An Inclusion-Exclusion Algorithm for Network Reliability with Minimal Cutsets

Size: px
Start display at page:

Download "An Inclusion-Exclusion Algorithm for Network Reliability with Minimal Cutsets"

Transcription

1 Amercan Journal of ompuaonal Mahemacs, 202, 2, 6-20 hp://dxdoorg/0426/acm Publshed Onlne December 202 (hp://wwwscrporg/ournal/acm) An Incluson-Excluson Algorhm for ework Relably wh Mnmal uses Yan-Ru Sun, We-Y Zhou School of Scences, orheasern Unversy, Shenyang, hna 2 School of Elecronc Informaon, Wuhan Unversy, Wuhan, hna Emal: yanrusun@26com, wey0564@26com Receved Augus 0, 202; revsed Ocober 9, 202; acceped Ocober 2, 202 ABSTRAT The ncluson-excluson formula (IEF) s a fundamenal ool for evaluang nework relably wh known mnmal pahs or mnmal cus However, he formula conans many pars of erms whch cancel Usng he noon of comparable node parons some properes of cancelng erms n IEF are gven Wh hese properes he hough of dynamc programmng mehod, a smple effcen ncluson-excluson algorhm for evaluang he source-o-ermnal relably of a nework sarng wh cuses s presened The algorhm generaes all he non-cancelng erms n he unrelably expresson The compuaonal complexy of he algorhm s Onm M, where n m are he numbers of nodes mnmal cus of he gven nework respecvely, M s he number of erms n he fnal symbolc unrelably expresson ha generaed usng he presened algorhm Examples are shown o llusrae he effecveness of he algorhm Keywords: Incluson-Excluson Formula; ework Relably; Mnmal use; Dynamc Programmng Inroducon The relably of a nework s an mporan parameer n desgn operaon of neworks There are many mehods o compue he relably of neworks [,2] Several algorhms exs n he leraure for evaluang he relably of a dreced graph by ncluson-excluson formula (IEF) based on eher pah (k-ree) enumeraon or cuse enumeraon [-8] In fndng he k-ermnal relably by IEF here are wo approaches, one based on enumerang all k-rees he oher based on enumerang all k-ermnal cus If here are m mnmal pahs (or cus) n a graph, here are 2 m possble nersecon erms n IEF However, he number of non-cancellng erms n IEF s consderably less Sarng wh he se of pahs (or k-rees) of a dreced graph, Sayanarayana coworkers [5,6] developed mehods of denfyng non-cancellng erms n IEF They showed ha he non-cancelng erms of he sourceo-ermnal relably correspond one-o-one wh he p-acyclc subgraphs of he gven graph An algorhm was gven for generang all he p-acyclc subgraphs of a dreced graph [5] Buzaco [] gave a correspondng resul for he non-cancellng erms n IEF sarng wh he se of cus of a graph Snce each erm n he resulng formula s assocaed wh a paron of he se of nodes of he graph, was called he node paron formula Fnd all he node parons of a graph s a very edous work Usng a lemma (he Lemma 4 of []) of ncomparable node parons of [] characerscs of cancelng erms n IEF, by he hough of dynamc programmng mehod a smple effcen ncluson-excluson algorhm s gven n hs paper for evaluang he sourceo-ermnal relably of a graph based on mnmal cus The algorhm generaes only he non-cancelng erms of he relably expresson of he graph 2 omenclaure, oaon Assumpon A nework s modeled as a dreced graph, E (abbrevaed o ) whch consss of a se of nodes a se of edges (lnks) E A node s s he source of s s he ermnal of 2 omenclaure Source o ermnal (s ) relably: he probably ha he source s s conneced o he ermnal node by pahs workng edges s cu: a subse of edges whose removal dvdes he node se of he nework no wo pars such ha, s, e he edge se from o opyrgh 202 ScRes

2 Y-R SU, W-Y ZHOU 7 Mnmal s cu: s cu whch no longer remans a s cu f s any edge s removed dae chld se of : an ordered se,,, 2 r conssng of all he node ses such ha, 2,, r, 2 r Incomparable node ses: a par of node ses 2 such ha oaon : subse of such ha s : complemen of,, : cu, e he edge se from o : ) cu, ) even ha all he edges of cu fal : ) unon of all he edges of cus 2) nersecon of evens U : unon of cus ( ): cdae chld se of Pr : he probably of even Q s, : source-o-ermnal (s ) unrelably of 2 Assumpon ) has perfecly relable nodes s-ndependen 2-sae (good faled) edges, he relably of each edge has been gven 2) Le,, be mncus of, hen, s also a mn- cu of Prelmnares 2 Le,,, m be he se of mncus of a gven nework where corresponds one-o-one wh he node se, e, (, 2,, m) The s unrelably of, by IEF, can be expressed as Q s, Pr 2 Pr Pr m m m Pr k Pr 2m km m he summaons are over all mncus mncu combnaons In formula (), here exs 2 m possble erms Bu s possble ha U U for some,, Indeed he mos vexng problem n relably analyss usng () s he appearance of large numbers of pars of dencal erms wh oppose sgn, whch cancel Fnd he charac- () ersc of cancelng erms n () s he keysone of an effcen algorhm Buzaco gave a smple very useful lemma (Lemma 4 of Ref []) o denfy some cancelng erms n () Lemma ven any wo mncus,, of such ha are ncom- parable, all erms n IEF conanng boh, s also a mncu [] cancel f In formula (), assume ha 2 m Accordng o Lemma, () can be changed no: Q s, Pr 2 m Pr Pr m m k km Pr k k Pr 2 k 2 k m 2 he summaons are over all mncus mncu combnaons ha sasfy he gven condons The erms n (2) can correspond one-o-one he verex of he m rooed rees wh he followng properes ) The roo verex of each rooed ree s he verex correspondng o cu se, s wegh s, sgn s + 2) Sons of each verex n every rooed ree are all elemens n, each son s wegh s he unon of s faher s wegh he cu se correspondng o hs son verex, sgn s s faher s sgn mes For example, le, 2 4, k (2), 2,, 4 Fgure are four rooed rees In Fgure (a), ree ( ) has a only verex, he roo verex Is wegh s 4, sgn s In Fgure (c), ree ( 2 ) s roo verex s 2, s wegh s 2, sgn s 2 has wo sons: The son verex s wegh equals o 2, sgn s ; s wegh equals o 4, sgn s s son s, here s wegh equals o 2 4, sgn s The wegh wh s sgn of each node n he rooed rees one-o-one corresponds wh he erm n he expresson of formula (2) So we dscuss m rooed rees gener- ang The rooed ree whose roo s s denoed as ree In fac, f we generae he rooed rees n non-ncreasng order of roo s modulus generae he sons of each verex n non-decreasng order of he son s modulus, we can use he rees whch have already been generaed o generae he followng rees For example, Fgure are four rooed rees, where ree s a branch of ree 2, ree 2 ree are he branches of ree If ree s generaed frsly, we can use he resul when we generae ree And when we 2 opyrgh 202 ScRes

