Banks Are Where The Liquidity Is


 Alice Mathews
 2 years ago
 Views:
Transcription
1 Prelmary Bas Are Where The Lquy Is Olver Har Harvar Uversy & NBER a Lug Zgales* Uversy of Chcago, NBER & CEPR February 04 Absrac Wha s so secal abou bas ha her emse ofe rggers goverme erveo? I hs aer we show ha, eve gorg ercoeceess ssues, he falure of a large ba causes a larger welfare loss ha he falure of oher suos The reaso s ha ages ee of lquy e o cocerae her holgs bas Thus, a shoc o bas srooroaely affecs he ages who ee lquy he mos, reucg aggregae ema a he level of ecoomc acvy We also show ha he goverme wll choose a larger fscal erveo whe a large ba fals Key Wors: lquy, balou, bag JEL Coes: E4 G, E5 * Olver Har graefully acowleges facal suor from he US Naoal Scece Fouao hrough he Naoal Bureau of Ecoomc Research Lug Zgales graefully acowleges facal suor from he Ceer for Research Secury Prces (CRSP) a he Iave o Global Mares a he Uversy of Chcago
2 Durg he facal crss usral frms, clug maor oes le Geeral Moors, were allowe o go baru By coras, facal frms, wh he oable eceo of Lehma, were bale ou Oe ossble reaso for hs ffereal reame ca be fou he olcal clou of hese wo usres Facal frms were a are maor oors of rece amsraos May of he rece Treasury Secreares a Whe House Chefs of Saff came from he facal usry The greaer aeo show by he goverme owar he facal usry, hus, mgh be urely a maer of olcs o ecoomcs Whle o eyg hs ossbly, hs aer we elore a alerave erreao: ha goverme erveo s usfe by a rsc fferece he welfare cosequeces whe a ba, raher ha a equallysze usral frm, fals A ofemeoe raoal for hs fferece s he egree of ercoeceess of facal suos Bu whle here s o oub ha large facal suos e o be hghly ercoece, large usral frms le Geeral Moors a For are very ercoece oo, as s srogly suggese he followg esmoy of For s CEO Ala Mulally: The omesc auo usry s hghly ereee A collase of oe of our comeors woul o oly affec For a our rasformao la, bu woul have a evasag rle effec across he ecoomy Aoher oular erreao amog facal ecoomss for he fferece bewee large maufacurg frms a bas focuses o he ably of eosors o ru (eg, Damo a Dybvg, 983), rasg he ossbly of effce lquao Ye, f hs were he oly roblem, eos surace woul f Furhermore, sulers a cusomers of GM ca ru oo, as show by he fac ha 008 he Goverme ervee o guaraee GM warraes So wha maes bas ffere? I hs aer, we focus o a ffere meso: bas are uque because hey are where eole ee of lquy ee her wealh I a worl where he reur from huma caal s o fully legeable, he falure of ay frm mles wo losses: a rec loss of wealh a a rec loss lquy, ue o he reuco legeable asses Ths ual effec s rese boh for a ba a for a Ala Mulally s esmoy o he Ue Saes Seae Commee o Bag, Housg a Urba Affars, November 8, 008
3 usral frm Ye, he mac of a loss of legeable asses s ffere eeg o he lquy ees of he holers of hose asses We wll show ha ages who ee lquy for rasaco uroses wll srooroaely hol her wealh he form of ba eoss Hece a ba falure hs hese ages, who are lquy cosrae, more severely ha he falure of a usral frm, causg a larger ro ema for labor servces, whch was suore by ha lquy, a a larger fall GDP( Smlarly, a loss bore by ebholers affecs GDP srooroaely more ha a equallysze loss bore by equy vesors) Bulg o Har a Zgales (03), we coser a smle geeral equlbrum ecoomy where secury mares are comlee, bu fuure labor come s o legeable There are wo grous of ages, ocors a eachers, a he lac of a smulaeous ouble cocece of was bewee he wo grous geeraes he ee for a relavely safe asse for rasaco uroses I hs coe we show ha ages wh lquy ees (ocors) wll choose o hol a srooroae amou of (Arrow) secures ha ay off he low saes of he worl We also show ha hese Arrow secures ca be maufacure by rachg vesme ayoffs orer of seory If we bul Arrow secures hs way, he ages ee of lquy wll hol srooroaely more of he mos seor raches We he coser he macroecoomc effecs of mosg a loss o ffere raches There s a bul correlao bewee eremely egave aggregae oucomes a losses bore by he mos seor raches To elmae he effec of hs surous correlao, we coser oly losses ue o a osycrac frau a secfc rache: a Maoffye loss If we o, he effec we observe woul be eve larger We show ha, whe a loss falls o seor raches, he macroecoomc effec of hs loss s more severe a so s he welfare loss assocae wh Losses bore by seor rache holers erve he ages who ee lquy, our moel he ocors, of collaeral I so og, hey reuce he effecve ema of hese ages for eachg servces, ecreasg he amou of come he eachers ca mae Uable o sell her labor servces o favorable erms, eachers wll cural her ow ema for ocors 3
4 servces, furher reucg he overall level of ecoomc acvy Ths effec s more lme or eve comleely abse whe a uor raches face a loss, because uor raches are hel srooroaely by ages wh low lquy ees The very seor raches hel by he ocors loo smlar o ba eoss Iee, bas ca be cosere muual fus ha o he asse se ves facal clams a o he lably se have a srooroae share of very seor clams (eoss) Accorg o hs vew, bas are ohg bu a coseffecve way o maufacure Arrow secures eee for lquy uroses Ths smle heory of bag s able o ela why bas ee o have eoss ha o o flucuae value Deosors are he ages wh he hghes ee for lquy a hus hey ema surace agas ossble falls he value of her vesmes, eve f hey are rs eural I our moel hs surace s rove by he eachers, e, he ages less ee of lquy Ths very smle heory of bag s able o ela o oly why he efaul of a ba s worse ha he efaul of a smlarlysze comay, bu also ha hese effecs are o uque o bas: hey are commo o all he mos seor secures Ths mgh ela why govermes are so reluca o mose losses o bos, esecally secure bos The reaso s he same: hey are hel srooroaely by eole ee of lquy The res of he aer rocees as follows Seco reses he framewor Seco 3 characerzes he olegeable equlbrum for he case where here s suffce lquy each sae of he worl o susa he frsbes level of rae (Oe of he subcases s relegae o a Ae) Seco 4 shows how hs ema ca be sasfe by secures raches a how he mos seor raches wll be srooroaely hel by ocors Seco 5 ees he aalyss o he case where here s suffce lquy he bes sae of he worl o susa he frsbes level of rae Seco 6 cosers he role of fscal olcy Seco 7 elas how our very seor fus ca be erree as bas Fally, Seco 8 coclues The Framewor 4
5 There s a large umber of ages E ae each age s equally lely o be a ocor or a eacher; ages lear her ye a he begg of ero Docors wa o cosume eacher servces ero a eachers wa o cosume ocor servces ero 3 Docors a eachers ca also cosume whea ero 4 a here s o scoug Each ocor a eacher has a eowme of whea ero equal o e Whea ca be vese roecs; hese roecs yel whea ero 4 We wll assume ha e>0 The mele s as Fgure Ages lear wheher Sae of worl Trae of Trae of Ouu hey are ocors or eachers realze eacher servces ocor servces from Whea vese Secures rae roecs/ whea cosume Fgure We wre ages ules as: Docors: ( ) U w l Teachers: ( ) U w l where s he quay of eacher servces cosume by a ocor; l s he labor sule by a ocor; s he quay of ocor servces cosume by a eacher; l s he labor sule by a eacher; a s he quay of whea cosume by vual, ero 4 We assume cosa reurs o scale: oe u of eacher labor yels oe u of eacher servces a oe u of ocor labor yels oe u of ocor servces w 5
