features : information services physical benchmarksin an on - line world startup on - line commodity exchanges seem not to realize that there are several good reasons why benchmarks derived from volumetric indices may not work - - and may not be useful - - for physical products , such as oil and petrochemicals . but there are other routes to price discovery
by neil fleming
apr . 1 , 2001
global energy business
page 37
copyright 2001 mcgraw - hill , inc .
somewhere near 500 internet - based exchanges for energy , oil , petrochemicals , and hydrocarbon shipping have been launched worldwide over the past two years . but most have since folded or been acquired . it ' s interesting to note that virtually all of those exchanges - - both the survivors and the deceased - - have two things in common . - - they were created from scratch by entrepreneurs from the technology world , or from the industries their exchanges are ( or were ) intended to serve . - - the businessmen and women behind them have followed the same train of thought in concluding that on - line trading was a business they could succeed at .
this train of thought can be broken down into four parts .
premise 1 : on - line trading is ` ` more efficient ' ' and will save its participants money .
premise 2 : i can capture some of those savings as site revenue .
premise 3 : my site will be the best site out there , so everyone will use it .
premise 4 : with all this liquidity , i can construct benchmarks . my benchmarks will be unimpeachably scientific and replace the existing , unscientific benchmarks currently used by the industry . as a result , i will make even more money , because i will become an information provider .
this article will examine those premises and the limits of their inherent logic , with a view to showing why very few - - if any - - of the 500 launched exchanges will survive . in particular , it will show why the logic of premise 4 is not valid for markets in physical commodities , such as oil and petrochemicals .
premise 1 : on - line trading is more efficient
few would question that , in theory , electronic trading has the potential to make liquid markets much more efficient . the history of enrononline , probably the most successful electronic energy trading system in the world today , supports that view . according to enron , enrononline has succeeded in raising the number of transactions completed by each of its market makers from an average 672 in 1999 to 3 , 084 last year , while lowering the marginal cost per transaction by 75 % over the two - year period .
however , the markets that enrononline predominantly serves are two of the most liquid and homogeneous in the energy business : natural gas and electricity . moreover , the exchange has achieved its efficiency gains using a model very different from any deployed by - - or available to - - most on - line exchanges . essentially , enron has used on - line trading to extend and complement its existing , off - line trading business . enron ' s focus remains the transaction , rather than the transaction system .
by contrast , the majority of transaction platforms for physical commodities are interlopers . their operators assume that commodities traders are willing to depersonalize their business by having their complex transactions executed not by humans , but by an electronic system that can do deals faster and more anonymously , and - - at least in theory - - with more players .
this assumption is a risky one , largely because it is based on a buyer ' s perception of how marketplaces should operate . it fails to take into account the concerns of physical commodity sellers and - - more importantly - - producers . typically , producers wish to ensure that the uptake of their commodity is continuous , because that minimizes the volatility of the commodity ' s price . relationships become all - important under such conditions - - but relationships are hardly enhanced by electronic dealing . to quote the november / december 2000 issue of the harvard business review , ` ` few suppliers want to be anonymous contestants in ruthless bidding wars . ' '
premise 2 : i can make money selling gains in efficiency
the logic underlying premise 2 is potentially seriously flawed . for while there is no doubt that market participants are prepared to pay for more efficient services , such as electronic trading , the nature of the forces driving physical energy commodity markets casts doubt on whether a trading system can , in the long term , make a profit by charging per - transaction fees .
here ' s why : although the era of competition is just beginning , it ' s clear that pressure on transaction fees is already downward , and will remain in that direction even if the number of participants in an exchange shrinks to a handful . in the energy business , the potential for efficiency gains - - as a percentage of total industry cost - - simply is not that great . for example , research done for platts , a division of the mcgraw - hill companies , new york , indicates that the total potential efficiency ` ` pool ' ' in the oil industry is only $ 150 million / year . even if only 10 businesses were to try to carve a profitable enterprise out of such a pool , each would be hard - pressed to do so .
what ' s more , such businesses would have an even harder time turning a profit because some of their competitors wouldn ' t be using a per - trade revenue model . in particular , sites operated by market participants , such as enrononline , will tend to charge nothing per trade , because their business models seek to profit not from commissions on transactions , but rather from the transactions themselves . by implication , it seems likely that an exchange will be able to make money only if its owners sell something else : clearing facilities , back - office integration , or information to decision - makers .
