The Implied Volatility of Forward Starting Options: ATM Short-Time Level, Skew and Curvature
|
|
- Cornelius Gerard Gibbs
- 5 years ago
- Views:
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 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 informationAn 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 informationAn 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 informationExplicit 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 informationOn 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 informationFractional 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 informationGeneralized 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
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 informationUtility 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 informationLaplace 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 informationEECE 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 informationCash 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 informationRough 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 informationIntroduction 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 informationMathematische 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 informationESTIMATES 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 informationON 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 information6.8 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 informationarxiv: 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 informationANSWERS 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 informationOn 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 informationStability 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 informationFIXED 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 informationStochastic 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 informationLECTURE 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 informationFlow 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 informationUndetermined 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 informationU 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 informationf(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 informationRandomized 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 informationDiscussion 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 informationarxiv: 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 informationEE 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 informationOn 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 informationLoss 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 informationMacroeconomics 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 informationResearch 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 information6.302 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 informationAMartingaleApproachforFractionalBrownian 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 informationPhysics 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 informationResearch 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 informationExample 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 informationApproximation 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 informationCentre 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 information6. 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 informationCHAPTER 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 informationū(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 informationSMT 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 informationPH2130 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 informationNotes 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 informationLet. 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 informationLecture #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 informationCH 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 informationBackward 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 information2. 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 informationNECESSARY 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 informationCHAPTER. 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 informationChapter 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 informationLecture 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 informationHedging 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 informationTo 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 informationBackward 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 informationFractional 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 informationResearch 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 informationGeneralized 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 informationOn 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 informationRicci 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 informationThe 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 informationPricing 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 informationPricing 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 informationApproximate 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 informationIntroduction 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 informationWhat 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 informationu(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 informationLecture 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 informationSimulation 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 informationMALLIAVIN 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 information18.03SC 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 informationCurvature. 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 informationSection 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 information4.2 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 informationMath 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 informationHamilton- 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 informationFLAT 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 informationParameter 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 informationChapter 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 informationTransform 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 informationInternational 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 informationApplied 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 informationLinear 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 informationTime 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 informationNote 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 informationReflected 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 informationEE202 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 informationPhysics 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 informationOscillation 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 informationDESIGN 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 informationAnn. 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 informationClark 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 informationStochastic 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