The Implied Volatility of Forward Starting Options: ATM Short-Time Level, Skew and Curvature

Size: px
Start display at page:

Download "The Implied Volatility of Forward Starting Options: ATM Short-Time Level, Skew and Curvature"

Transcription

1 The Implied Volailiy of Forward Saring Opion: ATM Shor-Time Level, Skew and Crvare Elia Alò Anoine Jacqier Jorge A León May 017 Barcelona GSE Working Paper Serie Working Paper nº 988

2 The implied volailiy of forward aring opion: ATM hor-ime level, kew and crvare Elia Alò Dp d Economia i Emprea Univeria Pompe Fabra and Barcelona GSE c/ramon Tria Farga, Barcelona, Spain Jorge A León Conrol Aomáico CINVESTAV-IPN Aparado Poal México, DF, Mexico Anoine Jacqier Deparmen of Mahemaic Imperial College London London SW7 AZ, UK Abrac For ochaic volailiy model, we dy he hor-ime behavior of he a-he-money implied volailiy level, kew and crvare for forwardaring opion Or analyi i baed on Malliavin Calcl echniqe Keyword: Forward aring opion, implied volailiy, Malliavin calcl, ochaic volailiy model JEL code: C0 1 Inrodcion Conider wo momen ime > A forward-ar call opion wih mariy T > allow he holder o receive, a ime and wih no addiional co, a call opion expirying a T, wih rike e eqal o KS, for ome K > 0 So, he opion life ar a, b he holder pay a ime he price of he opion Some claical applicaion of forward aring opion inclde employee ock opion and cliqe opion, among oher ee for example Rbinein 1991 Under he Black-Schole formla, a condiional expecaion argmen lead o how ha he price of a forward aring opion i he price of a plain vanilla Sppored by gran ECO P and MTM P MINECO/FEDER, UE Parially ppored by he CONACyT gran

3 opion wih ime o mariy T In he ochaic volailiy cae, a change-ofmeare link he price of he forward opion wih he price of a claical vanilla ee, for example, Rbinein 1991, Miela and Rkowki 1997, Wilmo 1998, or Zhang 1998 For ochaic volailiy model, change of nmeraire echniqe can be applied o obain a cloed-form pricing formla in he conex of he Heon model ee Kre and Nögel 005 The implied volailiy rface for forward aring opion exhibi banial difference o he claical vanilla cae ee for example Jacqier and Roome 015 Thi paper i devoed o he dy of he a-he-money ATM hor ime limi of he implied volailiy for forward aring opion More preciely, we will e Malliavin Calcl echniqe o compe he ATM hor-ime limi of he implied volailiy level, kew and crvare In pariclar, we will ee ha -conrary o he claical vanilla cae- he ATM hor-ime level depend on he correlaion parameer ee Lemma 6 and Theorem 7 We will alo prove ha he kew depend of he Malliavin derivaive of he volailiy proce in a imilar way a for vanilla opion, while he crvare ee Theorem 14 and 15 i of order OT The paper i organized a follow Secion i devoed o inrodce forward aring opion and he main noaion ed rogho he paper In Secion 3 we obain a decompoiion of he opion price ha will allow o compe, in Secion 4, 5 and 6, he limi for he ATM implied volailiy level, kew and crvare Forward ar opion We will conider he Heon model for ock price on a ime inerval 0, T nder a rik neral probabiliy P : ds = ˆrS d σ S ρdw 1 ρ B, 0, T, 1 where ˆr i he inananeo inere rae ppoed o be conan, W and B are independen andard Brownian moion defined on a probabiliy pace Ω, F, P and σ i a poiive and qare-inegrable proce adaped o he filraion generaed by W In he following we will denoe by F W and F B he filraion generaed by W and B, repecively Moreover we define F := F W F B I will be convenien in he following ecion o make he change of variable X = log S, 0, T Now, we conider a poin 0, T We wan o evalae he following opion price: V = e ˆrT E e X T e α e X, where E denoe he condiional expecaion given F and α i a real conan Noice ha if hi i he payoff of a call opion, while in he cae < hi define a forward ar opion We will make e of he following noaion

4 BS, x, K, σ will repreen he price of a Eropean call opion nder he claical Black-Schole model wih conan volailiy σ, crren log ock price x, ime o mariy T, rike price K and inere rae ˆr Remember ha in hi cae BS, x, K, σ = e x Nd e ˆrT KNd, where N i he cmlaive probabiliy fncion of he andard normal law and x ln K ˆrT d ± := σ ± σ T T L BS σ will denoe he Black-Schole differenial operaor in he log variable wih volailiy σ : L BS σ = 1 σ ˆr 1 σ ˆr I i well known ha L BS σ BS, ; K, σ = 0 G, x, K, σ := xx x BS, x, K, σ H, x, K, σ := x xx x BS, x, K, σ BS 1 a := BS 1, x, K, a denoe he invere of BS a a fncion of he volailiy parameer α := ˆrT We recall he following rel, ha can be proved following he ame argmen a in ha of Lemma 41 in Alò, León and Vive 007 Lemma 1 Le 0, < T and G := F FT W here exi C = Cn, ρ ch ha Then for every n 0, a If E n G, X, M, v G CE e X G σd b If E n G, X, M, v G CE e X G σd 1 n1 1 n1 3

5 3 A decompoiion formla for forward opion price We will and for M := E e α e X, 0, T We oberve ha M = e α eˆr e X0 v := 0 0 σ 1 0, e α eˆr e X ρdw 1 ρ db = M 0 e α σ eˆr e ρdw X 1 ρ db Y T 1, wih Y := σ 1,T d = Noice ha, if <, v T = v T σ d We will need he following hypohee: H1 There exi wo conan 0 < c < C ch ha c < σ < C, for all 0, T, wih probabiliy one H σ, σe X L, L 1,4 Now we are in a poiion o prove he following heorem, ha allow o idenify he impac of correlaion in he forward opion price Theorem Conider he model 1 and ame ha hypohee H1 and H hold Then, for all 0 T, V = E expx BS, 0, e α, v where Λ W := ρ DW e ˆr H, X, M, v σ Λ W ρ G, 0, eα, v σ θ dθ d e ˆr e X σ Λ W Proof Remember ha M := E e α e X and noice ha V = e ˆrT E e X T e α e X = e ˆrT E e X T M T = e ˆrT BST, X T, M T, ν T 4

6 Therefore, he anicipaing Iô formla for he Skorohod inegral ee for example Nalar 006 allow o wrie e ˆrT BST, X T, M T, v T E = E e ˆr BS, X, M, v ˆr 1 ˆr e ˆr e, X, M, v d, X, M, v e ˆr BS, X, M, v d ˆr σ d e ˆr BS, X, M, v σd e ˆr BS K, X, M, v d X, M e ˆr BS K, X, M, v d M, M e ˆr e ˆr K e ˆr BS, X, M, v v σ 1,T d BS, X, M, v σ ρλ W BS, X, M, v σ e α eˆr e X ρλ W 1 0, d 5

7 Tha i, ing, V = E BS, X, M, v e ˆr L BS v BS, X, M, v d e ˆr BS, X, M, v σ v d e ˆr BS K, X, M, v d X, M e ˆr BS K, X, M, v d M, M e ˆr e ˆr e ˆr K Th, we ge, for, V = E BS, X, M, v e ˆr L BS v BS, X, M, v d BS, X, M, v v σ 1,T d BS, X, M, v σ ρλ W BS, X, M, v σ e α eˆr e X ρλ W e ˆr BS, X, M, v σ σ 1,T d e ˆr BS K, X, M, v d X, M e ˆr BS K, X, M, v d M, M e ˆr e ˆr K Now, aking ino accon he fac ha L BS v BS, X, M, v = 0, BS, X, M, v σ ρλ W d M, X = σ e α eˆr e X 1 0, d, BS, X, M, v σ e α eˆr e X ρλ W 6

