Approximate hedging for non linear transaction costs on the volume of traded assets

Size: px
Start display at page:

Download "Approximate hedging for non linear transaction costs on the volume of traded assets"

Transcription

1 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 facal marke wh frcos, due o deermsc order books or regulaory cosras. The correspodg rasaco coss rewre as a o lear fuco G of he volume of raded asses, wh G >. For a sock wh Black-Scholes md-prce dyamcs, we exhb a asympocally coverge replcag porfolo, defed o a regular me grd wh radg daes. Up o a well chose regularzao h of he payoff fuco, we frs roduce he frcoless replcag porfolo of h S, where S s a fcve sock wh elarged local volaly dyamcs. I he marke wh frcos, a proper modfcao of hs porfolo sraegy provdes a ermal wealh, whch coverges probably o he clam of eres hs, as goes o fy. I erms of order book shapes, he exhbed replcag sraegy oly depeds o he sze G of he bd-ask spread. The ma ovao of he paper s he roduco of a Lelad ype sraegy for o-vashg o-lear rasaco coss o he volume of raded shares, sead of he commoly cosdered raded amou of moey. Ths duces los of echcales, ha we pass hrough usg a ovave approach based o he Mallav calculus represeao of he Greeks. Key words Lelad Lo sraegy, Dela hedgg, Mallav Calculus, rasaco coss, order book. Mahemacs Subjec Classfcao 9G ; 6G44 ; 6H7 JEL Classfcao G G3 Iroduco The curre hgh frequecy of radg o he facal markes does o allow o eglec he frcos duced by marke orders for buyg or sellg a gve umber of shares. Depedg o he lqudy of he sock of eres, he margal prce of CEREMADE, CNRS, UMR 7534, Uversé Pars-Dauphe E-mal: [email protected] [email protected]

2 Romuald Ele, Emmauel Lépee ay exra u of sock ca be sgfcaly dffere. The shape of he order book ad he sze of he bd-ask spread deerme he uderlyg cos duced by passg a order o he marke. Modelg order book dyamcs ad more mporaly quafyg he mpac of he rades o he uderlyg prce have brough a lo of aeo he rece leraure. Our cocer hs paper s o look owards effce aleraves order o replcae opos he presece of rasaco coss, relaed o he presece of order books. Ths kd of duced cos rewres as a fuco of he raded amou of shares sead of he more classcal ad less realsc raded amou of moey. For smplcy here, he order book shape s supposed o be deermsc ad has a saoary asympoc behavor whe he umber of raded shares goes o zero. More precsely, radg γ shares of sock a me duces a cos G, γ where he possbly o-lear fuco G sasfes G, γ G γ + O γ, for γ small eough. We cosder a facal marke wh oe bod ormalzed o ad oe sock S wh Black Scholes md-prce dyamcs. Observe ha G erpres as he half sze of he bd-ask spread. The order book duces frcos o ay poso ake o he sock ad we vesgae he replcao of a Europea opo wh payoff hs, where h s a covex fuco. I he classcal framework of proporoal rasaco coss o he amou of raded moey, Lelad [8 roduced a geous mehod order o hedge effcely call opos o a dscree me grd. Hs dea reles o he use of he frcoless hedgg sraegy assocaed o a Black Scholes sock wh a suably elarged volaly, relaed o he chose frequecy of radg. As he umber of radg daes goes o fy, Lo [ or Kabaov ad Safara [6 verfed ha he ermal value of he correspodg porfolo coverges o he clam hs of eres, uder he addoal codo ha he rasaco coss coeffce vashes suffcely fas as well. Ths urealsc assumpo has recely bee releved by Lépee [9 va a proper modfcao of he replcag sraegy. The ma movao of he paper s he roduco of Lelad-Lo approxmae hedgg sraeges he realsc framework descrbed above, where he amou of rasaco coss s a o lear fuco of he umber of raded shares of asse. Ths parcular feaure mples ha he aural Lelad-ype elarged volaly s assocaed o a local volaly model sead of a Black Scholes oe. Ideed, we cosder he prcg fuco Ĉ ad assocaed dela hedgg sraegy Ĉx duced by a fcve asse wh local volaly 8 ˆσ :, x σx + σg π x,. where σ s he Black Scholes volaly of he sock ad / s he mesh sze of he regular revso grd. I he mperfec marke of eres, we exhb a porfolo sarg wh al wealh Ĉ, S ad duced by a proper modfcao of he dela hedgg sraegy Ĉ x, S T, he spr of [9. The ma resul of he paper s he covergece probably of he ermal value of hs porfolo o he clam of

3 Asympoc Hedgg 3 eres hs, as he umber of revso daes eds o fy. Ths covergece requres o cosder payoff fucos h wh bouded secod dervaves. For dervaves wh less regular payoff fucos such as he classcal call opo, oe smply eeds o replace h by a well chose more regular payoff fuco h, characerzed erms of umber of radg daes of he hedgg sraegy. The approxmae hedgg sraegy roduced hs paper allows herefore o replcae asympocally a covex coge clam hs a marke wh o vashg rasaco coss coeffce relaed o deermsc order books. The ehaced sraegy oly reles o he sze G of he bd-ask spread ad o o he global shape of he order book. The cosderao of a fcve asse wh local volaly dyamcs of he form. duces los of echcales sce he Lo Kabaov mehodology requres precse esmaes o he sesves of he prcg fuco Ĉ erms of he umber of radg daes. The raher compuaoal obeo of hese esmaes reles o a ovave approach based o he Mallav represeao of he Greeks roduced [4. The paper s orgazed as follows: The ex seco preses he facal marke wh frcos ad he replcao problem of eres. Seco 3 s dedcaed o he ma resuls of he paper: he cosruco of he modfed volaly ad correspodg fcve prcg ad hedgg fucos, he Dela correco for he cosderao of o-vashg rasaco coss coeffce, he payoff regularzao ad he covergece of he ehaced replcag sraegy. Seco 4 deals he proof of he covergece, whereas echcal esmaes o he dervaves of he fcve prcg fuco Ĉ are repored Seco 5. Noaos. For a fuco f from [, R o R, we deoe by f, f x, f x, f xx,... he me ad space paral dervaves. For a fuco f from R o R, he frs ad secod dervaves are smply deoed f ad f. We deoe by C a geerc cosa, whch may vary from le o le. For possbly radom cosas, we use he oao C ω. Hedgg uder rasaco coss o he raded volume of shares I hs seco, we roduce he marke model ad formulae he facal dervave replcao problem uder rasaco coss duced by order book frcos.. The marke model We cosder a facal marke defed o a probably space Ω, F, Q, edowed wh a -dmesoal Browa moo W. We deoe by F = F he compleo of he flrao geeraed by W. Our model s he sadard wo-asse model wh he me horzo T = assumg ha s specfed uder he uque margale measure Q. The o-rsky asse s he umérare S =, ad he dyamcs of he rsky asse s gve by he

4 4 Romuald Ele, Emmauel Lépee sochasc equao S = S + σs udw u,, where σ > s a cosa. Up o cosderg dscoued processes, all he resuls of he paper exed as usual o facal markes wh o zero deermsc eres raes. I a frcoless complee marke of hs form, he prce a me of a facal dervave hs s gve by C, S where C s he uque soluo of he PDE { C, x + e = σ x C xx, x =,, x [,, C, x = hx, x,. I presece of realsc rasaco coss, where couous hedgg s o adequae aymore, hs paper develops a asympoc hedgg sraegy for he facal dervave hs.. The order book frcos We ed o ake o accou he frcos duced by he use of marke orders he facal marke. Whe a porfolo maager buys or sells a gve quay γ of sock S, he presece of order books mples a addoal cos, whch s relaed o he volume γ of he order. We model hese order book relaed coss va he roduco of a o lear couous deermsc cos fuco G. Wheever a age rades a possbly egave quay γ of socks S o he facal marke a me, he shall pay a mmedae cos G, γ >. We make he followg saoary assumpo o he asympoc behavor of he cos fuco G o he eghborhood of γ =. Codo G: There exss a cosa G > such ha G, γ = G γ + O γ,. Remark. Whe S represes he md-prce dyamcs of he rsky facal asse, G erpres smply as he bd-ask spread of he asse he order book of eres. We shall see he followg ha for asympoc replcao purpose, oly he sze G of he bd-ask spread s releva our approach. Remark. Of course, assumg ha he order book s deermsc ad ha he bd-ask spread remas cosa s urealsc ad hece resrcve. Neverheless, we oule hs paper ha hs smple framework already rases eresg mahemacal problems ad leads o promsg coclusos. The cosderao of dyamc radom order books, for whch o uamous model has emerged he leraure, shall be lef for furher research.

5 Asympoc Hedgg 5.3 Porfolo dyamcs ad replcao Due o he presece of frcos o he marke, ducg drec or drec rasaco coss, we oly cosder porfolo sraeges, where he maager chages hs marke poso o a fe umber of revso daes. For smplcy, we assume he paper ha he revso daes defe a uform deermsc me grd,.e. := /, for. Remark.3 As observed [ or [3, he use of o uform me grd, where he umber of radg daes creases as he maury s geg closer, allows o mprove he covergece of he Lelad ype approxmae hedgg sraegy. Oe ca expec hs propery o rema sasfed our coex. A rgorous proof of hs resul requres very compuaoal fer esmaes, whch go beyod he scope of hs already echcal paper. For he cosderao of radom me es, we refer o he ce resuls of [5, whch produces a robus asympoc hedgg sraegy for vashg lear rasaco coss wre erms of he raded amou of moey. A porfolo o he me erval [, s gve by a al capal x R ad a F-adaped pecewse-cosa process H N, where H L Ω represes he umber of shares of sock hold he porfolo o he me erval [, +, for ay <. Due o he order book frcos, he value of he porfolo process V assocaed o he pecewse-cosa vesme sraegy H s gve by V = V + Hu ds u G, H H,, N.. We am a hedgg he coge clam wh payoff hs, where h s a covex fuco, for whch precse regulary requremes are gve Seco 3.3 below. We look owards a porfolo V, wh ermal value covergg o hs as he umber of radg daes eds o fy. 3 Asympoc hedgg va volaly modfcao ad payoff regularzao I order o exhb a porfolo sraegy, whose asympoc ermal value aas he clam of eres hs despe he frcos, we formally expla Seco 3. he Lelad mehodology ad cosder a fcve asse wh upgraded volaly. Sce rasaco coss rewre our framework as a fuco of he volume of raded asse, he fcve asse has o Lpschz local volaly dyamcs. Afer verfyg Seco 3. ha hs sochasc dffereal equao has a uque soluo, we roduce he correspodg prcg ad hedgg fucos of he clam hs for a frcoless marke. Up o a proper sraegy modfcao, we exhb Seco 3.4 a asympoc hedgg sraegy for he covex clam hs. For payoff fucos wh few regulary such as call opo, a well chose addoal regularzao mehod s exposed Seco Cosruco of he elarged volaly fuco I he frcoless Black Scholes model, he prce fuco of he covex clam hs s he uque soluo C.,. of he PDE e ad he exac self-facg

