But asymmetric reporting requires this flow, and if you can get your model to
spit out a 4.35% discrepancy like the real survey while still keeping your
"all banks discrepancy" low then it requires that much larger (and
therefore that much less likely) of a net-flow from one group to the other.
If, on the other hand, you allow the "all banks discrepancy" to rise
(perhaps offset by derivatives in other correlated markets), you eliminate the
asymmetric reporting and the need for this unlikely net-flow of gold, be it
paper or physical. So whatever the actual net-flow from non-reporting members
in aggregate to reporting members in aggregate is in reality, half of that
number is the portion of the 7,575 tonne discrepancy which can be accounted
for. If it's not the whole thing (i.e. 15K tonnes in one quarter), then that
forces your "All Banks Discrepancy" up which is a net-position they
must be hedging elsewhere.
You wrote: "However, that minimal net position of 17t
"appears" when we look only at the reporting banks to be a 153t
position over 11407 of turnover. The key point is the non-reporting banks are
net short 136t.
The mathematics don't work out as you think."
Your statement backs my point. You need the non-reporting banks to be net in
the opposite direction which requires a net flow of twice the
discrepancy. My math is fine. I think the true net position is higher, close to
the reported discrepancy, and that the non-reporting banks are probably not as
different from the reporting banks as to be exactly opposite as your model
requires to keep the "net position" low without the need for
derivative hedges.
Bron SucheckiDecember 4, 2012 9:59 PM
"Do you still disagree with the statement given these
changes?"
Yes, because I'm running various iterations of my toy model and it doesn't
produce such a flow. I can produce a situation where net overall the buying and
selling of clients is net and banks are net, but still result in a reporting
banks discrepancy with no x2 flow between reporting and non-reporting.
Note also that just because overall BBs are net doesn't mean that all
individual BBs are net.
My model isn't perfect and I want to refine it, but I think the interaction of 56
banks trading with each other is more complex than you realise.
I think the idea the BBs are naked short massive amounts is unrealistic, but I
think "offset by derivatives in other correlated markets" is pretty
close to unrealistic as well.
I'm going to have to leave this here and maybe I'll get time over Christmas to
look at my toy model and come to understand the dynamics of 56 banks better and
get back to you.
I the meantime, letting Joe think this is a 100% resolved issue, and then him
misapplying it to say there is 10 times more physical demand than physical
supply is not right. I can see an incorrect meme starting from that.
I think you are missing the point. I understand you are short on time, but
perhaps you could provide a sample iteration of this, in the same format as the
one above:
"Yes, because I'm running various iterations of my toy model and it
doesn't produce such a flow. I can produce a situation where net overall the
buying and selling of clients is net and banks are net, but still result in a
reporting banks discrepancy with no x2 flow between reporting and
non-reporting."
Also, I never claimed that the LBMA survey was showing "10 times more
physical demand than physical supply". First of all it's not physical
demand, it's paper "foreign exchange" demand for gold held and traded
as a currency, and in the case of the survey it was only one quarter. I'm
guessing that the next quarter demand was probably flat and the quarter after
that a lot of that paper was probably unwound/sold. So it's not constant
demand. It's that demand *shocks* in both directions are being absorbed
more so by paper expansion/contraction than by simply letting the price take
care of it. Like a shock absorber.
I don't think you are accounting for this "FOREX currency use" of
unallocated gold which could easily overwhelm the gold market at current prices
without such a shock absorber.
BTW, Bron, I just let GBIJoe know about this thread so that
he can come read it and see for himself that this issue is not resolved between
you and me. I told him that in my earlier email anyway, but at least this will
reassure him that we are still not in agreement! ;D
Regarding complexity, you wrote: "I think the interaction of 56 banks
trading with each other is more complex than you realise."
The LBMA asked for reporting down to the individual trade, and the survey even
reported the total number of trades (385,852) broken out into purchases
(184,140) and sales (201,713). The daily average number of trades was 6,125
over 63 trading days among 36 reporting members. So each member reported, on average,
170 gold trades per day, averaging 81 purchases and 89 sales per day.
We can even calculate the average size of each transaction, although it will be
off a little due to the interbank halving. The average individual sales trade
was $38.4M+. And the average purchase trade was $40.3M+, or a little less than
one tonne per trade.
This is the kind of complexity that I'm taking into account that I don't think
your model is. Remember what I wrote in one of our emails last July?…
"The key difference between your random iterations and mine is that you
are keeping the magnitude of complexity the same with each iteration. I was
comparing different magnitudes of complexity. Whether we randomize the
individual transactions or the choice of reporting banks doesn’t make a big
difference because the point of my exercise was that the more complex the
model, the lower the discrepancy.