3 8 Y-R SU, W-Y ZHOU (a) 2 he generang processes of he rees wh above properes ) 2) Such ha rees verces correspond wh he non-cancelng erms of (2) 4 Algorhm (b) (c) Ths secon presens an algorhm for effcenly generang all he non-cancelng erms n (2) The algorhm has four pars, The man par s o generae all rees whose verces correspond wh he non-cancelng erms of (2) 2 (d) Fgure Rooed sub-rees (a) Sub-ree( ); (b) Sub-ree( ); (c) Sub-ree( 2 ); (d) Sub-ree( ) generae ree, we can use ree ree 2 drecly Ths s he hough of dynamc programmng By hs hough he process of generang rees s grealy smplfed In formula (2) here are sll many erms ha can cancel each oher The properes of cancelng erms are dscussed as follow Theorem Le,, 2, be hree mncus of a dreced graph, 2, 2 Then for any mncus, ha, ha of, , 2 4 4,, 2, be hree Theorem 2 Le mncus of a dreced graph wh 2 If 2 hen doesn exs mncu 0, 0 0 of, 0 such ha ; doesn exs 4, 4 of, 4 such ha Theorems 2 mply ha f we fnd ou he all pars of cancelng erms ha unon of wo cus hree cus, e fnd ou all U 2 U wh U2 U, he all cancelng erms n (2) can be deermned The followng lemma gves a condon ha U2 U n (2) Lemma 2 Le,, 2, be hree mnmal cus of a dreced graph 2 Then 2 f only f 2, 2 Accordng o Theorems 2 Lemma 2 we gve 4 Algorhm ) Fnd all he mncus of he gven dreced nework, E whch sasfes assumpons Le, 2,, m be he mncus correspondng o he node parons, 2,, m, respecvely Order he node parons as, 2,, m such ha 2 m 2) Fnd, 2,, m ) enerae m rooed rees by he followng Algorhm-Tree, e generae all he non-cancelng erms of Q s, 4) Sum up he weghs wh sgn of verces of all he rees o oban he symbolc expresson of Q s, Fnally, we ge he symbolc relably expresson of R s, Q s, 42 Algorhm Tree By heorems 2, all he pars of cancelng erms n IEF can be known f we fnd he cancelng erms whch unon of wo hree cu ses Usng hs propery an algorhm Algorhm Tree s gven I has wo pars: Trees eneraon Weghed Trees I generaes rooed rees n he non-ncrease order of he roo verex s modulus, e generaes ree ( m ), ree ( m- ),, ree ( ) successvely 42 Trees eneraon We shall gve an algorhm o generae all rees as follow Algorhm Trees eneraon Inpu: ) he node parons of :, 2,, m such ha 2 m he correspondng mncus, 2,, m 2) he cdae chld ses:,,,,, 2 k k k2 k,,,,,, m m m Oupu: all he rees Begn Sep enerae he frs ree m wh he only verex, e roo verex m Sep 2 enerae he second ree wh a roo k m opyrgh 202 ScRes

4 Y-R SU, W-Y ZHOU 9 verex m s only son verex m Sep Suppose ha rees,, 2,, k m m have been generaed enerae ree mk ) The roo of ree m k s mk 2) enerae sons (we call hem he frs-generaon offsprng) of :,,, (where m k mk, mk,2 mk, m k mk,,, 2,, mk, k mk m, m k m k, mk m); mk,, mk,2,,, m k m k m k m k ) Whle m k do Begn Denoe he elemen wh he mnmal modulus n m k as m k,, mk m k m k m k, m k wh ree m k, Subsue s son m k, Denoe he son se of hs verex Suppose mk,,, m k, mk, mk, 2 k, k non-decreasng order of her modulus) mk mk 0,, For = o Begn If k mk, mk, m m k m k m k mk mk m k mk mkm k, ; as m k,,,, m k,, hen,,, ; (n he u m k s son m k, m k, s son mk, wh s offsprng from curren ree ; mk, Else nex End If 0 m k, m k,, hen mark, m k, called sll node, denoed as sll node m k, End Sep 4 Repea sep unl all he rees, m, m,,2, have been generaed End 422 Weghed Trees Tree s wegh s defned he sum of roo s all verces weghs wh her sgn In he order of generang rees, sarng from each ree s roo verex gves each verex a wegh n dephfrs-search The roo s wegh s he all edges of he cu se correspondng o he roo verex, sgn s + Each non-sll verex s wegh equals o he unon of s fa- s Fgure 2 ework her s wegh all he edges of he cu se correspondng hs verex, s sgn s s faher s sgn mes Each sll node s wegh equals o he unon of s faher s wegh he ree s wegh whose roo s hs verex s sgn s s faher s sgn mes 5 ompuaonal omplexy The man par of he presened algorhm s Algorhm Sub-ree I has wo pars One s Sub-rees eneraon, he oher s Weghed Sub-rees The man work of algorhm Sub-rees eneraon s o deermne wheher here exs edges beween wo ses I a mos needs m m2mm 2 comparson operaons for each sub-ree Weghed sub-rees runs n OM, where M s he number of erms n he las symbolc un-relably expresson In fac, M s more smaller han 2 m For example,he nework n Fgure 2, m = 8, m , bu M = 5 For each of sub-rees, oher operaons a mos ake Om me I akes On me o fnd all mncus, where n s he number of nodes of a gven nework I akes Om me o fnd each cdae chld se So he compuaonal complex- y of he presened algorhm s O nm M 6 oncluson Ths paper presens an effcen algorhm for evaluang he relably of nework based mncus The algorhm generaes all he non-cancelng erms n he unrelably expresson By he hough of dynamc programmng mehod each verex a mos generaes wo generaons chldren n every sub-ree The number of verces of he generaed sub-rees are more smaller han he number of non-cancelng erms n Q s, s expresson The algorhm has smaller me complexy REFEREES [] J olbourn, The ombnaorcs of ework Relably, Oxford Unversy Press, ew York, Oxford, 987 [2] M O Ball, J olbourn J S Provan, ework Relably, Hbook of Operaons Research: ework Models, Elsever orh-holl, Amserdam, Vol 7, 995, pp opyrgh 202 ScRes

5 20 Y-R SU, W-Y ZHOU [] J A Buzaco, ode Paron Formula for Dreced raph Relably, eworks, Vol 7, o 2, 987, pp do:0002/ne [4] J A Buzaco S K hang, u Se Inersecons ode Paron, IEEE Transacons on Relably, Vol, o 4, 982, pp [5] A Sayanarayana A Prabhakar, ew Topologcal Formula Rapd Algorhm for Relably Analyss of omplex eworks, IEEE Transacons on Relably, Vol 27, o, 978, pp do:009/tr [6] A Sayanarayana J Hagsrom, A ew Algorhm for Relably Analyss of Mul-Termnal eworks, IEEE Transacons on Relably, Vol 0, o 4, 98, pp 25-4 do:009/tr [7] L Zhao F J Kong, A ew Formula an Algorhm for Relably Analyss of ework, Mcroelecron Relably, Vol 7, o 4, 997, pp 5-58 [8] W Yeh, A reedy Branch--Bound Incluson-Excluson Algorhm for alculang he Exac Mul-Sae ework Relably, IEEE Transacons on Relably, Vol 57, o, 2008, pp 88-9 do:009/tr opyrgh 202 ScRes