6 6 Romuald Ele, Emmauel Lépee replcao porfolo s gve by C, S = EhS + C xu, S uds u,. I exacly replcaes he coge clam hs ad s self-facg. I he presece of rasaco coss, Lelad suggesed hs famous paper [8 o subsue he volaly σ by a arfcally elarged oe σ, relaed o he mesh / of he radg replcao grd. We brefly recall he ma deas behd hs volaly elargeme ad deal formally how adaps o he framework of frcos cosdered here. For a sequece of volaly fucos σ o be deermed below, cosder he followg PDEs { u, x + σ xx u xx, x =,, x [,,, u, x = hx, x, for N. The soluo C of hs equao f exss s he frcoless prcg fuco of a facal dervave wh payoff fuco h, wheever he sock has σ local volaly dyamcs. We look owards a volaly fuco σ allowg o ake o accou he rasaco coss duced o he radg daes. More precsely, Io s formula mples ha he formally supposed smooh fuco C verfes C, S = C, S + C x u, S uds u + [ σ σ S u SuC xxu, S udu, for ad N. Hece, he process C, S ca be approxmaely defed as a porfolo process wh dyamcs of he form. wheever he las erm o he rgh had sde above correspods o he rasaco coss cumulave sum,.e. equalzg he varaos: [ σ σ S u SuC xxu, S u u G u, Cx u + u, S u+ u Cx u, S u, for N. A formal Taylor approxmao gves C x u + u, S u+ u C x u, S u = C xu, S u u + C xxu, S u S u+ u S u, C xxu, S u S u+ u S u, for N. Sce h s a covex fuco, we expec Cxx ad follows formally from Codo G ogeher wh he relao S u+ u S u σs u W u+ u W u ha [ σ σ S u u G σ W u+ u W u S u, N. Takg he codoal expecao gve F u ad pluggg he classcal esmae E W u+ u W u = u/π, hs leads o [ σ σ S u u G σ u S u π, N.

7 Asympoc Hedgg 7 For he regular radg grd cosdered here, u = / provdes he followg caddae for he upgraded volaly fuco: σ : x, σ + G / 8 π σ, N. 3.3 x Observe ha hs caddae upgraded local volaly fuco s degeerae a ad we prove he ex paragraph he well posed-ess of he correspodg local volaly fcve asse ad assocaed prcg fuco. 3. The fcve asse dyamcs Le us cosder a sequece of fcve asses, whose dyamcs are gve by he caddae upgraded volaly σ defed 3.3. We expec he fcve asses Ŝ o solve he followg sochasc dffereal equao Ŝ = S + γ Ŝu dw u, T, N, 3.4 where we roduced he oao γ : x σ xx = σ x + σγ x, wh γ := G / 8, N. 3.5 π Sce he dffuso coeffces γ are o Lpschz, he exsece of a uque process wh such dyamcs does o follow from he classcal heorems. We puzzle ou hs dffculy usg he Egelber & Schmd crero as dealed he followg lemma. Lemma 3. Whaever al codo, x [,,, he sochasc dffereal equao 3.4 adms a uque srog soluo Ŝ s s, sarg from x a me. Furhermore, hs soluo remas o-egave. Proof. We fx N ad, x [,,. For ay z R, observe ha he dffuso coeffce γ defed 3.5 sasfes: f ε ε dy =, for ay ε >, he γz =. 3.6 γ z + y Ideed, for z, akg ε = z /, we ge ε ε dy < γ z+y, so ha he lef had sde codo of 3.6 mples z =, leadg o γ z =. Hece, he dffuso coeffce γ sasfes he Egelber & Schmd crero, ad, here exss a weak soluo o 3.4 wh al codo, x, see Theorem 5.4 Seco 5 of [7. We ow observe ha he dffuso coeffce γ also sasfes γ z γ y = σ z + σγ z σ y + σγ y σ z y + σ y + σγ z σ y + σγ y, z, y R,

8 8 Romuald Ele, Emmauel Lépee sce he dervave of y σ y + σγ z s upper bouded by σ. We deduce γ z γ y σ z y + σγ z y l z y, z, y R, wh l : u σu + σγ u. Sce ε du lu =, for ay ε >, we deduce from Proposo.3 Seco 5 of [7 ha pahwse uqueess holds for he sochasc dffereal equao 3.. Togeher wh he exsece of a weak soluo verfed above, hs mples he exsece of a uque srog soluo o 3. for ay al codo, x, see Corollary 3.3 Seco 5 of [7. Fally, Ŝ remas o-egave, sce s couous ad Markova, ad he uque srog soluo sarg a s he ull oe. 3.3 Payoff regularzao ad relaed prcg fuco We ow qure he properes of he prcg fucos assocaed o he fcve asses Ŝ ad frs dscuss he regulary of he payoff fuco of eres. We am a hedgg he coge clam wh payoff hs, where he payoff fuco h s supposed o sasfy he followg: Codo P: The covex fuco h : [, R s affe ousde he erval Codo P: [/K, K, wh K >. Observe ha mos of he classcal covex payoffs sasfy hs codo. I parcular, uder Codo P, he map h s Lpschz ad we deoe by L > s smalles Lpschz cosa. I he followg, we shall somemes requre he payoff fuco o be couously dffereable. Besdes, order o cosder o-vashg rasaco coss, we eed a corol o he secod order varaos of he payoff fuco. I order o do so, we regularze he covex map h, as dealed he followg lemma. Lemma 3. There exss a sequece of covex maps h valued C [,, R such ha, for large eough, h h L l γ /6, h L, h 3L γ/6 l [/K,K. 3.7 Proof. We observe ha h s affe o [, /K ad roduce he exeso of h o R, whch remas affe wh he same slope o,. For smplcy, hs exeded map s also deoed h. For N, we roduce he covoluo bewee h ad he square kerel wh suppor [ l/γ /6, l/γ /6 : h : x [, 4 3 h x + y l γ /6 y dy. Sce h s L-Lpschz ad y dy = 3/4, we compue h h 4 L ly 3 γ /6 y dy = L l L l, N. 3 γ /6 γ /6

9 Asympoc Hedgg 9 Fx N. Observe ha h C [,, R ad, deog abusvely h he rgh dervave of h, we have h x = 4 h x + y l y dy 3 γ /6 = 4 x+ γ hz /6 z x γ/6 3 l l dz, x. x Sce h L, we deduce ha h L. Dffereag he secod expresso of h above, we deduce ha h x = 4 x+ hz γ/6 γ/6 x z 3 l l dz = 8 h x + y l γ /6 3 γ /6 l ydy, x for x. Usg oce aga ha h L, hs yelds h 8L 3 y γ/6 l dy = 8L 3 γ /6 l γ/6 3L l. 3.8 Besdes, sce h s affe o [K,, we deduce ha h x = 4 hk x K + y l y dy = hkx K = hx, 3 γ /6 for ay x K + l/γ /6. The exac same reasog apples for x /K l/γ /6. Hece, for large eough such ha γ /6 / l K, h s affe ad herefore h = ousde he erval [/K, K. Combed wh 3.8, hs complees he proof. Remark 3.4 Wheever h s valued C [,, R, he regularzao procedure s o ecessary sce 3.7 s sasfed as soo as s large eough. Hece oe ca smply use h sead of h. The sequece of regularzed approxmag payoff fucos h had, we ca ow roduce he assocaed valuao PDEs, gve by: { Ĉ e =, x + σ xx Ĉxx, x =,, x [,,, Ĉ, x = h x, x,., for N. The exsece of a uque srog soluo for hs PDE s gve Proposo 3.3 below. For sake of compleeess ad sce he correspodg dffereal operaor s o uformly parabolc o [,,, he proof of hs proposo s repored Appedx. As expeced, he soluo of he PDE erpres as he valuao fuco of he opo wh payoff h o he ermal value of he fcve asse Ŝ, roduced he prevous seco. Proposo 3.3 For ay N, he PDE e has a uque soluo deoed Ĉ, whch moreover sasfes Ĉ, x = E,x [h Ŝ,, x [,,, N. 3.9

10 Romuald Ele, Emmauel Lépee 3.4 Dela correco ad asympoc hedgg for o vashg rasaco coss coeffce Eve a frcoless complee seg, a coge clam ca ever be perfecly replcaed pracce, sce couous me hedgg s o feasble. As dealed Seco.3, we cosder porfolos where he poso he asses chages o he regular dscree me grd. I hs framework, we clam ha he upgrade σ of volaly ad he regularzao h of he payoff dealed Seco 3. ad Seco 3.3 allows o couerbalace asympocally he frcos due o order book relaed rasaco coss. Ths clam s he coe of he ex heorem, whch s he ma resul of he paper. Theorem 3.4 Cosder he sequece of porfolos V assocaed o he al codos Ĉ, S ad he vesme sraeges H defed by H := Ĉx, S Ĉ x j, S Ĉ j x j, S j, j for [, + ad <. The, he sequece of porfolo values rewre V = Ĉ, S + Hu ds u G, H H,, N, ad V coverges probably o he payoff hs as goes o. 3. The proof of hs heorem s preseed Seco 4 below, ad requres sharp esmaes o he dervaves of Ĉ, whose proofs are pospoed o Seco 5. Remark 3.5 Observe ha he hedgg sraegy does o smply coss cosderg he Dela assocaed o he fcve asse Ŝ. Ideed, as observed [6, for he classcal framework of rasaco coss proporoal o he amou of moey, hs orgal Lelad replcag sraegy does o coverge o he clam of eres, uless he rasaco coss vash fas eough as he umber of radg daes creases. As [9, he exra erm he defo of H allows o cosder o vashg rasaco coss. I parcular, observe ha he chage of poso a me, for, he porfolo V s gve by Ĉ x, S Ĉ x, S. Remark 3.6 Our ma resul also allows o quafy he effecs of a volume based radg axao, o he cos of hedgg sraeges for covex dervaves. Ideed, order o reder mos of he hgh frequecy radg arbrage opporues rreleva, he regulaor s sll lookg owards he bes way o creae a ax o radg orders. Neverheless, he exac cosequeces of such a regulao o asse maageme sraeges or more geerally rsk maageme sraeges s o ye compleely udersood. Smple quesos o hs subjec sll lack fully sasfyg aswers: Should he regulaor creae a ax o he volume of raded asse or he quay of raded moey? Should he use a lear ax? Wha are he cosequeces of usg a dffere shape of ax fuco? I our smplfyg Black Scholes framework, our coclusos are ha he global shape of he axao does o really maers from a hedgg perspecve sce oly he asympoc behavor aroud s releva. Besdes, Theorem 3.4 exhbs he volaly chage relaed o a volume based axao sead of a more classcal amou based oe.