6 Ages are rs eural The mares for eacher a ocor servces are erfecly comeve I s crucal for our aalyss ha here s o smulaeous ouble cocece of was: a eacher oes o wa o cosume ocor servces ero from he ocor who s buyg hs eacher servces Coser ow rouco There s a rsy echology ha rasfers eowme bewee eros a 4 There are (aggregae) saes of he worl Wh robably >0, oe u of ero whea s rasforme o R us of ero 4 whea (=, ) Whou loss of geeraly we label he saes so ha 0 R R R Ages lear abou he sae of he worl bewee eros a All ages are rs eural We also assume ha here s free ery of frms ossessg he echology escrbe a ha hese frms face cosa reurs o scale (he frms echologes are erfecly correlae) Ths framewor s smlar o Har a Zgales (03), ece hree resecs Oe (mor) s ha here are saes sea of us The seco (more mora) s ha we o o assume ha a age ca sure agas becomg a ocor raher ha a eacher before ero ; a usfcao s ha ero eowme cao be lege avace Thr, a mos mora, hs moel here s o rsless sorage As we show Har a Zgales (03), he resece of mulle vesme choces creaes a soro bewee rvae a socal ceves Sce hs soro has alreay bee aalyze our oher aer, we wa o elmae here I se of hese ffereces, some of he basc resuls are he same For eamle, he absece of ay legeably roblem he ecoomy has a uque ArrowDebreu (or sequeal Arrow) equlbrum I hs equlbrum, here s a searao bewee cosumo a rouco The rces of ocor a eacher servces, he wage raes of ocors a eachers, a he rce of whea wll be he same, a we ca ormalze hem o be each sae of he worl A hese rces each ocor a eacher rouces a cosumes oe u of servces, curs a labor cos of /, a receves a cosumer surlus of ½ Ages also receve eece surlus e R from vesg her eowme, a so he uly of each 6
7 age s e å R + Fally, he equlbrum rce of a Arrow secury, whch ays off oe u of whea sae, s / R, where R= å R 3 Nolegeable Equlbrum for he case where er Coser ow he case where fuure labor come cao be lege (e, worers ca he her come from leers) Suose, however, ha roec reurs ca be lege Tha s, frms ca ves ero whea he rsy roec a ssue secures collaeralze by he roec reurs (roec reurs cao be sole by frms maagers) These secures wll be urchase ero by ocors a eachers a use as a meas of ayme for servces eros a 3 Sce here are o furher frcos s aural o assume ha frms ssue a full se of Arrow secures bace by her roecs (where secury, =,,, ays off a u of whea f a oly f sae occurs) Le us revew he mg Ages lear her ye a he begg of ero Arrow secury mares oe, a frms ves The sae of he worl, sae say, s leare a he e of ero A hs o Arrow secury has value ( erms of ero 4 whea) a all oher Arrow secures have value zero I ero ocors use her holgs of Arrow secury o buy eachg servces I ero 3 eachers use her holgs of secury acqure ero, lus wha hey have accumulae from ocors reur for eacher servces ero, o buy ocor servces I ero 4 vesmes ay off a whea s cosume I hs seco we suose ha er ; we coser he case er > Seco 5 To comue he olegeable equlbrum, ormalze so ha he rces of whea ero, whea ero 4, Arrow secury ero, a Arrow secury ero 3, are all oe (f sae occurs) Coser a ocor s uly mamzao roblem I equlbrum he rce of eacher servces ero cao ecee sce oherwse ocors woul srcly refer o use her secures o urchase ero 4 7
8 whea raher ha eacher servces, a so he eacher mare woul o clear (Docors are ffere bewee eacher servces a whea) Thus, we ca assume for he urose of calculag uly ha ocors use all her Arrow secures o buy eacher servces (By a arallel argume he rce of ocor servces ero 3 cao ecee a so for uroses of calculag uly we ca assume ha eachers se all her Arrow secures o ocor servces) Ne coser a ocor s labor suly ecso ero 3 Igore he subscr o he sae The a ocor wll choose hs labor suly l o mamze l ( ) l, e, se l =, where s he rce of ocor servces Noe ha s oo lae for he ocor o buy more eacher servces a so hs margal reur from wor s (he wll use he rocees o buy whea ero 4) A ocor s labor yels reveue ( ), whch he reeems for whea ero 4; ao he curs a effor cos of ( ), a so hs e uly from wor s ( ) I follows ha ero a ocor chooses hs holgs of Arrow secures (, =, ) o solve: (*) Ma s q e, where, are he rces of ocor a eacher servces, resecvely, sae a q s he ero rce of he h Arrow secury Noe ha frm rofs are zero equlbrum gve cosa reurs o scale, a so we o o ee o ee rac of ay ves receve by cosumers A smlar calculao ales o eachers The fferece s ha a eacher ero chooses her labor suly l o mamze l  (l ), where s he rce of eacher servces The reaso s 8
9 ha a eacher s margal reur from wor s, sce she wll use her come o buy ocor servces Thus a eacher s e uly from wor s Hece ero a eacher chooses her holgs of Arrow secures (, =, ) o solve: (**) Ma subec o q e As oe, rof mamzao a cosa reur o scale mly zero rof: (3) qr Sce all he whea s vese, he suly of he h Arrow secury s er Hece, he secures mare clearg coos are (3) er, for =, I s easy o see ha equlbrum 0 for all : f 0, he rce of he eachg servces sae woul be zero a he reur o a ocor of urchasg a Arrow secury ha sae woul be fe Also, sce we have assume ha er, eve he bes sae he suly of lquy s o eough o suor he ArrowDebreu level of rae a so rces of ocor a eacher servces wll be srcly below I follows ha ocors a eachers wll se all her avalable wealh o each oher s servces eros a 3 Gve he suly fucos for ocor a eacher servces obae earler, we ca wre he mare clearg coos for ocor a eacher servces sae as (33), 9
10 (34), for =, (*), (**), (3)  (34) characerze a olegeable equlbrum Alhough 0 for all, s less clear ha 0 for all I he e we wll cocerae o he case where 0 for all : we show ha a suffce coo for hs s R R I he ae we esablsh ha our resuls geeralze o he case where 0 for some Prooso : Le S R J, a suose ha S R The here s a uque olegeable equlbrum characerze by q, RS er S, er er S 0, ( er ) = 4 R 3 e 4 S for all Proof: Suose 0 The, he frs orer coos for (*) a (**) are: (35) q (36), q for all, for some, 0
11 From (3) (34), (37) ( er ) a 4 ( ) ( ) er a so (38) 4 q ( ) ( er ) (39) q ( er ) I ur, hs mles (30) ( ) ( ) 4 er From (3) a (39) (3) å R = = S (er ) (e) Subsug (3) o (39), we have (3) q = (R ) S From (30) (3) a he buge cosra å q = e, we have (33) = S a so we ca wre 3 e, (34) er S (35) = 4 (R ) 3 e 4 S
12 We have esablshe he formulae Prooso The oly remag hg o chec s ha 0, e, er Ths wll be rue as log as (36) er S er for all, e, S R I s clear ha he above s a o legeable equlbrum The argume he ae esablshes uqueess Prooso ells us ha a ocor s ema for Arrow secures QED s srcly creasg I s also easy o see ha a eacher s ema for Arrow secures, er er S, s srcly creasg (gve R S) Boh of hese resuls are uve I beer saes he suly of Arrow secures s greaer, her rce lower, a ares wll hol more of hem The e rooso ells us ha a ocor s ema for Arrow secures ecees a eacher s ema he lowes sae = a he reverse s he case he hghes sae = Also he emas cross oly oce Prooso :,, a here ess a * such ha for * a for * Proof: Coser (35) e[ R S R ] Ths s osve for = sce R J R a egave for = sce RJ R Also f * * 0, e, R* RJ, he for * R RJ a so 0
13 QED Prooso s also uve Lquy s more mora for ocors ha for eachers, a s relavely more hs way low saes of he worl 4 Trachg of Secures So far we have show ha ocors wa relavely more Arrow secures he low saes a eachers he hgh saes Gve he umber of saes of he worl, s worhwhle o coser wheher hs ema ca be sasfe by smler secures I hs seco we wll rove ha boh yes of ages ema for secures ca be sasfe by rachg he ayoff of a vesme he esg echology o he bass of seory, smlar o he rachg of collaeralze eb oblgaos ha was so oular before he 008 facal crss (For a ffere elaao of rachg, base o asymmerc formao, see DeMarzo (005))We wll also argue ha hs way o maufacure secures s less roe o e os maulao by he facal ermeares Fally, we wll aalyze he welfare effecs of losses curre by secures wh ffere levels of seory 4 Traches as a vable subsue for Arrow secures Le s sar by rovg ha boh he ocors a he eachers ema for Arrow secures ca be sasfe by raches o he ecoomy s reur sream ( R, R, R ) A rache corresos o a eb level of a cera seory If he raches are,, 3,, esceg orer of seory, he sae he frs rache receves M(, R), he seco rache M(, R M(, R) ), he hr rache M( 3, R M(, R) M(, R M(, R)) ), a so o Prooso 3: The ema for Arrow secures by ocors ( ) a eachers ( ) ca be sasfe by raches,, 3,, of he rsy roec reur R, R, R 3
