EXCHANGE RATE ECONOMICS LECTURE 3 ASYMMETRIC INFORMATION AND EXCHANGE RATES. A. Portfolio Shifts Model and the Role of Order Flow
|
|
- Thomas Sanders
- 5 years ago
- Views:
Transcription
1 EXCHANGE RATE ECONOMICS LECTURE 3 ASYMMETRIC INFORMATION AND EXCHANGE RATES A. Porfolio Shifs Model and he Role of Order Flow Porfolio shifs by public cause exchange rae change no common knowledge when hey occur large enough ha marke clearing requires exchange rae o change There are T periods & asses: riskless & fx wih sochasic payoff F F where innovaions are iid ~ N( 0, Σ ) = T r = 1 r observed before rading each period (public info.) Decenralized marke wih N dealers i and coninuum of non-dealer cusomers all have idenical neg. exponenial uil. defined over wealh a T 3 rading rounds each day r M. Melvin,
2 TRADING ROUND 1 a) observe r a beginning of period b) all dealers simulaneously & independenly quoe a scalar price Pi1 a which any amoun may be bough or sold c) cusomer orders ci1 a Pi1 ci1 < 0 is cusomer sale (dealer buy) ci1 ~ N( 0, Σ c1 ) for each of N orders orders are indep. across dealers orders are disribued indep. of public info. r hese are he porfolio shifs ha are no publicly observable M. Melvin, 004.
3 TRADING ROUND a) each dealer simulaneously & indep. quoes a scalar price o oher dealers Pi a which any amoun may be bough & sold inerdealer quoes observable & available o all dealers b) dealers rade on oher's quoes a any given P, orders evenly spli across any dealers quoing ha P x = N T i i = 1 is ne inerdealer order flow inerdealer rade ransparen o all dealers (no noise) M. Melvin,
4 TRADING ROUND 3 a) dealers quoe scalar price Pi3 Pi3 condiioned on inerdealer order flow (dealers know amoun public mus absorb) any amoun may be raded observable & available o public a large public absorbs dealer unwaned invenory each dealer ends day wih 0 ne posiion b) public rades a Pi3 c3 = γ ( E [ P3, + 1 Ω 3 ] P3, ) so public's oal demand is funcion of expeced reurn γ capures agg. risk-bearing capaciy of public M. Melvin,
5 EQUILIBRIUM Dealer's problem is: max E [ exp( θw i3 s.. Ω i )] wi3 = wi0 + ci1( Pi1 ' Pi ) + ( Di + ' E [Ti Ω i ])( Pi3 ' Pi ) ' ( Ti Pi3 Pi ' denoes inerdealer quoe or rade yields price equaion: P = r + λ x for r use (i-i*) esimae: P = β1 ( i i * ) + β x + η M. Melvin,
6 ESTIMATION Daa: Reuers Dealing *ime, price, and signed rade (+ buy, - sell) *no quaniy, no quoes *ake price from 4pm o 4pm GMT *(i-i*) overnigh raes from Daasream (4pm GMT) Sig. & pos. order flow effec *high R *esimaes indicae ha day wih 1000 more purchases han sales DM/$ by.1% If order flow drives prices, wha drives order flow? References Evans & Lyons, "Order Flow and Exchange Rae Dynamics," JPE, 000 (hp:// M. Melvin,
7 ASYMMETRIC INFORMATION AND PRICE DISCOVERY IN THE FX MARKET: Does Tokyo Know More Abou he Yen? Viceniu Covrig and Michael Melvin M. Melvin,
8 I. INTRODUCTION Microsrucure heory informed rader presence affecs marke dynamics Empirical problem: idenifying informed Suggesed experimen: Tokyo pre- and pos-dec., 1994 Io, Lyons, and Melvin, Journal of Finance, *Informed rader concenraion in pre-lunch BEFORE period M. Melvin,
9 Some iniial sylized facs: U-shaped volailiy for Japan BEFORE no U-shape for non-japan BEFORE no U-shape for eiher AFTER Wha kind of privae informaion? cusomer order flow early knowledge of governmen acion invenory posiions GOAL: Tes implicaions of microsrucure heories regarding marke dynamics wih many informed raders presen M. Melvin,