11 Asympoc Hedgg 4 Proof of he ma resul Due o he cosderao of volume relaed o lear rasaco coss, he exhbed radg sraegy s based o a prcg fuco of a sock model wh o lear dyamcs. Hece, classcal esmaes are o avalable for he sesves of he prce fuco erms of he volaly parameer. Bu, we requre o udersad precsely he depedece of he prce sesves wh respec o he umber of radg daes whch affecs he modfed volaly parameer. We overcome hs dffculy, usg Mallav dervave ype represeao of he Greeks, as dealed he ex subseco. Ths leads o sharp esmaes, whch allow o derve he covergece of he approxmag replcag porfolo o he clam of eres a maury. 4. Represeao ad esmaes for he modfed prce fuco sesves Recall ha he prce fuco Ĉ s gve by Ĉ :, x E,x [h Ŝ. 4. A well chose probably chage leads classcally o a ce represeao of he Dela of he opo preseed below. Lemma 4. For N ad ay al codo, x [,,, he s.d.e. d S u = γ S u dw u + γ γ S u du 4. has a uque soluo S, whch moreover remas srcly posve. Besdes, we have Ĉx, x = E,x [ h S,, x [,,, N. 4.3 Proof. Fx N. The exsece of a uque soluo o 4. follows from smlar argumes as he oe preseed Lemma 3.. Besdes, sce ρudu = where { σ } y + σγ ρ : u exp u σ y + σγ dy = σ + σγ y σ u + σγ, u Theorem.6 ad.7, [, esure ha S remas srcly posve for a gve posve al codo. The mappgs y σ e y ad y σ e y adm locally Lpschz frs dervaves because her secod dervaves are locally bouded. Le deoe S := l Ŝ. By vrue of Theorem 39 V.7 ad Theorem 38 V.7[, we deduce ha here exss a verso of he mappg y S,y, whch s couously dffereable ad so s x Ŝ,x o,, for ay,. Precsely, for a gve al codo, x [,,, he age process Ŝ s gve by Ŝ u = + γ Ŝs Ŝs dw s, s T.

12 Romuald Ele, Emmauel Lépee Besdes, dffereag expresso 4. provdes Ĉx, x = E,x [ h Ŝ Ŝ. Assume for he mome ha Ŝ s a posve margale ad roduce he ew equvale probably P defed by dp = Ŝ dq, so ha Ĉ x, x = E P,x [ h Ŝ. 4.4 Grsaov heorem assers ha he process W gve by dw u = dw u γ Ŝ u du s a sadard Browa moo uder P. Hece, he dyamcs of Ŝ uder P are gve by dŝ u = γ Ŝ u dw u + γ γ Ŝ u du. Therefore, he law of Ŝ uder P s decal o he oe of S uder Q ad 4.4 rewres as 4.3. The res of he proof s dedcaed o he verfcao ha Ŝ s deed a posve margale. For ay p N, le us roduce he soppg me τ p := f{s : Ŝ s x/ + p}, wh he coveo ha f =. Applyg Growall s lemma, we verfy ha sup s Ŝs τ s square egrable, hece Ŝṇ τ s a margale. Le us defe he chage of measure dq p := Ŝ τ pdq. The, E[ Ŝ E[ Ŝ τ p τ p = = Q p τ p =, p N. 4.5 As τ p p, le us defe he sequece τ p p assocaed o he process S gve by 4.. By cosruco, observe ha τ p has he same law uder Q p ha τ p uder Q, for ay p N. I follows ha Q p τ p = = Q τ p = Q τ = where τ s he frs me whe S hs zero. Bu S remas srcly posve, so ha 4.5 mples ha E[ Ŝ. Sce Ŝ s a supermargale, we he coclude. We ow provde a expresso for he secod dervave of he prce fuco Ĉ, he spr of he Mallav represeao of he Greeks preseed [4. Lemma 4. For ay N, we have Ĉxx, x = E,x [ h S πudw u,, x [,,, 4.6 where π s defed by π u := S u γ S u, u. 4.7 Proof. Fx ay al codo, x [,, ad N. Dffereag 4.3 wh respec o x, we drecly compue Ĉ xx, x = E,x [ h S S = E,x [ h S D s S S s ˆγ S s ds.

13 Asympoc Hedgg 3 Recall ha he Mallav dervave ad he age process oly dffer by her al codos. Hece, recallg he defo 4.7 of π, he egrao by pars formula yelds [ Ĉxx, x = E,x D s[ h S πs ds = E,x [ h S πs dw s. Smlarly, he hrd dervave of he prce fuco also has such ype of represeao expecao, where we emphasze ha he sochasc egrals cosdered below are of Skorokhod ype, sce he egrad s o ecessarly F-adaped. Lemma 4.3 For ay N, we have Ĉxxx, x = E,x [ h S π udw u,, x [,,, 4.8 where π s defed by π u := xπu + πu πs dw s, u. 4.9 Proof. Fx ay al codo, x [,, ad N. Dffereag 4.6 wh respec o x ad followg a smlar reasog as above yelds Ĉxxx, x = E,x [ h S xπs dw s + h S S πudw u = E,x [ h S xπs dw s + D s[ h S πs πudw u ds. Hece, he Mallav egrao by pars formula provdes Ĉxxx, x = E,x [ h S xπs dw s + h S ad he defo 4.9 cocludes he proof. π s π udw u dw s, The exac same le of argumes provdes a smlar represeao for he fourh dervave of he prcg fuco. Lemma 4.4 For ay N, we have Ĉxxxx, x = E,x [ h S ˆπ udw u,, x [,,, 4. where ˆπ s defed by ˆπ u := x π u + πu π s dw s, u. 4. These represeaos allow o derve esmaes o he depedace of he dervaves of he prcg fuco Ĉ, erms of he parameer. The raher compuaoal obeo of hese esmaes s repored Seco 5 below.

14 4 Romuald Ele, Emmauel Lépee Proposo 4.5 There exs a cosa C ad a couous fuco f o, whch do o deped o N, such ha Ĉx, x C, 4. Ĉxx, C x x / γ 4.3 Ĉ xxx, x Ĉ xxxx, x fx γ + Ĉ x, x C C γ x + γ x 3/, 4.4 fx γ + fx 5/4 γ 5/4 + fx 3/ γ 3/, 4.5 fx 4/3 l, 4.6 for ay, x [,, ad N. Remark 4.7 Observe ha 4.3 also dcaes ha he prce fuco Ĉ s covex wh respec o he space varable. Ideed, he prcg fuco hers he covexy of he payoff. Ths observao s crucal order o esure ha a volaly upgrade allows o compesae he rasaco coss. 4. Asympocs of he hedgg error The subseco s dedcaed o he proof of Theorem 3.4, he ma resul of he paper. We verfy below ha he sequece V of ermal values for approxmae replcag porfolos coverges o hs, as he umber of radg daes eds o fy. For ay N, we rewre he hedgg sraegy H as H = Ĥ + K wh Ĥ := Ĉ x, S ad K := j Ĉ x j, S j Ĉ x j, S j, 4.7 for [, + ad. We also deoe Ĥ := Ĥ+ Ĥ ad K := K+ K. Therefore he ermal value of he caddae replcag porfolo V rewres V = Ĉ, S + Hu ds u G, Ĥ < Besdes, he dyamcs of Ĉ ad he defo 3.3 of ˆσ yelds h S = Ĉ, S + Ĉ x u, S uds u + + K, N. 4.8 σγ S u Ĉ xxu, S udu, N. Pluggg he wo expressos above ogeher drecly leads o he followg racable decomposo of he hedgg error V hs = F + F + F + F 3 + F 4,

15 Asympoc Hedgg 5 for ay N, where F := h S hs + F := F := Ĥ Ĉx, S ds, K ds F3 := G Ĥ + K F 4 := = Ĥ Ĉx, S ds G, Ĥ + K, = σγ S Ĉxx, S d = G, Ĥ + K, G Ĥ + K. We ow prove ha each sequece of radom varables F j for j =,..., 4 goes o zero probably, as goes o fy. Proposo 4.6 The sequeces F, F, F ad F 3 coverge o probably as goes o. Proof. We prove he covergece of each sequece separaely. Sep. Covergece of F. By cosruco of h, 3.7 mples ha he frs erm h S hs eds o as h h. The secod oe coverges o because Ĉx., S. s bouded accordg o 4.. As for he las erm, observe from 4.3 ha [ Ĥ = Ĉx, S Ĉx, S = h S E h S S = S h E [ S S S = S, N. As E S S C /, we deduce from 3.7 ha Ĥ C γ/6 + C γ/6 l l E S S, N. From he dyamcs 4. of S, we compue drecly E S S C γ / so ha Ĥ goes o as goes o fy. Very smlarly, we show ha K coverges also o ad Codo G provdes he covergece of F o. Sep. Covergece of F. Applyg he Io formula, we drecly compue ha Ĥ Ĉ x, S = M M + A A, < +, <, 4.9 where he sequece of processes M ad A are gve by.. [ M := σs u Ĉxxu, S udw u ad A := Ĉxu, S u + σ S uĉ xxxu, S u du,

16 6 Romuald Ele, Emmauel Lépee for ay N. Sce S has bouded momes, 4.3 ogeher wh he Cauchy Schwarz equaly yeld EM M 4 C γ + du C u γ, < +, <. Besdes, 4.4 ogeher wh 4.6 dcae ha E A A 4 C C l du u l γ + 4/3 γ / + 4/3 4 du u + γ / du u Pluggg he las wo esmaes 4.9 leads drecly o E F C γ + C l γ + 8/3 4, < +, <. d Cγ + for ay N, so ha E F goes o as goes o fy. Sep. Covergece of F. From he defo of K gve 4.7, we drecly compue C l /3 + C γ, F = Ĉ xu, S S S du Combg he Cauchy Schwarz equaly ogeher wh 4.6 yelds E F C l C l Sep 3. Covergece of F 3. For ay, observe ha E[ S S / du u 4/3 5/6 C l. Ĥ + K = Ĉ x, S Ĉ x, S = Ĉ xx, S S S, where he radom varable S s bewee S ad S. Hece, 4.3 ogeher wh Codo G yeld F 3 C ωχ 3 where χ 3 := γ S S, for ay N. Bu Eχ 3 Cγ l, hece F 3 as goes o. Proposo 4.7 The sequece F 4 coverges o probably as goes o.