8 and d M, M = σe α e ˆr e X 1 0, d, BS K, x, K, σ = 1 BS K, x, K, σ BS 1 BS, x, K, σ = K K, x, K, σ, i follow ha V = E BS, X, M, v 1 e ˆr 1 K and = E e ˆr BS, X, M, v Since we have BS e ˆr e ˆr e ˆr K BS, X, M, v σ ρλ W BS, X, M, v σ e α eˆr e X ρλ W BS, X, M, v σ ρλ W BS, X, M, v σ ρλ W BS, X, M, v σ M ρλ W, x, k, σ = ex N d σ 1 T d σ T BS K, x, k, σ = ex N d Kσ d T σ, T hen i i eay o ee ha V = E BS, X, M, v ρ ρ e ˆr H, X, M, v σ Λ W d e ˆr G, X, M, v σ Λ W 7

9 Hence, de o and, for all <, BS, X, M, v α ˆrT = expx N v T v T α ˆrT e α expx e ˆrT N v T v T = expx BS, 0, e α, v he proof i complee e X N αˆrt v T G, X, M, v = v T = e X G, 0, e α, v, v T Remark 3 We have choen Hypohee H1 and H for he ake of impliciy, which can be bied by adeqae inegrabiliy condiion Remark 4 Noice ha, if = we recover he decompoiion formla for call opion price preened in Alò, León and Vive 007 Remark 5 If he volailiy proce i conan σ = σ, for ome poiive conan σ, hen v = σ and Λ W 0, which implie ha, for, V = expx BS, 0, e α, σ 3 Coneqenly, we recover he well-known opion pricing formla for forward ar opion nder he Black-Schole model ee for example Rbinein 1991, Wilmo 1998, or Zhang The ATM hor-ime limi of he implied volailiy For 0,, we define he implied volailiy I, ; α a he F -adaped proce aifying V = expx BS, 0, e α, I, ; α 4 Noice ha, from 3, in he conan volailiy cae σ = σ, I, ; α = σ For he ake of impliciy, we will conider fir he ncorrelaed cae ρ = 0 In hi cae, we have he following rel 8

10 Lemma 6 Conider he model 1 wih ρ = 0 and ame ha hypohee in Theorem hold Then, for all <, Proof We know ha I, ; α lim T I, ; α = E σ = BS 1 E BS, 0, expα, v = E BS 1 BS, 0, expα, v BS 1 E BS, 0, expα, v BS 1 BS, 0, expα, v = E v BS 1 E BS, 0, expα, v BS 1 BS, 0, expα, v Noice ha a direc applicaion of Clark-Ocone formla give ha wih BS, 0, expα, v = E BS, 0, expα, v U r dw r, D U = E W T, 0, expα, v σ rdr 5 T v Then, applying Iô formla o he proce BS 1 A, where A := E BS, 0, expα, v U r dw r and aking expecaion, we ge BS 1 E BS, 0, expα, v BS 1 BS, 0, expα, v Hence E = 1 E lim I,, T α = E σ 1 lim T E Now, conidering ha BS 1 E r BS, 0, expα, v U r dr BS 1 E r BS, 0, expα, v U r dr BS 1 E r BS, 0, expα, v 1 BS 1 = N d 1 T E r BS, 0, expα, v T, 4 9

11 where d 1 := BS 1 E rbs,0,expα,v T, we ge ha 1 lim T E BS 1 E r BS, 0, expα, v U r dr = 0 Therefore, he proof i complee A a coneqence of he previo rel, we have he following limi in he correlaed cae Theorem 7 Conider he model 1 and ame ha hypohee in Theorem hold Then, for all <, lim I, ; T α = E σ ρ 1 e X E e ˆr e X σ D W σd Proof We know, by Theorem, ha I, ; α = BS 1 E BS, 0, expα, v ρ exp X E ρ exp X E σ e ˆr H, X, M, v σ Λ W G, 0, expα, v e ˆr e X σ Λ W By he mean vale heorem, we can find θ beween E BS, 0, expα, v and BS, 0, expα, v E ch ha ρ exp X ρ exp X G, 0, expα, v e ˆr H, X, M, v σ Λ W d e ˆr e X σ Λ W I, ; α BS 1 E BS, 0, expα, v π exp BS 1 θ 8 T ρ = T exp X T E e ˆr H, X, M, v σ Λ W ρ E G, 0, expα, v e ˆr e X σ Λ W = I 1 I 6 10

12 From Lemma 1, we have lim I exp X T 1 C lim T T T Finally, 6 and Lemma 6 imply lim T I, ; α = E σ ρ expx = E σ ρ expx E E e X G T 1 Λ W d C exp X lim T T 1/ E e X G 1/ = 0 lim T E 1 Therefore, he proof i complee σ v exp T 8 v T e ˆr e X σ D W σd e ˆr e X σ Λ W Remark 8 Noice ha, in he cae =, we recover he rel by Drrleman 008 for claical vanilla opion Alo, conrary o he claical vanilla cae, he ATM hor-ime limi depend on he correlaion parameer We will need he following lemma laer on Lemma 9 Conider he model 1 and ame ha E exp a T I, ; α T 0 0 σ < Then Proof We know ha, in hi cae, V = e ˆrT E e X T eˆrt e X, which converge o zero a T Indeed, we have e X T eˆrt e X 0, a T wih probabiliy 1 Moreover, by Novikov heorem ee for example Karaza and Shreve 1991, we have e X T eˆrt e X e X T eˆrt e X and E e X T eˆrt e X = eˆrt eˆr 11

13 a T Th, he dominaed convergence heorem implie ha V 0 a T, wih probabiliy one On he oher hand, V = expx N I, ; α T = expx 1 N I, ; α T which allow o complee he proof N I, ; α T, 5 An expreion for he derivaive of he implied volailiy Eqaion 4 lead o ge where k V α = expx k, 0, eα, I, ; α expx := ln K Then, from Theorem we are able o wrie = = E, 0, eα, I, ; α I, ; α, α I, ; α α V α expx k, 0, eα, I, ; α expx, 0, eα, I, ; α k E, 0, eα, v, 0, eα, I, ; α H e ˆr k, 0, eα, I, ; α k, X, e α e X, v σ Λ W ρ expx, 0, eα, I, ; α ρ E G k, 0, eα, v ex e ˆr σ Λ W expx, 0, eα, I, ; α d 7 Remark 10 Noe ha in he ncorrelaed cae ie, ρ = 0, eqaliy 7 implie I α, ; α = E k, 0, eα, v k, 0, eα, I, ; α, 0, eα, I, ; α 1

14 In hi cae, for α = α, we ge, by Theorem, E k, 0, expα, v = E N v T = E N v T N BS, 0, expα = E, v 1 and = V exp X 1 So, we conclde ha E k, 0, expα, v and, coneqenly, I α, ; α = 0 v T k, 0, expα, I, ; α = N I, ; α T = V exp X 1 1 k, 0, expα, I, ; α = 0 We will need he following hypohei H3 There exi wo conan C > 0 and δ 0 ch ha, for all θ < < r E D W σ r Cr δ 8 and E D W θ D W σ r C r δ r θ δ 9 Theorem 11 Conider he model 1, and ame ha hypohee H1, H and H3 hold and ha here exi a F -mearable random variable D σ ch ha E E D W σθ D σ 4 0 a T 10 Then, for all <, p θ T I lim T α, ; α = ρ exp ˆr E e X D σ 4 expx σ 13

15 Proof From 7 we obain I α, ; α = E k, 0, expα, v k, 0, expα, I, ; α, 0, expα, I, ; α ρ E ρ E = T 1 T T 3 H e ˆr k, X, M, v σ Λ W d expx, 0, expα, I, ; α G k, 0, expα, v ex e ˆr σ Λ W expx, 0, expα, I, ; α Proceeding a in Remark 10 and ing Theorem,we ge E k, 0, expα, v k, 0, expα, I, ; α BS, 0, expα = E, v 1 V exp X 1 { = exp X E ρ T e ˆr H, X, M, v σ Λ W α=α Then, where and exp X ρ G, 0, expα, v 4 exp X ρe I 1 = lim T 1 = lim I 1 lim I, T T T } e ˆr σ e X Λ W e ˆr H, X, M, v σ Λ W α=α 4, 0, expα, I, ; α I = exp X ρe G, 0, expα, v e ˆr σ e X Λ W 4, 0, expα, I, ; α I i eay o ee ha, nder H3, lim T I 1 = 0 de o Lemma 1 On he oher hand, G, 0, expα, v = G k, 0, expα, v, which yield I = ρ exp X E G k, 0, expα, v e ˆr e X σ Λ W, 0, expα, I, ; α = T 3 14