Chain-linking and seasonal adjustment of the quarterly national accounts

Chain-linking and seasonal adjustment of the quarterly national accounts Sascs Denmark Naonal Accouns 6 July 00 Chan-lnkng and seasonal adjusmen of he uarerly naonal accouns The mehod of chan-lnkng he uarerly naonal accouns was changed wh he revsed complaon of daa hrd uarer

More information

Online Technical Appendix: Estimation Details. Following Netzer, Lattin and Srinivasan (2005), the model parameters to be estimated

Online Technical Appendix: Estimation Details. Following Netzer, Lattin and Srinivasan (2005), the model parameters to be estimated Onlne Techncal Appendx: Esmaon Deals Followng Nezer, an and Srnvasan 005, he model parameers o be esmaed can be dvded no hree pars: he fxed effecs governng he evaluaon, ncdence, and laen erence componens

More information

Michał Kolupa, Zbigniew Śleszyński SOME REMARKS ON COINCIDENCE OF AN ECONOMETRIC MODEL

Michał Kolupa, Zbigniew Śleszyński SOME REMARKS ON COINCIDENCE OF AN ECONOMETRIC MODEL M I S C E L L A N E A Mchał Kolupa, bgnew Śleszyńsk SOME EMAKS ON COINCIDENCE OF AN ECONOMETIC MODEL Absrac In hs paper concep of concdence of varable and mehods for checkng concdence of model and varables

More information

FITTING EXPONENTIAL MODELS TO DATA Supplement to Unit 9C MATH Q(t) = Q 0 (1 + r) t. Q(t) = Q 0 a t,

FITTING EXPONENTIAL MODELS TO DATA Supplement to Unit 9C MATH Q(t) = Q 0 (1 + r) t. Q(t) = Q 0 a t, FITTING EXPONENTIAL MODELS TO DATA Supplemen o Un 9C MATH 01 In he handou we wll learn how o fnd an exponenal model for daa ha s gven and use o make predcons. We wll also revew how o calculae he SSE and

More information

IFX-Cbonds Russian Corporate Bond Index Methodology

IFX-Cbonds Russian Corporate Bond Index Methodology Approved a he meeng of he Commee represenng ZAO Inerfax and OOO Cbonds.ru on ovember 1 2005 wh amendmens complan wh Agreemen # 545 as of ecember 17 2008. IFX-Cbonds Russan Corporae Bond Index Mehodology

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Normal Random Varable and s dscrmnan funcons Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped sngle prooype

More information

Section 6 Short Sales, Yield Curves, Duration, Immunization, Etc.

Section 6 Short Sales, Yield Curves, Duration, Immunization, Etc. More Tuoral a www.lledumbdocor.com age 1 of 9 Secon 6 Shor Sales, Yeld Curves, Duraon, Immunzaon, Ec. Shor Sales: Suppose you beleve ha Company X s sock s overprced. You would ceranly no buy any of Company

More information

American basket and spread options. with a simple binomial tree

American basket and spread options. with a simple binomial tree Amercan baske and spread opons wh a smple bnomal ree Svelana orovkova Vre Unverse Amserdam Jon work wh Ferry Permana acheler congress, Torono, June 22-26, 2010 1 Movaon Commody, currency baskes conss of

More information

Improving Forecasting Accuracy in the Case of Intermittent Demand Forecasting

Improving Forecasting Accuracy in the Case of Intermittent Demand Forecasting (IJACSA) Inernaonal Journal of Advanced Compuer Scence and Applcaons, Vol. 5, No. 5, 04 Improvng Forecasng Accuracy n he Case of Inermen Demand Forecasng Dasuke Takeyasu The Open Unversy of Japan, Chba

More information

Deriving Reservoir Operating Rules via Fuzzy Regression and ANFIS

Deriving Reservoir Operating Rules via Fuzzy Regression and ANFIS Dervng Reservor Operang Rules va Fuzzy Regresson and ANFIS S. J. Mousav K. Ponnambalam and F. Karray Deparmen of Cvl Engneerng Deparmen of Sysems Desgn Engneerng Unversy of Scence and Technology Unversy

More information

SkyCube Computation over Wireless Sensor Networks Based on Extended Skylines

SkyCube Computation over Wireless Sensor Networks Based on Extended Skylines Proceedngs of he 2010 IEEE Inernaonal Conference on Informaon and Auomaon June 20-23, Harbn, Chna SkyCube Compuaon over Wreless Sensor Neworks Based on Exended Skylnes Zhqong Wang 1, Zhyue Wang 2, Junchang

More information

The Financial System. Instructor: Prof. Menzie Chinn UW Madison

The Financial System. Instructor: Prof. Menzie Chinn UW Madison Economcs 435 The Fnancal Sysem (2/13/13) Insrucor: Prof. Menze Chnn UW Madson Sprng 2013 Fuure Value and Presen Value If he presen value s $100 and he neres rae s 5%, hen he fuure value one year from now

More information

Lab 10 OLS Regressions II

Lab 10 OLS Regressions II Lab 10 OLS Regressons II Ths lab wll cover how o perform a smple OLS regresson usng dfferen funconal forms. LAB 10 QUICK VIEW Non-lnear relaonshps beween varables nclude: o Log-Ln: o Ln-Log: o Log-Log:

More information

Fugit (options) The terminology of fugit refers to the risk neutral expected time to exercise an

Fugit (options) The terminology of fugit refers to the risk neutral expected time to exercise an Fug (opons) INTRODUCTION The ermnology of fug refers o he rsk neural expeced me o exercse an Amercan opon. Invened by Mark Garman whle professor a Berkeley n he conex of a bnomal ree for Amercan opon hs

More information

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory Onlne appendces fro Counerpary sk and Cred alue Adusen a connung challenge for global fnancal arkes by Jon Gregory APPNDX A: Dervng he sandard CA forula We wsh o fnd an expresson for he rsky value of a

More information

A valuation model of credit-rating linked coupon bond based on a structural model

A valuation model of credit-rating linked coupon bond based on a structural model Compuaonal Fnance and s Applcaons II 247 A valuaon model of cred-rang lnked coupon bond based on a srucural model K. Yahag & K. Myazak The Unversy of Elecro-Communcaons, Japan Absrac A cred-lnked coupon

More information

SOCIETY OF ACTUARIES FINANCIAL MATHEMATICS. EXAM FM SAMPLE SOLUTIONS Interest Theory

SOCIETY OF ACTUARIES FINANCIAL MATHEMATICS. EXAM FM SAMPLE SOLUTIONS Interest Theory SOCIETY OF ACTUARIES EXAM FM FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS Ineres Theory Ths page ndcaes changes made o Sudy Noe FM-09-05. January 4, 04: Quesons and soluons 58 60 were added. June, 04

More information

Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. Hongliang Yan 2017/06/21

Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. Hongliang Yan 2017/06/21 nd he class wegh bas: weghed maxmum mean dscrepancy for unsupervsed doman adapaon Honglang Yan 207/06/2 Doman Adapaon Problem: Tranng and es ses are relaed bu under dfferen dsrbuons. Tranng (Source) DA

More information

Pricing and Valuation of Forward and Futures

Pricing and Valuation of Forward and Futures Prcng and Valuaon of orward and uures. Cash-and-carry arbrage he prce of he forward conrac s relaed o he spo prce of he underlyng asse, he rsk-free rae, he dae of expraon, and any expeced cash dsrbuons

More information

Online appendices from The xva Challenge by Jon Gregory. APPENDIX 14A: Deriving the standard CVA formula.