17 Asympoc Hedgg 7 Proof. For ay N, we wre F 4 = 4 = L wh he summads L := σγ S Ĉxx, S d σγ S Ĉxx, S, = L := Ĉxx σγ, S G σ W = L 3 := σg S Ĉxx, S W = L 4 := G =, σs u Ĉ xxu, S udw u σs u Ĉxxu, S udw u G H + K. Observe ha he prevous decomposo uses he covexy of he prce fuco gve 4.3, see Remark 4.7. I ow suffces o show ha L for =,... 4 as dealed he seps below. Sep. Covergece of L. We have L C ω L + L where, by vrue of 4.3, L S S := γ γ d, L := γ = = = S Ĉ xx, S Ĉxx, S d. We have E L C γ /. For he secod erm, we use he Taylor expaso Ĉ xx, S Ĉ xx, S = Ĉ xxx, S S S + Ĉ xx, S, for some radom varables ad S, for <. Besdes, dffereag he dyamcs of Ĉ, we observe ha Ĉ x = σ Ĉ xx σ x + σγ xĉ xxx σ x + σγ xĉ xxxx, 4.3 for ay x, ad N. Hece, combg 4.3, 4.4 ad 4.5, we ge L C ωγ + C ω γ = S S γ d d γ + d γ + d + d, 5/4 γ 3/ 3/ for ay N. Hece he Cauchy Schwarz equaly ad a drec compuao yeld EL l C + +. /4 3/8 Sep. Covergece of L. γ 5/4,

18 8 Romuald Ele, Emmauel Lépee We use he equaly E W = /π from whch we deduce σγ E [ G σ W = V ar G σ W = σ G, for ay. The depedece of he cremes of he Browa moo ogeher wh 4.3 yeld EL C γ C l. Sep 3. Covergece of L 3. We use he equaly a b a b. Therefore, he Cauchy-Schwarz equaly ad he Io somery gve us E L 3 C = [ S E Ĉ xx, S S u Ĉxxu, S u / du. By he Io formula, we ge d[s Ĉ xx, S = f dw + g d where f := σs Ĉ xx, S + σs Ĉ xxx, S, g := S Ĉ xx, S + σ S 3 Ĉ xxxx, S + σ S Ĉ xxx, S, for ad N. Hece, we derve E L 3 C E fs ds + = Esmaes 4.3 ad 4.4 provde E f u du C γ + E g s ds /. 4.3,. γ Besdes, combg 4.3 ogeher wh 4.3, 4.4 ad 4.5, we ge E gu du Cγ γ / + γ + γ 5/4 γ 3/ + 5/4. 3/ Pluggg hese las wo esmaes 4.3, smlar compuaos as Sep yeld o he covergece of E L 3 o zero. Sep 4. Covergece of L 4. We frs verfy ha we may replace K by K where K := Ĉxu, S udu,. To do so, suffces o show ha χ where χ := Ĉ x u, S u Ĉ xu, S du.

19 Asympoc Hedgg 9 Usg a Taylor expaso, we compue Ĉ xu, S u Ĉ xu, S = Ĉ xxu, S S u S, for some radom varable S bewee S u ad S, for ay u. Hece 4.3 ogeher wh 4.3, 4.4 ad 4.5 mply ha χ C ω χ where χ Su S := γ + Su S Su S + + Su S du, = / γ γ 5/4 γ 5/4 3/ γ 3/ for N. As E S u S C / for u +, we easly coclude ha E χ. A las, replacg K by K ad usg he equaly a b a b, we deduce from Io s formula ogeher wh 4.4 ha L 4 C ω Ĉ xxxu, S udu c ω du uγ + du uγ. 5 Prce sesves esmao Ths seco s dedcaed o he obeo of he esmaes preseed Proposo 4.5 above, whch allow o upper boud he sesves of he prce fuco Ĉ erms of he umber of radg daes. The corol of each sesvy s preseed separaely. These esmaes, amely 4., 4.3, 4.4, 4.5 ad 4.6, are obaed usg he Mallav represeao of he Greeks dealed Seco 4.. Ths parcular feaure s ew he classcal scheme of proof for he obeo of Lelad ype covergece heorems. I all he seco, we fx, x [,, ad om he subscrp {, x} order o allevae he oaos. 5. Esmaes 4. ad 4.3 o he frs ad secod dervaves Frs observe ha esmae 4. drecly follows from he represeao 4.3, sce h s bouded. The res of hs subseco s dedcaed o he obeo of 4.3. We fx, x [,,. Usg 4.6 ogeher wh he Cauchy Schwarz equaly, we derve Ĉxx, x h E / π u du, N. 5.3 We ow focus more closely o he dyamcs of he processes π defed by 4.7. Frs, accordg o he dyamcs of S, he age process S sasfes d S u = γ S u S u dw u + γ S u + γ S u γ S u S u du,

20 Romuald Ele, Emmauel Lépee for N. Besdes, Io s formula mples ha / γ S has he followg dyamcs d = γ S u γ S u γ S u d S u + γ S u γ S u γ S u du γ S u = γ S u γ S u dwu γ S u du, N. A drec applcao of he egrao by pars formula hece mples dπ u = γ S u S u Therefore, we deduce ha { πu = π σ γ } exp 8 γ S s ds du = σ γ 8 γ S u π udu, N γ x, u, N Pluggg hs expresso ogeher wh γ x σγ x 5.3 provdes 4.3. Ideed 5.34 also dcaes ha π ad hece S are o-egave, so ha Ĉ xx, x = E,x [ h S S. 5. Esmae 4.4 o he hrd dervave Ths subseco s dedcaed o he obeo of 4.4 ad dvdes 3 seps. Sep. Esmae decomposo Usg 4.8, we derve Ĉ xxx, x h E Z where Z := Le us roduce he sequece of processes Z gve by Z s := s By he defo of π gve 4.9, we compue π udw u, N π udw u, N Z = xπudw u + πuz dw u = xπudw u + Z πud uz du = xπudw u + Z πu du πu D uπs dw s du u = Z πu du + xπu πs D sπuds dw u, N.

21 Asympoc Hedgg Pluggg hs expresso 5.35 ad usg Io s formula, we deduce Ĉxxx, x C A / + B, N, 5.37 where A ad B are respecvely defed by A := E π uz u dw u ad B := E xπu πs D sπuds dw u, for N. We ow fx N ad ed o corol he erms A ad B separaely. Sep. Corol of A Recall from 5.34 ha π / γ x. Hece, we ge from a drec applcao of Io s formula ha A = E πu π s dw s dw u = E π u πs dw s du. We recall from 5.34 ha π / γ x ad deduce from he prevous expresso A = E π u πs dw s du γ x E π s dsdu Usg oce aga he same relao ogeher wh ˆγ x σγ x yelds A / γ x 4 σ γx Sep 3. Corol of B We ow ur o he more rcae erm B. Le us roduce he oao. b := xπ [D sπuπ s ds, so ha B = E b udw u. 5.4 By vrue of he margale mome equales, here exss C > such ha B C E / b u du C E sup b u, N. 5.4 u I order o corol he las erm o he r. h. s., we look owards he dyamcs of b. Dffereag he dyamcs of π gve 5.33, we compue separaely d xπu = σ γ 8 γ S u xπ udu + σ γ γ S u S u πudu, γ S u 3 dd sπu = σ γ 8 γ S u Dsπ udu + σ γ γ S u D s S u πudu, s, γ S u 3

22 Romuald Ele, Emmauel Lépee Sce D s S r = S r γ S s / S s = S r /{ π s } for s r, we deduce πs D sπuds σ γ = π s σ γ s 8 γ S r Dsπ r drds + γ S r D s S r π s 4 γ S r 3 r πs drds r = πs D sπ σ γ r ds dr 8 γ S r + r σ γ γ S r S r π 4 γ S r 3 r dr, for u. Combg hs expresso wh 5.4, we ge db u = σ γ 8 γ S u b udu + σ γ γ S u πu udu γ S u Noce ha b = γ x/ γ x <. From he dyamcs of b, we observe ha b creases as log as b s egave. Oce becomes posve, mus rema o egave, sce he egave par of he drf dsappears as soo as b reaches. Ideed, b = L π /π where. L := b σ γ + γ S r π 4 γ S r r π rdr 5.45 s srcly creasg. From here, we deduce ha b ad L have he same sg. Hece b s always o egave o [τ, where τ := f{s [,, b s = }. Therefore, we ge b u b {bu } + b u {u τ } b σ γ b r {u τ } τ 8 γ S r dr + σ γ γ S r {u τ } π τ 4 γ S r r rdr, for ay u, whch drecly leads o b u b + Γ u, wh Γ := Sce γ s o-egave ad π s decreasg, we deduce ha E sup b u u. γ S r rπ r dπ r γ x γ x + EΓ We ow focus o he las erm of hs expresso ad observe from a drec applcao of he egrao by pars formula ha Γu = γ x π u γ S u πu + πr rd γ S r πr γ S r dr, u We compue γ x = σ x + σγ σ x + σγ, γ x = σ γ x 4 γ x 3, 3 γ x = 3σ γ γ x 4 γ x 4,

23 Asympoc Hedgg 3 ad deduce from he applcao of Io s formula ha d γ S u = σ γ dw u γ S u du = σ γ 4 γ S u 4 γ S u dwu γ S u dπ u πu Pluggg hs expresso 5.48 drecly leads o Γ u γ x π + N u + Γ u, u, where N :=. π r r σ γ 4 γ S dwr. Sce Γ, follows ha N r u u s a supermargale whece EN. We deduce a upper boud o EΓ whch plugged 5.47 provdes E sup b u u γ x γ x + γx π = Togeher wh 5.4 ad he expresso γ x/ γ x C/x, we ge B C x γ x C γ x 3/, whch, combed wh 5.37 ad 5.39, provdes γ x γ x Esmae 4.5 o he fourh dervave Ths subseco s dedcaed o he obeo of 4.5. Fx N. The represeao 4. drecly provdes Ĉ xxxx, x h E ˆπ udw u, 5.5 ad we ow ed o corol he erm E ˆπ udw u several seps. Sep. A racable Decomposo for E. ˆπ udw u Le roduce he oao Z u := b s + π s Z s dw s, u, so ha Z = π s dw s, where b s defed above ad gve by b := π. π r D rπ dr. The defo of ˆπ gve 4. mples ˆπ udw u = π udw u + πu Z dw u = Z + π u Z dw u. 5.5

24 4 Romuald Ele, Emmauel Lépee Usg egrao by pars formulae, observe ha π u Z dw u rewres Z Z πud u[ Z du = Z Z πub u + πuz u du πu D ub s + D u[πs Zs dw s du u s s = Zu d Z u + Z u dzu πud ub s du Zs πud uπs du dw s s s r πs πu du dw s πs πud uπr du dw r dw s. Pluggg hs expresso ogeher wh Z = b s + Zs πs + πs Zs dw s ad he defo of b 5.5, we oba ˆπ udw u = c s dw s + Zu d Z u + Z u dzu { s s } + Zs b s + πs b r dw r πs πu du dw s, where c := b. π r D rb dr. Iroducg he dyamcs of Z ad Z he prevous expresso, we ge s ˆπ udw u = c s dw s + 3 Zs b s + πs b r dw r dw s s s + Zs + πr Zr dw r πr dr dw s. π s Usg Io s formula ogeher wh he defo of Z, we deduce E ˆπ udw u 3C + 3C + C3, 5.53 where we se C := E π s s Z r dw r dw s, C := E We ow requre o corol hese hree erms separaely. Zs b s dw s, C3 := E Sep. Corol of C Usg wce he margale mome equaly, we compue c s dw s. C C π E Z u du c π E sup Z u u C π E sup C π u b u + π E sup E sup b u + c π u. u Z u