16 Thi give ha I T 3 = 0 On he oher hand, ing Lemma 1 and 9, and he anicipaing Iô formla again, i follow ha ρ T E H e ˆr k, X, M, v σ Λ W lim T = lim T T = ρ E lim T = ρ lim T E = expx, 0, expα, I, ; α H k, X, M, v e ˆr T ρ expx lim T E Now, 10 allow o wrie lim T = T and now he proof i complee σ Λ W expx, 0, expα, I, ; α exp X exp ˆr T expx vt 3 σ Λ W exp X exp ˆr T σt 3 ρ exp ˆr E 4 expx D σ σ, σ Λ W Remark 1 If =, hi formla agree wih he a-he-money hor-ime limi kew proved in Alò, Leòn and Vive The econd derivaive of he implied volailiy In hi ecion we figre o I α, ; α Toward hi end, noe ha implie V α = expx, 0, expα, I, ; α k expx I, 0, expα, I, ; α, ; α α V α = expx BS k, 0, expα, I, ; α expx BS I, 0, expα, I, ; α, ; α k α expx BS I, 0, expα, I, ; α, ; α α expx, 0, expα, I, ; α I, ; α 11 α 15

17 61 The ncorrelaed cae In hi becion we ame ha ρ = 0 We will need he following rel, imilar o Theorem 5 in Alò and León 015 Lemma 13 Conider he model 1 wih ρ = 0 and ame ha hypohee in Theorem 11 hold Then where, 0, expα, I, ; α I α, ; α = 1 T Ψ E a E BS, 0, expα, v Ud, Ψa := BS k, 0, expα, BS 1 a and U i given in 5 Proof Thi proof i imilar o ha in Alò and León 015 Theorem 5, o we only kech i Noice ha 11 and Remark 10 give V α α=α = expx BS k, 0, expα, I, ; α expx, 0, expα, I, ; α I α, ; α Then, aking ino accon Theorem and he fac ha I, ; α = BS 1 exp X V, we are able o wrie expx, 0, expα, I, ; α I α, ; α = V α α=α expx BS k, 0, expα, I, ; α = expx E BS k, 0, expα, v BS k, 0, expα, I, ; α = expx E BS, 0, expα k, BS 1 BS, 0, expα, v BS, 0, expα k, BS 1 E BS, 0, expα, v Now, ing 4, applying Iô formla o he proce A := BS k, 0, expα, BS 1 E BS, 0, expα, v and aking expecaion, he rel follow 16

18 Theorem 14 Conider he model 1 wih ρ = 0 and ame ha hypohee in Theorem are aified Then lim T I T α, ; α = 1 D 4 E E W σ E σ 3 d Proof Lemma 13 yield lim T I T α, ; α = lim T T lim T T E = lim T T 1 T E σ Ψ a E BS, 0, expα, v Ud, 0, expα, I, ; α Ψ a E BS, 0, α, v Ud, 0, expα, I, ; α Remember ha we are aming ha here exi wo poiive conan c, C > 0 ch ha c σ C Th, he fac ha BS, 0, eˆrt, i an increaing fncion, ogeher wih H3 and Ψ a E BS, 0, α, v BS π exp 1 E BS,0,eˆrT,v T 8 = BS 1 E BS, 0, eˆrt, v 3, T 3/ allow o dedce ha, conidering ha C i a conan ha may change from line o line, T CT 0 < T CE exp 8 c 3 U C T T d E U T CT 1/ which implie ha lim T T = 0 Finally, he dominaed convergence heorem and Lemma 6 give ha lim T 1 = π lim T T = 1 4 lim T E = 1 4 E exp I,;α T 8 I, ; α 3 T E 1 I, ; α 3 T D W σr E σ U d E E σ 3 d DW σrdr d v T 17

19 and hi allow o complee he proof 6 General cae Now we are ready o analyze he crvare in he correlaed cae So we ame ρ 0 Theorem 15 Under he ampion of Theorem, we have lim T I T α, ; α = 1 D 4 E E W σ σ r E σ 3 d 1 E σ 1 E σ ρ exp X E 1 σ e ˆr e X σ D W σd ρ 1 exp X E e ˆr e X σ D W d Proof By Theorem and 11, we dedce σ 3 expx, 0, expα, I, ; α I, ; α α = expx E BS k, 0, expα, v BS k σ, 0, expα, I, ; α expx BS k, 0, expα, I, ; α I, ; α α expx BS I, 0, expα, I, ; α α, ; α ρ T E e ˆr H k, X, M, v σ Λ W α=α G k, 0, expα, v e ˆr e X σ Λ W 1 Th we can wrie 18

20 T I α, ; α E = T E T BS k, 0, expα, v BS k, 0, expα, I 0, ; α BS k, 0, expα, I, ; α, 0, expα, I 0, ; α BS k, 0, expα, I, ; α, 0, expα, I, ; α T BS k, 0, expα, I, ; α I α, ; α, 0, expα, I, ; α BS T, 0, expα, I, ; α I α, ; α, 0, expα, I, ; α ρ exp X E e ˆr H k, X T, M, v σ Λ W, 0, expα, I, ; α α=α ρ exp X E G k, 0, expα, v T e ˆr e X σ Λ W, 0, expα, I, ; α = T 1 T T 3 T 4 T 5 T 6 Here I 0, ; α denoe he implied volailiy in he ncorrelaed cae ρ = 0 I i eay o ee e definiion of BS lead o lim T T 3 T 4 T 5 = 0 Moreover, we can eaily ee ha lim T Then, he proof of Lemma 13 yield By comping BS k, 0, expα, I, ; α, 0, expα, I 0, ; α = 1 lim T 1 = lim T I 0,, α T T k i i eay o ee ha lim T = lim T T 1 I 0, ; α 1 I, ; α Therefore, we can e Lemma 6 and Theorem 7 o figre o hi limi On he oher hand lim T T 6 = ρ E G exp X k, 0, expα, v lim T e ˆr e X σ Λ W T, 0, expα, I, ; α = ρ 1 exp X E e ˆr e X σ D W σ d σ 3 19 d

21 Finally, he rel i a coneqence of Lemma 6, and Theorem 7 and 14 Aknowledgemen Elia Alò acknowledge nancial ppor from he Spanih Miniry of Economy and Compeiivene, hrogh he Severo Ochoa Programme for Cenre of Excellence in R&D SEV Jorge A León hank Univeria Barcelona, Univeria Pompe Fabra and he Cenre de Recerca Maemàica of Univeria Aònoma de Barcelona for heir hopialiy and economical ppor Reference 1 Alò, E, León, JA: On he econd derivaive of he a-he-money implied volailiy in ochaic volailiy model Working paper UPF, 015 Alò, E, León, JA and Vive, J: On he hor-ime behavior of he implied volailiy for jmp-diffion model wih ochaic volailiy Finance and Sochaic 11, , Drrleman, V Convergence of a-he-money implied volailiie o he po volailiy Jornal of Applied Probabiliy 45 0: , Hll, J C and Whie, A: The pricing of opion on ae wih ochaic volailiie Jornal of Finance 4, , Jacqier, Aand Roome, P: Aympoic of forward implied volailiy SIAM Jornal on Financial Mahemaic, 61, , Kre, S and Nögel, U: On he pricing of forward aring opion in Heon model on ochaic volailiy Finance and Sochaic 9, 33-50, Miela, M, Rkowki, M: Maringale mehod in financial modeling Berlin, Heidelberg, New York: Springer Nalar, D: The Malliavin Calcl and Relaed Topic Second Ediion Probabiliy and i Applicaion Springer-Verlag, Rbinein, M: Pay now, chooe laer Rik 4,

On the goodness of t of Kirk s formula for spread option prices

On the goodness of t of Kirk s formula for spread option prices On he goodne of of Kirk formula for pread opion price Elia Alò Dp. d Economia i Emprea Univeria Pompeu Fabra c/ramon ria Farga, 5-7 08005 Barcelona, Spain Jorge A. León y Conrol Auomáico CINVESAV-IPN Aparado

More information

An Introduction to Backward Stochastic Differential Equations (BSDEs) PIMS Summer School 2016 in Mathematical Finance.

