European Option Pricing for a Stochastic Volatility Lévy Model with Stochastic Interest Rates

Size: px
Start display at page:

Download "European Option Pricing for a Stochastic Volatility Lévy Model with Stochastic Interest Rates"

Transcription

1 Jonal of Mahemacal Fnance 98-8 do:436/mf33 Pblshed Onlne Noembe (hp://wwwscrpog/onal/mf) Eopean Opon Pcng fo a Sochasc Volaly Léy Model wh Sochasc Inees Raes Sasa Pnkham Paoe Saayaham School of Mahemacs Inse of Scence Sanaee Unesy of echnology Nakhon Rachasma haland E-mal: sasa@mahsach paoe@sach Receed Ags 7 ; esed Sepembe 9 ; acceped Sepembe 8 Absac We pesen a Eopean opon pcng when he ndelyng asse pce dynamcs s goened by a lnea combnaon of he me-change Léy pocess and a sochasc nees ae whch follows he Vascek pocess We oban an explc fomla fo he Eopean call opon n em of he chaacesc fncon of he al pobables Keywods: me-change Léy Pocess Sochasc Inees Rae Vascek Pocess Fowad Mease Opon Pcng Inodcon Le F P be a pobably space A sochasc pocess L s a Léy pocess f has ndependen and saonay ncemens and has a sochascally connos sample pah e fo any lm P L L he sm- h h ples possble Léy pocesses ae he sandad Bownan moon W Posson pocess N and compond Posson N pocess Y whee N s Posson pocess wh nensy and Y ae d andom aables Of cose we can bld a new Léy pocess fom known ones by sng he echnqe of lnea ansfomaon Fo example N he mp dffson pocess W Y whee ae consans s a Léy pocess whch comes fom a lnea ansfomaon of wo ndependen Léy pocesses e a Bownan moon wh df and a compond Posson pocess Assme ha a sk-neal pobably mease exss and all pocesses n secon wll be consdeed nde hs sk-neal mease In he Black-Scholes model he pce of a sky asse S nde a sk-neal mease and wh non ddend paymen follows S SexpL SexpW () whee s a sk-fee nees aes s a o- laly coeffcen of he sock pce Insead of modelng he log ens L W wh a nomal dsbon We now eplace wh a moe sophscaed pocess L whch s a Léy pocess of he fom L W J () whee J and denoes a pe Léy mp componen (e a Léy pocess wh no Bownan moon pa) and s conexy adsmen We assme ha he pocesses W and J ae ndependen o ncopoae he olaley effec o he model () we follow he echnqe of Ca and W [] by sbodnang a pa of a sandad Bownan moon W and a pa of mp Léy pocess J by he me negal of a mean eeng Cox Ingesoll Ross (CIR) pocess ds s whee follows he CIR pocess d d d W (3) Hee W s a sandad Bownan moon whch coesponds o he pocess he consan s he ae a whch he pocess ees owad s long em mean and s he olaly coeffcen of he pocess Copygh ScRes

2 S PINKHAM E AL 99 Hence he model () has been changed o L W J (4) and hs new pocess s called a sochasc olaly Ley pocess One can nepe as he sochasc clock pocess wh acy ae pocess By eplacng L n () wh L we oban a model of an ndelyng asse nde he sk-neal mease wh sochasc olaly as follows: S S expw J (5) In hs pape we shall consde he poblem of fndng a fomla fo Eopean call opons based on he ndelyng asse model (5) fo whch he consan nees aes s eplaced by he sochasc nees aes and J s compond Posson pocess e he model nde o consdeaon s gen by S S exp W J (6) Hee we assme ha follows he Vascek pocess d d dw (7) W s a sandad Bownan moon wh espec o he pocess and dw d d W W dw he consan s he ae a whch he nees ae ees owad s long em mean s he olaly coeffcen of he nees ae pocess (7) he consan s a speed eeson Leae Reews Many fnancal engneeng sdes hae been ndeaken o modfy and mpoe he Black-Scholes model Fo example he mp dffson models of Meon [] he sochasc Volaly mp dffson model of Baes [3] and Yan and Hanson [4] Fhemoe he me change Léy models poposed by Ca and W [] he poblem of opon pcng nde sochasc nees aes has been nesgaed fo along me Km [5] consced he opon pcng fomla based on Black-Scholes model nde seeal sochasc nees ae pocesses e Vascek CIR Ho-Lee ype He fond ha by ncpoang sochasc nees aes no he Black-Scholes model fo a sho may opon does no conbe o mpoemen n he pefomance of he ognal Black- Scholes pcng fomla Bgo and Meco [6] menon ha he sochasc feae of nees aes has a songe mpac on he opon pce when pcng fo a long may opon Ca and W [] conne hs sdy by gng he opon pcng fomla based on a me-changed Léy pocess model B hey sll se consan nees aes n he model In hs pape we ge an analyss on he opon pcng model based on a me-changed Léy pocess wh sochasc nees aes he es of he pape s oganzed as follows he dynamcs nde he fowad mease s descbed n Secon 3 he opon pcng fomla s gen n Secon 4 Fnally he close fom solon fo a Eopean call opon n ems of he chaacesc fncon s gen n Secon 5 3 he Ddynamcs nde he Fowad Mease We begn by gng a bef eew of he defnon of a coelaed Bownan moon and some of s popees (fo moe deals one see Bmmelhs [7]) Recallng n ha a sandad Bownan moon n R s a sochasc pocess Z whose ale a me s smply a eco of n ndependen Bownan moons a Z Z Zn We se Z nsead of W snce we wold lke o esee he lae fo he moe geneal case of coelaed Bownan moon whch wll be defned as follows: be a (consan) pose symmec Le n max sasfyng and By Cholesky s decomposon heoem one can fnd an ppe angl n n max h sch ha whee Η s he anspose of he max Η Le Z Z Zn be a sandad Bownan moon as nodced aboe we defne a new eco-aled pocess W W Wn by W Z o n em of componens n W hz n he pocess W s called a coelaed Bownan moon wh a (consan) coelaon max Each componen pocess W s self a sandad Bownan moon Noe ha f Id (he deny max) hen W s a sandad Bownan moon Fo example f we le a symmec max (3) hen has a Cholesky decomposon of he fom HH whee H s an ppe angla max of he fom Copygh ScRes