25 Asympoc Hedgg 5 Pluggg 5.5 hs expresso, follows ha C C γ x γx γ x +. γx Sce γ x σγ x ad γ x / γ x 3/x, we deduce ha C C γ x x γx Sep 3. Corol of C Applyg he margale mome equaly ogeher wh he relao 5.46, we deduce C C E sup b uzu C u b E sup Zu + E sup Γu Zu u u where Γ defed 5.46 s o egave ad creasg. Usg oce aga he margale mome equaly, we derve C C b π + C E sup Γu Zu u Observe ha he egrao by pars formula yelds dγ u Z u = γ S u uz u π udπ u + Γ u π udw u. The Jese equaly appled o he cocave fuco x x yelds he equaly fuudu fudu fuu du. Sce π s decreasg, we deduce ha sup Γu Zu u sup Γr π rdw r + Γ Γ /, 5.56 u u where, usg 5.49, we have Γ u := = γ S r r Zr πr dπr πr 4 γ S r rdr πr Zr γ S r dr πr Zr r γ S r dπ r πr + πr 3 rd Z, γ S + N u, r for u, wh N a local margale. Hece, we deduce ha Γ u Nu + χ + χ where χ := π r 4 γ S r rdr, χ := 4 r Z r π r dπ r. Applyg Io s formula o πr 4 γ S r r r ogeher wh he relao 5.49 yelds χ = π 4 γ x + 4 r π r 4 γ S r dπ r π r r γ S r π r 3 dπ r + N,,,

26 6 Romuald Ele, Emmauel Lépee where N, s a lower bouded local margale, so ha E χ π 4 γ x. From he margale equaly ogeher wh Io s formula, we ge E χ 4E sup Zr r r πr dπr 4 π π 3, 3 where he las equaly follows from he mooocy of π ogeher wh Doob s equaly. We deduce ha E χ +χ <, so ha Γ u Nu +E[ χ +χ F u, whch mples ha N s a supermargale. Therefore EN ad EΓ E[χ + χ. Hece, he wo prevous equales ogeher wh 5.56 lead o E sup u Γ u Z u E sup u Γr πr dw r + EΓ π 4 π 4 γx / The margale mome equaly ad he mooocy of Γ ad π esure u E sup Γr πr dw r CE Γr πr dr Cπ EΓ. u Pluggg EΓ γ x π observed 5.5 ogeher wh he defos of π ad b he prevous expressos ad 5.55 leads o C C γx γ x 3 + γx γ x + γ x γ x 4 + γ x 4 3/ Sce γ x/ γ x 3/x ad γ x σγ x, we compue C for some couous fuco f. fx γ + fx 5/4 γ 5/4, 5.57 Sep 4. Corol of C3 We ow ur o he las erm C3 ad observe from he margale mome equaly ha C3 = E c s dw s CE c s ds C E sup c s s I order o corol hs las erm, we compue he dyamcs of c defed as b. π s D sb ds. We deduce from he dyamcs of b gve 5.44 ha d b γ u = 8 γ S u b udu + γ γ 4 γ 3 S u S u b udu [ + γ γ γ S u πu π u udu + γ 4 γ γ S u S u πu udu.

27 Asympoc Hedgg 7 Smlarly, we compue dd sb γ u = 8 γ S u Dsb udu + γ γ 4 γ 3 S u D s S u b udu [ + γ γ γ S u πud sπu udu + γ 4 γ γ S u D s S u πu udu, for s u. Sce D. S u = Sṇ /{ πs }, we deduce followg he same le of argumes as Sep 3 of he prevous seco ha dc γ u = 8 γ S u c udu + γ γ 4 γ 3 S u b u u S u du [ + γ γ γ S u πu ub udu + γ 4 γ γ Su Su π u u du. Therefore, Io s formula ogeher wh he defo of π leads o c u π u = c π γ 4 [ γ γ γ S r r b r π r γ 8 π r dr γ S r γ γ S r πr r dr, u. Sce π ad γ are decreasg, hs relao combed wh 5.58 mples C3 C c + EX + EY, wh X := γ S r r b r πr π r dπr,. Y := γ S r γ S r γ S r πr r dπr. We frs focus o he process Y ad, sce π s decreasg, observe ha. Y π γ S r γ S r γ S r r dπr. 5.6 Applyg Io s formula o he process u πu γ S u π γ x = 3 γ S r rπ r γ S r dr + r γ S r dπ r u π u/ γ S u u, we ge γ S r π r γ S r dr γ S r 4 γ S r 3 r πr γ S r r γ S r dr N Y u, where N Y s a local margale gve by N Y :=. r πr γ S r / γ S r dw r. Pluggg dπr = πr γ S r γ S r dr he prevous equaly provdes βu := r π γ S r r γ S r dr 5.6 γ S r γ S r = u π u γ S u π γ x + rπ r γ S r dr + N Y u, u. 5.6

28 8 Romuald Ele, Emmauel Lépee Le pck v [, ad defe for r [v,, N r := r v N Y u du. By vrue of Theorem 65, IV -6, [, N r r [v, s a local margale. Moreover, 5.6 mples ha r r βu du v v Besdes, observe ha 3 v u πu [ γ S u du + rπ r γ S r dr = 8 γ r v rπ r γ S r drdu + N r uπ u γ S u du + N r 5.63 rdπr 8 γ π Ths esmae ogeher wh 5.63 ad β mply ha N r r [v, s a supermargale, as a local margale bouded from below. Therefore, sce β s creasg, we deduce from 5.63 ad 5.64 ha Eβv v E v βu du 4 γ π, v <. As v, usg he Faou lemma sce β, we derve EY γ 4 π Eβ 6 π We ow focus o he erm X ad observe from 5.44 ha b d r πr = σ γ γ S r π 4 γ S r r dr = γ S r dπr, so ha b /π s creasg ad herefore d b r /πr γ S r dπr. Hece, Io s formula mples drecly Xu γ x b π π γ S r b r πr πr dr γ S r πr r dπr b r + πr r πr d γ S r, for u. Pluggg 5.49 hs expresso, we deduce X u γ x b π + Y + N X u, u, 5.66 where N X s a local margale. Sce EY <, we deduce ha N X s a supermargale so ha EN X. Hece, combg 5.59 ogeher wh 5.65 ad 5.66 provdes C3 C c + γ x b π + π 3 = C γ x γ x + 3 γx γ x 3 + γ x 3. Sce γ γ x Cγ /x, γ / γ x C/x ad / γ x C γ x, hs yelds C3 C γ x / γ x 3/ Pluggg 5.54, 5.57 ad ad 5.53 provdes 4.5.

29 Asympoc Hedgg Esmae 4.6 o he crossed dervave Ths subseco s dedcaed o he obeo of 4.6. Ths fer esmae s ecessary order o cosder rasaco coss coeffces whch do o vash as he umber of radg daes goes o fy. I requres he obeo of sroger esmaes o Ĉ xx ad Ĉ xxx whch are made possble va he corol 3.7 o he sequece of payoff fucos h. We recall ha he al codo, x s fxed ad E,x deoes E[. S Le us frs derve some a pror esmaes o S ad S. = x. Lemma 5. There exs a cosa C ad a couous fuco f o, whch do o deped o such ha E,x S u C, u, 5.68 E,x S u Cfx, u, 5.69 E,x S u Cfx, u, 5.7 E,x S u 3/ Cfx, u, 5.7 E,x S u C γ fx, u, 5.7 Proof. We fx N ad u [, order o verfy each esmae separaely. Proof of Recall ha S sasfes d S u = γ S u S u dw u + σ S u du. Usg he dyamc of S ad he Io formula, we verfy easly ha S has fe momes of all orders. As S u = π u γ S u, we deduce ha S u has also fe momes of all orders. We also kow ha he process S s posve ad. γ S u S u dw u s a local margale whch urs ou o be a margale oce sopped by a sequece of soppg mes τ k, a.s. as k. By he Faou Lemma, we deduce ha τ k, E S u + lm f E σ S u du + E k Usg he Growall lemma, we coclude abou σ S r dr., Proof of By vrue of 5.68, we have xe,x S u = E,x S u C. Hece, a Taylor expaso drecly leads o Proof of 5.7 E,x S u = E,x S u E, S u Cx.

30 3 Romuald Ele, Emmauel Lépee From he s.d.e. sasfed by S u, we deduce ha here s a cosa C such ha E S u Cγ gx for some couous fuco g. To do so, suffces o use equaly 5.69 ad apply he Growall lemma. Recall ha S = π γ S = π σ S + σγ S. As π π, we coclude abou 5.7. Proof of 5.7. We have xe,x S u 3/ = 3/E,x S u / S u. Usg he Cauchy-Schwarz equaly ad Iequales 5.69 ad 5.7, we deduce ha xe,x S u 3/ Cgx, for some couous fuco g. Hece 5.69 follows from a Taylor expaso. Proof of 5.7. We have xe,x S u = E,x S u S u. We he use he Cauchy Schwarz equaly wh Iequaly 5.7 ad he equaly E,x S u Cγ gx. The cocluso follows as prevously. We ow provde fer esmaes o Ĉ xx ad Ĉ xxx. Lemma 5. There exss a couous fuco f such ha Ĉ xx, x fx,, x [,,, N. 4/3 γ l Proof. Fx N. From 4.7 ad 5.34, we compue [ Ĉxx, x = E[ h S S = E h S γ S π C γ h E [π, sce h vashes ousde a compac subse of, whch does o deped of ad hece h S γ S s bouded by C γ h. We ow look owards a sharp esmae of E[π. The expresso of π gve 5.34 ogeher wh Jese equaly yeld [ E [π π γ E e σ 8 γ S u du π γ E γ γ S u e σ 6 γ S u du, 5.73 where we used he boud xe x C, x, for he las equaly. We spl he expecao of he r.h.s. he expresso above wo pars. The frs oe s bouded for large eough as follows, by vrue of 5.69 ad 5.7: [ E γ S u e σ γ 6γ S u { S u γ } γ e σ / γ 6 fx, 5.74 where f s a couous fuco whch may chage from le o le. Observe ha he Cauchy-Schwarz equaly ad 5.7 yelds E[ S u S u γ γ /6 fx. Therefore, he secod erm s bouded by [ E γ S u e σ γ 6γ S u { S u σ γ γ } + γ 5/6 fx γ 5/6 fx. 5.75

31 Asympoc Hedgg 3 Togeher wh x /3 e x C, x, pluggg 5.74 ad yelds E [π π γ γ 5/6 fx fx /3 γ 5/ /3 Togeher wh 3.7, pluggg hs esmae he frs equaly of hs proof cocludes he proof. Lemma 5.3 Fx N. There exss a couous fuco f such ha Ĉ xxx, x fx,, x [,,, N. 4/3 γ l Proof. Fx N. As observed Seco 5., we have [ Ĉxxx, x h A / + B, where B := E h S b udw u 5.77 ad A ad b are respecvely gve 5.37 ad 5.4. As already observed 5.38, we have A γ x E π s dsdu π γ x s Ee s σ γ 4 γ S r drdsdu. s Usg he boud x / e x C for x, we deduce A C π [ s γ x E γ S γ s 3/ r e s σ γ 8 γ S r drdsdu. Sce he expoeal o he r.h.s s smaller ha, we drecly deduce from 5.69 ha C π γ /4 fx γ x γ / fx 3/4 5/4 γ 5/4 A / fx 4/3 γ l We ow focus o he secod erm o he r.h.s. of 5.77 ad rewre B = E b u h S D u S du = E Observe from 5.45 ha he process b s gve by b u = π u π b + πu S b u h S πu du, u. σ γ γ S r π 4 γ r rdr. S r Moreover, recall ha S = π γ S ad h vashes ousde a compac subse depede of. Pluggg hese esmaes he expresso of B, we ge B C [ γ h b π E[π + E π σ γ γ S r π 4 γ r r dr. S r