Online appendices from The xva Challenge by Jon Gregory. APPENDIX 14A: Deriving the standard CVA formula. Onlne appendces fro he xa Challenge by Jon Gregory APPNDX 4A: Dervng he sandard CA forla We wsh o fnd an expresson for he rsky vale of a need se of dervaves posons wh a ax ary dae Denoe he rsk-free vale

More information

Hardware-Assisted High-Efficiency Ray Casting of Unstructured Time-Varying Flows Using Temporal Coherence

Hardware-Assisted High-Efficiency Ray Casting of Unstructured Time-Varying Flows Using Temporal Coherence Hardware-Asssed Hgh-Effcency Ray Casng of Unsrucured Tme-Varyng Flows Usng Temporal Coherence Qanl Ma, Lang Zeng, Huaxun Xu, Wenke Wang, Skun L Absrac Advances n compuaonal power are enablng hgh-precson

More information

EXPLOITING GEOMETRICAL NODE LOCATION FOR IMPROVING SPATIAL REUSE IN SINR-BASED STDMA MULTI-HOP LINK SCHEDULING ALGORITHM

EXPLOITING GEOMETRICAL NODE LOCATION FOR IMPROVING SPATIAL REUSE IN SINR-BASED STDMA MULTI-HOP LINK SCHEDULING ALGORITHM Inernaonal Journal of Technology (2015) 1: 53 62 ISSN 2086 9614 IJTech 2015 EXLOITING GEOMETRICAL NODE LOCATION FOR IMROVING SATIAL REUSE IN SINR-BASED STDMA MULTI-HO LINK SCHEDULING ALGORITHM Nachwan

More information

Recall from last time. The Plan for Today. INTEREST RATES JUNE 22 nd, J u n e 2 2, Different Types of Credit Instruments

Recall from last time. The Plan for Today. INTEREST RATES JUNE 22 nd, J u n e 2 2, Different Types of Credit Instruments Reall from las me INTEREST RATES JUNE 22 nd, 2009 Lauren Heller Eon 423, Fnanal Markes Smple Loan rnpal and an neres paymen s pad a maury Fxed-aymen Loan Equal monhly paymens for a fxed number of years

More information

STOCK PRICES TEHNICAL ANALYSIS

STOCK PRICES TEHNICAL ANALYSIS STOCK PRICES TEHNICAL ANALYSIS Josp Arnerć, Elza Jurun, Snježana Pvac Unversy of Spl, Faculy of Economcs Mace hrvaske 3 2 Spl, Croaa jarnerc@efs.hr, elza@efs.hr, spvac@efs.hr Absrac Ths paper esablshes

More information

Fairing of Polygon Meshes Via Bayesian Discriminant Analysis

Fairing of Polygon Meshes Via Bayesian Discriminant Analysis Farng of Polygon Meshes Va Bayesan Dscrmnan Analyss Chun-Yen Chen Insue of Informaon Scence, Academa Snca. Deparmen of Compuer Scence and Informaon Engneerng, Naonal Tawan Unversy. 5, Tawan, Tape, Nankang

More information

Floating rate securities

Floating rate securities Caps and Swaps Floang rae secures Coupon paymens are rese perodcally accordng o some reference rae. reference rae + ndex spread e.g. -monh LIBOR + 00 bass pons (posve ndex spread 5-year Treasury yeld 90

More information

PFAS: A Resource-Performance-Fluctuation-Aware Workflow Scheduling Algorithm for Grid Computing

PFAS: A Resource-Performance-Fluctuation-Aware Workflow Scheduling Algorithm for Grid Computing PFAS: A Resource-Performance-Flucuaon-Aware Workflow Schedulng Algorhm for Grd Compung Fangpeng Dong and Selm G. Akl School of Compung, Queen's Unversy Kngson, ON Canada, K7L N6 {dong, akl}@cs.queensu.ca

More information

A Backbone Formation Algorithm in Wireless Sensor Network Based on Pursuit Algorithm

A Backbone Formation Algorithm in Wireless Sensor Network Based on Pursuit Algorithm Ysong Jang, Weren Sh A Backbone Formaon Algorhm n Wreless Sensor Nework Based on Pursu Algorhm YISONG JIANG, WEIREN SHI College of Auomaon Chongqng Unversy No 74 Shazhengje, Shapngba, Chongqng Chna jys398@6com,

More information

Dynamic Relationship and Volatility Spillover Between the Stock Market and the Foreign Exchange market in Pakistan: Evidence from VAR-EGARCH Modelling

Dynamic Relationship and Volatility Spillover Between the Stock Market and the Foreign Exchange market in Pakistan: Evidence from VAR-EGARCH Modelling Dynamc Relaonshp and Volaly pllover Beween he ock Marke and he Foregn xchange marke n Paksan: vdence from VAR-GARCH Modellng Dr. Abdul Qayyum Dr. Muhammad Arshad Khan Inroducon A volale sock and exchange

More information

UNN: A Neural Network for uncertain data classification

UNN: A Neural Network for uncertain data classification UNN: A Neural Nework for unceran daa classfcaon Jaq Ge, and Yun Xa, Deparmen of Compuer and Informaon Scence, Indana Unversy Purdue Unversy, Indanapols, USA {jaqge, yxa }@cs.upu.edu Absrac. Ths paper proposes

More information

Terms and conditions for the MXN Peso / US Dollar Futures Contract (Physically Delivered)

Terms and conditions for the MXN Peso / US Dollar Futures Contract (Physically Delivered) The Englsh verson of he Terms and Condons for Fuures Conracs s publshed for nformaon purposes only and does no consue legal advce. However, n case of any Inerpreaon conroversy, he Spansh verson shall preval.