14 Proof: The roof wll be by cosruco Le R, R R, 3 R3 R,, R R be raches esceg orer of seory The he reur vecors of he raches,, 3,, across ffere saes of he worl are gve by = [ R, R, R ], =[ 0, R  R, R  R R  R ], a =[ 0, 0, R3 R, R3 R R3 R], 3 = [ 0, 0, 0, 0 R R ] A ocor s orfolo yels he reur vecor (, ) Sce s mooocally creasg, f he ocor buys R of he frs rache, R R of he seco rache, R R 3 3 of he hr rache u o R R of he las rache we ca relcae he same ayoff he ocors woul have obae wh he Arrow secures Tha s, R R R R R R R (,0,0,0) (0,,0,0) 3 (0,0,,0,0) (0,0,0,) The same logc ales o eachers QED Whle here are may ways o maufacure he Arrow secures eee, rachg s arcularly aracve because s robus o maagers esroyg some of he ayoff o favor oe se or aoher Coser a secury ha ays oly f he sae of he worl s For he ower of ha secury here s a srog ceve o clam ha he sae of he worl s, eve whe he rue sae s +, sce oe case 4
15 he wll be a a lo, he oher ohg We have assume ha maagers cao seal ay ayoff, bu hey may be able o esroy some Thus, f he rue sae s above may be ossble for he ower of secury o brbe he maager o esroy ouu a ree he rue sae of he worl s The same roblem oes o arse wh raches, sce he ayoff of hese secures s mooocally creasg he rue sae of he worl a so here s o ceve o falsfy he sae of aure by esroyg ayoff 4 Dsrbuo of owersh of he varous raches Havg esablshe ha raches are a covee way o acheve he same ayoff as ha obae by Arrow secures, we as e how he varous raches wll be allocae across vesors Prooso 4 says ha ocors wll ves more seor raches a eachers wll ves more uor raches Prooso 4: The amou vese by a ocor rache R R s srcly ecreasg =, (where 0 = R 0 =0) The amou vese by a eacher rache R R s srcly creasg =, (where 0 =0) Proof: Usg Prooso we ca wre (4) e( R R ) S, R R R R whch s srcly ecreasg sce R s a srcly cocave fuco Also from (3), (4) R R R R e, 5
16 a so R R mus be srcly creasg QED 43 Welfare effecs of losses ffere raches Now we wa o suy he ffereal macroecoomc a welfare effecs of losses ffere raches There s a obvous reaso why a loss suffere by a very seor rache has worse welfare cosequeces: sce he rache s seor, s mare oly whe he loss he uerlyg vesme s so severe as o go hrough all he oher layers Hece, a loss a very seor rache s a caor of a very egave realzao of he sae of he worl To elmae hs effec, we assume ha each rache s maage by a searae fu a ha oe of hese fus faces a ueece loss ue o a accoug frau (a Maoffye shoc), e, a oally osycrac eve I s ow legmae o as whch fu s losses wll have he wors mac o he ecoomy To be recse, suose ha he ecoomy has arrve a he begg of ero, a s ow ha sae has occurre A hs o he fu maagg rache eereces a shoc: a small (fesmal) ueece chage s wealh equal o (We are assumg ha sae s such ha rache s worh a srcly osve amou, e, ) Ths wealh shoc s srbue amog ocors a eachers accorg o her relave holgs of rache Recall ha a ocor hols R R us a a eacher sasfy (43) (44) R R us These sum o e, a so he wealh chages he realze sae wll = =, R R e R R e 6
17 If we efe (45) = we ca rewre hs more comacly as, R R e (46), = ( ) As Har a Zgales (03), we use he sum of ules of ocors a eachers as our measure of welfare Ths s reasoable sce e ae each age s equally lely o be a ocor or a eacher From (*) a (**), he sum of ules sae s (47) W = Usg (33)(34) o solve for,, we ca wre (47) as (48) ( ) 4 W = ( ) ( ) ( ) Noe ha he absece of he shoc er Dffereag (48) wh resec o a usg (46), we oba, a =0, (49) W = ( er ) ( ) ( ) ( er ) ( er ) ( ) ( ) 4 er ( er ) ( er ) 4 ( er ) = ( ) ( er ) ( ) ( er ) ( )( er ) ( er ) ( er ) The coeffce of (49) s 7
18 (40) 4 ( ) ( er ) ( er ), a hs s easly see o be srcly osve, sce (4) er ( er ), gve ha er Hece, we have esablshe ha he effec o welfare of a small shoc o rache s greaer f s large Bu we ow from Prooso 4 ha s ecreasg Therefore, we have esablshe ha he welfare loss from a small egave shoc o a rache wll be greaer he more seor he rache s The uo s smle Each ollar los has wo effecs: a rec effec o welfare (equal o a ollar sce ages have lear uly) lus a rec effec o welfare rouce by he reuce level of rae (whch geeraes a osve surlus), cause by a reuce level of lquy The ocors are he ages more ee of lquy Thus, a loss ha falls srooroaely o he shoulers of he ocors wll have srooroaely large welfare cosequeces As Prooso 4 shows he eole who ee lquy he mos ( our moel he ocors) ves more of her wealh seor fus I fac, he more so, he more seor hey are Thus, a loss he more seor fus wll fall srooroaely o he shoulers of he ocors a hus wll have a srooroaely large effec o he level of ecoomc acvy a a srooroaely large loss aggregae welfare I s aural o as how large W s Oe s frs hough s ha W >, e, he effec of a wealh shoc wll be mulle, gve lquy cosras However, hs oes o have o be he case I fac, s eve ossble ha W <0! The reaso s ha a osve wealh shoc ha affecs maly eachers ( close o zero) wll rve u he rce of ocors servces (see (34)) a herefore reuce he suly a crease he rce of eacher servces (see (33)) Ths ca mae he ocors so much worse off ha he sum of he ocor a he eacher ules falls 8
19 However, hs cao hae for very seor raches, arcular, = For =, er er sce er S s cocave R Therefore, a lower bou for W s gve by (49) wh relace by er Subsug er o (49) yels (4) ( ) ( ), 4 4 ( er ) ( er ) ( er ) whch ecees sce (43) er ( er ) mles ha er ( ) ( ) ( er ) ( ) The cocluso s ha a egave shoc o he mos seor rache causes a welfare loss ha s greaer ha he shoc self 5 Nolegeable Equlbrum whe er a R s large We ow coser how he aalyss chages whe he bes sae here s eough lquy, so ha a he level of rae s effce Ths case s acually much smler a wll erm a suy of fscal olcy Seco 6 To smlfy maers we coue o assume ha er for all Much of he aalyss of Seco 3 coues o aly Frs, 0 for all a so he frs orer coo for (*) s (5) for all q O he oher ha, he frs orer coo for (**) s 9
20 (5) for all, q wh equaly f 0 We also ow ha sae, rces wll be srcly less ha a so (33) (34) escrbe he equlbrum he goos mare Hece, by (3), (53) ( er ) (54) for ( er ) 4, We ow show ha he above mles 0 for all Suose o: 0 for some < The, (5)(5) a = mly: (55), sce O he oher ha, (5)(5) a = mly: (56) 4 er Hece, 4 (57) er whch s mossble sce, from (3),, er er, gve ha er Therefore, 0 for all, a, sce eachers have he wealh o buy some secures, 0 I follows ha (58) er for all, a so we ca rewre (54) as 0
21 (59) 3 ( er ) 4 for all Combg (5) wh (59), a usg, yels (50) q q er 3 4 Fally, we ca subsue (50) o (3) o oba (5) q R 3 4 er R (53), (59), (50), (5), a (58) (lus he sasfaco of ocor a eacher buge cosras) escrbe a olegeable equlbrum where rces are he hghes sae There s a furher feasbly coo:, e, a ocor mus be able o affor a leas oe u of Arrow secury orer o be able o urchase oe u of eacher servces a rce sae Usg (58), we requre (5) q er q e, whch, from (50) a (5), ca be smlfe o (53) I oher wors er er R mus be large ( arcular, er a so er ) I s easy o ee he rachg resul of Seco 4 o hs case A ocor s holg of rache, gve by R R, s cosa a equal o e for =,  from (58); whle for =, sce 0, er a so
22 (54) R R e I oher wors, a ocor hols equal amous of all raches ece he mos uor oe a srcly less of ha I s also easy o ee he welfare resuls of Seco 4 o show ha a egave shoc o a seor rache wll creae a larger welfare loss ha a equvale shoc o he mos uor rache We wll o rove he eals here, bu sea carry ou a smlar calculao whe we scuss fscal olcy he e seco 6 Fscal Polcy So far we have o cosere how he goverme mgh reso o he lquy roblems ha we have hghlghe I hs seco we aalyze fscal olcy alog he les of Har a Zgales (03) Secfcally, we assume ha here s a mllg echology ero 4 ha allows ocors a eachers o cover whea o flour, a ha hey eoy cosumg flour as well as whea The goverme ca mose a er u sales a o flour somehg ha he rvae secor cao o a ca ssue bos ero, afer he sae of he worl s realze, bace by hs sales a As Har a Zgales (03), we suose ha he goverme bos (b us of hem) are hae recly o ocors (ohg our aalyss reles o he ea ha he ey of a age s overfable) The eals of he mllg echology a refereces for flour versus whea ca be fou Har a Zgales (03) For our uroses s eough o rely o he followg resul from ha aer: he goverme ca crease he lquy of a ocor ero sae from o b, bu hs moses a loss o he ecoomy ero 4 of b () b, where he frs erm reflecs he bo reayme a () b s he eawegh loss of he sales a requre o rase b Here () b sasfes (6) (0) 0, '(0) 0, ( b) 0 for all b 0