An Introduction to Backward Stochastic Differential Equations (BSDEs) PIMS Summer School 2016 in Mathematical Finance. 1 An Inroducion o Backward Sochasic Differenial Equaions (BSDEs) PIMS Summer School 2016 in Mahemaical Finance June 25, 2016 Chrisoph Frei cfrei@ualbera.ca This inroducion is based on Touzi [14], Bouchard

More information

An Introduction to Malliavin calculus and its applications

An Introduction to Malliavin calculus and its applications An Inroducion o Malliavin calculus and is applicaions Lecure 5: Smoohness of he densiy and Hörmander s heorem David Nualar Deparmen of Mahemaics Kansas Universiy Universiy of Wyoming Summer School 214

More information

Explicit form of global solution to stochastic logistic differential equation and related topics

Explicit form of global solution to stochastic logistic differential equation and related topics SAISICS, OPIMIZAION AND INFOMAION COMPUING Sa., Opim. Inf. Compu., Vol. 5, March 17, pp 58 64. Publihed online in Inernaional Academic Pre (www.iapre.org) Explici form of global oluion o ochaic logiic

More information

On Line Supplement to Strategic Customers in a Transportation Station When is it Optimal to Wait? A. Manou, A. Economou, and F.

On Line Supplement to Strategic Customers in a Transportation Station When is it Optimal to Wait? A. Manou, A. Economou, and F. On Line Spplemen o Sraegic Comer in a Tranporaion Saion When i i Opimal o Wai? A. Mano, A. Economo, and F. Karaemen 11. Appendix In hi Appendix, we provide ome echnical analic proof for he main rel of

More information

Fractional Ornstein-Uhlenbeck Bridge

Fractional Ornstein-Uhlenbeck Bridge WDS'1 Proceeding of Conribued Paper, Par I, 21 26, 21. ISBN 978-8-7378-139-2 MATFYZPRESS Fracional Ornein-Uhlenbeck Bridge J. Janák Charle Univeriy, Faculy of Mahemaic and Phyic, Prague, Czech Republic.

More information

Generalized Orlicz Spaces and Wasserstein Distances for Convex-Concave Scale Functions

Generalized Orlicz Spaces and Wasserstein Distances for Convex-Concave Scale Functions Generalized Orlicz Space and Waerein Diance for Convex-Concave Scale Funcion Karl-Theodor Surm Abrac Given a ricly increaing, coninuou funcion ϑ : R + R +, baed on he co funcional ϑ (d(x, y dq(x, y, we

More information

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max ecure 8 7. Sabiliy Analyi For an n dimenional vecor R n, he and he vecor norm are defined a: = T = i n i (7.) I i eay o how ha hee wo norm aify he following relaion: n (7.) If a vecor i ime-dependen, hen

More information

Utility maximization in incomplete markets

Utility maximization in incomplete markets Uiliy maximizaion in incomplee markes Marcel Ladkau 27.1.29 Conens 1 Inroducion and general seings 2 1.1 Marke model....................................... 2 1.2 Trading sraegy.....................................

More information

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2)

Laplace Transform. Inverse Laplace Transform. e st f(t)dt. (2) Laplace Tranform Maoud Malek The Laplace ranform i an inegral ranform named in honor of mahemaician and aronomer Pierre-Simon Laplace, who ued he ranform in hi work on probabiliy heory. I i a powerful

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

Cash Flow Valuation Mode Lin Discrete Time

Cash Flow Valuation Mode Lin Discrete Time IOSR Journal of Mahemaics (IOSR-JM) e-issn: 2278-5728,p-ISSN: 2319-765X, 6, Issue 6 (May. - Jun. 2013), PP 35-41 Cash Flow Valuaion Mode Lin Discree Time Olayiwola. M. A. and Oni, N. O. Deparmen of Mahemaics

More information

Rough Paths and its Applications in Machine Learning

Rough Paths and its Applications in Machine Learning Pah ignaure Machine learning applicaion Rough Pah and i Applicaion in Machine Learning July 20, 2017 Rough Pah and i Applicaion in Machine Learning Pah ignaure Machine learning applicaion Hiory and moivaion

More information

Introduction to SLE Lecture Notes

Introduction to SLE Lecture Notes Inroducion o SLE Lecure Noe May 13, 16 - The goal of hi ecion i o find a ufficien condiion of λ for he hull K o be generaed by a imple cure. I urn ou if λ 1 < 4 hen K i generaed by a imple curve. We will

More information

Mathematische Annalen

Mathematische Annalen Mah. Ann. 39, 33 339 (997) Mahemaiche Annalen c Springer-Verlag 997 Inegraion by par in loop pace Elon P. Hu Deparmen of Mahemaic, Norhweern Univeriy, Evanon, IL 628, USA (e-mail: elon@@mah.nwu.edu) Received:

More information

ESTIMATES FOR THE DERIVATIVE OF DIFFUSION SEMIGROUPS

ESTIMATES FOR THE DERIVATIVE OF DIFFUSION SEMIGROUPS Elec. Comm. in Probab. 3 (998) 65 74 ELECTRONIC COMMUNICATIONS in PROBABILITY ESTIMATES FOR THE DERIVATIVE OF DIFFUSION SEMIGROUPS L.A. RINCON Deparmen of Mahemaic Univeriy of Wale Swanea Singleon Par

More information

ON JENSEN S INEQUALITY FOR g-expectation

ON JENSEN S INEQUALITY FOR g-expectation Chin. Ann. Mah. 25B:3(2004),401 412. ON JENSEN S INEQUALITY FOR g-expectation JIANG Long CHEN Zengjing Absrac Briand e al. gave a conerexample showing ha given g, Jensen s ineqaliy for g-expecaion sally

More information

6.8 Laplace Transform: General Formulas

6.8 Laplace Transform: General Formulas 48 HAP. 6 Laplace Tranform 6.8 Laplace Tranform: General Formula Formula Name, ommen Sec. F() l{ f ()} e f () d f () l {F()} Definiion of Tranform Invere Tranform 6. l{af () bg()} al{f ()} bl{g()} Lineariy

More information

arxiv: v1 [math.pr] 21 May 2010

arxiv: v1 [math.pr] 21 May 2010 ON SCHRÖDINGER S EQUATION, 3-DIMENSIONAL BESSEL BRIDGES, AND PASSAGE TIME PROBLEMS arxiv:15.498v1 [mah.pr 21 May 21 GERARDO HERNÁNDEZ-DEL-VALLE Absrac. In his work we relae he densiy of he firs-passage

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER John Riley 6 December 200 NWER TO ODD NUMBERED EXERCIE IN CHPTER 7 ecion 7 Exercie 7-: m m uppoe ˆ, m=,, M (a For M = 2, i i eay o how ha I implie I From I, for any probabiliy vecor ( p, p 2, 2 2 ˆ ( p,

More information

On Delayed Logistic Equation Driven by Fractional Brownian Motion

On Delayed Logistic Equation Driven by Fractional Brownian Motion On Delayed Logiic Equaion Driven by Fracional Brownian Moion Nguyen Tien Dung Deparmen of Mahemaic, FPT Univeriy No 8 Ton Tha Thuye, Cau Giay, Hanoi, 84 Vienam Email: dungn@fp.edu.vn ABSTRACT In hi paper

More information

Stability in Distribution for Backward Uncertain Differential Equation

Stability in Distribution for Backward Uncertain Differential Equation Sabiliy in Diribuion for Backward Uncerain Differenial Equaion Yuhong Sheng 1, Dan A. Ralecu 2 1. College of Mahemaical and Syem Science, Xinjiang Univeriy, Urumqi 8346, China heng-yh12@mail.inghua.edu.cn