More information

The Empirical Research of Price Fluctuation Rules and Influence Factors with Fresh Produce Sequential Auction Limei Cui

The Empirical Research of Price Fluctuation Rules and Influence Factors with Fresh Produce Sequential Auction Limei Cui 6h Inernaonal Conference on Sensor Nework and Compuer Engneerng (ICSNCE 016) The Emprcal Research of Prce Flucuaon Rules and Influence Facors wh Fresh Produce Sequenal Aucon Lme Cu Qujng Normal Unversy,

More information

A Novel Approach to Model Generation for Heterogeneous Data Classification

A Novel Approach to Model Generation for Heterogeneous Data Classification A Novel Approach o Model Generaon for Heerogeneous Daa Classfcaon Rong Jn*, Huan Lu *Dep. of Compuer Scence and Engneerng, Mchgan Sae Unversy, Eas Lansng, MI 48824 rongn@cse.msu.edu Deparmen of Compuer

More information

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm An Application to the Data of Operating equipment and supplies

A Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm An Application to the Data of Operating equipment and supplies A Hyrd Mehod o Improve Forecasng Accuracy Ulzng Genec Algorhm An Applcaon o he Daa of Operang equpmen and supples Asam Shara Tax Corporaon Arkne, Shzuoka Cy, Japan, e-mal: a-shara@arkne.nfo Dasuke Takeyasu

More information

Batch Processing for Incremental FP-tree Construction

Batch Processing for Incremental FP-tree Construction Inernaonal Journal of Compuer Applons (975 8887) Volume 5 No.5, Augus 21 Bach Processng for Incremenal FP-ree Consrucon Shashkumar G. Toad Deparmen of CSE, GMRIT, Rajam, Srkakulam Dsrc AndraPradesh, Inda.

More information

DEA-Risk Efficiency and Stochastic Dominance Efficiency of Stock Indices *

DEA-Risk Efficiency and Stochastic Dominance Efficiency of Stock Indices * JEL Classfcaon: C61, D81, G11 Keywords: Daa Envelopmen Analyss, rsk measures, ndex effcency, sochasc domnance DEA-Rsk Effcency and Sochasc Domnance Effcency of Sock Indces * Marn BRANDA Charles Unversy

More information

Network Security Risk Assessment Based on Node Correlation

Network Security Risk Assessment Based on Node Correlation Journal of Physcs: Conference Seres PAPER OPE ACCESS ewor Secury Rs Assessmen Based on ode Correlaon To ce hs arcle: Zengguang Wang e al 2018 J. Phys.: Conf. Ser. 1069 012073 Vew he arcle onlne for updaes

More information

Optimum Reserve Capacity Assessment and Energy and Spinning Reserve Allocation Based on Deterministic and Stochastic Security Approach

Optimum Reserve Capacity Assessment and Energy and Spinning Reserve Allocation Based on Deterministic and Stochastic Security Approach Ausralan Journal of Basc and Appled Scences, 4(9): 4400-4412, 2010 ISS 1991-8178 Opmum Reserve Capacy Assessmen and Enery and Spnnn Reserve Allocaon Based on Deermnsc and Sochasc Secury Approach Farzad

More information

Explaining Product Release Planning Results Using Concept Analysis

Explaining Product Release Planning Results Using Concept Analysis Explanng Produc Release Plannng Resuls Usng Concep Analyss Gengshen Du, Thomas Zmmermann, Guenher Ruhe Deparmen of Compuer Scence, Unversy of Calgary 2500 Unversy Drve NW, Calgary, Albera T2N 1N4, Canada

More information

Optimal Combination of Trading Rules Using Neural Networks

Optimal Combination of Trading Rules Using Neural Networks Vol. 2, No. Inernaonal Busness Research Opmal Combnaon of Tradng Rules Usng Neural Neworks Subraa Kumar Mra Professor, Insue of Managemen Technology 35 Km Mlesone, Kaol Road Nagpur 44 502, Inda Tel: 9-72-280-5000

More information

CENTRO DE ESTUDIOS MONETARIOS Y FINANCIEROS T. J. KEHOE MACROECONOMICS I WINTER 2011 PROBLEM SET #6

CENTRO DE ESTUDIOS MONETARIOS Y FINANCIEROS T. J. KEHOE MACROECONOMICS I WINTER 2011 PROBLEM SET #6 CENTRO DE ESTUDIOS MONETARIOS Y FINANCIEROS T J KEHOE MACROECONOMICS I WINTER PROBLEM SET #6 This quesion requires you o apply he Hodrick-Presco filer o he ime series for macroeconomic variables for he

More information

Accuracy of the intelligent dynamic models of relational fuzzy cognitive maps

Accuracy of the intelligent dynamic models of relational fuzzy cognitive maps Compuer Applcaons n Elecrcal Engneerng Accuracy of he nellgen dynamc models of relaonal fuzzy cognve maps Aleksander Jasrebow, Grzegorz Słoń Kelce Unversy of Technology 25-314 Kelce, Al. Tysącleca P. P.

More information

Tax Dispute Resolution and Taxpayer Screening

Tax Dispute Resolution and Taxpayer Screening DISCUSSION PAPER March 2016 No. 73 Tax Dspue Resoluon and Taxpayer Screenng Hdek SATO* Faculy of Economcs, Kyushu Sangyo Unversy ----- *E-Mal: hsao@p.kyusan-u.ac.jp Tax Dspue Resoluon and Taxpayer Screenng

More information

Trade, Growth, and Convergence in a Dynamic Heckscher-Ohlin Model*

Trade, Growth, and Convergence in a Dynamic Heckscher-Ohlin Model* Federal Reserve Ban of Mnneapols Research Deparmen Saff Repor 378 Ocober 8 (Frs verson: Sepember 6) Trade, Growh, and Convergence n a Dynamc Hecscher-Ohln Model* Clausre Bajona Ryerson Unversy Tmohy J.

More information

Associating Absent Frequent Itemsets with Infrequent Items to Identify Abnormal Transactions

Associating Absent Frequent Itemsets with Infrequent Items to Identify Abnormal Transactions Assocang Absen Frequen Iemses wh Infrequen Iems o Idenfy Abnormal Transacons L-Jen Kao Deparmen of Compuer Scence and Informaon Engneerng Hwa Hsa Insue of Technology New Tape Cy, Tawan 23568 ljenkao@cc.hwh.edu.w

More information

San Francisco State University ECON 560 Summer 2018 Problem set 3 Due Monday, July 23

San Francisco State University ECON 560 Summer 2018 Problem set 3 Due Monday, July 23 San Francisco Sae Universiy Michael Bar ECON 56 Summer 28 Problem se 3 Due Monday, July 23 Name Assignmen Rules. Homework assignmens mus be yped. For insrucions on how o ype equaions and mah objecs please

More information

Supplement to Chapter 3

Supplement to Chapter 3 Supplemen o Chaper 3 I. Measuring Real GD and Inflaion If here were only one good in he world, anchovies, hen daa and prices would deermine real oupu and inflaion perfecly: GD Q ; GD Q. + + + Then, he

More information

Chapter - IV. Total and Middle Fuzzy Graph

Chapter - IV. Total and Middle Fuzzy Graph Chapter - IV otal and Mddle Fuzzy Graph CHAPER - IV OAL AND MIDDLE FUZZY GRAPH In ths chapter for the gven fuzzy graph G:(σ, µ), subdvson fuzzy graph sd(g) : ( σ sd, µ sd ), square fuzzy graph S 2 ( G)