premise 3 : my site will be the best , so it will capture all the market liquidity available
this statement - - which essentially says that given enough marketing support , a site can change the trading patterns of an industry to capture all of its liquidity - - is a fallacy .
the logical problem with the premise is clear . because no one has yet developed a trading software package that is clearly superior to others , what ` ` best site ' ' really means is ` ` most liquid site . ' ' so the pre - condition for capturing ` ` all ' ' the liquidity is that the site must already be the most liquid site .
some startup exchanges have attempted to solve this problem by insisting that their participants make volume guarantees . but volume guarantees conflict directly with traders ' focus on profits . trading businesses that tell their traders to sacrifice profits for the sake of the common good are playing a risky game ; if the common good means lower trading profits , the traders will simply leave for greener pastures .
to become the ` ` best ' ' site , then , a site must somehow find a way to bootstrap its liquidity , perhaps by offering substantial , non - volume - related incentives or efficiencies to users .
however , the paradox is that most sites ' revenue models depend almost exclusively on volume .
premise 4 : once i capture lots of liquidity , i can build benchmarks
this premise is the most startling and - - in some ways - - the most flawed of the four . it raises many serious questions , the answers to any of which can invalidate the model . worse , these questions are all highly theoretical , making persuasive answers even harder to come by .
among the questions are : - - how much liquidity is enough ? - - do indexes ( volumetric averages ) make good physical benchmarks ? - - can a trading site benchmark with the bid / offer range ? - - can a trading site generate a close ? and , last but not least : what is a benchmark , anyway ?
benchmarks and indices
a benchmark is a price or series of prices that a market ' s participants agree to use as the basis for determining other prices . useful benchmarks are good indicators of transactable value . a benchmark allows market participants to determine at what price they should buy or sell the commodity .
benchmarks are typically used in complex markets with multi - dimensional variables for arbitrage . in oil , for example , benchmark pricing has grown up around the need to compute relative values for differing commodities across time , geographical distance , commodity type , and the degree to which one commodity may be substituted for another .
it is a common error , however , to believe that markets should or do use their most liquidly traded commodities as benchmarks . although liquidity is a major asset of benchmarks , many markets use illiquid benchmarks whose other characteristics outweigh the liquidity advantage . these include market transparency ; the free and open availability of the commodity ; the absence of political or partisan control of the commodity ; the absence of delivery restrictions on it ; and the size of the end - user market where the commodity will be consumed . for example , although the physical volume of dubai crude oil actually produced is tiny , it nonetheless is used as a key benchmark for all of asia because - - unlike its persian gulf competitors - - it is the only crude oil that has these characteristics .
one way to compute a commodity ' s benchmark price is through strict indexation , the process of constructing a price marker from a volumetric average of concluded business . however , this process will have little chance of producing a useful benchmark unless the contributing volumes are high . if it doesn ' t reflect a sufficient number of trades , an index has little or no statistical validity as an indication of transactable price .
this represents a problem for benchmarking oil prices , whether for crude or products . of the 77 million barrels of crude produced worldwide each day , the vast majority are sold on the long - term contract market , leaving perhaps as little as 10 % to be traded ` ` spot . ' ' in reality , this figure is boosted in multiple ways , primarily by transaction chaining - - the repeated on - selling of cargos - - but , even assuming threefold transaction growth as a result of electronic trading , is still perilously low to be used as a valid basis for strict indexation , especially considering the great diversity of crude oil specifications around the world . for many crude oils , the trading basis for establishing price can be just a few transactions per month . even for ` ` liquid ' ' benchmark crudes , the basis is typically just a handful of trades per day . oil markets , however , don ' t seem to care . for example , the settlement basis for the international petroleum exchange ' s brent crude oil futures contract is an index based on a smattering of
physical trades taking place on the day of expiry . acceptance of an indexation mechanism such as this has difficult prerequisites . these include open participation in the mechanism , the perceived existence of a ` ` level playing field , ' ' the existence of comparative historical data , and achieving ` ` buy in ' ' from the market ' s dominant players .