More information

FIXED POINTS AND STABILITY IN NEUTRAL DIFFERENTIAL EQUATIONS WITH VARIABLE DELAYS

FIXED POINTS AND STABILITY IN NEUTRAL DIFFERENTIAL EQUATIONS WITH VARIABLE DELAYS PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 136, Number 3, March 28, Page 99 918 S 2-9939(7)989-2 Aricle elecronically publihed on November 3, 27 FIXED POINTS AND STABILITY IN NEUTRAL DIFFERENTIAL

More information

Stochastic Modelling in Finance - Solutions to sheet 8

Stochastic Modelling in Finance - Solutions to sheet 8 Sochasic Modelling in Finance - Soluions o shee 8 8.1 The price of a defaulable asse can be modeled as ds S = µ d + σ dw dn where µ, σ are consans, (W ) is a sandard Brownian moion and (N ) is a one jump

More information

LECTURE 1: GENERALIZED RAY KNIGHT THEOREM FOR FINITE MARKOV CHAINS

LECTURE 1: GENERALIZED RAY KNIGHT THEOREM FOR FINITE MARKOV CHAINS LECTURE : GENERALIZED RAY KNIGHT THEOREM FOR FINITE MARKOV CHAINS We will work wih a coninuous ime reversible Markov chain X on a finie conneced sae space, wih generaor Lf(x = y q x,yf(y. (Recall ha q

More information

Flow networks. Flow Networks. A flow on a network. Flow networks. The maximum-flow problem. Introduction to Algorithms, Lecture 22 December 5, 2001

Flow networks. Flow Networks. A flow on a network. Flow networks. The maximum-flow problem. Introduction to Algorithms, Lecture 22 December 5, 2001 CS 545 Flow Nework lon Efra Slide courey of Charle Leieron wih mall change by Carola Wenk Flow nework Definiion. flow nework i a direced graph G = (V, E) wih wo diinguihed verice: a ource and a ink. Each

More information

Undetermined coefficients for local fractional differential equations

Undetermined coefficients for local fractional differential equations Available online a www.isr-publicaions.com/jmcs J. Mah. Compuer Sci. 16 (2016), 140 146 Research Aricle Undeermined coefficiens for local fracional differenial equaions Roshdi Khalil a,, Mohammed Al Horani

More information

U T,0. t = X t t T X T. (1)

U T,0. t = X t t T X T. (1) Gauian bridge Dario Gabarra 1, ommi Soinen 2, and Eko Valkeila 3 1 Deparmen of Mahemaic and Saiic, PO Box 68, 14 Univeriy of Helinki,Finland dariogabarra@rnihelinkifi 2 Deparmen of Mahemaic and Saiic,

More information

f(s)dw Solution 1. Approximate f by piece-wise constant left-continuous non-random functions f n such that (f(s) f n (s)) 2 ds 0.

f(s)dw Solution 1. Approximate f by piece-wise constant left-continuous non-random functions f n such that (f(s) f n (s)) 2 ds 0. Advanced Financial Models Example shee 3 - Michaelmas 217 Michael Tehranchi Problem 1. Le f : [, R be a coninuous (non-random funcion and W a Brownian moion, and le σ 2 = f(s 2 ds and assume σ 2

More information

Randomized Perfect Bipartite Matching

Randomized Perfect Bipartite Matching Inenive Algorihm Lecure 24 Randomized Perfec Biparie Maching Lecurer: Daniel A. Spielman April 9, 208 24. Inroducion We explain a randomized algorihm by Ahih Goel, Michael Kapralov and Sanjeev Khanna for

More information

Discussion Session 2 Constant Acceleration/Relative Motion Week 03

Discussion Session 2 Constant Acceleration/Relative Motion Week 03 PHYS 100 Dicuion Seion Conan Acceleraion/Relaive Moion Week 03 The Plan Today you will work wih your group explore he idea of reference frame (i.e. relaive moion) and moion wih conan acceleraion. You ll

More information

arxiv: v1 [math.pr] 19 Feb 2011

arxiv: v1 [math.pr] 19 Feb 2011 A NOTE ON FELLER SEMIGROUPS AND RESOLVENTS VADIM KOSTRYKIN, JÜRGEN POTTHOFF, AND ROBERT SCHRADER ABSTRACT. Various equivalen condiions for a semigroup or a resolven generaed by a Markov process o be of

More information

EE Control Systems LECTURE 2

EE Control Systems LECTURE 2 Copyrigh F.L. Lewi 999 All righ reerved EE 434 - Conrol Syem LECTURE REVIEW OF LAPLACE TRANSFORM LAPLACE TRANSFORM The Laplace ranform i very ueful in analyi and deign for yem ha are linear and ime-invarian

More information

On a Fractional Stochastic Landau-Ginzburg Equation

On a Fractional Stochastic Landau-Ginzburg Equation Applied Mahemaical Sciences, Vol. 4, 1, no. 7, 317-35 On a Fracional Sochasic Landau-Ginzburg Equaion Nguyen Tien Dung Deparmen of Mahemaics, FPT Universiy 15B Pham Hung Sree, Hanoi, Vienam dungn@fp.edu.vn

More information

Loss of martingality in asset price models with lognormal stochastic volatility

Loss of martingality in asset price models with lognormal stochastic volatility Loss of maringaliy in asse price models wih lognormal sochasic volailiy BJourdain July 7, 4 Absrac In his noe, we prove ha in asse price models wih lognormal sochasic volailiy, when he correlaion coefficien

More information

Macroeconomics 1. Ali Shourideh. Final Exam

Macroeconomics 1. Ali Shourideh. Final Exam 4780 - Macroeconomic 1 Ali Shourideh Final Exam Problem 1. A Model of On-he-Job Search Conider he following verion of he McCall earch model ha allow for on-he-job-earch. In paricular, uppoe ha ime i coninuou

More information

Research Article G-Doob-Meyer Decomposition and Its Applications in Bid-Ask Pricing for Derivatives under Knightian Uncertainty

Research Article G-Doob-Meyer Decomposition and Its Applications in Bid-Ask Pricing for Derivatives under Knightian Uncertainty Hindawi Pblihing Corporaion Applied Mahemaic Volme 215, Aricle ID 9189, 13 page hp://dx.doi.org/1.1155/215/9189 Reearch Aricle G-Doob-Meyer Decompoiion and I Applicaion in Bid-Ak Pricing for Derivaive

More information

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson 6.32 Feedback Syem Phae-locked loop are a foundaional building block for analog circui deign, paricularly for communicaion circui. They provide a good example yem for hi cla becaue hey are an excellen

More information

AMartingaleApproachforFractionalBrownian Motions and Related Path Dependent PDEs

AMartingaleApproachforFractionalBrownian Motions and Related Path Dependent PDEs AMaringaleApproachforFracionalBrownian Moions and Relaed Pah Dependen PDEs Jianfeng ZHANG Universiy of Souhern California Join work wih Frederi VIENS Mahemaical Finance, Probabiliy, and PDE Conference

More information

Physics 127b: Statistical Mechanics. Fokker-Planck Equation. Time Evolution

Physics 127b: Statistical Mechanics. Fokker-Planck Equation. Time Evolution Physics 7b: Saisical Mechanics Fokker-Planck Equaion The Langevin equaion approach o he evoluion of he velociy disribuion for he Brownian paricle migh leave you uncomforable. A more formal reamen of his

More information

Research Article Existence and Uniqueness of Solutions for a Class of Nonlinear Stochastic Differential Equations

Research Article Existence and Uniqueness of Solutions for a Class of Nonlinear Stochastic Differential Equations Hindawi Publihing Corporaion Abrac and Applied Analyi Volume 03, Aricle ID 56809, 7 page hp://dx.doi.org/0.55/03/56809 Reearch Aricle Exience and Uniquene of Soluion for a Cla of Nonlinear Sochaic Differenial

More information

Example on p. 157

Example on p. 157 Example 2.5.3. Le where BV [, 1] = Example 2.5.3. on p. 157 { g : [, 1] C g() =, g() = g( + ) [, 1), var (g) = sup g( j+1 ) g( j ) he supremum is aken over all he pariions of [, 1] (1) : = < 1 < < n =