You picked a specific level of complexity – 3,136 transactions – and then you
reiterated that model and found that the average discrepancy (for your model)
was .11% and the maximum was 2.96%.
What I did was to simply run 1 iteration on 3 different models of gradually
increasing complexity. The fact that I randomized the reporting banks and you
randomized the size of the individual trades is inconsequential. It’s also
inconsequential whether we had 75% reporting or 64% reporting.
Your original model had 9 trades, so my three levels of increasing complexity
were 63 trades, 126 trades and 252 trades. What we saw from a single iteration
of each level of magnitude was this:
9 trades – 10%
63 trades – 5.6%
126 trades – 2.7%
252 trades – 1.69%
I’m calculating the percent a little different than you. I’m saying there X%
more sales than purchases, and you’re saying there are X% less purchases than
sales. That’s why you came up with 4.35% on the LBMA survey and I came up with
4.5%. But that’s not a big deal.
So your new model has a complexity of 3,136 trades as compared to my most
complex model with 252 trades. What we find is that we’d expect a single
iteration to yield less than 1%, or maybe around 1%. So it looks like this:
9 trades – 10%
63 trades – 5.6%
126 trades – 2.7%
252 trades – 1.69%
3,136 trades – 1%
The LBMA survey reported a total of 385,852 trades.
If you want to try skewing the reporting banks toward sales, that’s fine. But
you need to run comparisons of increasing magnitude, not just iterations of the
same magnitude. I’ll bet if you keep it realistic we’ll see the same results…
it’ll still trend toward zero too quickly."
Perhaps you could expand on how you are taking into account more complexity
than I realize.
Bron SucheckiDecember 5, 2012 5:02 PM
"So your new model has a complexity of 3,136 trades ...
The LBMA survey reported a total of 385,852 trades."
That statement tells me you do not understand how the LBMA survey was
constructed. If every bank reported the LBMA could only have got a maximum of
3,136 data points. The LBMA was not given 385,852 individual trades.
If a bank had sold 1oz to 100 clients and covered that with 10oz purchase from
10 BBs it would have reported this to the LBMA:
Sell to Clients - 100oz
Buy from Clients - 0oz
Sell to BBs - 0oz
Buy from BBs - 100oz
It doesn't matter how many trades or clients it did, it will only report 4 data
points to the LBMA.
BTW, I had not thought about this "So each member reported, on average,
170 gold trades per day, averaging 81 purchases and 89 sales per day."
170 trades per day is impossible, that makes no sense to me when I look at how
many individual trades we do with bullion banks per day.
Those "trade" numbers must, at a minimum, be net settlement with a
counterparty. For example, if a BB bought 5 times from client A and sold 6
times to client A on one day, that was reported as 1 sale trade.
On average each BB having net settlements with 170 of its clients/other BBs
each day makes more sense.
I am going to have to write to the LBMA about that survey.
Of course the banks didn't report each trade to the LBMA.
The banks essentially performed their own internal survey and then reported the
aggregate numbers to the LBMA.
"That statement tells me you do not understand how the LBMA survey was
constructed."
Um, okay. If you say so. Your statements likewise tell me that you do not
understand the points I'm making. So there! ;D
The point is that 385,852 is how many trades (or net daily settlements with
individual counterparties as you pointed out) are reflected in those aggregate
numbers we see on the survey. Each trade (or net settlement) is presumably the
result of some counterparty's independent decision(s) and subsequent action(s).
So the coordination of actions essentially rests at the (resolution) level of
the trade (net daily settlement per counterparty), which was, on average,
around $40 million.
The point of running iterations at increasing levels of complexity (increasing
numbers of presumably random trades/daily counterparties) was to show you that
the more presumably random trades you aggregate, the lower the expected
discrepancy (deviation) between purchases and sales after applying the
statistical methodology of the survey.
As I showed, at 9 random trades, we found a 10% discrepancy between purchases
and sales. But as we increase the number of random trades, we quickly drop
toward zero. Even at 3,136 trades we were close to 1% discrepancy on average.
In fact the largest discrepancy/deviation your model produced after 100
iterations was 2.96% and the standard deviation was 1.21%. This is compared to
4.35% in the actual survey.
Can you see a trend forming here?
9 trades – 10%
63 trades – 5.6%
126 trades – 2.7%
252 trades – 1.69%
3,136 trades – 1.21%
Does this next one fit the trend or stand out?
385,852 trades – 4.35%
There's a point here which you don't seem to understand.