the nature of today ' s on - line exchanges makes these conditions hard to meet . not everyone uses exchanges ; they may be owned by industry players ; they typically have little or no historical data ; their daily volume fluctuates wildly ; and major players ' commitment to them is either fragmentary or compromised by volume commitments . in today ' s environment , the chance that an index will be able to gain market acceptance as a benchmark is extremely slim .
are strict volumetric indices useful ? another big question surrounding volumetric indices is how useful they are as benchmarks for physical commodities . advocates of indexation argue that its statistical methodology helps eliminate market distortions caused , for example , by market closes . in addition , they argue that the alternative approach - - ` ` market assessment , ' ' based on human judgements about transactable prices - - is too subjective to be relied on to determine benchmark prices . indexation , its proponents argue , ` ` eliminates ' ' this subjectivity .
in practice , however , this argument doesn ' t pass the ` ` so what ? ' ' test . even if a system such as indexation succeeds in taking judgement out of the determination of market prices , it cannot prevent attempts to distort the index by exploiting the ` ` rules ' ' on which the system is based .
in many markets , human judgement is the only effective defense against market plays whose goal is profit , not price transparency . even in highly liquid markets - - such as , for example , the monthly natural gas market in the u . s . - - the indices generated by information companies like platts are subject to human review and analysis . during their generation , platts ' market specialists undertake a string of comparative tests to unearth potential distortions in reported prices . whenever such distortions become apparent , platts ' editors investigate the market further and , where necessary , eliminate certain trades from the assessment picture . there are good reasons for their diligence . a volumetric index can be manipulated by volume plays , by a few players under the cloak of ` ` anonymous dealing , ' ' by selective trading , or by hedging in one marketplace and trading in another .
figures 1 and 2 illustrate how players with exposure to a particular price on the buy side might - - and actually do - - manipulate a volumetric index . by doing lots of business early in the day when the market is rising , they can skew the volumetric average for the day lower ( figure 1 ) . when the market is falling , they reverse the pattern and shift their transactions to late in the day , again pushing the market down ( figure 2 ) . when players do this consistently , the impact on an index can be considerable over time .
there are other , potentially more serious problems with indices . because an index is a theoretical construct , in a rapidly moving market it will manifest lag - - and become useless to market participants seeking the answer to the question , ` ` at what price should i buy or sell ? ' ' indeed , an index may even actively mislead people asking this question . for example , in a fast rising market , an index generated over the course of one day will be significantly below the opening market price on the following day . this creates the impression that prices have somehow moved overnight , whereas in fact they may not have moved at all .
but perhaps the biggest problem with indices relates to their use in heavily interlinked markets , where prices are constrained by a complex of spread and arbitrage values . this description applies to most energy markets , and is particularly apt for today ' s global , physical market for oil . in markets where spread relationships are as or more important than outright prices , the process of generating averaged indices tends to generate sets of prices that cannot be reconciled with each other .
figure 3 depicts a dramatized version of this problem . price 1 and price 2 are interdependent ; here , there is always a 5 cents difference between them . however , the volume pattern for the day ' s trade ( shown in the lower part of the figure ) is such that while price 1 has very high volume early in the day , price 2 does not enjoy its volume boost until later . the result : contrary to market reality , the volumetric averages for price 1 and price 2 say that they only differ by 1 cents .
other routes to price discovery
so , if an on - line trading system cannot generate a reliable benchmark from an index , are there other routes to price discovery ? yes , two . the first is to benchmark from a bid - offer range , and the second is to use a close of some kind .
bid - offer ranges are widely used as market measurement tools by platts and others . however , the automated application of bid - offer ranges in pursuit of ` ` scientific ' ' benchmarks is fraught with difficulty . what happens when there is a bid , but no offer ? an offer , but no bid ? what rules can be written to discriminate between an ` ` off - market ' ' bid and an ` ` on - market ' ' one ? how should a system decide when a bid or offer ' s timing is not representative of the typical market ? most on - line exchanges have not even begun to answer these questions , and at best offer their users a ` ` last bid - last offer ' ' price range that can accidentally distort their perception of the market , if the two are not aligned in time , or if it turns out , for example , that the ` ` last bid ' ' was actually withdrawn in response to the ` ` last offer . ' '
some exchanges , implicitly acknowledging that creating useful benchmarks is difficult , have tried to incorporate their transactions and bid / offers into published benchmarks . obviously , they hope that their system will attract more liquidity if it is tied to a traditional benchmark . but it ' s safe to say that a publisher will be interested in incorporating an exchange ' s proprietary markers only if transaction bid / offers are open and transparent .