23 I oher wors, he margal eawegh loss s zero whe he a rae s zero bu s srcly osve a creasg whe he a rae s osve We wll be arcularly erese how he goverme shoul reso o a Maoff shoc However, gve ha here s a shorage of lquy he ecoomy, he goverme wll wa o reso eve he absece of such a shoc Thus, we wll aalyze he omal fscal olcy wh a whou a shoc Aalyzg fscal olcy he moel of Seco 3 ur ou o be har, a so we wll focus o he moel of seco 5 where er a R s large We wll suose ha he goverme chooses fscal olcy sae ero o mamze he sum of ocor a eacher ules We wll also assume ha he goverme cao comm o s fscal olcy avace: he goverme wll choose b sae o mamze he sum of ocor a eacher ules afer, are eerme Le s coser he case here s o shoc a ae he oerveo equlbrum as a sarg o Clearly, here s o ee for erveo sae sce he effce level of rae s realze here Coser < If he goverme ssues b us of bos o a ocor (each bo ays oe u of whea ero 4 sae ), he sce er, 0, he ew equlbrum he goos mare s gve by (6) (63) er b, er b, for =,  Tha s, (64) er b, er b 3 4 From (*) a (**), he sum of ocor a eacher ules sae s (65) b b b W = ( ) 3
24 The goverme wll choose b o mamze frs orer coo s ecessary a suffce: 4 er b er b er b b ( b ) = W Sce 3 er 4 b er b '( b ) 4 4 (66) W s srcly cocave b, he followg Noe ha he lefha se of (66) s srcly osve whe b 0 a zero whe er b, a so he omal b wll sasfy 0 b er Now suose ha ages acae ha he goverme wll choose b each sae < o sasfy (66) Wll hey chage her eae behavor? We argue ha hey wll o: he raoal eecaos equlbrum er, 0 To see hs se he ew rce sae o be as (64) Also le (67) q q er b 3 4, (68) q R 3 4 er b R The, s easy o see ha (5) s sasfe, whle (5) hols wh src equaly for < So 0 for sae < Hece, f we se er, (3) s sasfe for < Fally, he feasbly coo (53) becomes (69) er er, 3 4 ( er b ) whch s mle by (53) 4
25 Thus, we have cosruce a ew equlbrum wh goverme erveo where ocors coue o hol all he Arrow secures sae =, , a he goverme omzes accorgly Noe ha sce er for all =, , a er, he rachg resuls of Seco 4 a 5 coue o hol Le us ow coser how he goverme wll reso o a uacae Maoff shoc Noe ha sae < oly seor raches are he moey (raches ), a sce ocors hol all hese raches ( 0 for all <), oes o maer whch rache s h by he Maoff shoc Thus, f we are cocere wh how goverme erveo ees o he seory of he rache h, he eresg case s whe we are sae I sae a small Maoff shoc wll have o effec f So le s assume ha he ocors are us o he marg erms of lquy: Noe ha hs mles, so ha for small shocs here wll be eough lquy he mare for ocor servces o susa Suose ha here s a small (egave) shoc o he aggregae amou of lquy, whch s ve amog ocors a eachers as (46) Before he shoc he goverme ha a zero fscal olcy sae ( b 0 ), bu afer he shoc wll ervee As we have us argue, afer he shoc, a so he equlbrum he eacher mare s gve by (60) e, b, We coue o assume ha he goverme mamzes he sum of ocor a eacher ules eve hough hs welfare crero may o be so comellg he case of a uacae shoc 5
26 (6) b Welfare sae afer he shoc s herefore gve by (6) b b b W = = ( ) b b b ( b ) W s srcly cocave b a so he followg frs orer coo s ecessary a suffce: (63) We ca use (63) o comue alyg (46), yels (64) 3 b '( b ) b b b b ''( b ), 4 Dffereag (63) wh resec o, a (65) b 4 ''( b ) b 4 3 b 3 Calculag hs a 0, where b 0 a, we have (66) b 4 0 ''(0) 4 I follows from (66) ha he omal fscal resose sae o a uacae Maoff shoc (e, a egave ) s: a) osve; b) wll be bgger f he shoc hs a seor rache (where e ) ha f hs he mos uor rache (where ) e 6
27 7 Very Seor Fus a Bas So far, we have cosere oly absrac secures Ye, he mos seor raches hel by he ocors ca be erree as ba eoss O he asse se bas ves facal clams a o he lably se hey have a srooroae share of very seor clams (eoss) Thus, bas are a coseffecve way o maufacure he Arrow secures eee by ocors a eachers I arcular, ba eoss lay he role of he mos seor secury hel by he ages wh he hghes lquy ees (ocors) I realworl bas here s a aoal feaure (o coae our moel) ha gves eoss a hghly seor saus: her callably o ema If we coser callably o ema as a form of suer seory, moey mare fus e o have hs feaure oo, esecally f hey are (mlcly) guaraee by he equy of he sosorg orgazao (Kacerczy a Schabl (03)) a ossbly by he goverme Accorg o hs vew, wha maes bas secal s o he aure of her vesmes (e, formaosesve ba loas, as Share (990) a Raa (99)) or her ercoeceess (as Alle a Gale, (000)), bu he eole who eos hem Bas are secal because her eoss are hel by eole wh he hghes lquy ees As a resul, losses amog eosors have eremely egave macroecoomc cosequeces because hey erve of lquy he ages who ee lquy he mos o suor her urchases A loss her lquy buffer wll lea hese ages o cural her ema for goos a servces, reucg he come (a he ably o ay) of oher ages he ecoomy Ths effec may reuce he level of ecoomc acvy a he aggregae welfare by a mulle of he loss bore Ths feaure s o uque o bas, bu s share by moey mare fus, sce hey rove a almos erfec subsue for eoss for ages who ee lquy As a resul, losses 7
28 bore by moey mare fu vesors woul have smlarly sruve effecs o he ecoomy Cosse wh hs erreao, 008 moey mare fus were bale ou a way smlar o bas, eve hough her leg was o secal a hey were o hghly ercoece The same logc ha ales o bas a moey mare muual fus ales o a lesser ee o bos geeral, arcular secure a hghly rae bos These bos are also hel hgher rooro by eole who ee lquy he mos Hece, losses bor by boholers ca have some of he same macroecoomc cosequeces (albe less severe) as losses bore by eosors Ths resul mgh ela why govermes are so reluca o le boholers suffer a loss Ths smle heory of bag s also able o ela why bas (a moey mare muual fus) ee o have eoss ha o o flucuae value Deosors are he ages wh he hghes ee for lquy a hus hey ema surace agas ossble falls he value of her vesmes, eve f hey are rs eural Ths surace s rove by he ages less ee of lquy ( our moel he eachers) Ths surace comoe ca ela why he yel of eoss a of very shorerm US reasury blls s lower ha he curve of rs a reur woul rec (Krshamurhy a Vssg Jorgese, 0) 8 Coclusos Ths aer elas why a comlee mare framewor wh legeably cosras  here s a ema for relavely safe asses for rasaco uroses I also elas why ages ee of lquy ves srooroaely hs asse a why losses mose o hs ye of asse have a srooroae mac o he ecoomy The characersc of hs relavely safe asse s ha s very seor, us le eoss a moer ba Our argume s ha he essece of bas s ha ba eoss are hel srooroaely by eole ee of lquy I oher wors, bas are where he lquy s 8
REVISTA INVESTIGACION OPERACIONAL Vol. 25, No. 1, 2004. k n ),
REVISTA INVESTIGACION OPERACIONAL Vol 25, No, 24 RECURRENCE AND DIRECT FORMULAS FOR TE AL & LA NUMBERS Eduardo Pza Volo Cero de Ivesgacó e Maemáca Pura y Aplcada (CIMPA), Uversdad de Cosa Rca ABSTRACT
More informationChapter 4 MultipleDegreeofFreedom (MDOF) Systems. Packing of an instrument
Chaper 4 MulpleDegreeofFreedom (MDOF Sysems Eamples: Pacg of a srume Number of degrees of freedom Number of masses he sysem X Number of possble ypes of moo of each mass Mehods: Newo s Law ad Lagrage
More informationPORTFOLIO CHOICE WITH HEAVY TAILED DISTRIBUTIONS 1. Svetlozar Rachev 2 Isabella Huber 3 Sergio Ortobelli 4
PORTFOLIO CHOIC WITH HAVY TAILD DISTRIBUTIONS Sveloar Rachev Isabella Huber 3 Sergo Orobell 4 We are graeful o Boryaa RachevaJoova Soya Soyaov ad Almra Bglova for he comuaoal aalyss ad helful commes.