10 II. IMPLICATIONS OF INFORMED TRADER CONCENTRATION A. A Represenaive Theory ( 1) F = F + δ. ( ) P = F + λω, Informed rader demand: β ( δ + u) ( 3) β = 1/{(1+ φ) λ( + 1) + A[ φ + λ σ z (1+ φ)]} 4) λ[ k (1 + φ) + σ Z α ] = kα (, k, α = β 1 = [ φ + λ (1 + φ) σ ] + ( k + 1) λ(1+ φ ) z A. informaional efficiency, ( 5) Q 1+ {1/[ φ + ( σ / k β z ]} =, Q = [var( δ ω)] 1 IMPLICATION: Prices are more informaive and converge more quickly o full informaion levels when here are many informed raders in he marke M. Melvin,
11 B. Esimaing Speed of Adjusmen Sample *10:30-1noon Tokyo *0 days BEFORE and AFTER *Reuers quoes on yen/dollar *1-minue reurns MA(1)-GARCH(1,1) () 6 r = α + ε 0 + α ε 1 1 () 7 h * * = γ + γ h dum h dum γ ε 1 + γ γ ε 4 1 Esimaed half-life of shock o volailiy: ½ minues BEFORE 14 minues AFTER M. Melvin,
12 III. JAPANESE AND NON-JAPANESE BANK DYNAMICS If Tokyo knows more, hen Japanese quoes should lead non-japanese? Causaliy ess: ( 9) r d a br i cr d = e Sample: *0 days BEFORE and AFTER *early-morning, lae-morning, and afernoon *filer ick-by-ick r i reurns.5 basis poins *consruc r d reurns around r i FINDINGS: *-way causaliy in all periods bu one *Japan causes non-japan in lae-morning BEFORE M. Melvin,
13 Nonsynchronous Quoing and Cross Correlaions Difference beween observed quoes equals sums of reurns of underlying unobserved quoe process (i) i + 1 q q = q i + 1 [ ] J J N N (ii) E( y ) = E ( q q )( q q ) ij = E i + 1 q = i + 1 i J i + 1 k j + 1 i q J = i + 1 k = k j + 1 N s j + 1 = i + 1 j j + 1 γ k = i + 1k = j + 1 (ii) γ k = Cov( r, r k ), r q q 1 N (iii) χ k) max[ 0,min(, + k) max(, )] ij ( = i + 1 j + 1 i j + k (iv) (v) E( y ij χ ) = χ ( l ) γ ij ij ij y = χ γ + e k k = k ij ij c k for k = 5, Wald es for lead/lag: q J lead q N BEFORE 16. AFTER 1.6 q N lead q J M. Melvin,
14 IV. PRICE DISCOVERY IN JAPAN AND ELSEWHERE Follow Hasbrouck (1995) o esimae conribuion of Japanese and non-japanese quoes o price discovery ( 10) r = Ψ( L) e ( 11) r = α( β' q 1 Eβ ' q + Γ k 1 r k+ 1 + e ) + Γ 1 r 1 + Γ r +... e = Fz, Ez = 0, Var(z ) = I ( 1) S ([ ] ) j = ψf j /( ψωψ ' ) M. Melvin,
15 Japanese/non-Japanese info. share: BEFORE 96% AFTER 89% Japanese/Hong Kong info. share: BEFORE 18% AFTER 111% Japanese conribuion o price discovery higher BEFORE han AFTER M. Melvin,
16 V. CONCLUSIONS Marke dynamics differ depending upon he presence of informed raders *greaer he number of informed raders he faser price adjuss o full-informaion value *informed-rader quoes lead he res of he marke when high concenraion of informed raders *informed raders conribuion o price discovery peaks when informed cluser Does Tokyo Know More Abou he Yen? *qualified yes... Reference: hp:// M. Melvin,
17 Figure 1: Variance of Yen/Dollar Quoes in Asian Morning -- BEFORE Variance am am 11-1 am Japan Non-Japan Variance Figure : Variance of Yen/Dollar Quoes in Asian Morning -- AFTER 9-10am am 11-1 am Japan Non-Japan M. Melvin,
Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.
Inroducion Gordon Model (1962): D P = r g r = consan discoun rae, g = consan dividend growh rae. If raional expecaions of fuure discoun raes and dividend growh vary over ime, so should he D/P raio. Since
More information1. Consider a pure-exchange economy with stochastic endowments. The state of the economy
Answer 4 of he following 5 quesions. 1. Consider a pure-exchange economy wih sochasic endowmens. The sae of he economy in period, 0,1,..., is he hisory of evens s ( s0, s1,..., s ). The iniial sae is given.