can an on - line site benchmark from a close ? in theory , yes . but if human hands and brains intervene , the process is no longer a mechanical one , but rather an editorial / judgement effort . here too , there are many obstacles . one of the biggest is that closing prices work best in two kinds of markets : completely ` ` open ' ' markets , in which assessments are derived by surveying all participants ; and ` ` sole - operator ' ' markets - - like futures markets - - where the traded instrument exists only on that exchange .
physical commodities , by contrast , are traded across a broad spectrum of instruments and sub - markets . it would be virtually impossible for the owner of a single sub - market - - or on - line exchange - - to assert the superiority of its close over someone else ' s , or to build market acceptance for a price derived from a trading pool that doesn ' t include all players .
even a system used by most players - - for example , the oil industry ' s derivatives - trading intercontinental exchange - - would have a hard time convincing anyone to rely exclusively on its closing prices . that ' s not only because ` ` most ' ' is not the same as ` ` all , ' ' but also because liquidity is frequently too low to assure that there will in fact be a traded market to close every day . what happens to the benchmark on days when no one comes out to play ?
in conclusion , it appears that price discovery may not necessarily be an emergent property of on - line trading systems . that may be the case because the creators of most on - line trading systems have confused its mechanism with its function . trading systems are neither new markets nor new marketplaces ; they are simply new vehicles for participating in existing marketplaces . a telephone is also a vehicle for participating in markets - - but no one expects their phone to be able to tell them the price of a commodity .
gebol 11600001
related topics :
dow jones topics
publication : global energy business
news :
engl
copyright ? 2001 dow jones reuters business interactive llc . all rights reserved .
features : information services
physical benchmarksin an on - line world startup on - line commodity exchanges seem not to realize that there are several good reasons why benchmarks derived from volumetric indices may not work - - and may not be useful - - for physical products , such as oil and petrochemicals . but there are other routes to price discovery
by neil fleming
apr . 1 , 2001
global energy business
page 37
copyright 2001 mcgraw - hill , inc .
somewhere near 500 internet - based exchanges for energy , oil , petrochemicals , and hydrocarbon shipping have been launched worldwide over the past two years . but most have since folded or been acquired . it ' s interesting to note that virtually all of those exchanges - - both the survivors and the deceased - - have two things in common . - - they were created from scratch by entrepreneurs from the technology world , or from the industries their exchanges are ( or were ) intended to serve . - - the businessmen and women behind them have followed the same train of thought in concluding that on - line trading was a business they could succeed at .
this train of thought can be broken down into four parts .
premise 1 : on - line trading is ` ` more efficient ' ' and will save its participants money .
premise 2 : i can capture some of those savings as site revenue .
premise 3 : my site will be the best site out there , so everyone will use it .
premise 4 : with all this liquidity , i can construct benchmarks . my benchmarks will be unimpeachably scientific and replace the existing , unscientific benchmarks currently used by the industry . as a result , i will make even more money , because i will become an information provider .
this article will examine those premises and the limits of their inherent logic , with a view to showing why very few - - if any - - of the 500 launched exchanges will survive . in particular , it will show why the logic of premise 4 is not valid for markets in physical commodities , such as oil and petrochemicals .
premise 1 : on - line trading is more efficient
few would question that , in theory , electronic trading has the potential to make liquid markets much more efficient . the history of enrononline , probably the most successful electronic energy trading system in the world today , supports that view . according to enron , enrononline has succeeded in raising the number of transactions completed by each of its market makers from an average 672 in 1999 to 3 , 084 last year , while lowering the marginal cost per transaction by 75 % over the two - year period .