32 3 Romuald Ele, Emmauel Lépee Recallg he process β defed 5.6, observe ha he expresso of b ogeher wh γ + γ ad 5.34 lead o B C [ γ h γx γ x E[π + E π r dπr C γ h x E[π + π E [π + E [π β + E [π β h fx + C γ h E [π 4/3 γ 7/6 β, 5.79 where he las equaly follows from The res of he proof s dedcaed o he corol of E [π β. We follow he oaos of he prevous seco ad observe from he mooocy of β ogeher wh 5.63 ha Eπ β lm Eπ v v v βu du lm Eπ rπr 3 v γ S r dr + v v N Y r dr Sce he frs erm he parehess s bouded by Cπ γ, 5.76 yelds Eπ β Cπ γ fx + lm Eπ 4/3 γ 7/6 v v Regardg he las erm, we frs observe from 5.63 ha v v N Y r dr. 5.8 Nr Y rπr dr 3 v γ S r dr C vπ γ, v u. Ths provdes a upper boud for. v N Y r dr ad he egrao by pars formula yelds π u v N Y r dr C vπ γ dπr + v v π r N Y r dr, v u. Moreover, he las erm o he r.h.s s a supermargale as a bouded from below local margale. Hece, by vrue of he Lebesgue heorem, we fally deduce ha lm v Eπ v v N Y r dr C vπ γ lm E v v π d r π =. Combg hs esmae wh 5.77, 5.78, 5.79 ad 5.8 ad 3.7 cocludes he proof. Proof of 4.6. I order o derve he upper boud 4.6, suffces o derve he expresso of Ĉ x, x from Ĉ xx, x ad Ĉ xxx, x by dffereag he p.d.e. e ad o plug he esmaes of Lemma 5. ad Lemma 5.3..

33 Asympoc Hedgg 33 6 Appedx: proof of Proposo 3.3 Noe ha we cao mmedaely coclude abou he exsece of a soluo of e because he operaor s o uformly parabolc o, [ [, [. Tha s why, we shall brg he problem back o aoher oe he doma of whch sasfes he requred uform parabolcy. Fx N. By vrue of Lemma 3., recall ha Ŝ s he uque soluo of he sochasc equao Ŝ,,x s = x + s γ Ŝ,,x u dw u, s,, x [,,, where we use he overscrp, x order o emphasze he al codo. Iroducg γ m : x σ x + σγ x + m, we deoe by Ŝ,m he soluo of Ŝ,m,,x s = x + s γ m Ŝ,m,,x u dw u, s,, x [,,, for ay m >. Sce γ m γ m /, for m >, hece Ŝ,m,,x Ŝ,,x L Ω, P as m goes o, uformly, x [,,. We deduce ha Ĉ,m :, x E h Ŝ,m,,x coverges uformly o Ĉ :, x E h Ŝ,,x. Applyg Lemma 3.3 p wh Codo A p 3 [3, mples, ogeher wh h L, ha Ĉ,m, x Ĉ,m u, y L E Ŝ,x, Ŝ,u,y K x y + u, for m >,, u ad x, y R for a gve R,, where he cosa K depeds o, m ad R. We deduce ha Ĉ,m s couous for ay m > ad hece so s Ĉ. Fx m >. We use argumes of Seco 6.3 [3 ad ry o follow her oaos. Le us cosder he followg ses Q m :=, m, m, B m := {} m, m, { } T m := {} m, m, S m := [, m, m, For each y S m, s easy o observe ha here exss a closed ball Ky m such ha Ky m Q m = ad Ky m Q m = {y}. I follows ha he fuco W y proposed p 34 [3 defes a barrer for each y S m. Besdes, Ĉ ad h are couous ad σ s Lpschz o Q m. By vrue of Theorem 3.6 p 38 [3, we deduce ha he Drchle problem u, x + σ xx u xx, x =, x Q m T m ut, x = h x x B m u, x = Ĉ, x, x S m

34 34 Romuald Ele, Emmauel Lépee adms a uque soluo u,m, couous o Q m wh couous dervaves u,m, u,m xx o Q m T m. Moreover, Theorem 5. p 47 [3 mples ha u,m has he followg sochasc represeao u,m [Ĉ, x = E τ m, Ŝ,,x τ m τ m < + h Ŝ,x τ m =,, x Q m, where τ m s he frs me where Ŝ,,x exs Q m. The defo of Ĉ mples u,m [Ĉ [, x = E τ m, Ŝ,,x τ = E m h Ŝ,x = Ĉ, x,, x Q m. As m, we deduce ha Ĉ solves he PDE e. Moreover, C :, y Ĉ, e y solves he followg uformly parabolc PDE { v, y + σ e y v yy, y σ e y v y, y =,, y [, R v, y = he y, y R. By vrue of Theorem 3.6 [3, C s also he uque soluo of he same PDE resrced o a arbrary smooh bouded doma. Moreover, Theorem 5. p 47 [3, mples ha C has a uque probablsc represeao. We deduce ha Ĉ s he uque soluo of e. Refereces. Chery A.S. ad Egelber H.J. Sgular Sochasc Dffereal Equaos. Lecure Noes Mahemacs. Sprger.. Des E., Yur Kabaov. Mea Square Error for he Lelad-Lo Hedgg Sraegy: Covex Pay-off. Face ad Sochascs, 4, 4, ,. 3. Fredma A. Sochasc Dffereal Equaos ad Applcaos. Volume. Academc Press, Fouré E., Lasry J.M., Lebuchoux J., Los P.L., Touz N. Applcaos of Mallav Calculus o Moe Carlo Mehods Face, Face ad Sochascs 3, 39-4, Fukasawa, M.. Coservave dela hedgg uder rasaco coss, Prepr. 6. Kabaov Y., Safara M. O Lelad s sraegy of Opo Prcg wh Trasaco Coss. Face ad Sochascs,, 3, 39-5, Karazas.I, Shreve.S.E. Browa Moo ad Sochasc Calculus. Sprger Verlag. 8. Lelad H. Opo prcg ad Replcao wh Trasacos Coss, Joural of Face, XL, 5, 83 3, Lépee E. Modfed Lelad s Sraegy for Cosa Trasaco Coss Rae. Mahemacal Face. do:./j x. To appear.. Lo K. E Verfahre zur Replkao vo Opoe uer Trasakokose seger Ze, Dsserao. Uversä der Budeswehr Müche. Isu für Mahemak ud Daeverarbeug, Pergamechkov S. Lm Theorem for Lelad s Sraegy. Aals of Appled Probably, 3, 99 8, 3.. Proer P.E. Sochasc Iegrao ad Dffereal Equaos. d. Ed.. Sochasc Modellg ad Appled Probably. Sprger. 3. Seke J., Yao J. Hedgg Errors of Lelad s Sraeges wh me-homogeeous Rebalacg. Prepr.

HIGH FREQUENCY MARKET MAKING

HIGH 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 information

A quantization tree method for pricing and hedging multi-dimensional American options

A quantization tree method for pricing and hedging multi-dimensional American options A quazao ree mehod for prcg ad hedgg mul-dmesoal Amerca opos Vlad BALLY Glles PAGÈS Jacques PRINTEMS Absrac We prese here he quazao mehod whch s well-adaped for he prcg ad hedgg of Amerca opos o a baske

More information

Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument

Chapter 4 Multiple-Degree-of-Freedom (MDOF) Systems. Packing of an instrument Chaper 4 Mulple-Degree-of-Freedom (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 information

Proving the Computer Science Theory P = NP? With the General Term of the Riemann Zeta Function

Proving the Computer Science Theory P = NP? With the General Term of the Riemann Zeta Function Research Joural of Mahemacs ad Sascs 3(2): 72-76, 20 ISSN: 2040-7505 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 information

A new proposal for computing portfolio valueat-risk for semi-nonparametric distributions

A new proposal for computing portfolio valueat-risk for semi-nonparametric distributions A ew proposal for compug porfolo valuea-rsk for sem-oparamerc dsrbuos Tro-Mauel Ñíguez ad Javer Peroe Absrac Ths paper proposes a sem-oparamerc (SNP) mehodology for compug porfolo value-a-rsk (VaR) ha

More information

7.2 Analysis of Three Dimensional Stress and Strain

7.2 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 wo-dmesoal saes of sress. Here he fully hree dmesoal sress

More information

Lecture 13 Time Series: Stationarity, AR(p) & MA(q)

Lecture 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 FRB-MIT-Pe. See

More information

Vladimir PAPI], Jovan POPOVI] 1. INTRODUCTION

Vladimir PAPI], Jovan POPOVI] 1. INTRODUCTION Yugoslav Joural of Operaos Research 200 umber 77-9 VEHICLE FLEET MAAGEMET: A BAYESIA APPROACH Vladmr PAPI] Jova POPOVI] Faculy of Traspor ad Traffc Egeerg Uversy of Belgrade Belgrade Yugoslava Absrac:

More information

No Regret Learning in Oligopolies: Cournot vs Bertrand

No 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 information

APPENDIX III THE ENVELOPE PROPERTY

APPENDIX 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 information

Analysis of Coalition Formation and Cooperation Strategies in Mobile Ad hoc Networks

Analysis 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 Sopha-Apols, Frace Absrac. Ths paper focuses o he formal assessme of he properes of

More information

Determinants of Foreign Direct Investment in Malaysia: What Matters Most?