More informationLecture 13 Time Series: Stationarity, AR(p) & MA(q)
RS C  ecure 3 ecure 3 Tme Seres: Saoar AR & MAq Tme Seres: Iroduco I he earl 97 s was dscovered ha smle me seres models erformed beer ha he comlcaed mulvarae he oular 96s macro models FRBMITPe. See
More informationEuropean Exotic Options
Hado # for B9.38 rg lecre dae: 4/3/ * RskNeral Valao Eroea Exoc Oos e. Prce rocess of he derlyg secry. e. Payoff of he dervave. e 3. Execao of dscoed ayoff der RNPM.. Chooser Oo oo o oo A me : rchase
More informationProving the Computer Science Theory P = NP? With the General Term of the Riemann Zeta Function
Research Joural of Mahemacs ad Sascs 3(2): 7276, 20 ISSN: 20407505 Maxwell Scefc Orgazao, 20 Receved: Jauary 08, 20 Acceped: February 03, 20 Publshed: May 25, 20 Provg he ompuer Scece Theory P NP? Wh
More informationINVESTIGATION OF HMNETWORK WITH PRIORITY MESSAGES AND DEPENDING ON TIME INCOMES FROM TRANSITIONS BETWEEN ITS STATES
Joral of Ale Maheacs a Coaoal Mechacs 0  INVETIGATION OF HMNETWORK WITH PRIORITY MEAGE AND DEPENDING ON TIME INCOME FROM TRANITION BETWEEN IT TATE Olga Kro Mhal Maalys Facly of Maheacs a Coer cece Groo
More informationCHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING
CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING Q.1 Defie a lease. How does i differ from a hire purchase ad isalme sale? Wha are he cash flow cosequeces of a lease? Illusrae.
More informationValuation Methods of a Life Insurance Company
Valuao Mehods of a Lfe Isurace Comay ISORY...3 2 PRODUC ASSESSMEN : PROFI ESING...4 2. E PROFI ESING IN 3 SEPS...5 2.. Equalece Prcle...5 2..2 radoal Marg...6 2..3 Prof esg...6 2.2 COMMON CRIERIA O EVALUAE
More informationJorge Ortega Arjona Departamento de Matemáticas, Facultad de Ciencias, UNAM jloa@fciencias.unam.mx
Usg UML Sae Dagrams for Moellg he Performace of Parallel Programs Uso e Dagramas e Esao UML para la Moelacó el Desempeño e Programas Paralelos Jorge Orega Aroa Deparameo e Maemácas, Facula e Cecas, UNAM
More informationCritical Approach of the Valuation Methods of a Life Insurance Company under the Traditional European Statutory View
Crcal Aroach of he Valuao Mehods of a Lfe Isurace Comay uder he radoal Euroea Sauory Vew Dr. PaulAoe Darbellay ParerRe Belleresrasse 36 C8034 Zürch Swzerlad Phoe: 4 385 34 63 Fa: 4 385 37 04 Emal: aulaoe.darbellay@arerre.com
More informationNo Regret Learning in Oligopolies: Cournot vs Bertrand
No Regre Learg Olgopoles: Couro vs Berrad Ur Nadav Georgos Plouras Absrac Couro ad Berrad olgopoles cosue he wo mos prevale models of frm compeo. The aalyss of Nash equlbra each model reveals a uque predco
More informationWhy we use compounding and discounting approaches
Comoudig, Discouig, ad ubiased Growh Raes Near Deb s school i Souher Colorado. A examle of slow growh. Coyrigh 00004, Gary R. Evas. May be used for orofi isrucioal uroses oly wihou ermissio of he auhor.
More informationVladimir PAPI], Jovan POPOVI] 1. INTRODUCTION
Yugoslav Joural of Operaos Research 200 umber 779 VEHICLE FLEET MAAGEMET: A BAYESIA APPROACH Vladmr PAPI] Jova POPOVI] Faculy of Traspor ad Traffc Egeerg Uversy of Belgrade Belgrade Yugoslava Absrac:
More informationThe Unintended Consequences of Tort Reform: Rent Seeking in New York State s Structured Settlements Statutes
The Ueded Cosequeces of Tor Reform: Re Seeg ew Yor Sae s Srucured Selemes Saues Publshed Joural of Foresc Ecoomcs, Volume 3 o, Wer 2 By Lawrece M. Spzma* Professor of Ecoomcs Mahar Hall Sae Uversy of ew
More information7.2 Analysis of Three Dimensional Stress and Strain
eco 7. 7. Aalyss of Three Dmesoal ress ad ra The cocep of raco ad sress was roduced ad dscussed Par I..5. For he mos par he dscusso was cofed o wodmesoal saes of sress. Here he fully hree dmesoal sress
More informationGARCH Modelling. Theoretical Survey, Model Implementation and
Maser Thess GARCH Modellg Theorecal Survey, Model Imlemeao ad Robusess Aalyss Lars Karlsso Absrac I hs hess we survey GARCH modellg wh secal focus o he fg of GARCH models o facal reur seres The robusess
More informationClaims Reserving When There Are Negative Values in the Runoff Triangle
Clams Reservg Whe There Are Negave Values he Ruo Tragle Erque de Alba ITAM Meco ad Uversy o Waerloo Caada 7 h. Acuaral Research Coerece The Uversy o Waerloo Augus 70 00 . INTRODUCTION The may uceraes
More informationProfessional Liability Insurance Contracts: Claims Made Versus Occurrence Policies
ARICLES ACADÉMIQUES ACADEMIC ARICLES Assuraces e geso des rsques, vol. 79(34), ocobre 2011 javer 2012, 251277 Isurace ad Rsk Maageme, vol. 79(34), Ocober 2011 Jauary 2012, 251277 Professoal Lably
More informationA new proposal for computing portfolio valueatrisk for seminonparametric distributions
A ew proposal for compug porfolo valuearsk for semoparamerc dsrbuos TroMauel Ñíguez ad Javer Peroe Absrac Ths paper proposes a semoparamerc (SNP) mehodology for compug porfolo valuearsk (VaR) ha
More informationThe following model solutions are presented for educational purposes. Alternate methods of solution are, of course, acceptable.
The followg model soluos are preseed for educaoal purposes. Alerae mehods of soluo are, of course, accepable.. Soluo: C Gve he same prcpal vesed for he same perod of me yelds he same accumulaed value,
More informationThe Advertising Market in a Product Oligopoly
The Adversg Mare a Produc Olgooly Ahoy Dues chool o Ecoocs ad Maagee Uversy o Aarhus Århus Dear Ocober 003 Absrac A odel s develoed whch roducers a dereaed roduc are coee rces ad orave adversg. The odel
More informationDeterminants of Foreign Direct Investment in Malaysia: What Matters Most?