however , the markets that enrononline predominantly serves are two of the most liquid and homogeneous in the energy business : natural gas and electricity . moreover , the exchange has achieved its efficiency gains using a model very different from any deployed by - - or available to - - most on - line exchanges . essentially , enron has used on - line trading to extend and complement its existing , off - line trading business . enron ' s focus remains the transaction , rather than the transaction system .
by contrast , the majority of transaction platforms for physical commodities are interlopers . their operators assume that commodities traders are willing to depersonalize their business by having their complex transactions executed not by humans , but by an electronic system that can do deals faster and more anonymously , and - - at least in theory - - with more players .
this assumption is a risky one , largely because it is based on a buyer ' s perception of how marketplaces should operate . it fails to take into account the concerns of physical commodity sellers and - - more importantly - - producers . typically , producers wish to ensure that the uptake of their commodity is continuous , because that minimizes the volatility of the commodity ' s price . relationships become all - important under such conditions - - but relationships are hardly enhanced by electronic dealing . to quote the november / december 2000 issue of the harvard business review , ` ` few suppliers want to be anonymous contestants in ruthless bidding wars . ' '
premise 2 : i can make money selling gains in efficiency
the logic underlying premise 2 is potentially seriously flawed . for while there is no doubt that market participants are prepared to pay for more efficient services , such as electronic trading , the nature of the forces driving physical energy commodity markets casts doubt on whether a trading system can , in the long term , make a profit by charging per - transaction fees .
here ' s why : although the era of competition is just beginning , it ' s clear that pressure on transaction fees is already downward , and will remain in that direction even if the number of participants in an exchange shrinks to a handful . in the energy business , the potential for efficiency gains - - as a percentage of total industry cost - - simply is not that great . for example , research done for platts , a division of the mcgraw - hill companies , new york , indicates that the total potential efficiency ` ` pool ' ' in the oil industry is only $ 150 million / year . even if only 10 businesses were to try to carve a profitable enterprise out of such a pool , each would be hard - pressed to do so .
what ' s more , such businesses would have an even harder time turning a profit because some of their competitors wouldn ' t be using a per - trade revenue model . in particular , sites operated by market participants , such as enrononline , will tend to charge nothing per trade , because their business models seek to profit not from commissions on transactions , but rather from the transactions themselves . by implication , it seems likely that an exchange will be able to make money only if its owners sell something else : clearing facilities , back - office integration , or information to decision - makers .
premise 3 : my site will be the best , so it will capture all the market liquidity available
this statement - - which essentially says that given enough marketing support , a site can change the trading patterns of an industry to capture all of its liquidity - - is a fallacy .
the logical problem with the premise is clear . because no one has yet developed a trading software package that is clearly superior to others , what ` ` best site ' ' really means is ` ` most liquid site . ' ' so the pre - condition for capturing ` ` all ' ' the liquidity is that the site must already be the most liquid site .
some startup exchanges have attempted to solve this problem by insisting that their participants make volume guarantees . but volume guarantees conflict directly with traders ' focus on profits . trading businesses that tell their traders to sacrifice profits for the sake of the common good are playing a risky game ; if the common good means lower trading profits , the traders will simply leave for greener pastures .
to become the ` ` best ' ' site , then , a site must somehow find a way to bootstrap its liquidity , perhaps by offering substantial , non - volume - related incentives or efficiencies to users .
however , the paradox is that most sites ' revenue models depend almost exclusively on volume .
premise 4 : once i capture lots of liquidity , i can build benchmarks
this premise is the most startling and - - in some ways - - the most flawed of the four . it raises many serious questions , the answers to any of which can invalidate the model . worse , these questions are all highly theoretical , making persuasive answers even harder to come by .
among the questions are : - - how much liquidity is enough ? - - do indexes ( volumetric averages ) make good physical benchmarks ? - - can a trading site benchmark with the bid / offer range ? - - can a trading site generate a close ? and , last but not least : what is a benchmark , anyway ?
benchmarks and indices
a benchmark is a price or series of prices that a market ' s participants agree to use as the basis for determining other prices . useful benchmarks are good indicators of transactable value . a benchmark allows market participants to determine at what price they should buy or sell the commodity .