More informationExamples of Dynamic Programming Problems
M.I.T. 5.450-Fall 00 Sloan School of Managemen Professor Leonid Kogan Examples of Dynamic Programming Problems Problem A given quaniy X of a single resource is o be allocaed opimally among N producion
More informationLicenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A
Licenciaura de ADE y Licenciaura conjuna Derecho y ADE Hoja de ejercicios PARTE A 1. Consider he following models Δy = 0.8 + ε (1 + 0.8L) Δ 1 y = ε where ε and ε are independen whie noise processes. In
More informationMacroeconomic Theory Ph.D. Qualifying Examination Fall 2005 ANSWER EACH PART IN A SEPARATE BLUE BOOK. PART ONE: ANSWER IN BOOK 1 WEIGHT 1/3
Macroeconomic Theory Ph.D. Qualifying Examinaion Fall 2005 Comprehensive Examinaion UCLA Dep. of Economics You have 4 hours o complee he exam. There are hree pars o he exam. Answer all pars. Each par has
More informationDEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND
DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND Asymmery and Leverage in Condiional Volailiy Models Michael McAleer WORKING PAPER
More informationFinancial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS
Name SOLUTIONS Financial Economerics Jeffrey R. Russell Miderm Winer 009 SOLUTIONS You have 80 minues o complee he exam. Use can use a calculaor and noes. Try o fi all your work in he space provided. If
More informationFINM 6900 Finance Theory
FINM 6900 Finance Theory Universiy of Queensland Lecure Noe 4 The Lucas Model 1. Inroducion In his lecure we consider a simple endowmen economy in which an unspecified number of raional invesors rade asses
More informationProblem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims
Problem Se 5 Graduae Macro II, Spring 2017 The Universiy of Nore Dame Professor Sims Insrucions: You may consul wih oher members of he class, bu please make sure o urn in your own work. Where applicable,
More informationSchool and Workshop on Market Microstructure: Design, Efficiency and Statistical Regularities March 2011
2229-12 School and Workshop on Marke Microsrucure: Design, Efficiency and Saisical Regulariies 21-25 March 2011 Some mahemaical properies of order book models Frederic ABERGEL Ecole Cenrale Paris Grande
More informationChapter 5. Heterocedastic Models. Introduction to time series (2008) 1
Chaper 5 Heerocedasic Models Inroducion o ime series (2008) 1 Chaper 5. Conens. 5.1. The ARCH model. 5.2. The GARCH model. 5.3. The exponenial GARCH model. 5.4. The CHARMA model. 5.5. Random coefficien
More informationA Dynamic Model of Economic Fluctuations
CHAPTER 15 A Dynamic Model of Economic Flucuaions Modified for ECON 2204 by Bob Murphy 2016 Worh Publishers, all righs reserved IN THIS CHAPTER, OU WILL LEARN: how o incorporae dynamics ino he AD-AS model
More informationEcon107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)
I. Definiions and Problems A. Perfec Mulicollineariy Econ7 Applied Economerics Topic 7: Mulicollineariy (Sudenmund, Chaper 8) Definiion: Perfec mulicollineariy exiss in a following K-variable regression
More informationEffects of Finite-Sample and Realized Kernels on Standardized Returns on the Tokyo Stock Exchange
Effecs of Finie-Sample and Realized Kernels on Sandardized Reurns on he Toyo Soc Exchange The Third Inernaional Conference High-Frequency Daa Analysis in Financial Mares /7/0 Tesuya Taaishi Hiroshima Universiy
More informationT L. t=1. Proof of Lemma 1. Using the marginal cost accounting in Equation(4) and standard arguments. t )+Π RB. t )+K 1(Q RB
Elecronic Companion EC.1. Proofs of Technical Lemmas and Theorems LEMMA 1. Le C(RB) be he oal cos incurred by he RB policy. Then we have, T L E[C(RB)] 3 E[Z RB ]. (EC.1) Proof of Lemma 1. Using he marginal
More informationCooperative Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS. August 8, :45 a.m. to 1:00 p.m.
Cooperaive Ph.D. Program in School of Economic Sciences and Finance QUALIFYING EXAMINATION IN MACROECONOMICS Augus 8, 213 8:45 a.m. o 1: p.m. THERE ARE FIVE QUESTIONS ANSWER ANY FOUR OUT OF FIVE PROBLEMS.
More informationDiebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles
Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance
More informationSolutions Problem Set 3 Macro II (14.452)
Soluions Problem Se 3 Macro II (14.452) Francisco A. Gallego 04/27/2005 1 Q heory of invesmen in coninuous ime and no uncerainy Consider he in nie horizon model of a rm facing adjusmen coss o invesmen.
More informationVolatility. Many economic series, and most financial series, display conditional volatility
Volailiy Many economic series, and mos financial series, display condiional volailiy The condiional variance changes over ime There are periods of high volailiy When large changes frequenly occur And periods
More information1 Consumption and Risky Assets
Soluions o Problem Se 8 Econ 0A - nd Half - Fall 011 Prof David Romer, GSI: Vicoria Vanasco 1 Consumpion and Risky Asses Consumer's lifeime uiliy: U = u(c 1 )+E[u(c )] Income: Y 1 = Ȳ cerain and Y F (
More information5. Stochastic processes (1)
Lec05.pp S-38.45 - Inroducion o Teleraffic Theory Spring 2005 Conens Basic conceps Poisson process 2 Sochasic processes () Consider some quaniy in a eleraffic (or any) sysem I ypically evolves in ime randomly
More information2) Of the following questions, which ones are thermodynamic, rather than kinetic concepts?