Deermas of Foreg Drec Ivesme Maaysa: Wha Maers Mos? Nursuha Shahrud, Zarah Yusof ad NuruHuda Mohd. Saar Ths paper exames he deermas of foreg drec vesme Maaysa from 970008. The causay ad dyamc reaoshp
More informationAPPENDIX III THE ENVELOPE PROPERTY
Apped III APPENDIX III THE ENVELOPE PROPERTY Optmzato mposes a very strog structure o the problem cosdered Ths s the reaso why eoclasscal ecoomcs whch assumes optmzg behavour has bee the most successful
More informationSOCIETY OF ACTUARIES FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS
SOCIETY OF ACTUARIES EXAM FM FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS Ths page dcaes chages made o Sudy Noe FM0905. Aprl 8, 04: Queso ad soluo 6 added. Jauary 4, 04: Quesos ad soluos 58 60 were
More informationThe Economics of Administering Import Quotas with LicensesonDemand
The Ecoomcs of Admserg Impor uoas wh LcesesoDemad Jaa Hraaova, James Falk ad Harry de Gorer Prepared for he World Bak s Agrculural Trade Group Jauary 2003 Absrac Ths paper exames he effecs of raog mpor
More informationEXAMPLE 1... 1 EXAMPLE 2... 14 EXAMPLE 3... 18 EXAMPLE 4 UNIVERSAL TRADITIONAL APPROACH... 24 EXAMPLE 5 FLEXIBLE PRODUCT... 26
EXAMLE... A. Edowme... B. ure edowme d Term surce... 4 C. Reseres... 8. Bruo premum d reseres... EXAMLE 2... 4 A. Whoe fe... 4 B. Reseres of Whoe fe... 6 C. Bruo Whoe fe... 7 EXAMLE 3... 8 A.ure edowme...
More informationMETHODOLOGY ELECTRICITY, GAS AND WATER DISTRIBUTION INDEX (IDEGA, by its Spanish acronym) (Preliminary version)
MEHODOLOGY ELEY, GAS AND WAE DSBUON NDEX (DEGA, by s Sash acroym) (Prelmary verso) EHNAL SUBDEOAE OPEAONS SUBDEOAE Saago, December 26h, 2007 HDA/GGM/GMA/VM ABLE OF ONENS Pages. roduco 3 2. oceual frameork
More information Models:  Classical: : Mastermodel (clay( Curves.  Example:  Independent variable t
Compue Gaphcs Geomec Moelg Iouco  Geomec Moelg (GM) sce e of 96  Compue asssace fo  Desg: CAD  Maufacug: : CAM  Moels:  Classcal: : Masemoel (cla( cla, poopes,, Mockup)  GM: mahemacal escpo fo
More informations :risk parameter for company size
UNDESTANDING ONLINE TADES: TADING AND EFOMANCE IN COMMON STOCK INVESTMENT Y. C. George L, Y. C. Elea Kag 2 ad ChugL Chu 3 Deparme of Accoug ad Iformao Techology, Naoal Chug Cheg Uversy, Tawa,.O.C acycl@ccu.edu.w;
More informationWHAT ARE OPTION CONTRACTS?
WHAT ARE OTION CONTRACTS? By rof. Ashok anekar An oion conrac is a derivaive which gives he righ o he holder of he conrac o do 'Somehing' bu wihou he obligaion o do ha 'Somehing'. The 'Somehing' can be
More information10.5 Future Value and Present Value of a General Annuity Due
Chapter 10 Autes 371 5. Thomas leases a car worth $4,000 at.99% compouded mothly. He agrees to make 36 lease paymets of $330 each at the begg of every moth. What s the buyout prce (resdual value of the
More informationThe Term Structure of Interest Rates
The Term Srucure of Ieres Raes Wha is i? The relaioship amog ieres raes over differe imehorizos, as viewed from oday, = 0. A cocep closely relaed o his: The Yield Curve Plos he effecive aual yield agais
More informationAnalysis of Coalition Formation and Cooperation Strategies in Mobile Ad hoc Networks
Aalss of oalo Formao ad ooperao Sraeges Moble Ad hoc ewors Pero Mchard ad Ref Molva Isu Eurecom 9 Roue des rêes 06904 SophaApols, Frace Absrac. Ths paper focuses o he formal assessme of he properes of
More informationOptimal Combination of International and Intertemporal Diversification of Disaster Risk: Role of Government. Tao YE, Muneta YOKOMATSU and Norio OKADA
京 都 大 学 防 災 研 究 所 年 報 第 5 号 B 平 成 9 年 4 月 Auals of Disas. Prev. Res. Is., Kyoo Uiv., No. 5 B, 27 Opimal Combiaio of Ieraioal a Ieremporal Diversificaio of Disaser Risk: Role of Goverme Tao YE, Muea YOKOMATSUaNorio
More informationClassic Problems at a Glance using the TVM Solver
C H A P T E R 2 Classc Problems at a Glace usg the TVM Solver The table below llustrates the most commo types of classc face problems. The formulas are gve for each calculato. A bref troducto to usg the
More informationPerformance Comparisons of Load Balancing Algorithms for I/O Intensive Workloads on Clusters
Joural of ewor ad Compuer Applcaos, vol. 3, o., pp. 3246, Jauary 2008. Performace Comparsos of oad Balacg Algorhms for I/O Iesve Worloads o Clusers Xao Q Deparme of Compuer Scece ad Sofware Egeerg Aubur
More informationBusiness School Discipline of Finance. Discussion Paper 2014005. Modelling the crash risk of the Australian Dollar carry trade
Dscusso Paper: 2014005 Busess School Dscple of Face Dscusso Paper 2014005 Modellg he crash rsk of he Ausrala Dollar carry rade SukJoog Km Uversy of Sydey Busess School Modellg he crash rsk of he Ausrala
More informationOnline Appendix: Measured Aggregate Gains from International Trade
Ole Appedx: Measured Aggregate Gas from Iteratoal Trade Arel Burste UCLA ad NBER Javer Cravo Uversty of Mchga March 3, 2014 I ths ole appedx we derve addtoal results dscussed the paper. I the frst secto,
More informationDuration Outline and Reading
Deb Isrumes ad Markes Professor Carpeer Duraio Oulie ad Readig Oulie Ieres Rae Sesiiviy Dollar Duraio Duraio Buzzwords Parallel shif Basis pois Modified duraio Macaulay duraio Readig Tuckma, Chapers 5
More informationEvaluation and Modeling of the Digestion and Absorption of Novel Manufacturing Technology in Food Enterprises
Advace Joural of Food Scece ad Techology 9(6): 482486, 205 ISSN: 20424868; eissn: 20424876 Mawell Scefc Orgazao, 205 Submed: Aprl 9, 205 Acceped: Aprl 28, 205 Publshed: Augus 25, 205 Evaluao ad Modelg
More informationCOMPETING ADVERTISING AND PRICING STRATEGIES FOR LOCATIONBASED COMMERCE
COMPTING ADVRTISING AND PRICING STRATGIS FOR LOCATIONBASD COMMRC NngYao Pa, Insue of Informaon Managemen Naonal Chao Tung Unversy, Tawan, krssy.a@msa.hne.ne YungMng L, Insue of Informaon Managemen Naonal
More informationAPPLICATIONS OF GEOMETRIC
APPLICATIONS OF GEOMETRIC SEQUENCES AND SERIES TO FINANCIAL MATHS The mos powerful force i he world is compoud ieres (Alber Eisei) Page of 52 Fiacial Mahs Coes Loas ad ivesmes  erms ad examples... 3 Derivaio
More informationThe Time Value of Money
The Tme Value of Moey 1 Iversemet Optos Year: 1624 Property Traded: Mahatta Islad Prce : $24.00, FV of $24 @ 6%: FV = $24 (1+0.06) 388 = $158.08 bllo Opto 1 0 1 2 3 4 5 t ($519.37) 0 0 0 0 $1,000 Opto
More informationThe Design of a Forecasting Support Models on Demand of Durian for Domestic Markets and Export Markets by Time Series and ANNs.