3 S PINKHAM E AL H Le Z Z Z moons hen W W W Z be hee ndependen Bownan W defned by W Z o n ems of componens W Z Z W Z W Z (3) Now le s n o o poblem Noe ha by Io s lemma he model (6) has he dynamc gen by Y ds S d dw S e d N m d d d W d d d W whee e Y m E (33) dwdw dwdw and dwdw d We can e-we he dynamc (33) n ems of hee ndependen Bownan moons Z Z Z follows (3) we ge ds S md dz dz (34) Y S e d N d d d Z (35) d d d Z (36) hs decomposon makes ease o pefom a mease ansfomaon In fac fo any fxed may le s denoe by he -fowad mease e he pobably mease ha s defned by he Radon- Nkodym deae exp d d (37) d P Hee P s he pce a me of a zeo-copon bond wh may and s defned as e ds s P E F (38) Nex Consde a connos-me economy whee nees aes ae sochasc and sasfy (35) Snce he SDE (35) sasfes all he necessay condons of heoem 3 see Poe [8] hen he solon of (35) has he Mako popey As a conseqence he zeo copon bond pce a me nde he mease n (38) sasfes P E exp sds (39) Noe ha P depends on only nsead of dependng on all nfomaon aalable n F p o me As sch becomes a fncon F of P F meanng ha he pcng poblem can now be fomlaed as a seach fo he fncon F Lemma he pce of a zeo copon bond can be deed by compng he expecaon (39) We oban P exp a b (3) whee b e 3 a e e 3 Poof See Pal [9] (pp 38-39) Lemma he pocess followng he dynamcs n (35) can be wen n he fom whee he pocess x w fo each (3) x sasfes dx d d x Z x (3) Moeoe he fncon w() s deemnsc and well defned n he me neal [] whch sasfed e e w In pacla w (33) Poof o sole he solon of SDE (35) g e and sng Io s Lemma Le g g g dg d d d hen de e de ddz (34) = e d e d Z Inegaed on boh sde he aboe eqaon fom o whee and smplfed one ge e e e dz By sng he defnon of w fom (33) Copygh ScRes

4 S PINKHAM E AL d w e Z (35) e e w Noe ha he solon of (3) s whee e e d e d (36) x x Z Z Hence w x fo each he poof s now complee Nex we shall calclae he Radon-Nkodym deae as appea n (37) By Lemma and we hae x w and P Sbsng and P no (37) we hae x w d d exp d exp a b (37) exp x d e d Sochasc negaon by pas mples ha x d x d x d x (38) By sbsng he expesson fo dx fom (3) dx (39) x d d Z Moeoe by sbsng he expesson fo x fom (36) he fs negal on he gh hand sde of (39) becomes x d s d o e Z d Usng negal by pas we hae (Eqaon 3) Sbsng (3) no (39) we oban dx d e Z (3) Hence x d e d Z (3) Sbsng (3) no (37) once ge d d exp e dz e d (33) he Gsano heoem hen mples ha he hee poc esses Z Z and Z defned by d d e Z d Z (34) dz d Z dz dz ae hee ndependen Bownan moons nde he meas- e heefoe he dynamcs of and S nde ae gen by Y d ds S d dz dz m S e N d e dd Z d d d Z (35) 4 he Pcng of a Eopean Call Opon on he Gen Asse Le S be he pce of a fnancal asse modeled as a sochasc pocess on a fleed pobably space FF F s sally aken o be he pce hsoy p o me All pocesses n hs secon wll be defned n hs space We denoe C he pce a me of a Eopean call opon on he cen pce of an ndelyng asse S wh ske pce K and expaon me s e dz d o e dz e d e d e dz e e d dz e dz s s e dz e d e dz d e d s s d Z (3) Copygh ScRes

5 S PINKHAM E AL he emnal payoff of a Eopean opon on he ndelyng sock S wh ske pce K s max S K (4) hs means he holde wll execse hs gh only S K and hen hs gan s S K Ohewse f S K hen he holde wll by he ndelyng asse fom he make and he ale of he opon s zeo We wold lke o fnd a fomla fo pcng a Eopean call opon wh ske pce K and may based on he model (35) Consde a connos-me economy whee nees aes ae sochasc and he pce of he Eopean call opon a me nde he -fowad mease s CS K ; PE max S K S P ( ) max S K p S S ds whee E s he expecaon wh espec o he -fo- wad pobably mease p s he coespondng con- donal densy gen S and P s a zeo copon bond whch s defned n Lemma Wh a change n aable X ln S C S ; K X P max e K p X X dx X ln P e K p X X dx ln K X ln K = P e p X X dx KP p X X dx ln K X e e p X X X e X X ln K E e S d ln K p X X X e dx X X K KP p X X X X ln K E (e S ) KP p X X d X ln K (4) Wh he fs negand n (4) beng pose and negang p o one he fs negand heefoe defnes a new pobably mease ha we denoe by C S ; K ln K X e q X X dx q below d ln K X K KP X K X K X P ln KP p X X X X e P ; P ; X e P ln KP X K X (43) whee hose pobables n (43) ae calclaed nde he pobably mease he Eopean call opon fo log asse pce X ln S wll be denoed by ˆ X C X ; e P X ; (44) e P P X ; whee ln K and P X ; := P X ; K Noe ha we do no hae a closed fom solon fo hese pobables Howee hese pobables ae elaed o chaacesc fncons whch hae closed fom solons as wll be seen n Lemma 4 he followng lemma shows he elaonshp beween P and P n he opon ale of (44) Lemma 3 he fncons P and P n he opon ales of (44) sasfy he PIDEs (45): and sbec o he bonday condon a expaon = P x ; x (46) Moeoe P sasfes he Eqaon (47) P P y AP e P x y ; P x ; kydy (45) P P P AP b x x a b ( ) P b P e b (47) P y P x y ; P x ; (e ) kydy x Copygh ScRes