The point of looking at it this way is to help us determine if the 4.35%
discrepancy/deviation found in the actual survey is statistically significant
or insignificant. Clearly it is significant, and therefore it requires an
explanation beyond the "halving methodology" under which the survey
was published. We are well past this point, but you don't seem to understand
that, because you keep coming back to your model trying to tweak it, make it
more complex, or reiterate it enough times to get an outcome so rare that it is
more than three standard deviations, a 4.35% discrepancy. You'll probably have
to run more than 1,000 iterations to get just one of those in your simple model
with only 3,136 random trades.
As I say, each reporting LBMA member essentially conducted its own internal
survey of, on average, apparently, 170 "trades" per day. If those
trades were "random" (as in not coordinated/one-directional) then,
looking at my "trend" above, we could probably expect random
discrepancy/deviation between purchases and sales from each reporting member after
applying the "halving methodology" of the survey on the order
of about 2% in any given day. When we (as a BB conducting our own internal
"survey") aggregate 63 presumably random days, we now have an
aggregate of 10,710 data points. So, after applying the final survey
methodology, we'd expect to see, on average, a deviation of below 1.21% between
purchase and sales.
And then, as the LBMA aggregating these numbers from 36 separate reporting
members, we would expect (presuming randomness/insignificance) a discrepancy of
1% or less given the aggregate number contains data points from 395,852
presumably random "trades" *after applying the "halving
methodology"*.
If it's not 1% or less (which it wasn't), then there must be another
explanation."
As I said, Bron, I thought we were well past this point and yet you keep coming
back to your model to show how we can get to such a large discrepancy as we saw
in the actual survey. Fine, you keep playing with your toy model and I'll keep
pointing out that in every iteration where you show a high discrepancy between
purchases and sales, there must be one of three explanations:
1. There must be a net flow of twice the magnitude of the discrepancy
between reporting and non-reporting members…
or
2. There must be a net-change in the volume of paper gold equal to or
greater than the magnitude of the discrepancy during the reporting
period…
or
3. A combination of the two.
You seem to be saying, "no, no, there must be another explanation. You
just don't understand how complex these bullion banks are. I do, because I'm a
bullion professional who has direct dealings with them, and believe me, they
are complex!"
Fine. Let's see your alternative explanation. The only one I can infer so far
from your comments is perhaps:
4. A reckless abandon when reporting numbers (i.e., bogus numbers) which would
render the entire survey meaningless.
I suppose that is valid alternative explanation, but I thought we were working
on the assumption that this is not the case. If so, I eagerly await your new,
alternative explanation (as well as that sample iteration that doesn't produce
a flow).
Bron SucheckiDecember 5, 2012 8:34 PM
We've been over this in our previous email exchange.
I am not modelling 3,136 trades, which is the point you do not get. You cannot
go any further in terms of data points. The "trend forming" was about
getting more accurate models that reflects the survey, not about modelling more
underlying trades.
It does not matter if we could construct a model with 300,000 underlying trades
or 900,000 trades, they only aggregate up to a maximum of 3136 data points upon
which the LBMA methodology is applied and that is as far as you can model.
What I am trying to do is to model various scenarios with those 3136 data
points - eg
1) totally random, which my model is currently doing
2) asymmetric BBs, which I haven't even started to create scenarios for in my
model
3) volume of client trading to BB trading, ie how much speculative trade do
they do off client flow?
4) for the above, either zero net position, or some long or short position, for
each BB
It is only by exploring these scenarios that we can determine how much the LBMA
methodology may distort the underlying buy-sell difference reality.
Before I want to come to any conclusions about the results of the LBMA survey I
want to understand how the methodology works. You don't to understand that we
are not “well past this point” and I think we are talking at cross purposes.
“each reporting LBMA member essentially conducted its own internal survey” –
just to check, when saying “survey” do you mean “sampled”?
“There must be a net flow of twice the magnitude of the discrepancy between
reporting and non-reporting members” – I don’t think the maths of Client A to
Non-Reporting to Reporting to Client B work out as simply as this when you are
talking about 3136 interrelationships between 56 banks
BTW, I had far too little client volume vs BB-to-BB volume. Tweaking the model
gives an average discrepancy of -0.15% with a standard deviation of 5.14% over 1000
iterations. While that could explain away the LBMA result, I will not accept it
and continue to refine my understanding. You'll just have to wait for me to do
that.
Sounds good, Bron! I think I've made my case for any prying
eyes to decide for themselves and, like I said, I (we) eagerly await your
studied conclusions and your Occam's-worthy explanation for the discrepancy
between purchases and sales!
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