The 2 d RMUTP Ieraoal Coferece 2010 Page 108 The Desg of a Forecasg Suppor Models o Demad of Dura for Domesc Markes ad Expor Markes by Tme Seres ad ANNs. Udomsr Nohacho, 1* kegpol Ahakor, 2 Kazuyosh Ish,
More informationCONVERGENCE AND SPATIAL PATTERNS IN LABOR PRODUCTIVITY: NONPARAMETRIC ESTIMATIONS FOR TURKEY 1
CONVERGENCE AND SPAIAL PAERNS IN LABOR PRODUCIVIY: NONPARAMERIC ESIMAIONS FOR URKEY ugrul emel, Ays asel & Peer J. Alberse Workg Paper 993 Forhcomg he Joural of Regoal Aalyss ad Polcy, 999. We would lke
More informationA binary powering Schur algorithm for computing primary matrix roots
Numercal Algorhms manuscr No. (wll be nsered by he edor) A bnary owerng Schur algorhm for comung rmary marx roos Federco Greco Bruno Iannazzo Receved: dae / Acceed: dae Absrac An algorhm for comung rmary
More informationConfidence Intervals for Paired Means
Chaper 496 Cofidece Iervals for Paired Meas Iroducio This rouie calculaes he sample size ecessary o achieve a specified disace from he paired sample mea erece o he cofidece limi(s) a a saed cofidece level
More informationExam FM/2 Interest Theory Formulas
Exm FM/ Iere Theory Formul by (/roprcy Th collboro of formul for he ere heory eco of he SO Exm FM / S Exm. Th uy hee free ocopyrghe ocume for ue g Exm FM/. The uhor of h uy hee ug ome oo h uque o h o
More informationThe Solow Growth Model
The Solow Growh Moel The Solow Growh Moel is a moel of capial accumulaio i a pure proucio ecoomy: here are o prices because we are sricly ierese i oupu = real icome. Everyoe wors all he ime, so here is
More informationLecture 40 Induction. Review Inductors Selfinduction RL circuits Energy stored in a Magnetic Field
ecure 4 nducon evew nducors Selfnducon crcus nergy sored n a Magnec Feld 1 evew nducon end nergy Transfers mf Bv Mechancal energy ransform n elecrc and hen n hermal energy P Fv B v evew eformulaon of
More informationSolving Fuzzy Linear Programming Problems with Piecewise Linear Membership Function
Avalable a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 9966 Vol., Issue December ), pp. Prevously, Vol., Issue, pp. 6 6) Applcaos ad Appled Mahemacs: A Ieraoal Joural AAM) Solvg Fuzzy Lear Programmg Problems
More informationEQUITY VALUATION USING DCF: A THEORETICAL ANALYSIS OF THE LONG TERM HYPOTHESES
Ivesme Maaeme ad Facal Iovaos Volume 4 Issue 007 9 EQUIY VALUAION USING DCF: A HEOREICAL ANALYSIS OF HE LONG ERM HYPOHESES Luco Cassa * Adrea Pla ** Slvo Vsmara *** Absrac hs paper maches he sesvy aalyss
More informationSpline. Computer Graphics. Bsplines. BSplines (for basis splines) Generating a curve. Basis Functions. Lecture 14 Curves and Surfaces II
Lecure 4 Curves and Surfaces II Splne A long flexble srps of meal used by drafspersons o lay ou he surfaces of arplanes, cars and shps Ducks weghs aached o he splnes were used o pull he splne n dfferen
More informationPricing and Valuation of Forward and Futures
Prcng and Valuaon of orward and uures. Cashandcarry arbrage he prce of he forward conrac s relaed o he spo prce of he underlyng asse, he rskfree rae, he dae of expraon, and any expeced cash dsrbuons
More informationAn Effectiveness of Integrated Portfolio in Bancassurance
A Effectveess of Itegrated Portfolo Bacassurace Taea Karya Research Ceter for Facal Egeerg Isttute of Ecoomc Research Kyoto versty Sayouu Kyoto 606850 Japa arya@eryotouacp Itroducto As s well ow the
More informationTrust Evaluation and Dynamic Routing Decision Based on Fuzzy Theory for MANETs
JOURNAL OF SOFTWARE, VOL. 4, NO. 10, ECEBER 2009 1091 Trus Evaluao ad yamc Roug ecso Based o Fuzzy Theory for ANETs Hogu a, Zhpg Ja ad Zhwe Q School of Compuer Scece ad Techology, Shadog Uversy, Ja, Cha.P.R.
More informationINTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT
IJSM, Volume, Number, 0 ISSN: 5554 INTERNATIONAL JOURNAL OF STRATEGIC MANAGEMENT SPONSORED BY: Angelo Sae Unversy San Angelo, Texas, USA www.angelo.edu Managng Edors: Professor Alan S. Khade, Ph.D. Calforna
More informationANOVA Notes Page 1. Analysis of Variance for a OneWay Classification of Data
ANOVA Notes Page Aalss of Varace for a OeWa Classfcato of Data Cosder a sgle factor or treatmet doe at levels (e, there are,, 3, dfferet varatos o the prescrbed treatmet) Wth a gve treatmet level there
More informationFINANCIAL MATHEMATICS 12 MARCH 2014
FINNCIL MTHEMTICS 12 MRCH 2014 I ths lesso we: Lesso Descrpto Make use of logarthms to calculate the value of, the tme perod, the equato P1 or P1. Solve problems volvg preset value ad future value autes.
More informationEquities: Positions and Portfolio Returns
Foundaions of Finance: Equiies: osiions and orfolio Reurns rof. Alex Shapiro Lecure oes 4b Equiies: osiions and orfolio Reurns I. Readings and Suggesed racice roblems II. Sock Transacions Involving Credi
More informationHomework 6  Solution
Howork 6  oluo 364: 79 Rfr o xal 7 Th aou of fll ss by a bolg ach s orally srbu wh σ= ouc If = 9 bols ar raoly slc fro h ouu of h ach w fou ha h robably ha h sal a wll b wh 3 ouc of h ru a s 638 uos ha
More informationBullwhip Effect Measure When Supply Chain Demand is Forecasting
J. Basic. Appl. Sci. Res., (4)4743, 01 01, TexRoad Publicaio ISSN 0904304 Joural of Basic ad Applied Scieific Research www.exroad.com Bullwhip Effec Measure Whe Supply Chai emad is Forecasig Ayub Rahimzadeh
More informationRecurrence Relations
CMPS Aalyss of Algorthms Summer 5 Recurrece Relatos Whe aalyzg the ru tme of recursve algorthms we are ofte led to cosder fuctos T ( whch are defed by recurrece relatos of a certa form A typcal example
More informationFinancial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression
Ieraoal Joural of Grd Dsrbuo Compug, pp.5364 hp://dx.do.org/10.1457/jgdc.014.7.5.05 Facal Tme Seres Forecasg wh Grouped Predcors usg Herarchcal Cluserg ad Suppor Vecor Regresso ZheGao a,b,* ad JajuYag
More informationValue of information sharing in marine mutual insurance
Value of formao sharg mare muual surace Kev L, Joh Lu, Ja Ya 3 ad Je M Deparme of Logscs & Marme Sudes, The Hog Kog Polechc Uvers, Hog Kog. Emal address:.x.l@polu.edu.h. Deparme of Logscs & Marme Sudes,
More informationAmerican Journal of Business Education September 2009 Volume 2, Number 6
Amerca Joural of Bue Educao Sepember 9 Volume, umber 6 Tme Value Of Moe Ad I Applcao I Corporae Face: A Techcal oe O L Relaohp Bewee Formula JeHo Che, Alba Sae Uver, USA ABSTRACT Tme Value of Moe (TVM
More informationA Way of Hedging Mortality Rate Risks in Life Insurance Product Development
A Way of Hegig Moraliy ae iss i Life Isurace Prouc Develome Chagi Kim Absrac Forecasig moraliy imrovemes i he fuure is imora a ecessary for isurace busiess. A ieresig observaio is ha moraliy raes for a
More informationObject Tracking Based on Online Classification Boosted by Discriminative Features
Ieraoal Joural of Eergy, Iformao ad Commucaos, pp.920 hp://dx.do.org/10.14257/jec.2013.4.6.02 Objec Trackg Based o Ole Classfcao Boosed by Dscrmave Feaures Yehog Che 1 ad Pl Seog Park 2 1 Qlu Uversy of
More informationApproximate hedging for non linear transaction costs on the volume of traded assets
Noame mauscrp No. wll be sered by he edor Approxmae hedgg for o lear rasaco coss o he volume of raded asses Romuald Ele, Emmauel Lépee Absrac Ths paper s dedcaed o he replcao of a covex coge clam hs a
More informationOverview. Eingebettete Systeme. Model of periodic tasks. Model of periodic tasks. Echtzeitverhalten und Betriebssysteme
Overvew Egebettete Systeme able of some kow preemptve schedulg algorthms for perodc tasks: Echtzetverhalte ud Betrebssysteme 5. Perodsche asks statc prorty dyamc prorty Deadle equals perod Deadle smaller
More informationCHAPTER 2. Time Value of Money 61
CHAPTER 2 Tme Value of Moey 6 Tme Value of Moey (TVM) Tme Les Future value & Preset value Rates of retur Autes & Perpetutes Ueve cash Flow Streams Amortzato 62 Tme les 0 2 3 % CF 0 CF CF 2 CF 3 Show