6 S PINKHAM E AL 3 and sbec o he bonday condon a expaon = P x ; x (48) whee fo = P P AP [ ] e x P P P P P (49) x x P y P x y ; P ( x ; ) e kydy x Noe ha x f x and ohewse x Poof See Appendx A 5 he Closed-Fom Solon fo Eopean Call Opons Fo = he chaacesc fncon fo P x ; wh espec o he aable ae defned by κ f ; : e d x P x ; (5) wh a mns sgn o accon fo he negay of he mease dp Noe ha f also sasfes smla PIDEs f A f x ; (5) wh he espece bonday condons f x ; e d P x ; x e x d e Snce P x d x d ; x d he followng lemma shows how o calclae he cha- acesc fncons fo P and P as hey appeaed n Lemma 3 Lemma 4 he fncons P and P can be calclaed by he nese Foe ansfomaons of he chaacesc fncon e e f ; x P x ; Re d π fo wh Re[] denong he eal componen of a complex nmbe By leng he chaacesc fncon f s gen by f x ; exp x B C E whee b b b b b b b x y y b e e kydy x y b e e k ydy b b b b e b B ln b b b b 3 e 3 4e e 3 4 C e b 4bb b 4bb b b e E b 4bb b b b e b b b b e b B ln b b b b 3 4 e ) 3 4e 4 4 Poof See Appendx B In smmay we hae s poed he followng man heoem Copygh ScRes

7 4 S PINKHAM E AL heoem 5 he ale of a Eopean call opon of SDE (35) s C S ; K SP X ; KP ( ) P X ; whee P and P ae gen n Lemma 4 and P s gen n Lemma 6 Acknowledgemens hs eseach s (paally) sppoed by he Cene of Excellen n Mahemacs he commsson on Hghe Edcaon (CHE) Addess: 7 Rama VI Road Rachahew Dsc Bangkok haland 7 Refeences [] P Ca and L W me Change Ley Pocesses and Opon Pcng Jonal of Fnancal Economcs Vol 7 No 4 pp 3-4 do:6/s34-45x(3)7-5 [] R C Meon Opon Pcng when Undelyng Sock Rens ae Dsconnos Jonal of Fnancal Economcs Vol 3 No pp 5-44 do:6/34-45x(76)9- [3] D Baes Jmp and Sochasc Volaly: Exchange Rae Pocesses Implc n Deche Mak n Opon Reew of Fnancal Sdes Vol 9 No 996 pp 69-7 do:93/fs/969 [4] G Yan and F B Hanson Opon Pcng fo Sochasc Volaly Jmp Dffson Model wh Log Unfom Jmp Ampldes Poceedng Amecan Conol Confeence Mnneapols 4-6 Jne 6 pp [5] Y J Km Opon Pcng nde Sochasc Inees aes: An Empcal Inesgaon Asa Pacfc Fnancal Makes Vol 9 No pp 3-44 do:3/a:55376 [6] D Bgo and F Meco Inees Rae Models: heoy and Pacce nd Edon Spnge Beln [7] R Bmmelhs Mahemacal Mehod fo Fnancal Engneeng Unesy of London 9 hp://wwwemsbbkack/fo_sdens/msc/mah_meho ds/lecepdf [8] PE Ploe Sochasc Inegaon and Dffeenal Eqaon Sochasc Modelng and Appled Pobably Vol nd Edon Spnge Beln 5 [9] N Pal An Elemenay Inodcon o Sochasc Inees Rae Modelng Adance Sees on Sascal Scence & Appled Pobably Vol Wold Scenfc Sngapoe 8 [] M G Kendall A Sa and J K Od Adance heoy of Sascs Vol Halsed Pess New Yok 987 Copygh ScRes

8 S PINKHAM E AL 5 Appendx A: Poof of Lemma 3 By Io s lemma Cˆ x follows he paal nego-dffeenal eqaon (PIDE) Cˆ LC D ˆ J ˆ LC (A) whee ˆ ˆ D ˆ C C LC e x ˆ C ˆ ˆ ˆ C C C x Cˆ ˆ C x and J LCˆ ˆ ˆ ˆ C y Cx y Cx e kydy x whee k( y) s he Léy densy We plan o sbse (44) no (A) Fsly we compe Cˆ x P P e e P P a b Cˆ x P P e P e P x x x Cˆ x P P e ep Cˆ x P P e e P Pb ˆ C x P P e P P e P x x x x Cˆ x P P e e P ˆ ˆ C x P P P C x P e e P e x x x P P e P b Pb Cˆ x P P P P e e P b x x x x ; ˆ ; Cˆ x y C x y P x y P x y P x x e e ; ; ; e P ( ) P x y ; P x ; Sbse all ems aboe no (A) and sepaae by assmed ndependen ems of P and P hs ges wo PIDEs fo he -fowad pobably fo P x ; : P P P e x P P P P P x x P y P x y ; P x ; e kyd y x y e P x y ; P x ; kyd y (A) and sbec o he bonday condon a he expaon me = accodng o (46) By sng he noaon n (49) hen (A) becomes Eqaon (A3) P P y AP e P x y ; P x ; kyd y P : A P (A3) Copygh ScRes

9 6 S PINKHAM E AL Fo P ( x ; ): P P P P P P P b P x x x a b P e b P e b P y P x y ; P x ; e kyd y x (A4) and sbec o he bonday condon a expaon me Agan by sng he noaon (49) hen (A4) becomes = accodng o (48) b P P P P a AP b P b x x P P e b( ): A P he poof s now compleed (A5) Copygh ScRes

10 S PINKHAM E AL 7 Appendx B: Poof of Lemma 4 o sole he chaacesc fncon explcly leng be he me-o-go we conece ha he fncon f s gen by f x ; (B) exp x B C E and he bonday condon B C E hs conece explos he lneay of he coeffcen n PIDEs (5) Noe ha he chaacesc fncon of f always exss In ode o sbse (B) no (5) fsly we compe f f B C E f f x f f f C f E f f x f E f C f f x C f E f x e f ( x ; ) f f x f ( x y ; ) f ( x ; ) Sbsng all he aboe ems no (5) afe cancellng he common faco of f we ge a smplfed fom as follows: C C x y y x E E E e e e e k ydy + B e C C E By sepaang he ode and odeng he emanng ems we can edce o hee odnay dffeenal eqaons (ODEs) as follows: C() C () (B) E E E x y y e e k yd y (B3) B e C E C (B4) I s clea fom (B) and C() ha Le C e (B5) b b x y y b e e k y dy and sbse all em aboe no (B3) we ge b b 4bb b b 4bb E b E E b b By mehod of aable sepaaon we hae Usng paal facon on he lef hand sde we ge de bd b b 4bb b b 4bb E E b b Copygh ScRes