More information

Approximation for Option Prices under Uncertain Volatility

Approximation for Option Prices under Uncertain Volatility Approximaion for Opion Price under Uncerain Volailiy Jean-Pierre Fouque Bin Ren February, 3 Abrac In hi paper, we udy he aympoic behavior of he wor cae cenario opion price a he volailiy inerval in an uncerain

More information

Centre for Computational Finance and Economic Agents WP Working Paper Series. Hengxu Wang, John G. O Hara, Nick Constantinou

Centre for Computational Finance and Economic Agents WP Working Paper Series. Hengxu Wang, John G. O Hara, Nick Constantinou Cenre for Compaional Finance and Economic Agens WP67-13 Working Paper Series Hengx Wang, John G. O Hara, Nick Consanino A pah-independen approach o inegraed variance nder he CEV model 13 www.ccfea.ne A

More information

6. Stochastic calculus with jump processes

6. Stochastic calculus with jump processes A) Trading sraegies (1/3) Marke wih d asses S = (S 1,, S d ) A rading sraegy can be modelled wih a vecor φ describing he quaniies invesed in each asse a each insan : φ = (φ 1,, φ d ) The value a of a porfolio

More information

CHAPTER 7. Definition and Properties. of Laplace Transforms

CHAPTER 7. Definition and Properties. of Laplace Transforms SERIES OF CLSS NOTES FOR 5-6 TO INTRODUCE LINER ND NONLINER PROBLEMS TO ENGINEERS, SCIENTISTS, ND PPLIED MTHEMTICINS DE CLSS NOTES COLLECTION OF HNDOUTS ON SCLR LINER ORDINRY DIFFERENTIL EQUTIONS (ODE")

More information

ū(e )(1 γ 5 )γ α v( ν e ) v( ν e )γ β (1 + γ 5 )u(e ) tr (1 γ 5 )γ α ( p ν m ν )γ β (1 + γ 5 )( p e + m e ).

ū(e )(1 γ 5 )γ α v( ν e ) v( ν e )γ β (1 + γ 5 )u(e ) tr (1 γ 5 )γ α ( p ν m ν )γ β (1 + γ 5 )( p e + m e ). PHY 396 K. Soluion for problem e #. Problem (a: A poin of noaion: In he oluion o problem, he indice µ, e, ν ν µ, and ν ν e denoe he paricle. For he Lorenz indice, I hall ue α, β, γ, δ, σ, and ρ, bu never

More information

SMT 2014 Calculus Test Solutions February 15, 2014 = 3 5 = 15.

SMT 2014 Calculus Test Solutions February 15, 2014 = 3 5 = 15. SMT Calculus Tes Soluions February 5,. Le f() = and le g() =. Compue f ()g (). Answer: 5 Soluion: We noe ha f () = and g () = 6. Then f ()g () =. Plugging in = we ge f ()g () = 6 = 3 5 = 5.. There is a

More information

PH2130 Mathematical Methods Lab 3. z x

PH2130 Mathematical Methods Lab 3. z x PH130 Mahemaical Mehods Lab 3 This scrip shold keep yo bsy for he ne wo weeks. Yo shold aim o creae a idy and well-srcred Mahemaica Noebook. Do inclde plenifl annoaions o show ha yo know wha yo are doing,

More information

Notes on cointegration of real interest rates and real exchange rates. ρ (2)

Notes on cointegration of real interest rates and real exchange rates. ρ (2) Noe on coinegraion of real inere rae and real exchange rae Charle ngel, Univeriy of Wiconin Le me ar wih he obervaion ha while he lieraure (mo prominenly Meee and Rogoff (988) and dion and Paul (993))

More information

Let. x y. denote a bivariate time series with zero mean.

Let. x y. denote a bivariate time series with zero mean. Linear Filer Le x y : T denoe a bivariae ime erie wih zero mean. Suppoe ha he ime erie {y : T} i conruced a follow: y a x The ime erie {y : T} i aid o be conruced from {x : T} by mean of a Linear Filer.

More information

Lecture #31, 32: The Ornstein-Uhlenbeck Process as a Model of Volatility

Lecture #31, 32: The Ornstein-Uhlenbeck Process as a Model of Volatility Saisics 441 (Fall 214) November 19, 21, 214 Prof Michael Kozdron Lecure #31, 32: The Ornsein-Uhlenbeck Process as a Model of Volailiy The Ornsein-Uhlenbeck process is a di usion process ha was inroduced

More information

CH Sean Han QF, NTHU, Taiwan BFS2010. (Joint work with T.-Y. Chen and W.-H. Liu)

CH Sean Han QF, NTHU, Taiwan BFS2010. (Joint work with T.-Y. Chen and W.-H. Liu) CH Sean Han QF, NTHU, Taiwan BFS2010 (Join work wih T.-Y. Chen and W.-H. Liu) Risk Managemen in Pracice: Value a Risk (VaR) / Condiional Value a Risk (CVaR) Volailiy Esimaion: Correced Fourier Transform

More information

Backward stochastic dynamics on a filtered probability space

Backward stochastic dynamics on a filtered probability space Backward sochasic dynamics on a filered probabiliy space Gechun Liang Oxford-Man Insiue, Universiy of Oxford based on join work wih Terry Lyons and Zhongmin Qian Page 1 of 15 gliang@oxford-man.ox.ac.uk

More information

2. VECTORS. R Vectors are denoted by bold-face characters such as R, V, etc. The magnitude of a vector, such as R, is denoted as R, R, V

2. VECTORS. R Vectors are denoted by bold-face characters such as R, V, etc. The magnitude of a vector, such as R, is denoted as R, R, V ME 352 VETS 2. VETS Vecor algebra form he mahemaical foundaion for kinemaic and dnamic. Geomer of moion i a he hear of boh he kinemaic and dnamic of mechanical em. Vecor anali i he imehonored ool for decribing

More information

NECESSARY AND SUFFICIENT CONDITIONS FOR LATENT SEPARABILITY

NECESSARY AND SUFFICIENT CONDITIONS FOR LATENT SEPARABILITY NECESSARY AND SUFFICIENT CONDITIONS FOR LATENT SEPARABILITY Ian Crawford THE INSTITUTE FOR FISCAL STUDIES DEPARTMENT OF ECONOMICS, UCL cemmap working paper CWP02/04 Neceary and Sufficien Condiion for Laen

More information

CHAPTER. Forced Equations and Systems { } ( ) ( ) 8.1 The Laplace Transform and Its Inverse. Transforms from the Definition.

CHAPTER. Forced Equations and Systems { } ( ) ( ) 8.1 The Laplace Transform and Its Inverse. Transforms from the Definition. CHAPTER 8 Forced Equaion and Syem 8 The aplace Tranform and I Invere Tranform from he Definiion 5 5 = b b {} 5 = 5e d = lim5 e = ( ) b {} = e d = lim e + e d b = (inegraion by par) = = = = b b ( ) ( )

More information

Chapter 6. Laplace Transforms

Chapter 6. Laplace Transforms 6- Chaper 6. Laplace Tranform 6.4 Shor Impule. Dirac Dela Funcion. Parial Fracion 6.5 Convoluion. Inegral Equaion 6.6 Differeniaion and Inegraion of Tranform 6.7 Syem of ODE 6.4 Shor Impule. Dirac Dela

More information

Lecture 4: Processes with independent increments

Lecture 4: Processes with independent increments Lecure 4: Processes wih independen incremens 1. A Wienner process 1.1 Definiion of a Wienner process 1.2 Reflecion principle 1.3 Exponenial Brownian moion 1.4 Exchange of measure (Girsanov heorem) 1.5

More information

Hedging strategy for unit-linked life insurance contracts in stochastic volatility models

Hedging strategy for unit-linked life insurance contracts in stochastic volatility models Hedging raegy for uni-linked life inurance conrac in ochaic volailiy model Wei Wang Ningbo Univeriy Deparmen of Mahemaic Feng Hua Sree 818, Ningbo Ciy China wangwei@nbu.edu.cn Linyi Qian Ea China Normal