benchmarks are typically used in complex markets with multi - dimensional variables for arbitrage . in oil , for example , benchmark pricing has grown up around the need to compute relative values for differing commodities across time , geographical distance , commodity type , and the degree to which one commodity may be substituted for another .
it is a common error , however , to believe that markets should or do use their most liquidly traded commodities as benchmarks . although liquidity is a major asset of benchmarks , many markets use illiquid benchmarks whose other characteristics outweigh the liquidity advantage . these include market transparency ; the free and open availability of the commodity ; the absence of political or partisan control of the commodity ; the absence of delivery restrictions on it ; and the size of the end - user market where the commodity will be consumed . for example , although the physical volume of dubai crude oil actually produced is tiny , it nonetheless is used as a key benchmark for all of asia because - - unlike its persian gulf competitors - - it is the only crude oil that has these characteristics .
one way to compute a commodity ' s benchmark price is through strict indexation , the process of constructing a price marker from a volumetric average of concluded business . however , this process will have little chance of producing a useful benchmark unless the contributing volumes are high . if it doesn ' t reflect a sufficient number of trades , an index has little or no statistical validity as an indication of transactable price .
this represents a problem for benchmarking oil prices , whether for crude or products . of the 77 million barrels of crude produced worldwide each day , the vast majority are sold on the long - term contract market , leaving perhaps as little as 10 % to be traded ` ` spot . ' ' in reality , this figure is boosted in multiple ways , primarily by transaction chaining - - the repeated on - selling of cargos - - but , even assuming threefold transaction growth as a result of electronic trading , is still perilously low to be used as a valid basis for strict indexation , especially considering the great diversity of crude oil specifications around the world . for many crude oils , the trading basis for establishing price can be just a few transactions per month . even for ` ` liquid ' ' benchmark crudes , the basis is typically just a handful of trades per day . oil markets , however , don ' t seem to care . for example , the settlement basis for the international petroleum exchange ' s brent crude oil futures contract is an index based on a smattering of
physical trades taking place on the day of expiry . acceptance of an indexation mechanism such as this has difficult prerequisites . these include open participation in the mechanism , the perceived existence of a ` ` level playing field , ' ' the existence of comparative historical data , and achieving ` ` buy in ' ' from the market ' s dominant players .
the nature of today ' s on - line exchanges makes these conditions hard to meet . not everyone uses exchanges ; they may be owned by industry players ; they typically have little or no historical data ; their daily volume fluctuates wildly ; and major players ' commitment to them is either fragmentary or compromised by volume commitments . in today ' s environment , the chance that an index will be able to gain market acceptance as a benchmark is extremely slim .
are strict volumetric indices useful ? another big question surrounding volumetric indices is how useful they are as benchmarks for physical commodities . advocates of indexation argue that its statistical methodology helps eliminate market distortions caused , for example , by market closes . in addition , they argue that the alternative approach - - ` ` market assessment , ' ' based on human judgements about transactable prices - - is too subjective to be relied on to determine benchmark prices . indexation , its proponents argue , ` ` eliminates ' ' this subjectivity .
in practice , however , this argument doesn ' t pass the ` ` so what ? ' ' test . even if a system such as indexation succeeds in taking judgement out of the determination of market prices , it cannot prevent attempts to distort the index by exploiting the ` ` rules ' ' on which the system is based .
in many markets , human judgement is the only effective defense against market plays whose goal is profit , not price transparency . even in highly liquid markets - - such as , for example , the monthly natural gas market in the u . s . - - the indices generated by information companies like platts are subject to human review and analysis . during their generation , platts ' market specialists undertake a string of comparative tests to unearth potential distortions in reported prices . whenever such distortions become apparent , platts ' editors investigate the market further and , where necessary , eliminate certain trades from the assessment picture . there are good reasons for their diligence . a volumetric index can be manipulated by volume plays , by a few players under the cloak of ` ` anonymous dealing , ' ' by selective trading , or by hedging in one marketplace and trading in another .
figures 1 and 2 illustrate how players with exposure to a particular price on the buy side might - - and actually do - - manipulate a volumetric index . by doing lots of business early in the day when the market is rising , they can skew the volumetric average for the day lower ( figure 1 ) . when the market is falling , they reverse the pattern and shift their transactions to late in the day , again pushing the market down ( figure 2 ) . when players do this consistently , the impact on an index can be considerable over time .