11 8 S PINKHAM E AL de b b E E( ) b b whee b 4bb Inegang boh sdes we hae b E b ln E b E b Usng bonday condon E ( ) we ge b E ln b Solng fo E ( ) we oban e bb E b b e b d (B6) whee b b b b In ode o sole B ( ) explcly we sbse C ( ) and E ( ) n (B5) and (B6) no (B4) B ' e e e bb e e e b b e b Inegang wh espec o and sng bonday condon B ( ) hen we ge B e 4e e 3 b b b b e b ln b b b b he deals of he poof fo he chaacesc fncon f ae smla o f Hence we hae f x ; exp x B CE whee B C and E ( ) ae as gen n hs Lem- ma We can hs ealae he chaacesc fncon n close fom Howee we ae neesed n he pobably P hese can be need fom he chaacesc fncons by pefomng he followng negaon P x ; κ e f x ; Re d fo whee X ln S and ln K see Kendall e al [] he poof s now complee Copygh ScRes

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova The XIII Inenaonal Confeence Appled Sochasc Models and Daa Analyss (ASMDA-009) Jne 30-Jly 3, 009, Vlns, LITHUANIA ISBN 978-9955-8-463-5 L Sakalaskas, C Skadas and E K Zavadskas (Eds): ASMDA-009 Seleced

More information

European Option Pricing for a Stochastic Volatility Lévy Model with Stochastic Interest Rates

European Option Pricing for a Stochastic Volatility Lévy Model with Stochastic Interest Rates Jonal of Mahemaical Finance 98-8 doi:.436/mf..33 Pblished Online Noembe (hp://www.scirp.og/onal/mf) Eopean Opion Picing fo a Sochasic Volailiy Léy Model wih Sochasic Inees Raes Saisa Pinkham Paioe Saayaham

More information

Name of the Student:

Name of the Student: Engneeng Mahemacs 05 SUBJEC NAME : Pobably & Random Pocess SUBJEC CODE : MA645 MAERIAL NAME : Fomula Maeal MAERIAL CODE : JM08AM007 REGULAION : R03 UPDAED ON : Febuay 05 (Scan he above QR code fo he dec

More information

Chapter Finite Difference Method for Ordinary Differential Equations

Chapter Finite Difference Method for Ordinary Differential Equations Chape 8.7 Fne Dffeence Mehod fo Odnay Dffeenal Eqaons Afe eadng hs chape, yo shold be able o. Undesand wha he fne dffeence mehod s and how o se o solve poblems. Wha s he fne dffeence mehod? The fne dffeence

More information

1 Constant Real Rate C 1

1 Constant Real Rate C 1 Consan Real Rae. Real Rae of Inees Suppose you ae equally happy wh uns of he consumpon good oday o 5 uns of he consumpon good n peod s me. C 5 Tha means you ll be pepaed o gve up uns oday n eun fo 5 uns

More information

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security 1 Geneal Non-Abiage Model I. Paial Diffeenial Equaion fo Picing A. aded Undelying Secuiy 1. Dynamics of he Asse Given by: a. ds = µ (S, )d + σ (S, )dz b. he asse can be eihe a sock, o a cuency, an index,

More information

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( ) 5-1. We apply Newon s second law (specfcally, Eq. 5-). (a) We fnd he componen of he foce s ( ) ( ) F = ma = ma cos 0.0 = 1.00kg.00m/s cos 0.0 = 1.88N. (b) The y componen of he foce s ( ) ( ) F = ma = ma

More information

Chapter 3: Vectors and Two-Dimensional Motion

Chapter 3: Vectors and Two-Dimensional Motion Chape 3: Vecos and Two-Dmensonal Moon Vecos: magnude and decon Negae o a eco: eese s decon Mulplng o ddng a eco b a scala Vecos n he same decon (eaed lke numbes) Geneal Veco Addon: Tangle mehod o addon

More information

Available online Journal of Scientific and Engineering Research, 2017, 4(2): Research Article

Available online   Journal of Scientific and Engineering Research, 2017, 4(2): Research Article Avlble onlne www.jse.com Jonl of Scenfc nd Engneeng Resech, 7, 4():5- Resech Acle SSN: 394-63 CODEN(USA): JSERBR Exc Solons of Qselsc Poblems of Lne Theoy of Vscoelscy nd Nonlne Theoy Vscoelscy fo echnclly

More information

The Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland

The Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland The Unque Soluon of Sochasc Dffeenal Equaons Dech Rye RyeDM@gawne.ch Mdaweg 3 CH-4500 Solohun Swzeland Phone +4132 621 13 07 Tme evesal n sysems whou an exenal df sngles ou he an-iô negal. Key wods: Sochasc

More information

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT V M Chacko E CONVE AND INCREASIN CONVE OAL IME ON ES RANSORM ORDER R&A # 4 9 Vol. Decembe ON OAL IME ON ES RANSORM ORDER V. M. Chacko Depame of Sascs S. homas Collee hss eala-68 Emal: chackovm@mal.com

More information

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED)

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED) FIRMS IN THE TWO-ERIO FRAMEWORK (CONTINUE) OCTOBER 26, 2 Model Sucue BASICS Tmelne of evens Sa of economc plannng hozon End of economc plannng hozon Noaon : capal used fo poducon n peod (decded upon n

More information

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions:

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions: esng he Random Walk Hypohess If changes n a sees P ae uncoelaed, hen he followng escons hold: va + va ( cov, 0 k 0 whee P P. k hese escons n un mply a coespondng se of sample momen condons: g µ + µ (,,

More information

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain Lecue-V Sochasic Pocesses and he Basic Tem-Sucue Equaion 1 Sochasic Pocesses Any vaiable whose value changes ove ime in an unceain way is called a Sochasic Pocess. Sochasic Pocesses can be classied as

More information

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables Opmal Conrol Why Use I - verss calcls of varaons, opmal conrol More generaly More convenen wh consrans (e.g., can p consrans on he dervaves More nsghs no problem (a leas more apparen han hrogh calcls of