More information

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship

To become more mathematically correct, Circuit equations are Algebraic Differential equations. from KVL, KCL from the constitutive relationship Laplace Tranform (Lin & DeCarlo: Ch 3) ENSC30 Elecric Circui II The Laplace ranform i an inegral ranformaion. I ranform: f ( ) F( ) ime variable complex variable From Euler > Lagrange > Laplace. Hence,

More information

Backward Stochastic Differential Equations and Applications in Finance

Backward Stochastic Differential Equations and Applications in Finance Backward Sochaic Differenial Equaion and Applicaion in Finance Ying Hu Augu 1, 213 1 Inroducion The aim of hi hor cae i o preen he baic heory of BSDE and o give ome applicaion in 2 differen domain: mahemaical

More information

Fractional Brownian Bridge Measures and Their Integration by Parts Formula

Fractional Brownian Bridge Measures and Their Integration by Parts Formula Journal of Mahemaical Reearch wih Applicaion Jul., 218, Vol. 38, No. 4, pp. 418 426 DOI:1.377/j.in:295-2651.218.4.9 Hp://jmre.lu.eu.cn Fracional Brownian Brige Meaure an Their Inegraion by Par Formula

More information

Research Article On Double Summability of Double Conjugate Fourier Series

Research Article On Double Summability of Double Conjugate Fourier Series Inernaional Journal of Mahemaic and Mahemaical Science Volume 22, Aricle ID 4592, 5 page doi:.55/22/4592 Reearch Aricle On Double Summabiliy of Double Conjugae Fourier Serie H. K. Nigam and Kuum Sharma

More information

Generalized Snell envelope and BSDE With Two general Reflecting Barriers

Generalized Snell envelope and BSDE With Two general Reflecting Barriers 1/22 Generalized Snell envelope and BSDE Wih Two general Reflecing Barriers EL HASSAN ESSAKY Cadi ayyad Universiy Poly-disciplinary Faculy Safi Work in progress wih : M. Hassani and Y. Ouknine Iasi, July

More information

On the Exponential Operator Functions on Time Scales

On the Exponential Operator Functions on Time Scales dvance in Dynamical Syem pplicaion ISSN 973-5321, Volume 7, Number 1, pp. 57 8 (212) hp://campu.m.edu/ada On he Exponenial Operaor Funcion on Time Scale laa E. Hamza Cairo Univeriy Deparmen of Mahemaic

More information

Ricci Curvature and Bochner Formulas for Martingales

Ricci Curvature and Bochner Formulas for Martingales Ricci Curvaure and Bochner Formula for Maringale Rober Halhofer and Aaron Naber Augu 15, 16 Abrac We generalize he claical Bochner formula for he hea flow on M o maringale on he pah pace, and develop a

More information

The Asymptotic Behavior of Nonoscillatory Solutions of Some Nonlinear Dynamic Equations on Time Scales

The Asymptotic Behavior of Nonoscillatory Solutions of Some Nonlinear Dynamic Equations on Time Scales Advances in Dynamical Sysems and Applicaions. ISSN 0973-5321 Volume 1 Number 1 (2006, pp. 103 112 c Research India Publicaions hp://www.ripublicaion.com/adsa.hm The Asympoic Behavior of Nonoscillaory Soluions

More information

Pricing and Hedging in Stochastic Volatility Regime Switching Models

Pricing and Hedging in Stochastic Volatility Regime Switching Models Jornal of Mahemaical Finance 3 3 7-8 hp://xoiorg/436/jmf336 Pblihe Online Febrary 3 (hp://wwwcirporg/jornal/jmf) Pricing an Heging in Sochaic Volailiy Regime Swiching Moel Séphane Goe Cenre Naional e la

More information

Pricing the American Option Using Itô s Formula and Optimal Stopping Theory

Pricing the American Option Using Itô s Formula and Optimal Stopping Theory U.U.D.M. Projec Repor 2014:3 Pricing he American Opion Uing Iô Formula and Opimal Sopping Theory Jona Bergröm Examenarbee i maemaik, 15 hp Handledare och examinaor: Erik Ekröm Januari 2014 Deparmen of

More information

Approximate Controllability of Fractional Stochastic Perturbed Control Systems Driven by Mixed Fractional Brownian Motion

Approximate Controllability of Fractional Stochastic Perturbed Control Systems Driven by Mixed Fractional Brownian Motion American Journal of Applied Mahemaic and Saiic, 15, Vol. 3, o. 4, 168-176 Available online a hp://pub.ciepub.com/ajam/3/4/7 Science and Educaion Publihing DOI:1.1691/ajam-3-4-7 Approximae Conrollabiliy

More information

Introduction to Congestion Games

Introduction to Congestion Games Algorihmic Game Theory, Summer 2017 Inroducion o Congeion Game Lecure 1 (5 page) Inrucor: Thoma Keelheim In hi lecure, we ge o know congeion game, which will be our running example for many concep in game

More information

What Ties Return Volatilities to Price Valuations and Fundamentals? On-Line Appendix

What Ties Return Volatilities to Price Valuations and Fundamentals? On-Line Appendix Wha Ties Reurn Volailiies o Price Valuaions and Fundamenals? On-Line Appendix Alexander David Haskayne School of Business, Universiy of Calgary Piero Veronesi Universiy of Chicago Booh School of Business,

More information

u(t) Figure 1. Open loop control system

u(t) Figure 1. Open loop control system Open loop conrol v cloed loop feedbac conrol The nex wo figure preen he rucure of open loop and feedbac conrol yem Figure how an open loop conrol yem whoe funcion i o caue he oupu y o follow he reference

More information

Lecture 10: The Poincaré Inequality in Euclidean space

Lecture 10: The Poincaré Inequality in Euclidean space Deparmens of Mahemaics Monana Sae Universiy Fall 215 Prof. Kevin Wildrick n inroducion o non-smooh analysis and geomery Lecure 1: The Poincaré Inequaliy in Euclidean space 1. Wha is he Poincaré inequaliy?

More information

Simulation of BSDEs and. Wiener Chaos Expansions

Simulation of BSDEs and. Wiener Chaos Expansions Simulaion of BSDEs and Wiener Chaos Expansions Philippe Briand Céline Labar LAMA UMR 5127, Universié de Savoie, France hp://www.lama.univ-savoie.fr/ Workshop on BSDEs Rennes, May 22-24, 213 Inroducion

More information

MALLIAVIN CALCULUS FOR BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS AND APPLICATION TO NUMERICAL SOLUTIONS

MALLIAVIN CALCULUS FOR BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS AND APPLICATION TO NUMERICAL SOLUTIONS The Annal of Applied Probabiliy 211, Vol. 21, No. 6, 2379 2423 DOI: 1.1214/11-AAP762 Iniue of Mahemaical Saiic, 211 MALLIAVIN CALCULUS FOR BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS AND APPLICATION TO

More information

18.03SC Unit 3 Practice Exam and Solutions

18.03SC Unit 3 Practice Exam and Solutions Sudy Guide on Sep, Dela, Convoluion, Laplace You can hink of he ep funcion u() a any nice mooh funcion which i for < a and for > a, where a i a poiive number which i much maller han any ime cale we care

More information

Curvature. Institute of Lifelong Learning, University of Delhi pg. 1

Curvature. Institute of Lifelong Learning, University of Delhi pg. 1 Dicipline Coure-I Semeer-I Paper: Calculu-I Leon: Leon Developer: Chaianya Kumar College/Deparmen: Deparmen of Mahemaic, Delhi College of r and Commerce, Univeriy of Delhi Iniue of Lifelong Learning, Univeriy

More information

Section 3.5 Nonhomogeneous Equations; Method of Undetermined Coefficients

Section 3.5 Nonhomogeneous Equations; Method of Undetermined Coefficients Secion 3.5 Nonhomogeneous Equaions; Mehod of Undeermined Coefficiens Key Terms/Ideas: Linear Differenial operaor Nonlinear operaor Second order homogeneous DE Second order nonhomogeneous DE Soluion o homogeneous

More information

4.2 Continuous-Time Systems and Processes Problem Definition Let the state variable representation of a linear system be

4.2 Continuous-Time Systems and Processes Problem Definition Let the state variable representation of a linear system be 4 COVARIANCE ROAGAION 41 Inrodcion Now ha we have compleed or review of linear sysems and random processes, we wan o eamine he performance of linear sysems ecied by random processes he sandard approach

More information

Math 10B: Mock Mid II. April 13, 2016

Math 10B: Mock Mid II. April 13, 2016 Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.