More informationT = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :
Bullets bods Let s descrbe frst a fxed rate bod wthout amortzg a more geeral way : Let s ote : C the aual fxed rate t s a percetage N the otoal freq ( 2 4 ) the umber of coupo per year R the redempto of
More informationIDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki
IDENIFICAION OF HE DYNAMICS OF HE GOOGLE S RANKING ALGORIHM A. Khak Sedgh, Mehd Roudak Cotrol Dvso, Departmet of Electrcal Egeerg, K.N.oos Uversty of echology P. O. Box: 163151355, ehra, Ira sedgh@eetd.ktu.ac.r,
More informationLecture Note on the Real Exchange Rate
Lecure Noe on he Real Exchange Rae Barry W. Ickes Fall 2004 0.1 Inroducion The real exchange rae is he criical variable (along wih he rae of ineres) in deermining he capial accoun. As we shall see, his
More informationPrice Volatility, Trading Activity and Market Depth: Evidence from Taiwan and Singapore Taiwan Stock Index Futures Markets
WeHsu Kuo Asa e Pacfc al./asa Maageme Pacfc Maageme evew (005) evew 0(), (005) 33 0(), 33 Prce Volaly, Tradg Acvy ad Marke Deph: Evdece from Tawa ad Sgapore Tawa Sock Idex Fuures Markes WeHsu Kuo a,*,
More informationChristopher Dougherty EC220  Introduction to econometrics: past examinations and marking schemes 2011 exam
Chrsopher Doughery EC0  Iroduco o ecoomercs: pas examaos ad markg schemes 011 exam Orgal cao: Doughery, C. (01) EC0  Iroduco o ecoomercs: pas examaos ad markg schemes. [Teachg Resource] 011 The Auhor
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y  ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationGUIDANCE STATEMENT ON CALCULATION METHODOLOGY
GUIDANCE STATEMENT ON CALCULATION METHODOLOGY Adopon Dae: 9/28/0 Effecve Dae: //20 Reroacve Applcaon: No Requred www.gpssandards.org 204 CFA Insue Gudance Saemen on Calculaon Mehodology GIPS GUIDANCE STATEMENT
More informationOn the Multiplicative Zagreb Indices of Bucket Recursive Trees
Iraa J a Cem 8 arc 07 37 5 Iraa Joural of aemacal Cemsry Joural omepage: wwwmcasauacr O e ulplcae agre Ices of Buce Recurse Trees RAIN KAI Deparme of Sascs Imam Kome Ieraoal ersy Qaz Ira ARTICL INFO Arcle
More informationStock Trading with Recurrent Reinforcement Learning (RRL) CS229 Application Project Gabriel Molina, SUID 5055783
Sock raing wih Recurren Reinforcemen Learning (RRL) CS9 Applicaion Projec Gabriel Molina, SUID 555783 I. INRODUCION One relaively new approach o financial raing is o use machine learning algorihms o preic
More informationChapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization
Chapter 3 Mathematcs of Face Secto 4 Preset Value of a Auty; Amortzato Preset Value of a Auty I ths secto, we wll address the problem of determg the amout that should be deposted to a accout ow at a gve
More informationHIGH FREQUENCY MARKET MAKING
HIGH FREQUENCY MARKET MAKING RENÉ CARMONA AND KEVIN WEBSTER Absrac. Sce hey were auhorzed by he U.S. Secury ad Exchage Commsso 1998, elecroc exchages have boomed, ad by 21 hgh frequecy radg accoued for
More informationApproximation Algorithms for Scheduling with Rejection on Two Unrelated Parallel Machines
(ICS) Iteratoal oural of dvaced Comuter Scece ad lcatos Vol 6 No 05 romato lgorthms for Schedulg wth eecto o wo Urelated Parallel aches Feg Xahao Zhag Zega Ca College of Scece y Uversty y Shadog Cha 76005
More informationAnomaly Detection of Network Traffic Based on Prediction and SelfAdaptive Threshold
Ieraoal Joural of Fuure Geerao Coucao ad eworkg Vol. 8, o. 6 (15), pp. 514 hp://d.do.org/1.1457/fgc.15.8.6. Aoaly Deeco of ework raffc Based o Predco ad SelfAdapve hreshold Haya Wag Depare of Iforao
More informationINFLUENCE OF DEBT FINANCING ON THE EFFECTIVENESS OF THE INVESTMENT PROJECT WITHIN THE MODIGLIANIMILLER THEORY
VOUME 2, 2 NFUENCE OF DEBT FNANCNG ON THE EFFECTVENE OF THE NVETMENT PROJECT WTHN THE MODGANMER THEORY Pr Brusov, Taaa Flaova, Naal Orhova, Pavl Brusov, Nasa Brusova Fac Uvrsy ur h Govrm of h Russa Frao,
More informationRevision: June 12, 2010 215 E Main Suite D Pullman, WA 99163 (509) 334 6306 Voice and Fax
.3: Inucors Reson: June, 5 E Man Sue D Pullman, WA 9963 59 334 636 Voce an Fax Oerew We connue our suy of energy sorage elemens wh a scusson of nucors. Inucors, lke ressors an capacors, are passe woermnal
More informationHarmony search algorithms for inventory management problems
Afrca Joural of Busess Maageme Vol.6 (36), pp. 98649873, 2 Sepember, 202 Avalable ole a hp://www.academcourals.org/ajbm DOI: 0.5897/AJBM2.54 ISSN 9938233 202 Academc Jourals Revew Harmoy search algorhms
More informationPROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE
Profi Tes Modelling in Life Assurance Using Spreadshees PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Erik Alm Peer Millingon 2004 Profi Tes Modelling in Life Assurance Using Spreadshees
More informationAbraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract
Preset Value of Autes Uder Radom Rates of Iterest By Abraham Zas Techo I.I.T. Hafa ISRAEL ad Uversty of Hafa, Hafa ISRAEL Abstract Some attempts were made to evaluate the future value (FV) of the expected
More informationGeneralized Difference Sequence Space On Seminormed Space By Orlicz Function
Ieaoa Joa of Scece ad Eee Reeach IJSER Vo Ie Decembe 4 5687 568X Geeazed Dffeece Seece Sace O Semomed Sace B Ocz Fco A.Sahaaa Aa ofeo G Ie of TechooCombaoeIda. Abac I h aewe defe he eece ace o emomed
More informationECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil
ECONOMIC CHOICE OF OPTIMUM FEEDER CABE CONSIDERING RISK ANAYSIS I Camargo, F Fgueredo, M De Olvera Uversty of Brasla (UB) ad The Brazla Regulatory Agecy (ANEE), Brazl The choce of the approprate cable
More informationStandardized Formula Sheet: Formulas Standard Normal Distribution Table Summary of Financial Ratios
Sadardzed Formula See: Formulas Sadard ormal Dsrbuo Table Summary o Facal Raos Formulas. Prese Value o a Sgle Cas Flow CF PV (. Fuure Value o a Sgle Cas Flow FV CF( 3. Prese Value o a Ordary Auy ( PV PT[
More informationA Reexamination of the Joint Mortality Functions
Norh merican cuarial Journal Volume 6, Number 1, p.166170 (2002) Reeaminaion of he Join Morali Funcions bsrac. Heekung Youn, rkad Shemakin, Edwin Herman Universi of S. Thomas, Sain Paul, MN, US Morali
More informationOPTIMAL KNOWLEDGE FLOW ON THE INTERNET
İstabul Tcaret Üverstes Fe Blmler Dergs Yıl: 5 Sayı:0 Güz 006/ s.  OPTIMAL KNOWLEDGE FLOW ON THE INTERNET Bura ORDİN *, Urfat NURİYEV ** ABSTRACT The flow roblem ad the mmum sag tree roblem are both fudametal
More informationMDM 4U PRACTICE EXAMINATION
MDM 4U RCTICE EXMINTION Ths s a ractce eam. It does ot cover all the materal ths course ad should ot be the oly revew that you do rearato for your fal eam. Your eam may cota questos that do ot aear o ths
More informationSmall Menu Costs and Large Business Cycles: An Extension of Mankiw Model *
Small enu Coss an Large Business Ccles: An Exension of ankiw oel * Hirana K Nah Deparmen of Economics an Inl. Business Sam Houson Sae Universi an ober Srecher Deparmen of General Business an Finance Sam
More informationDuration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613.
Graduae School of Business Adminisraion Universiy of Virginia UVAF38 Duraion and Convexiy he price of a bond is a funcion of he promised paymens and he marke required rae of reurn. Since he promised
More informationCommercial Pension Insurance Program Design and Estimated of Tax Incentives Based on Analysis of Enterprise Annuity Tax Incentives
Iteratoal Joural of Busess ad Socal Scece Vol 5, No ; October 204 Commercal Peso Isurace Program Desg ad Estmated of Tax Icetves Based o Aalyss of Eterprse Auty Tax Icetves Huag Xue, Lu Yatg School
More informationMorningstar Investor Return
Morningsar Invesor Reurn Morningsar Mehodology Paper Augus 31, 2010 2010 Morningsar, Inc. All righs reserved. The informaion in his documen is he propery of Morningsar, Inc. Reproducion or ranscripion
More information