More information

Lecture 5. Plane Wave Reflection and Transmission

Lecture 5. Plane Wave Reflection and Transmission Lecue 5 Plane Wave Reflecon and Tansmsson Incden wave: 1z E ( z) xˆ E (0) e 1 H ( z) yˆ E (0) e 1 Nomal Incdence (Revew) z 1 (,, ) E H S y (,, ) 1 1 1 Refleced wave: 1z E ( z) xˆ E E (0) e S H 1 1z H (

More information

Simulation of Non-normal Autocorrelated Variables

Simulation of Non-normal Autocorrelated Variables Jounal of Moden Appled Sascal Mehods Volume 5 Issue Acle 5 --005 Smulaon of Non-nomal Auocoelaed Vaables HT Holgesson Jönöpng Inenaonal Busness School Sweden homasholgesson@bshse Follow hs and addonal

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION HAPER : LINEAR DISRIMINAION Dscmnan-based lassfcaon 3 In classfcaon h K classes ( k ) We defned dsmnan funcon g () = K hen gven an es eample e chose (pedced) s class label as f g () as he mamum among g

More information

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have:

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have: Poblem Se #3 Soluons Couse 4.454 Maco IV TA: Todd Gomley, gomley@m.edu sbued: Novembe 23, 2004 Ths poblem se does no need o be uned n Queson #: Sock Pces, vdends and Bubbles Assume you ae n an economy

More information

Handling Fuzzy Constraints in Flow Shop Problem

Handling Fuzzy Constraints in Flow Shop Problem Handlng Fuzzy Consans n Flow Shop Poblem Xueyan Song and Sanja Peovc School of Compue Scence & IT, Unvesy of Nongham, UK E-mal: {s sp}@cs.no.ac.uk Absac In hs pape, we pesen an appoach o deal wh fuzzy

More information

( ) α is determined to be a solution of the one-dimensional minimization problem: = 2. min = 2

( ) α is determined to be a solution of the one-dimensional minimization problem: = 2. min = 2 Homewo (Patal Solton) Posted on Mach, 999 MEAM 5 Deental Eqaton Methods n Mechancs. Sole the ollowng mat eqaton A b by () Steepest Descent Method and/o Pecondtoned SD Method Snce the coecent mat A s symmetc,

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

Stochastic Programming handling CVAR in objective and constraint

Stochastic Programming handling CVAR in objective and constraint Sochasc Programmng handlng CVAR n obecve and consran Leondas Sakalaskas VU Inse of Mahemacs and Informacs Lhana ICSP XIII Jly 8-2 23 Bergamo Ialy Olne Inrodcon Lagrangan & KKT condons Mone-Carlo samplng

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr.

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr. Moden Enegy Funconal fo Nucle and Nuclea Mae By: lbeo noosa Teas &M Unvesy REU Cycloon 008 Meno: D. Shalom Shlomo Oulne. Inoducon.. The many-body poblem and he aee-fock mehod. 3. Skyme neacon. 4. aee-fock

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

The sound field of moving sources

The sound field of moving sources Nose Engneeng / Aoss -- ong Soes The son el o mong soes ong pon soes The pesse el geneae by pon soe o geneal me an The pess T poson I he soe s onenae a he sngle mong pon, soe may I he soe s I be wen as

More information

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic *

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic * Avalable onlne a wwwscencedeccom ScenceDec oceda Engneeng 69 4 85 86 4h DAAAM Inenaonal Smposum on Inellgen Manufacung and Auomaon Behavo of Inegal Cuves of he uaslnea Second Ode Dffeenal Equaons Alma

More information

Multistage Median Ranked Set Sampling for Estimating the Population Median

Multistage Median Ranked Set Sampling for Estimating the Population Median Jounal of Mathematcs and Statstcs 3 (: 58-64 007 ISSN 549-3644 007 Scence Publcatons Multstage Medan Ranked Set Samplng fo Estmatng the Populaton Medan Abdul Azz Jeman Ame Al-Oma and Kamaulzaman Ibahm

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

Part II CONTINUOUS TIME STOCHASTIC PROCESSES

Part II CONTINUOUS TIME STOCHASTIC PROCESSES Par II CONTINUOUS TIME STOCHASTIC PROCESSES 4 Chaper 4 For an advanced analyss of he properes of he Wener process, see: Revus D and Yor M: Connuous marngales and Brownan Moon Karazas I and Shreve S E:

More information

Go over vector and vector algebra Displacement and position in 2-D Average and instantaneous velocity in 2-D Average and instantaneous acceleration

Go over vector and vector algebra Displacement and position in 2-D Average and instantaneous velocity in 2-D Average and instantaneous acceleration Mh Csquee Go oe eco nd eco lgeb Dsplcemen nd poson n -D Aege nd nsnneous eloc n -D Aege nd nsnneous cceleon n -D Poecle moon Unfom ccle moon Rele eloc* The componens e he legs of he gh ngle whose hpoenuse

More information

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY Unvesy of Calfona, Davs Dae: June xx, 009 Depamen of Economcs Tme: 5 hous Mcoeconomcs Readng Tme: 0 mnues PRELIMIARY EXAMIATIO FOR THE Ph.D. DEGREE Pa I ASWER KEY Ia) Thee ae goods. Good s lesue, measued

More information

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1 ps 57 Machne Leann School of EES Washnon Sae Unves ps 57 - Machne Leann Assume nsances of classes ae lneal sepaable Esmae paamees of lnea dscmnan If ( - -) > hen + Else - ps 57 - Machne Leann lassfcaon

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002

More information

) from i = 0, instead of i = 1, we have =

) from i = 0, instead of i = 1, we have = Chape 3: Adjusmen Coss n he abou Make I Movaonal Quesons and Execses: Execse 3 (p 6): Illusae he devaon of equaon (35) of he exbook Soluon: The neempoal magnal poduc of labou s epesened by (3) = = E λ

More information

The shortest path between two truths in the real domain passes through the complex domain. J. Hadamard

The shortest path between two truths in the real domain passes through the complex domain. J. Hadamard Complex Analysis R.G. Halbud R.Halbud@ucl.ac.uk Depamen of Mahemaics Univesiy College London 202 The shoes pah beween wo uhs in he eal domain passes hough he complex domain. J. Hadamad Chape The fis fundamenal

More information

Variational method to the second-order impulsive partial differential equations with inconstant coefficients (I)