More information

Keywords: School bus problem, heuristic, harmony search

Keywords: School bus problem, heuristic, harmony search Journal of Emergng Trends n Compung and Informaon Scences 2009-2013 CIS Journal. All rghs reserved. hp://www.csjournal.org Model and Algorhm for Solvng School Bus Problem 1 Taehyeong Km, 2 Bum-Jn Par 1

More information

Cryptographic techniques used to provide integrity of digital content in long-term storage

Cryptographic techniques used to provide integrity of digital content in long-term storage RB/3/2011 Crypographc echnques used o provde negry of dgal conen n long-erm sorage REPORT ON THE PROBLEM Problem presened by Marn Šmka Paweł Wojcechowsk Polsh Secury Prnng Works (PWPW) 1 Repor auhors Małgorzaa

More information

To find a non-split strong dominating set of an interval graph using an algorithm

To find a non-split strong dominating set of an interval graph using an algorithm IOSR Journal of Mathematcs (IOSR-JM) e-issn: 2278-5728,p-ISSN: 219-765X, Volume 6, Issue 2 (Mar - Apr 201), PP 05-10 To fnd a non-splt rong domnatng set of an nterval graph usng an algorthm Dr A Sudhakaraah*,

More information

Dr. A. Sudhakaraiah* V. Rama Latha E.Gnana Deepika

Dr. A. Sudhakaraiah* V. Rama Latha E.Gnana Deepika Internatonal Journal Of Scentfc & Engneerng Research, Volume, Issue 6, June-0 ISSN - Splt Domnatng Set of an Interval Graph Usng an Algorthm. Dr. A. Sudhakaraah* V. Rama Latha E.Gnana Deepka Abstract :

More information

A Hybrid Method for Forecasting with an Introduction of a Day of the Week Index to the Daily Shipping Data of Sanitary Materials

A Hybrid Method for Forecasting with an Introduction of a Day of the Week Index to the Daily Shipping Data of Sanitary Materials Journal of Communcaon and Compuer (05) 0-07 do: 0.765/548-7709/05.0.00 D DAVID PUBLISHING A Hyrd Mehod for Forecasng wh an Inroducon of a Day of he Week Inde o he Daly Shppng Daa of Sanary Maerals Dasuke

More information

Financial Innovation and Asset Price Volatility. Online Technical Appendix

Financial Innovation and Asset Price Volatility. Online Technical Appendix Fnancal Innovaon and Asse Prce Volaly Onlne Techncal Aendx Felx Kubler and Karl Schmedders In hs echncal aendx we derve all numbered equaons dslayed n he aer Equaons For he wo models n he aer, he frs se

More information

Correlation of default

Correlation of default efaul Correlaon Correlaon of defaul If Oblgor A s cred qualy deeroraes, how well does he cred qualy of Oblgor B correlae o Oblgor A? Some emprcal observaons are efaul correlaons are general low hough hey

More information

Open Access Impact of Wind Power Generation on System Operation and Costs

Open Access Impact of Wind Power Generation on System Operation and Costs Send Orders for Reprns o reprns@benhamscence.ae 580 he Open Elecrcal & Elecronc Engneerng Journal, 2014, 8, 580-588 Open Access Impac of nd Power eneraon on Sysem Operaon and oss ang Fe 1,2,*, Pan enxa

More information

Estimating intrinsic currency values

Estimating intrinsic currency values Esmang nrnsc currency values Forex marke praconers consanly alk abou he srenghenng or weakenng of ndvdual currences. In hs arcle, Jan Chen and Paul Dous presen a new mehodology o quanfy hese saemens n

More information

NBER WORKING PAPER SERIES TRADE, GROWTH, AND CONVERGENCE IN A DYNAMIC HECKSCHER-OHLIN MODEL. Claustre Bajona Timothy J. Kehoe

NBER WORKING PAPER SERIES TRADE, GROWTH, AND CONVERGENCE IN A DYNAMIC HECKSCHER-OHLIN MODEL. Claustre Bajona Timothy J. Kehoe NBER WORKING PAPER SERIES TRADE, GROWTH, AND CONVERGENCE IN A DYNAMIC HECKSCHER-OHLIN MODEL Clausre Bajona Tmohy J. Kehoe Worng Paper 567 hp://www.nber.org/papers/w567 NATIONAL BUREAU OF ECONOMIC RESEARCH

More information

ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm

ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm Informaon 25, 6, 3-33; do:.339/nfo633 Arcle OPEN ACCESS nformaon ISSN 278-2489 www.mdp.com/journal/nformaon ANFIS Based Tme Seres Predcon Mehod of Bank Cash Flow Opmzed by Adapve Populaon Acvy PSO Algorhm

More information

Macroeconomics II A dynamic approach to short run economic fluctuations. The DAD/DAS model.

Macroeconomics II A dynamic approach to short run economic fluctuations. The DAD/DAS model. Macroeconomics II A dynamic approach o shor run economic flucuaions. The DAD/DAS model. Par 2. The demand side of he model he dynamic aggregae demand (DAD) Inflaion and dynamics in he shor run So far,

More information

Inventory Investment. Investment Decision and Expected Profit. Lecture 5

Inventory Investment. Investment Decision and Expected Profit. Lecture 5 Invenory Invesmen. Invesmen Decision and Expeced Profi Lecure 5 Invenory Accumulaion 1. Invenory socks 1) Changes in invenory holdings represen an imporan and highly volaile ype of invesmen spending. 2)

More information

Some Insights of Value-Added Tax Gap

Some Insights of Value-Added Tax Gap Ovdus Unversy Annals, Economc Scences Seres Some Insghs of Value-Added Tax Ga Cuceu Ionuţ-Consann Vădean Vorela-Lga Maşca Smona-Gabrela "Babeş-Bolya" Unversy Cluj-Naoca, Faculy of Economcs and Busness

More information

Differences in the Price-Earning-Return Relationship between Internet and Traditional Firms

Differences in the Price-Earning-Return Relationship between Internet and Traditional Firms Dfferences n he Prce-Earnng-Reurn Relaonshp beween Inerne and Tradonal Frms Jaehan Koh Ph.D. Program College of Busness Admnsraon Unversy of Texas-Pan Amercan jhkoh@upa.edu Bn Wang Asssan Professor Compuer

More information

Midterm Exam. Use the end of month price data for the S&P 500 index in the table below to answer the following questions.