Determinants 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 970-008. The causay ad dyamc reaoshp

More information

CONVERGENCE AND SPATIAL PATTERNS IN LABOR PRODUCTIVITY: NONPARAMETRIC ESTIMATIONS FOR TURKEY 1

CONVERGENCE 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 information

American Journal of Business Education September 2009 Volume 2, Number 6

American 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 Je-Ho Che, Alba Sae Uver, USA ABSTRACT Tme Value of Moe (TVM

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL 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 information

Professional Liability Insurance Contracts: Claims Made Versus Occurrence Policies

Professional Liability Insurance Contracts: Claims Made Versus Occurrence Policies ARICLES ACADÉMIQUES ACADEMIC ARICLES Assuraces e geso des rsques, vol. 79(3-4), ocobre 2011- javer 2012, 251-277 Isurace ad Rsk Maageme, vol. 79(3-4), Ocober 2011- Jauary 2012, 251-277 Professoal Lably

More information

Value of information sharing in marine mutual insurance

Value 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:[email protected]. Deparme of Logscs & Marme Sudes,

More information

Average Price Ratios

Average Price Ratios Average Prce Ratos Morgstar Methodology Paper August 3, 2005 2005 Morgstar, Ic. All rghts reserved. The formato ths documet s the property of Morgstar, Ic. Reproducto or trascrpto by ay meas, whole or

More information

Performance Comparisons of Load Balancing Algorithms for I/O- Intensive Workloads on Clusters

Performance Comparisons of Load Balancing Algorithms for I/O- Intensive Workloads on Clusters Joural of ewor ad Compuer Applcaos, vol. 3, o., pp. 32-46, 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 information

Business School Discipline of Finance. Discussion Paper 2014-005. Modelling the crash risk of the Australian Dollar carry trade

Business School Discipline of Finance. Discussion Paper 2014-005. Modelling the crash risk of the Australian Dollar carry trade Dscusso Paper: 2014-005 Busess School Dscple of Face Dscusso Paper 2014-005 Modellg he crash rsk of he Ausrala Dollar carry rade Suk-Joog Km Uversy of Sydey Busess School Modellg he crash rsk of he Ausrala

More information

T = 1/freq, T = 2/freq, T = i/freq, T = n (number of cash flows = freq n) are :

T = 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 information

GARCH Modelling. Theoretical Survey, Model Implementation and

GARCH 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 information

Jorge Ortega Arjona Departamento de Matemáticas, Facultad de Ciencias, UNAM [email protected]

Jorge 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 information

Abraham Zaks. Technion I.I.T. Haifa ISRAEL. and. University of Haifa, Haifa ISRAEL. Abstract

Abraham 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 information

Solving Fuzzy Linear Programming Problems with Piecewise Linear Membership Function

Solving Fuzzy Linear Programming Problems with Piecewise Linear Membership Function Avalable a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 9-966 Vol., Issue December ), pp. Prevously, Vol., Issue, pp. 6 6) Applcaos ad Appled Mahemacs: A Ieraoal Joural AAM) Solvg Fuzzy Lear Programmg Problems

More information

CHAPTER 22 ASSET BASED FINANCING: LEASE, HIRE PURCHASE AND PROJECT FINANCING

CHAPTER 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 information

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data

ANOVA Notes Page 1. Analysis of Variance for a One-Way Classification of Data ANOVA Notes Page Aalss of Varace for a Oe-Wa 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 information

TIME-VARYING RISK PREMIUM IN LARGE CROSS-SECTIONAL EQUITY DATASETS

TIME-VARYING RISK PREMIUM IN LARGE CROSS-SECTIONAL EQUITY DATASETS IME-VARYING RISK PREMIUM IN LARGE CROSS-SECIONAL EQUIY DAASES Parck Gaglard a, Elsa Ossola ad Olver Scalle c * Frs draf: Decemer 2 hs verso: Novemer 2 Asrac We develop a ecoomerc mehodology o fer he pah

More information

Price Volatility, Trading Activity and Market Depth: Evidence from Taiwan and Singapore Taiwan Stock Index Futures Markets

Price Volatility, Trading Activity and Market Depth: Evidence from Taiwan and Singapore Taiwan Stock Index Futures Markets We-Hsu Kuo Asa e Pacfc al./asa Maageme Pacfc Maageme evew (005) evew 0(), (005) 3-3 0(), 3-3 Prce Volaly, Tradg Acvy ad Marke Deph: Evdece from Tawa ad Sgapore Tawa Sock Idex Fuures Markes We-Hsu Kuo a,*,

More information

PORTFOLIO CHOICE WITH HEAVY TAILED DISTRIBUTIONS 1. Svetlozar Rachev 2 Isabella Huber 3 Sergio Ortobelli 4

PORTFOLIO 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 Racheva-Joova Soya Soyaov ad Almra Bglova for he comuaoal aalyss ad helful commes.

More information

1. The Time Value of Money

1. The Time Value of Money Corporate Face [00-0345]. The Tme Value of Moey. Compoudg ad Dscoutg Captalzato (compoudg, fdg future values) s a process of movg a value forward tme. It yelds the future value gve the relevat compoudg

More information

- Models: - Classical: : Mastermodel (clay( Curves. - Example: - Independent variable t

- 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,, Mock-up) - GM: mahemacal escpo fo

More information

Natural Gas Storage Valuation. A Thesis Presented to The Academic Faculty. Yun Li

Natural Gas Storage Valuation. A Thesis Presented to The Academic Faculty. Yun Li Naural Gas Sorage Valuao A Thess Preseed o The Academc Faculy by Yu L I Paral Fulfllme Of he Requremes for he Degree Maser of Scece he School of Idusral ad Sysem Egeerg Georga Isue of Techology December

More information

Mobile Data Mining for Intelligent Healthcare Support

Mobile Data Mining for Intelligent Healthcare Support Proceedgs of he 42d Hawa Ieraoal Coferece o ysem ceces - 2009 Moble Daa Mg for Iellge Healhcare uppor Par Delr Haghgh, Arkady Zaslavsky, hoal Krshaswamy, Mohamed Medha Gaber Ceer for Dsrbued ysems ad ofware

More information

Mobile Data Mining for Intelligent Healthcare Support

Mobile Data Mining for Intelligent Healthcare Support Moble Daa Mg for Iellge Healhcare uppor Absrac The growh umbers ad capacy of moble devces such as moble phoes coupled wh wdespread avalably of expesve rage of bosesors preses a uprecedeed opporuy for moble

More information

Internal model in life insurance : application of least squares monte carlo in risk assessment

Internal model in life insurance : application of least squares monte carlo in risk assessment Ieral model lfe surace : applcao of leas squares moe carlo rs assessme - Oberla euam Teugua (HSB) - Jae Re (Uversé yo, HSB) - rédérc Plache (Uversé yo, aboraore SA) 04. aboraore SA 50 Aveue Toy Garer -

More information

Financial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression

Financial Time Series Forecasting with Grouped Predictors using Hierarchical Clustering and Support Vector Regression Ieraoal Joural of Grd Dsrbuo Compug, pp.53-64 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 information

Harmony search algorithms for inventory management problems

Harmony search algorithms for inventory management problems Afrca Joural of Busess Maageme Vol.6 (36), pp. 9864-9873, 2 Sepember, 202 Avalable ole a hp://www.academcourals.org/ajbm DOI: 0.5897/AJBM2.54 ISSN 993-8233 202 Academc Jourals Revew Harmoy search algorhms

More information

Object Tracking Based on Online Classification Boosted by Discriminative Features

Object Tracking Based on Online Classification Boosted by Discriminative Features Ieraoal Joural of Eergy, Iformao ad Commucaos, pp.9-20 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 information

Standardized Formula Sheet: Formulas Standard Normal Distribution Table Summary of Financial Ratios

Standardized 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 information

Traditional Smoothing Techniques

Traditional Smoothing Techniques Tradoal Smoohg Techques Smple Movg Average: or Ceered Movg Average, assume s odd: 2 ( 2 ( Weghed Movg Average: W W (or, of course, you could se up he W so ha hey smply add o oe. Noe Lear Movg Averages

More information

ECONOMIC CHOICE OF OPTIMUM FEEDER CABLE CONSIDERING RISK ANALYSIS. University of Brasilia (UnB) and The Brazilian Regulatory Agency (ANEEL), Brazil

ECONOMIC 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 information

Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Edward W. Frees

Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Edward W. Frees Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces b Edward W. Frees Logudal ad Pael Daa: Aalss ad Applcaos for he Socal Sceces Bref Table of Coes Chaper. Iroduco PART I - LINEAR MODELS Chaper.

More information

IDENTIFICATION OF THE DYNAMICS OF THE GOOGLE S RANKING ALGORITHM. A. Khaki Sedigh, Mehdi Roudaki

IDENTIFICATION 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: 16315-1355, ehra, Ira [email protected],

More information

Claims Reserving When There Are Negative Values in the Runoff Triangle

Claims 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 7-0 00 . INTRODUCTION The may uceraes

More information

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R =

Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS R = Chapter 3. AMORTIZATION OF LOAN. SINKING FUNDS Objectves of the Topc: Beg able to formalse ad solve practcal ad mathematcal problems, whch the subjects of loa amortsato ad maagemet of cumulatve fuds are

More information

15. Basic Index Number Theory

15. Basic Index Number Theory 5. Basc Idex Numer Theory A. Iroduco The aswer o he queso wha s he Mea of a gve se of magudes cao geeral e foud, uless here s gve also he ojec for he sake of whch a mea value s requred. There are as may

More information

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time.

Preprocess a planar map S. Given a query point p, report the face of S containing p. Goal: O(n)-size data structure that enables O(log n) query time. Computatoal Geometry Chapter 6 Pot Locato 1 Problem Defto Preprocess a plaar map S. Gve a query pot p, report the face of S cotag p. S Goal: O()-sze data structure that eables O(log ) query tme. C p E

More information

Chapter 3 0.06 = 3000 ( 1.015 ( 1 ) Present Value of an Annuity. Section 4 Present Value of an Annuity; Amortization

Chapter 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 information

Quantifying Environmental Green Index For Fleet Management Model

Quantifying Environmental Green Index For Fleet Management Model Proceedgs of he Easer Asa Socey for Trasporao Sudes, Vol.9, 20 Quafyg Evromeal ree Idex For Flee Maageme Model Lay Eg TEOH a, Hoo Lg KHOO b a Deparme of Mahemacal ad Acuaral Sceces, Faculy of Egeerg ad

More information

Markit iboxx USD Liquid Leveraged Loan Index

Markit iboxx USD Liquid Leveraged Loan Index Mark Boxx USD Lqud Leveraged Loa Idex Sepember 20 Mark Boxx USD Leveraged Loa Idex Idex Gude Coe Overvew... 4 Seleco Crera... 5 Idex Icepo/Rebalacg... 5 Elgbly Crera... 5 Loa Type... 5 Mmum facly ze...

More information

Relaxation Methods for Iterative Solution to Linear Systems of Equations

Relaxation Methods for Iterative Solution to Linear Systems of Equations Relaxato Methods for Iteratve Soluto to Lear Systems of Equatos Gerald Recktewald Portlad State Uversty Mechacal Egeerg Departmet [email protected] Prmary Topcs Basc Cocepts Statoary Methods a.k.a. Relaxato

More information

of the relationship between time and the value of money.

of the relationship between time and the value of money. TIME AND THE VALUE OF MONEY Most agrbusess maagers are famlar wth the terms compoudg, dscoutg, auty, ad captalzato. That s, most agrbusess maagers have a tutve uderstadg that each term mples some relatoshp

More information

Derivative Securities: Lecture 7 Further applications of Black-Scholes and Arbitrage Pricing Theory. Sources: J. Hull Avellaneda and Laurence

Derivative Securities: Lecture 7 Further applications of Black-Scholes and Arbitrage Pricing Theory. Sources: J. Hull Avellaneda and Laurence Deivaive ecuiies: Lecue 7 uhe applicaios o Black-choles ad Abiage Picig heoy ouces: J. Hull Avellaeda ad Lauece Black s omula omeimes is easie o hik i ems o owad pices. Recallig ha i Black-choles imilaly

More information

10.5 Future Value and Present Value of a General Annuity Due

10.5 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 information

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0

The analysis of annuities relies on the formula for geometric sums: r k = rn+1 1 r 1. (2.1) k=0 Chapter 2 Autes ad loas A auty s a sequece of paymets wth fxed frequecy. The term auty orgally referred to aual paymets (hece the ame), but t s ow also used for paymets wth ay frequecy. Autes appear may

More information

Simple Linear Regression

Simple Linear Regression Smple Lear Regresso Regresso equato a equato that descrbes the average relatoshp betwee a respose (depedet) ad a eplaator (depedet) varable. 6 8 Slope-tercept equato for a le m b (,6) slope. (,) 6 6 8

More information

Y2K* Stephanie Schmitt-Grohé. Rutgers Uni ersity, 75 Hamilton Street, New Brunswick, New Jersey 08901 E-mail: [email protected].