Variational method to the second-order impulsive partial differential equations with inconstant coefficients (I) Avalable onlne a www.scencedrec.com Proceda Engneerng 6 ( 5 4 Inernaonal Worksho on Aomoble, Power and Energy Engneerng Varaonal mehod o he second-order mlsve aral dfferenal eqaons wh nconsan coeffcens

More information

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

CONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS

CONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS APPENDX J CONSSTENT EARTHQUAKE ACCEERATON AND DSPACEMENT RECORDS Earhqake Acceleraons can be Measred. However, Srcres are Sbjeced o Earhqake Dsplacemens J. NTRODUCTON { XE "Acceleraon Records" }A he presen

More information

ESS 265 Spring Quarter 2005 Kinetic Simulations

ESS 265 Spring Quarter 2005 Kinetic Simulations SS 65 Spng Quae 5 Knec Sulaon Lecue une 9 5 An aple of an lecoagnec Pacle Code A an eaple of a knec ulaon we wll ue a one denonal elecoagnec ulaon code called KMPO deeloped b Yohhau Oua and Hoh Mauoo.

More information

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model

( t) Outline of program: BGC1: Survival and event history analysis Oslo, March-May Recapitulation. The additive regression model BGC1: Survval and even hsory analyss Oslo, March-May 212 Monday May 7h and Tuesday May 8h The addve regresson model Ørnulf Borgan Deparmen of Mahemacs Unversy of Oslo Oulne of program: Recapulaon Counng

More information

ajanuary't I11 F or,'.

ajanuary't I11 F or,'. ',f,". ; q - c. ^. L.+T,..LJ.\ ; - ~,.,.,.,,,E k }."...,'s Y l.+ : '. " = /.. :4.,Y., _.,,. "-.. - '// ' 7< s k," ;< - " fn 07 265.-.-,... - ma/ \/ e 3 p~~f v-acecu ean d a e.eng nee ng sn ~yoo y namcs

More information

Variability Aware Network Utility Maximization

Variability Aware Network Utility Maximization aably Awae Newok ly Maxmzaon nay Joseph and Gusavo de ecana Depamen of Eleccal and Compue Engneeng, he nvesy of exas a Ausn axv:378v3 [cssy] 3 Ap 0 Absac Newok ly Maxmzaon NM povdes he key concepual famewok

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations Today - Lecue 13 Today s lecue coninue wih oaions, oque, Noe ha chapes 11, 1, 13 all inole oaions slide 1 eiew Roaions Chapes 11 & 1 Viewed fom aboe (+z) Roaional, o angula elociy, gies angenial elociy

More information

Mass-Spring Systems Surface Reconstruction

Mass-Spring Systems Surface Reconstruction Mass-Spng Syses Physally-Based Modelng: Mass-Spng Syses M. Ale O. Vasles Mass-Spng Syses Mass-Spng Syses Snake pleenaon: Snake pleenaon: Iage Poessng / Sae Reonson: Iage poessng/ Sae Reonson: Mass-Spng

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng

More information

Field due to a collection of N discrete point charges: r is in the direction from

Field due to a collection of N discrete point charges: r is in the direction from Physcs 46 Fomula Shee Exam Coulomb s Law qq Felec = k ˆ (Fo example, f F s he elecc foce ha q exes on q, hen ˆ s a un veco n he decon fom q o q.) Elecc Feld elaed o he elecc foce by: Felec = qe (elecc

More information

Chapters 2 Kinematics. Position, Distance, Displacement

Chapters 2 Kinematics. Position, Distance, Displacement Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen

More information

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay)

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay) Secions 3.1 and 3.4 Eponenial Funcions (Gowh and Decay) Chape 3. Secions 1 and 4 Page 1 of 5 Wha Would You Rahe Have... $1million, o double you money evey day fo 31 days saing wih 1cen? Day Cens Day Cens

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

3. A Review of Some Existing AW (BT, CT) Algorithms

3. A Review of Some Existing AW (BT, CT) Algorithms 3. A Revew of Some Exstng AW (BT, CT) Algothms In ths secton, some typcal ant-wndp algothms wll be descbed. As the soltons fo bmpless and condtoned tansfe ae smla to those fo ant-wndp, the pesented algothms

More information

SCIENCE CHINA Technological Sciences

SCIENCE CHINA Technological Sciences SIENE HINA Technologcal Scences Acle Apl 4 Vol.57 No.4: 84 8 do:.7/s43-3-5448- The andom walkng mehod fo he seady lnea convecondffuson equaon wh axsymmec dsc bounday HEN Ka, SONG MengXuan & ZHANG Xng *

More information

Backcalculation Analysis of Pavement-layer Moduli Using Pattern Search Algorithms

Backcalculation Analysis of Pavement-layer Moduli Using Pattern Search Algorithms Bakallaon Analyss of Pavemen-laye Modl Usng Paen Seah Algohms Poje Repo fo ENCE 74 Feqan Lo May 7 005 Bakallaon Analyss of Pavemen-laye Modl Usng Paen Seah Algohms. Inodon. Ovevew of he Poje 3. Objeve

More information

336 ERIDANI kfk Lp = sup jf(y) ; f () jj j p p whee he supemum is aken ove all open balls = (a ) inr n, jj is he Lebesgue measue of in R n, () =(), f

336 ERIDANI kfk Lp = sup jf(y) ; f () jj j p p whee he supemum is aken ove all open balls = (a ) inr n, jj is he Lebesgue measue of in R n, () =(), f TAMKANG JOURNAL OF MATHEMATIS Volume 33, Numbe 4, Wine 2002 ON THE OUNDEDNESS OF A GENERALIED FRATIONAL INTEGRAL ON GENERALIED MORREY SPAES ERIDANI Absac. In his pape we exend Nakai's esul on he boundedness

More information

8 Baire Category Theorem and Uniform Boundedness

8 Baire Category Theorem and Uniform Boundedness 8 Bae Categoy Theoem and Unfom Boundedness Pncple 8.1 Bae s Categoy Theoem Valdty of many esults n analyss depends on the completeness popety. Ths popety addesses the nadequacy of the system of atonal

More information

2/20/2013. EE 101 Midterm 2 Review

2/20/2013. EE 101 Midterm 2 Review //3 EE Mderm eew //3 Volage-mplfer Model The npu ressance s he equalen ressance see when lookng no he npu ermnals of he amplfer. o s he oupu ressance. I causes he oupu olage o decrease as he load ressance