Midterm Exam. Use the end of month price data for the S&P 500 index in the table below to answer the following questions. Universiy of Washingon Winer 00 Deparmen of Economics Eric Zivo Economics 483 Miderm Exam This is a closed book and closed noe exam. However, you are allowed one page of handwrien noes. Answer all quesions

More information

Using Fuzzy-Delphi Technique to Determine the Concession Period in BOT Projects

Using Fuzzy-Delphi Technique to Determine the Concession Period in BOT Projects Usng Fuzzy-Delph Technque o Deermne he Concesson Perod n BOT Projecs Khanzad Mosafa Iran Unversy of Scence and Technology School of cvl engneerng Tehran, Iran. P.O. Box: 6765-63 khanzad@us.ac.r Nasrzadeh

More information

An improved segmentation-based HMM learning method for Condition-based Maintenance

An improved segmentation-based HMM learning method for Condition-based Maintenance An mproved segmenaon-based HMM learnng mehod for Condon-based Manenance T Lu 1,2, J Lemere 1,2, F Carella 1,2 and S Meganck 1,3 1 ETRO Dep., Vre Unverse Brussel, Plenlaan 2, 1050 Brussels, Belgum 2 FMI

More information

A Series of ILP Models for the Optimization of Water Distribution Networks

A Series of ILP Models for the Optimization of Water Distribution Networks A Seres of ILP Models for he Opzaon of Waer Dsrbuon Neworks NIKHIL HOODA 1,*, OM DAMANI 1 and ASHUTOSH MAHAJAN 2 1 Deparen of Copuer Scence and Engneerng, Indan Insue of Technology, Bobay 2 Deparen of

More information

This specification describes the models that are used to forecast

This specification describes the models that are used to forecast PCE and CPI Inflaion Differenials: Convering Inflaion Forecass Model Specificaion By Craig S. Hakkio This specificaion describes he models ha are used o forecas he inflaion differenial. The 14 forecass

More information

OCR Statistics 1 Working with data. Section 2: Measures of location

OCR Statistics 1 Working with data. Section 2: Measures of location OCR Statstcs 1 Workng wth data Secton 2: Measures of locaton Notes and Examples These notes have sub-sectons on: The medan Estmatng the medan from grouped data The mean Estmatng the mean from grouped data

More information

Interest Rate Derivatives: More Advanced Models. Chapter 24. The Two-Factor Hull-White Model (Equation 24.1, page 571) Analytic Results

Interest Rate Derivatives: More Advanced Models. Chapter 24. The Two-Factor Hull-White Model (Equation 24.1, page 571) Analytic Results Ineres Rae Dervaves: More Advanced s Chaper 4 4. The Two-Facor Hull-Whe (Equaon 4., page 57) [ θ() ] σ 4. dx = u ax d dz du = bud σdz where x = f () r and he correlaon beween dz and dz s ρ The shor rae

More information

Pricing Model of Credit Default Swap Based on Jump-Diffusion Process and Volatility with Markov Regime Shift

Pricing Model of Credit Default Swap Based on Jump-Diffusion Process and Volatility with Markov Regime Shift Assocaon for Informaon Sysems AIS Elecronc brary (AISe) WICEB 13 Proceedngs Wuhan Inernaonal Conference on e-busness Summer 5-5-13 Prcng Model of Cred Defaul Swap Based on Jump-Dffuson Process and Volaly

More information

Assessing Long-Term Fiscal Dynamics: Evidence from Greece and Belgium

Assessing Long-Term Fiscal Dynamics: Evidence from Greece and Belgium Inernaonal Revew of Busness Research Papers Vol. 7. No. 6. November 2011. Pp. 33-45 Assessng Long-Term Fscal Dynamcs: Evdence from Greece and Belgum JEL Codes: Ε62 and Η50 1. Inroducon Evangela Kasma 1,2

More information

Bond Prices and Interest Rates

Bond Prices and Interest Rates Winer erm 1999 Bond rice Handou age 1 of 4 Bond rices and Ineres Raes A bond is an IOU. ha is, a bond is a promise o pay, in he fuure, fixed amouns ha are saed on he bond. he ineres rae ha a bond acually

More information

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019 5-45/65: Desgn & Analyss of Algorthms January, 09 Lecture #3: Amortzed Analyss last changed: January 8, 09 Introducton In ths lecture we dscuss a useful form of analyss, called amortzed analyss, for problems

More information

Return Calculation Methodology

Return Calculation Methodology Reurn Calculaon Mehodology Conens 1. Inroducon... 1 2. Local Reurns... 2 2.1. Examle... 2 3. Reurn n GBP... 3 3.1. Examle... 3 4. Hedged o GBP reurn... 4 4.1. Examle... 4 5. Cororae Acon Facors... 5 5.1.

More information

Time-domain Analysis of Linear and Nonlinear Circuits

Time-domain Analysis of Linear and Nonlinear Circuits Tme-doman Analyss of Lnear and Nonlnear Crcus Dr. José Erneso Rayas-Sáncez February 4, 8 Tme-doman Analyss of Lnear and Nonlnear Crcus Dr. José Erneso Rayas-Sáncez Inroducon Tme doman analyss can be realzed

More information

Recursive Data Mining for Masquerade Detection and Author Identification

Recursive Data Mining for Masquerade Detection and Author Identification Recursve Daa Mnng for Masquerade Deecon and Auhor Idenfcaon Boleslaw K. Szymansk, IEEE Fellow, and Yongqang Zhang Deparmen of Compuer Scence, RPI, Troy, NY 280, USA Absrac- In hs paper, a novel recursve

More information

Albania. A: Identification. B: CPI Coverage. Title of the CPI: Consumer Price Index. Organisation responsible: Institute of Statistics

Albania. A: Identification. B: CPI Coverage. Title of the CPI: Consumer Price Index. Organisation responsible: Institute of Statistics Albana A: Idenfcaon Tle of he CPI: Consumer Prce Index Organsaon responsble: Insue of Sascs Perodcy: Monhly Prce reference perod: December year 1 = 100 Index reference perod: December 2007 = 100 Weghs

More information

Geometric Algebra. Prof. Nigel Boston. Camera Network Research Group Meeting. Nov. 8 & 15, 2007

Geometric Algebra. Prof. Nigel Boston. Camera Network Research Group Meeting. Nov. 8 & 15, 2007 Geomerc Algebra Ngel Boso /5/07 Geomerc Algebra Prof. Ngel Boso Camera Nework esearch Group Meeg Nov. 8 & 5, 007 Sraegy: Use Clfford algebra o develop varas for projecve rasformaos. (eferece: J. Laseby,

More information

OPTIMAL EXERCISE POLICIES AND SIMULATION-BASED VALUATION FOR AMERICAN-ASIAN OPTIONS

OPTIMAL EXERCISE POLICIES AND SIMULATION-BASED VALUATION FOR AMERICAN-ASIAN OPTIONS OPTIMAL EXERCISE POLICIES AND SIMULATION-BASED VALUATION FOR AMERICAN-ASIAN OPTIONS RONGWEN WU Deparmen of Mahemacs, Unversy of Maryland, College Park, Maryland 20742, rxw@mah.umd.edu MICHAEL C. FU The

More information

Applications of Myerson s Lemma

Applications of Myerson s Lemma Applcatons of Myerson s Lemma Professor Greenwald 28-2-7 We apply Myerson s lemma to solve the sngle-good aucton, and the generalzaton n whch there are k dentcal copes of the good. Our objectve s welfare