More information

Hamilton- J acobi Equation: Explicit Formulas In this lecture we try to apply the method of characteristics to the Hamilton-Jacobi equation: u t

Hamilton- J acobi Equation: Explicit Formulas In this lecture we try to apply the method of characteristics to the Hamilton-Jacobi equation: u t M ah 5 2 7 Fall 2 0 0 9 L ecure 1 0 O c. 7, 2 0 0 9 Hamilon- J acobi Equaion: Explici Formulas In his lecure we ry o apply he mehod of characerisics o he Hamilon-Jacobi equaion: u + H D u, x = 0 in R n

More information

FLAT CYCLOTOMIC POLYNOMIALS OF ORDER FOUR AND HIGHER

FLAT CYCLOTOMIC POLYNOMIALS OF ORDER FOUR AND HIGHER #A30 INTEGERS 10 (010), 357-363 FLAT CYCLOTOMIC POLYNOMIALS OF ORDER FOUR AND HIGHER Nahan Kaplan Deparmen of Mahemaic, Harvard Univeriy, Cambridge, MA nkaplan@mah.harvard.edu Received: 7/15/09, Revied:

More information

Parameter Estimation for Fractional Ornstein-Uhlenbeck Processes: Non-Ergodic Case

Parameter Estimation for Fractional Ornstein-Uhlenbeck Processes: Non-Ergodic Case Parameer Eimaion for Fracional Ornein-Uhlenbeck Procee: Non-Ergodic Cae R. Belfadli 1, K. E-Sebaiy and Y. Ouknine 3 1 Polydiciplinary Faculy of Taroudan, Univeriy Ibn Zohr, Taroudan, Morocco. Iniu de Mahémaique

More information

Chapter 6. Laplace Transforms

Chapter 6. Laplace Transforms Chaper 6. Laplace Tranform Kreyzig by YHLee;45; 6- An ODE i reduced o an algebraic problem by operaional calculu. The equaion i olved by algebraic manipulaion. The reul i ranformed back for he oluion of

More information

Transform Techniques. Moment Generating Function

Transform Techniques. Moment Generating Function Transform Techniques A convenien way of finding he momens of a random variable is he momen generaing funcion (MGF). Oher ransform echniques are characerisic funcion, z-ransform, and Laplace ransform. Momen

More information

International Journal of Scientific & Engineering Research, Volume 4, Issue 10, October ISSN

International Journal of Scientific & Engineering Research, Volume 4, Issue 10, October ISSN Inernaional Journal of Scienific & Engineering Research, Volume 4, Issue 10, Ocober-2013 900 FUZZY MEAN RESIDUAL LIFE ORDERING OF FUZZY RANDOM VARIABLES J. EARNEST LAZARUS PIRIYAKUMAR 1, A. YAMUNA 2 1.

More information

Applied Mathematics Letters. Oscillation results for fourth-order nonlinear dynamic equations

Applied Mathematics Letters. Oscillation results for fourth-order nonlinear dynamic equations Applied Mahemaics Leers 5 (0) 058 065 Conens liss available a SciVerse ScienceDirec Applied Mahemaics Leers jornal homepage: www.elsevier.com/locae/aml Oscillaion resls for forh-order nonlinear dynamic

More information

Linear Motion, Speed & Velocity

Linear Motion, Speed & Velocity Add Iporan Linear Moion, Speed & Velociy Page: 136 Linear Moion, Speed & Velociy NGSS Sandard: N/A MA Curriculu Fraework (2006): 1.1, 1.2 AP Phyic 1 Learning Objecive: 3.A.1.1, 3.A.1.3 Knowledge/Underanding

More information

Time Varying Multiserver Queues. W. A. Massey. Murray Hill, NJ Abstract

Time Varying Multiserver Queues. W. A. Massey. Murray Hill, NJ Abstract Waiing Time Aympoic for Time Varying Mulierver ueue wih Abonmen Rerial A. Melbaum Technion Iniue Haifa, 3 ISRAEL avim@x.echnion.ac.il M. I. Reiman Bell Lab, Lucen Technologie Murray Hill, NJ 7974 U.S.A.

More information

Note on Matuzsewska-Orlich indices and Zygmund inequalities

Note on Matuzsewska-Orlich indices and Zygmund inequalities ARMENIAN JOURNAL OF MATHEMATICS Volume 3, Number 1, 21, 22 31 Noe on Mauzewka-Orlic indice and Zygmund inequaliie N. G. Samko Univeridade do Algarve, Campu de Gambela, Faro,85 139, Porugal namko@gmail.com

More information

Reflected Solutions of BSDEs Driven by G-Brownian Motion

Reflected Solutions of BSDEs Driven by G-Brownian Motion Refleced Soluion of BSDE Driven by G-Brownian Moion Hanwu Li Shige Peng Abdoulaye Soumana Hima Sepember 1, 17 Abrac In hi paper, we udy he refleced oluion of one-dimenional backward ochaic differenial

More information

EE202 Circuit Theory II

EE202 Circuit Theory II EE202 Circui Theory II 2017-2018, Spring Dr. Yılmaz KALKAN I. Inroducion & eview of Fir Order Circui (Chaper 7 of Nilon - 3 Hr. Inroducion, C and L Circui, Naural and Sep epone of Serie and Parallel L/C

More information

Physics 240: Worksheet 16 Name

Physics 240: Worksheet 16 Name Phyic 4: Workhee 16 Nae Non-unifor circular oion Each of hee proble involve non-unifor circular oion wih a conan α. (1) Obain each of he equaion of oion for non-unifor circular oion under a conan acceleraion,

More information

Oscillation of an Euler Cauchy Dynamic Equation S. Huff, G. Olumolode, N. Pennington, and A. Peterson

Oscillation of an Euler Cauchy Dynamic Equation S. Huff, G. Olumolode, N. Pennington, and A. Peterson PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON DYNAMICAL SYSTEMS AND DIFFERENTIAL EQUATIONS May 4 7, 00, Wilmingon, NC, USA pp 0 Oscillaion of an Euler Cauchy Dynamic Equaion S Huff, G Olumolode,

More information

DESIGN OF TENSION MEMBERS

DESIGN OF TENSION MEMBERS CHAPTER Srcral Seel Design LRFD Mehod DESIGN OF TENSION MEMBERS Third Ediion A. J. Clark School of Engineering Deparmen of Civil and Environmenal Engineering Par II Srcral Seel Design and Analysis 4 FALL

More information

Ann. Funct. Anal. 2 (2011), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL:

Ann. Funct. Anal. 2 (2011), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL: Ann. Func. Anal. 2 2011, no. 2, 34 41 A nnals of F uncional A nalysis ISSN: 2008-8752 elecronic URL: www.emis.de/journals/afa/ CLASSIFICAION OF POSIIVE SOLUIONS OF NONLINEAR SYSEMS OF VOLERRA INEGRAL EQUAIONS

More information

Clark s Representation of Wiener Functionals and Hedging of the Barrier Option

Clark s Representation of Wiener Functionals and Hedging of the Barrier Option aqarvelo mecnierebaa erovnuli akademii moambe, 8, #, 04 ULLEIN OF HE GEORGIAN NAIONAL ACADEMY OF SCIENCES, vol 8, no, 04 Mahemaic Clark Repreenaion of Wiener Funcional and Hedging of he arrier Opion Omar

More information

Stochastic Model for Cancer Cell Growth through Single Forward Mutation

Stochastic Model for Cancer Cell Growth through Single Forward Mutation Journal of Modern Applied Saisical Mehods Volume 16 Issue 1 Aricle 31 5-1-2017 Sochasic Model for Cancer Cell Growh hrough Single Forward Muaion Jayabharahiraj Jayabalan Pondicherry Universiy, jayabharahi8@gmail.com

More information