More information

Nanoparticles. Educts. Nucleus formation. Nucleus. Growth. Primary particle. Agglomeration Deagglomeration. Agglomerate

Nanoparticles. Educts. Nucleus formation. Nucleus. Growth. Primary particle. Agglomeration Deagglomeration. Agglomerate ucs Nucleus Nucleus omaon cal supesauaon Mng o eucs, empeaue, ec. Pmay pacle Gowh Inegaon o uson-lme pacle gowh Nanopacles Agglomeaon eagglomeaon Agglomeae Sablsaon o he nanopacles agans agglomeaon! anspo

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

Computer Propagation Analysis Tools

Computer Propagation Analysis Tools Compue Popagaion Analysis Tools. Compue Popagaion Analysis Tools Inoducion By now you ae pobably geing he idea ha pedicing eceived signal sengh is a eally impoan as in he design of a wieless communicaion

More information

ÖRNEK 1: THE LINEAR IMPULSE-MOMENTUM RELATION Calculate the linear momentum of a particle of mass m=10 kg which has a. kg m s

ÖRNEK 1: THE LINEAR IMPULSE-MOMENTUM RELATION Calculate the linear momentum of a particle of mass m=10 kg which has a. kg m s MÜHENDİSLİK MEKANİĞİ. HAFTA İMPULS- MMENTUM-ÇARPIŞMA Linea oenu of a paicle: The sybol L denoes he linea oenu and is defined as he ass ies he elociy of a paicle. L ÖRNEK : THE LINEAR IMPULSE-MMENTUM RELATIN

More information

Chapter Finite Difference Method for Ordinary Differential Equations

Chapter Finite Difference Method for Ordinary Differential Equations Chape 8.7 Finie Diffeence Mehod fo Odinay Diffeenial Eqaions Afe eading his chape, yo shold be able o. Undesand wha he finie diffeence mehod is and how o se i o solve poblems. Wha is he finie diffeence

More information

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System Communcaons n Sascs Theory and Mehods, 34: 475 484, 2005 Copyrgh Taylor & Francs, Inc. ISSN: 0361-0926 prn/1532-415x onlne DOI: 10.1081/STA-200047430 Survval Analyss and Relably A Noe on he Mean Resdual

More information

Contact, information, consultations

Contact, information, consultations ontact, nfomaton, consultatons hemsty A Bldg; oom 07 phone: 058-347-769 cellula: 664 66 97 E-mal: wojtek_c@pg.gda.pl Offce hous: Fday, 9-0 a.m. A quote of the week (o camel of the week): hee s no expedence

More information

Rotations.

Rotations. oons j.lbb@phscs.o.c.uk To s summ Fmes of efeence Invnce une nsfomons oon of wve funcon: -funcons Eule s ngles Emple: e e - - Angul momenum s oon geneo Genec nslons n Noehe s heoem Fmes of efeence Conse

More information

7 Wave Equation in Higher Dimensions

7 Wave Equation in Higher Dimensions 7 Wave Equaion in Highe Dimensions We now conside he iniial-value poblem fo he wave equaion in n dimensions, u c u x R n u(x, φ(x u (x, ψ(x whee u n i u x i x i. (7. 7. Mehod of Spheical Means Ref: Evans,

More information

Comparison of Differences between Power Means 1

Comparison of Differences between Power Means 1 In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,

More information

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation

Lecture 18: Kinetics of Phase Growth in a Two-component System: general kinetics analysis based on the dilute-solution approximation Lecue 8: Kineics of Phase Gowh in a Two-componen Sysem: geneal kineics analysis based on he dilue-soluion appoximaion Today s opics: In he las Lecues, we leaned hee diffeen ways o descibe he diffusion

More information

Supporting information How to concatenate the local attractors of subnetworks in the HPFP

Supporting information How to concatenate the local attractors of subnetworks in the HPFP n Effcen lgorh for Idenfyng Prry Phenoype rcors of Lrge-Scle Boolen Newor Sng-Mo Choo nd Kwng-Hyun Cho Depren of Mhecs Unversy of Ulsn Ulsn 446 Republc of Kore Depren of Bo nd Brn Engneerng Kore dvnced

More information

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION Inenaional Jounal of Science, Technology & Managemen Volume No 04, Special Issue No. 0, Mach 205 ISSN (online): 2394-537 STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE

More information

If there are k binding constraints at x then re-label these constraints so that they are the first k constraints.

If there are k binding constraints at x then re-label these constraints so that they are the first k constraints. Mathematcal Foundatons -1- Constaned Optmzaton Constaned Optmzaton Ma{ f ( ) X} whee X {, h ( ), 1,, m} Necessay condtons fo to be a soluton to ths mamzaton poblem Mathematcally, f ag Ma{ f ( ) X}, then

More information

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2

ANSWERS TO ODD NUMBERED EXERCISES IN CHAPTER 2 Joh Rley Novembe ANSWERS O ODD NUMBERED EXERCISES IN CHAPER Seo Eese -: asvy (a) Se y ad y z follows fom asvy ha z Ehe z o z We suppose he lae ad seek a oado he z Se y follows by asvy ha z y Bu hs oads

More information

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 9 Solutions [Theorems of Gauss and Stokes]

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 9 Solutions [Theorems of Gauss and Stokes] ENGI 44 Avance alculus fo Engineeing Faculy of Engineeing an Applie cience Poblem e 9 oluions [Theoems of Gauss an okes]. A fla aea A is boune by he iangle whose veices ae he poins P(,, ), Q(,, ) an R(,,

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

On the local convexity of the implied volatility curve in uncorrelated stochastic volatility models

On the local convexity of the implied volatility curve in uncorrelated stochastic volatility models On he local conexiy of he implied olailiy cue in uncoelaed sochasic olailiy models Elisa Alòs Dp. d Economia i Empesa and Bacelona Gaduae School of Economics Uniesia Pompeu Faba c/ramon Tias Fagas, 5-7

More information

Advanced Macroeconomics II: Exchange economy

Advanced Macroeconomics II: Exchange economy Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence

More information

Different kind of oscillation

Different kind of oscillation PhO 98 Theorecal Qeson.Elecrcy Problem (8 pons) Deren knd o oscllaon e s consder he elecrc crc n he gre, or whch mh, mh, nf, nf and kω. The swch K beng closed he crc s copled wh a sorce o alernang crren.