Y2K* Stephanie Schmitt-Grohé. Rutgers Uni ersity, 75 Hamilton Street, New Brunswick, New Jersey 08901 E-mail: grohe@econ.rutgers.edu. Revew of Economc Dynamcs 2, 850856 Ž 1999. Arcle ID redy.1999.0065, avalable onlne a hp:www.dealbrary.com on Y2K* Sephane Schm-Grohé Rugers Unersy, 75 Hamlon Sree, New Brunswc, New Jersey 08901 E-mal:

More information

Chapter Eight. f : R R

Chapter Eight. f : R R Chapter Eght f : R R 8. Itroducto We shall ow tur our atteto to the very mportat specal case of fuctos that are real, or scalar, valued. These are sometmes called scalar felds. I the very, but mportat,

More information

The Digital Signature Scheme MQQ-SIG

The Digital Signature Scheme MQQ-SIG The Dgtal Sgature Scheme MQQ-SIG Itellectual Property Statemet ad Techcal Descrpto Frst publshed: 10 October 2010, Last update: 20 December 2010 Dalo Glgorosk 1 ad Rue Stesmo Ødegård 2 ad Rue Erled Jese

More information

Evaluation and Modeling of the Digestion and Absorption of Novel Manufacturing Technology in Food Enterprises

Evaluation and Modeling of the Digestion and Absorption of Novel Manufacturing Technology in Food Enterprises Advace Joural of Food Scece ad Techology 9(6): 482-486, 205 ISSN: 2042-4868; e-issn: 2042-4876 Mawell Scefc Orgazao, 205 Submed: Aprl 9, 205 Acceped: Aprl 28, 205 Publshed: Augus 25, 205 Evaluao ad Modelg

More information

Valuation Methods of a Life Insurance Company

Valuation 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 information

Generating Intelligent Teaching Learning Systems using Concept Maps and Case Based Reasoning

Generating Intelligent Teaching Learning Systems using Concept Maps and Case Based Reasoning 17 Geerag Iellge Teachg Learg Sysems usg Cocep Maps ad Case Based Reasog Makel L. Esposa, MSc. Naala Maríez S. y Zeada García V. Deparme of Compuer Scece, Ceral Uversy of Las Vllas, Hghway o Camajuaí,

More information

MORE ON TVM, "SIX FUNCTIONS OF A DOLLAR", FINANCIAL MECHANICS. Copyright 2004, S. Malpezzi

MORE ON TVM, SIX FUNCTIONS OF A DOLLAR, FINANCIAL MECHANICS. Copyright 2004, S. Malpezzi MORE ON VM, "SIX FUNCIONS OF A DOLLAR", FINANCIAL MECHANICS Copyrgh 2004, S. Malpezz I wan everyone o be very clear on boh he "rees" (our basc fnancal funcons) and he "fores" (he dea of he cash flow model).

More information

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time

A Study of Unrelated Parallel-Machine Scheduling with Deteriorating Maintenance Activities to Minimize the Total Completion Time Joural of Na Ka, Vol. 0, No., pp.5-9 (20) 5 A Study of Urelated Parallel-Mache Schedulg wth Deteroratg Mateace Actvtes to Mze the Total Copleto Te Suh-Jeq Yag, Ja-Yuar Guo, Hs-Tao Lee Departet of Idustral

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Trust Evaluation and Dynamic Routing Decision Based on Fuzzy Theory for MANETs

Trust 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 information

The Term Structure of Interest Rates

The 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 information

Numerical Solution of the Incompressible Navier-Stokes Equations

Numerical Solution of the Incompressible Navier-Stokes Equations Nmercl Solo of he comressble Ner-Sokes qos The comressble Ner-Sokes eqos descrbe wde rge of roblems fld mechcs. The re comosed of eqo mss cosero d wo momem cosero eqos oe for ech Cres eloc comoe. The deede

More information

Critical Approach of the Valuation Methods of a Life Insurance Company under the Traditional European Statutory View

Critical 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. Paul-Aoe Darbellay ParerRe Belleresrasse 36 C-8034 Zürch Swzerlad Phoe: 4 385 34 63 Fa: 4 385 37 04 E-mal: [email protected]

More information

The Transport Equation

The Transport Equation The Transpor Equaion Consider a fluid, flowing wih velociy, V, in a hin sraigh ube whose cross secion will be denoed by A. Suppose he fluid conains a conaminan whose concenraion a posiion a ime will be

More information

Lecture 7. Norms and Condition Numbers

Lecture 7. Norms and Condition Numbers Lecture 7 Norms ad Codto Numbers To dscuss the errors umerca probems vovg vectors, t s usefu to empo orms. Vector Norm O a vector space V, a orm s a fucto from V to the set of o-egatve reas that obes three

More information

Numerical Methods with MS Excel

Numerical Methods with MS Excel TMME, vol4, o.1, p.84 Numercal Methods wth MS Excel M. El-Gebely & B. Yushau 1 Departmet of Mathematcal Sceces Kg Fahd Uversty of Petroleum & Merals. Dhahra, Saud Araba. Abstract: I ths ote we show how

More information

Credibility Premium Calculation in Motor Third-Party Liability Insurance

Credibility Premium Calculation in Motor Third-Party Liability Insurance Advaces Mathematcal ad Computatoal Methods Credblty remum Calculato Motor Thrd-arty Lablty Isurace BOHA LIA, JAA KUBAOVÁ epartmet of Mathematcs ad Quattatve Methods Uversty of ardubce Studetská 95, 53

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree

A New Bayesian Network Method for Computing Bottom Event's Structural Importance Degree using Jointree , pp.277-288 http://dx.do.org/10.14257/juesst.2015.8.1.25 A New Bayesa Network Method for Computg Bottom Evet's Structural Importace Degree usg Jotree Wag Yao ad Su Q School of Aeroautcs, Northwester Polytechcal

More information

The Increasing Participation of China in the World Soybean Market and Its Impact on Price Linkages in Futures Markets

The Increasing Participation of China in the World Soybean Market and Its Impact on Price Linkages in Futures Markets The Icreasg arcpao of Cha he Word Soybea Marke ad Is Ipac o rce Lkages Fuures Markes by Mara Ace Móz Chrsofoe Rodofo Margao da Sva ad Fabo Maos Suggesed cao fora: Chrsofoe M. A. R. Sva ad F. Maos. 202.

More information

Methodology of the CBOE S&P 500 PutWrite Index (PUT SM ) (with supplemental information regarding the CBOE S&P 500 PutWrite T-W Index (PWT SM ))

Methodology of the CBOE S&P 500 PutWrite Index (PUT SM ) (with supplemental information regarding the CBOE S&P 500 PutWrite T-W Index (PWT SM )) ehodology of he CBOE S&P 500 PuWre Index (PUT S ) (wh supplemenal nformaon regardng he CBOE S&P 500 PuWre T-W Index (PWT S )) The CBOE S&P 500 PuWre Index (cker symbol PUT ) racks he value of a passve

More information

STOCHASTIC approximation algorithms have several

STOCHASTIC approximation algorithms have several IEEE TRANSACTIONS ON INFORMATION THEORY, VOL 60, NO 10, OCTOBER 2014 6609 Trackg a Markov-Modulated Statoary Degree Dstrbuto of a Dyamc Radom Graph Mazyar Hamd, Vkram Krshamurthy, Fellow, IEEE, ad George

More information

Pricing Rainbow Options

Pricing Rainbow Options Prcng Ranbow Opons Peer Ouwehand, Deparmen of Mahemacs and Appled Mahemacs, Unversy of Cape Town, Souh Afrca E-mal address: [email protected] Graeme Wes, School of Compuaonal & Appled Mahemacs, Unversy

More information

Mathematical Finance

Mathematical Finance Mathematical Finance Option Pricing under the Risk-Neutral Measure Cory Barnes Department of Mathematics University of Washington June 11, 2013 Outline 1 Probability Background 2 Black Scholes for European

More information

Capacity Planning. Operations Planning

Capacity Planning. Operations Planning Operaons Plannng Capacy Plannng Sales and Operaons Plannng Forecasng Capacy plannng Invenory opmzaon How much capacy assgned o each producon un? Realsc capacy esmaes Sraegc level Moderaely long me horzon

More information

Fuzzy Forecasting Applications on Supply Chains

Fuzzy Forecasting Applications on Supply Chains WSEAS TANSACTINS o SYSTEMS Haa Toza Fuzzy Forecag Applcao o Supply Cha HAKAN TZAN ZALP VAYVAY eparme of Idural Egeerg Turh Naval Academy 3494 Tuzla / Iabul TUKIYE hoza@dhoedur Abrac: - emad forecag; whch

More information

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology

Statistical Pattern Recognition (CE-725) Department of Computer Engineering Sharif University of Technology I The Name of God, The Compassoate, The ercful Name: Problems' eys Studet ID#:. Statstcal Patter Recogto (CE-725) Departmet of Computer Egeerg Sharf Uversty of Techology Fal Exam Soluto - Sprg 202 (50

More information

The Unintended Consequences of Tort Reform: Rent Seeking in New York State s Structured Settlements Statutes

The 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 information

The Advertising Market in a Product Oligopoly

The 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 information

The Time Value of Money

The 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 information

Lecture 40 Induction. Review Inductors Self-induction RL circuits Energy stored in a Magnetic Field

Lecture 40 Induction. Review Inductors Self-induction RL circuits Energy stored in a Magnetic Field ecure 4 nducon evew nducors Self-nducon 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 information

EXAMPLE 1... 1 EXAMPLE 2... 14 EXAMPLE 3... 18 EXAMPLE 4 UNIVERSAL TRADITIONAL APPROACH... 24 EXAMPLE 5 FLEXIBLE PRODUCT... 26

EXAMPLE 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 information

Curve Fitting and Solution of Equation

Curve Fitting and Solution of Equation UNIT V Curve Fttg ad Soluto of Equato 5. CURVE FITTING I ma braches of appled mathematcs ad egeerg sceces we come across epermets ad problems, whch volve two varables. For eample, t s kow that the speed

More information

The Economics of Administering Import Quotas with Licenses-on-Demand

The Economics of Administering Import Quotas with Licenses-on-Demand The Ecoomcs of Admserg Impor uoas wh Lceses-o-Demad 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 information

The real value of stock

The real value of stock he real value of sock Collars ivolve he paye of a variable aou of sock, depedig o a average sock price. I his arcle, Ahoy Pavlovich uses he Black-Scholes fraework o value hese exoc derivaves ad explore

More information