there are other , potentially more serious problems with indices . because an index is a theoretical construct , in a rapidly moving market it will manifest lag - - and become useless to market participants seeking the answer to the question , ` ` at what price should i buy or sell ? ' ' indeed , an index may even actively mislead people asking this question . for example , in a fast rising market , an index generated over the course of one day will be significantly below the opening market price on the following day . this creates the impression that prices have somehow moved overnight , whereas in fact they may not have moved at all .
but perhaps the biggest problem with indices relates to their use in heavily interlinked markets , where prices are constrained by a complex of spread and arbitrage values . this description applies to most energy markets , and is particularly apt for today ' s global , physical market for oil . in markets where spread relationships are as or more important than outright prices , the process of generating averaged indices tends to generate sets of prices that cannot be reconciled with each other .
figure 3 depicts a dramatized version of this problem . price 1 and price 2 are interdependent ; here , there is always a 5 cents difference between them . however , the volume pattern for the day ' s trade ( shown in the lower part of the figure ) is such that while price 1 has very high volume early in the day , price 2 does not enjoy its volume boost until later . the result : contrary to market reality , the volumetric averages for price 1 and price 2 say that they only differ by 1 cents .
other routes to price discovery
so , if an on - line trading system cannot generate a reliable benchmark from an index , are there other routes to price discovery ? yes , two . the first is to benchmark from a bid - offer range , and the second is to use a close of some kind .
bid - offer ranges are widely used as market measurement tools by platts and others . however , the automated application of bid - offer ranges in pursuit of ` ` scientific ' ' benchmarks is fraught with difficulty . what happens when there is a bid , but no offer ? an offer , but no bid ? what rules can be written to discriminate between an ` ` off - market ' ' bid and an ` ` on - market ' ' one ? how should a system decide when a bid or offer ' s timing is not representative of the typical market ? most on - line exchanges have not even begun to answer these questions , and at best offer their users a ` ` last bid - last offer ' ' price range that can accidentally distort their perception of the market , if the two are not aligned in time , or if it turns out , for example , that the ` ` last bid ' ' was actually withdrawn in response to the ` ` last offer . ' '
some exchanges , implicitly acknowledging that creating useful benchmarks is difficult , have tried to incorporate their transactions and bid / offers into published benchmarks . obviously , they hope that their system will attract more liquidity if it is tied to a traditional benchmark . but it ' s safe to say that a publisher will be interested in incorporating an exchange ' s proprietary markers only if transaction bid / offers are open and transparent .
can an on - line site benchmark from a close ? in theory , yes . but if human hands and brains intervene , the process is no longer a mechanical one , but rather an editorial / judgement effort . here too , there are many obstacles . one of the biggest is that closing prices work best in two kinds of markets : completely ` ` open ' ' markets , in which assessments are derived by surveying all participants ; and ` ` sole - operator ' ' markets - - like futures markets - - where the traded instrument exists only on that exchange .
physical commodities , by contrast , are traded across a broad spectrum of instruments and sub - markets . it would be virtually impossible for the owner of a single sub - market - - or on - line exchange - - to assert the superiority of its close over someone else ' s , or to build market acceptance for a price derived from a trading pool that doesn ' t include all players .
even a system used by most players - - for example , the oil industry ' s derivatives - trading intercontinental exchange - - would have a hard time convincing anyone to rely exclusively on its closing prices . that ' s not only because ` ` most ' ' is not the same as ` ` all , ' ' but also because liquidity is frequently too low to assure that there will in fact be a traded market to close every day . what happens to the benchmark on days when no one comes out to play ?
in conclusion , it appears that price discovery may not necessarily be an emergent property of on - line trading systems . that may be the case because the creators of most on - line trading systems have confused its mechanism with its function . trading systems are neither new markets nor new marketplaces ; they are simply new vehicles for participating in existing marketplaces . a telephone is also a vehicle for participating in markets - - but no one expects their phone to be able to tell them the price of a commodity .
gebol 11600001
related topics :
dow jones topics
publication : global energy business
news :
engl
copyright ? 2001 dow jones reuters business interactive llc . all rights reserved .