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

Track Properities of Normal Chain

Track Properities of Normal Chain In. J. Conemp. Mah. Scences, Vol. 8, 213, no. 4, 163-171 HIKARI Ld, www.m-har.com rac Propes of Normal Chan L Chen School of Mahemacs and Sascs, Zhengzhou Normal Unversy Zhengzhou Cy, Hennan Provnce, 4544,

More information

APPLICATIONS OF SEMIGENERALIZED -CLOSED SETS

APPLICATIONS OF SEMIGENERALIZED -CLOSED SETS Intenatonal Jounal of Mathematcal Engneeng Scence ISSN : 22776982 Volume Issue 4 (Apl 202) http://www.mes.com/ https://stes.google.com/ste/mesounal/ APPLICATIONS OF SEMIGENERALIZED CLOSED SETS G.SHANMUGAM,

More information

2 shear strain / L for small angle

2 shear strain / L for small angle Sac quaons F F M al Sess omal sess foce coss-seconal aea eage Shea Sess shea sess shea foce coss-seconal aea llowable Sess Faco of Safe F. S San falue Shea San falue san change n lengh ognal lengh Hooke

More information

Weights of Markov Traces on Cyclotomic Hecke Algebras

Weights of Markov Traces on Cyclotomic Hecke Algebras Jounal of Algeba 238, 762775 2001 do:10.1006jab.2000.8636, avalable onlne a hp:www.dealbay.com on Weghs of Maov Taces on Cycloomc Hece Algebas Hebng Ru 1 Depamen of Mahemacs, Eas Chna Nomal Unesy, Shangha,

More information

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing Peenaon fo Theoecal Condened Mae Phyc n TU Beln Geen-Funcon and GW appoxmaon Xnzheng L Theoy Depamen FHI May.8h 2005 Elecon n old Oulne Toal enegy---well olved Sngle pacle excaon---unde developng The Geen

More information

s = rθ Chapter 10: Rotation 10.1: What is physics?

s = rθ Chapter 10: Rotation 10.1: What is physics? Chape : oaon Angula poson, velocy, acceleaon Consan angula acceleaon Angula and lnea quanes oaonal knec enegy oaonal nea Toque Newon s nd law o oaon Wok and oaonal knec enegy.: Wha s physcs? In pevous

More information

A hybrid method to find cumulative distribution function of completion time of GERT networks

A hybrid method to find cumulative distribution function of completion time of GERT networks Jounal of Indusal Engneeng Inenaonal Sepembe 2005, Vol., No., - 9 Islamc Azad Uvesy, Tehan Souh Banch A hybd mehod o fnd cumulave dsbuon funcon of compleon me of GERT newos S. S. Hashemn * Depamen of Indusal

More information

Chapter 6 Plane Motion of Rigid Bodies

Chapter 6 Plane Motion of Rigid Bodies Chpe 6 Pne oon of Rd ode 6. Equon of oon fo Rd bod. 6., 6., 6.3 Conde d bod ced upon b ee een foce,, 3,. We cn ume h he bod mde of e numbe n of pce of m Δm (,,, n). Conden f he moon of he m cene of he

More information

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or

More information

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue

More information

Fall 2009 Social Sciences 7418 University of Wisconsin-Madison. Problem Set 2 Answers (4) (6) di = D (10)

Fall 2009 Social Sciences 7418 University of Wisconsin-Madison. Problem Set 2 Answers (4) (6) di = D (10) Publc Affars 974 Menze D. Chnn Fall 2009 Socal Scences 7418 Unversy of Wsconsn-Madson Problem Se 2 Answers Due n lecure on Thursday, November 12. " Box n" your answers o he algebrac quesons. 1. Consder

More information

UNIT10 PLANE OF REGRESSION

UNIT10 PLANE OF REGRESSION UIT0 PLAE OF REGRESSIO Plane of Regesson Stuctue 0. Intoducton Ojectves 0. Yule s otaton 0. Plane of Regesson fo thee Vaales 0.4 Popetes of Resduals 0.5 Vaance of the Resduals 0.6 Summay 0.7 Solutons /

More information

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth

Should Exact Index Numbers have Standard Errors? Theory and Application to Asian Growth Should Exac Index umbers have Sandard Errors? Theory and Applcaon o Asan Growh Rober C. Feensra Marshall B. Rensdorf ovember 003 Proof of Proposon APPEDIX () Frs, we wll derve he convenonal Sao-Vara prce

More information

Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial Lindley Distribution

Stochastic Orders Comparisons of Negative Binomial Distribution with Negative Binomial Lindley Distribution Oen Jounal of Statcs 8- htt://dxdoog/46/os5 Publshed Onlne Al (htt://wwwscrpog/ounal/os) Stochac Odes Comasons of Negatve Bnomal Dbuton wth Negatve Bnomal Lndley Dbuton Chooat Pudommaat Wna Bodhsuwan Deatment

More information

THE EQUIVALENCE OF GRAM-SCHMIDT AND QR FACTORIZATION (page 227) Gram-Schmidt provides another way to compute a QR decomposition: n

THE EQUIVALENCE OF GRAM-SCHMIDT AND QR FACTORIZATION (page 227) Gram-Schmidt provides another way to compute a QR decomposition: n HE EQUIVAENCE OF GRA-SCHID AND QR FACORIZAION (page 7 Ga-Schdt podes anothe way to copute a QR decoposton: n gen ectos,, K, R, Ga-Schdt detenes scalas j such that o + + + [ ] [ ] hs s a QR factozaton of

More information

ROBUST EXPONENTIAL ATTRACTORS FOR MEMORY RELAXATION OF PATTERN FORMATION EQUATIONS

ROBUST EXPONENTIAL ATTRACTORS FOR MEMORY RELAXATION OF PATTERN FORMATION EQUATIONS IJRRAS 8 () Augus www.apapess.com/olumes/ol8issue/ijrras_8.pdf ROBUST EXONENTIAL ATTRACTORS FOR EORY RELAXATION OF ATTERN FORATION EQUATIONS WANG Yuwe, LIU Yongfeng & A Qaozhen* College of ahemacs and

More information