More information

Prediction of Oil Demand Based on Time Series Decomposition Method Nan MA * and Yong LIU

Prediction of Oil Demand Based on Time Series Decomposition Method Nan MA * and Yong LIU 2017 2nd Inernaonal Conference on Sofware, Mulmeda and Communcaon Engneerng (SMCE 2017) ISBN: 978-1-60595-458-5 Predcon of Ol Demand Based on Tme Seres Decomposon Mehod Nan MA * and Yong LIU College of

More information

Alternative methods to derive statistical distribution of Sharpe performance measure: Review, comparison, and extension

Alternative methods to derive statistical distribution of Sharpe performance measure: Review, comparison, and extension Alernave mehods o derve sascal dsrbuon of Sharpe performance measure: evew, comparson, and exenson Le-Jane Kao Deparmen of Fnance and Bankng, KaNan Unversy, aoyuan,awan Cheng-Few Lee Deparmen of Fnance

More information

Financial Econometrics Jeffrey R. Russell Midterm Winter 2011

Financial Econometrics Jeffrey R. Russell Midterm Winter 2011 Name Financial Economerics Jeffrey R. Russell Miderm Winer 2011 You have 2 hours o complee he exam. Use can use a calculaor. Try o fi all your work in he space provided. If you find you need more space

More information

Estimation of Optimal Tax Level on Pesticides Use and its

Estimation of Optimal Tax Level on Pesticides Use and its 64 Bulgaran Journal of Agrculural Scence, 8 (No 5 0, 64-650 Agrculural Academy Esmaon of Opmal Ta Level on Pescdes Use and s Impac on Agrculure N. Ivanova,. Soyanova and P. Mshev Unversy of Naonal and

More information

Market and Information Economics

Market and Information Economics Marke and Informaion Economics Preliminary Examinaion Deparmen of Agriculural Economics Texas A&M Universiy May 2015 Insrucions: This examinaion consiss of six quesions. You mus answer he firs quesion

More information

Numerical Evaluation of European Option on a Non Dividend Paying Stock

Numerical Evaluation of European Option on a Non Dividend Paying Stock Inernaonal Journal of Compuaonal cence and Mahemacs. IN 0974-389 olume Number 3 (00) pp. 6--66 Inernaonal Research Publcaon House hp://www.rphouse.com Numercal Evaluaon of European Opon on a Non Dvdend

More information

Dynamic Programming Applications. Capacity Expansion

Dynamic Programming Applications. Capacity Expansion Dynamic Programming Applicaions Capaciy Expansion Objecives To discuss he Capaciy Expansion Problem To explain and develop recursive equaions for boh backward approach and forward approach To demonsrae

More information

Bank of Japan. Research and Statistics Department. March, Outline of the Corporate Goods Price Index (CGPI, 2010 base)

Bank of Japan. Research and Statistics Department. March, Outline of the Corporate Goods Price Index (CGPI, 2010 base) Bank of Japan Research and Sascs Deparmen Oulne of he Corporae Goods Prce Index (CGPI, 2010 base) March, 2015 1. Purpose and Applcaon The Corporae Goods Prce Index (CGPI) measures he prce developmens of

More information

Unified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems

Unified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems IEEE Transacons on Susanable Energy Acceped for publcaon, November 2015 1 Unfed Un Commmen Formulaon and Fas Mul-Servce LP Model for Flexbly Evaluaon n Susanable Power Sysems Lngx Zhang, Suden Member,

More information

Baoding, Hebei, China. *Corresponding author

Baoding, Hebei, China. *Corresponding author 2016 3 rd Inernaonal Conference on Economcs and Managemen (ICEM 2016) ISBN: 978-1-60595-368-7 Research on he Applcably of Fama-French Three-Facor Model of Elecrc Power Indusry n Chnese Sock Marke Yeld

More information

Empirical analysis on China money multiplier

Empirical analysis on China money multiplier Aug. 2009, Volume 8, No.8 (Serial No.74) Chinese Business Review, ISSN 1537-1506, USA Empirical analysis on China money muliplier SHANG Hua-juan (Financial School, Shanghai Universiy of Finance and Economics,

More information

VI. Clickstream Big Data and Delivery before Order Making Mode for Online Retailers

VI. Clickstream Big Data and Delivery before Order Making Mode for Online Retailers VI. Clcksream Bg Daa and Delvery before Order Makng Mode for Onlne Realers Yemng (Yale) Gong EMLYON Busness School Haoxuan Xu *, Jnlong Zhang School of Managemen, Huazhong Unversy of Scence &Technology

More information

Distilling GRU with Data Augmentation for Unconstrained Handwritten Text Recognition

Distilling GRU with Data Augmentation for Unconstrained Handwritten Text Recognition Disilling GRU wih Daa Augmenaion for Unconsrained Handwrien Tex Recogniion Reporer: Zecheng Xie Souh China Universiy of Technology Augus 6,2018 Ouline Problem Definiion Daa Augmenaion Experimens Conclusion

More information

ANSWER ALL QUESTIONS. CHAPTERS 6-9; (Blanchard)

ANSWER ALL QUESTIONS. CHAPTERS 6-9; (Blanchard) ANSWER ALL QUESTIONS CHAPTERS 6-9; 18-20 (Blanchard) Quesion 1 Discuss in deail he following: a) The sacrifice raio b) Okun s law c) The neuraliy of money d) Bargaining power e) NAIRU f) Wage indexaion

More information

In-Arrears Interest Rate Derivatives under the 3/2 Model

In-Arrears Interest Rate Derivatives under the 3/2 Model Modern Economy, 5, 6, 77-76 Publshed Onlne June 5 n ScRes. hp://www.scrp.org/journal/me hp://d.do.org/.46/me.5.6667 In-Arrears Ineres Rae Dervaves under he / Model Joanna Goard School of Mahemacs and Appled

More information

Cointegration between Fama-French Factors

Cointegration between Fama-French Factors 1 Conegraon beween Fama-French Facors Absrac Conegraon has many applcaons n fnance and oher felds of scence researchng me seres and her nerdependences. The analyss s a useful mehod o analyse non-conegraon

More information

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

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

More information

Multiple Choice Questions Solutions are provided directly when you do the online tests.

Multiple Choice Questions Solutions are provided directly when you do the online tests. SOLUTIONS Muliple Choice Quesions Soluions are provided direcly when you do he online ess. Numerical Quesions 1. Nominal and Real GDP Suppose han an economy consiss of only 2 ypes of producs: compuers

More information

Noise and Expected Return in Chinese A-share Stock Market. By Chong QIAN Chien-Ting LIN

Noise and Expected Return in Chinese A-share Stock Market. By Chong QIAN Chien-Ting LIN Nose and Expeced Reurn n Chnese A-share Sock Marke By Chong QIAN Chen-Tng LIN 1 } Capal Asse Prcng Model (CAPM) by Sharpe (1964), Lnner (1965) and Mossn (1966) E ( R, ) R f, + [ E( Rm, ) R f, = β ] + ε

More information