AP Chemisry Tes (Chaper 12) Muliple Choice (40%) 1) Which of he following is a kineic quaniy? A) Enhalpy B) Inernal Energy C) Gibb s free energy D) Enropy E) Rae of reacion 2) Of he following quesions,
More informationA Specification Test for Linear Dynamic Stochastic General Equilibrium Models
Journal of Saisical and Economeric Mehods, vol.1, no.2, 2012, 65-70 ISSN: 2241-0384 (prin), 2241-0376 (online) Scienpress Ld, 2012 A Specificaion Tes for Linear Dynamic Sochasic General Equilibrium Models
More information1 Answers to Final Exam, ECN 200E, Spring
1 Answers o Final Exam, ECN 200E, Spring 2004 1. A good answer would include he following elemens: The equiy premium puzzle demonsraed ha wih sandard (i.e ime separable and consan relaive risk aversion)
More informationLecture Notes 5: Investment
Lecure Noes 5: Invesmen Zhiwei Xu (xuzhiwei@sju.edu.cn) Invesmen decisions made by rms are one of he mos imporan behaviors in he economy. As he invesmen deermines how he capials accumulae along he ime,
More informationAsymmetry and Leverage in Conditional Volatility Models*
Asymmery and Leverage in Condiional Volailiy Models* Micael McAleer Deparmen of Quaniaive Finance Naional Tsing Hua Universiy Taiwan and Economeric Insiue Erasmus Scool of Economics Erasmus Universiy Roerdam
More informationMonetary policymaking and inflation expectations: The experience of Latin America
Moneary policymaking and inflaion expecaions: The experience of Lain America Luiz de Mello and Diego Moccero OECD Economics Deparmen Brazil/Souh America Desk 8h February 7 1999: new moneary policy regimes
More information20. Applications of the Genetic-Drift Model
0. Applicaions of he Geneic-Drif Model 1) Deermining he probabiliy of forming any paricular combinaion of genoypes in he nex generaion: Example: If he parenal allele frequencies are p 0 = 0.35 and q 0
More information13.3 Term structure models
13.3 Term srucure models 13.3.1 Expecaions hypohesis model - Simples "model" a) shor rae b) expecaions o ge oher prices Resul: y () = 1 h +1 δ = φ( δ)+ε +1 f () = E (y +1) (1) =δ + φ( δ) f (3) = E (y +)
More informationDynamics of Firms and Trade in General Equilibrium. Robert Dekle, Hyeok Jeong and Nobuhiro Kiyotaki USC, KDI School and Princeton
Dynamics of Firms and Trade in General Equilibrium Rober Dekle, Hyeok Jeong and Nobuhiro Kiyoaki USC, KDI School and Princeon real exchange rae.5 2 Figure. Aggregae Exchange Rae Disconnec in Japan 98 99
More informationUnit Root Time Series. Univariate random walk
Uni Roo ime Series Univariae random walk Consider he regression y y where ~ iid N 0, he leas squares esimae of is: ˆ yy y y yy Now wha if = If y y hen le y 0 =0 so ha y j j If ~ iid N 0, hen y ~ N 0, he
More informationIntermediate Macro In-Class Problems
Inermediae Macro In-Class Problems Exploring Romer Model June 14, 016 Today we will explore he mechanisms of he simply Romer model by exploring how economies described by his model would reac o exogenous
More informationVectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1
Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies
More informationStochastic models and their distributions
Sochasic models and heir disribuions Couning cusomers Suppose ha n cusomers arrive a a grocery a imes, say T 1,, T n, each of which akes any real number in he inerval (, ) equally likely The values T 1,,
More informationEcon Autocorrelation. Sanjaya DeSilva
Econ 39 - Auocorrelaion Sanjaya DeSilva Ocober 3, 008 1 Definiion Auocorrelaion (or serial correlaion) occurs when he error erm of one observaion is correlaed wih he error erm of any oher observaion. This
More informationC. Theoretical channels 1. Conditions for complete neutrality Suppose preferences are E t. Monetary policy at the zero lower bound: Theory 11/22/2017
//7 Moneary policy a he zero lower bound: Theory A. Theoreical channels. Condiions for complee neuraliy (Eggersson and Woodford, ). Marke fricions. Preferred habia and risk-bearing (Hamilon and Wu, ) B.
More informationThe Real Exchange Rate, Real Interest Rates, and the Risk Premium. Charles Engel University of Wisconsin
The Real Exchange Rae, Real Ineres Raes, an he Risk Premium Charles Engel Universiy of Wisconsin 1 Define he excess reurn or risk premium on Foreign s.. bons: λ i + Es+ 1 s i = r + Eq+ 1 q r The famous
More informationTesting for a Single Factor Model in the Multivariate State Space Framework
esing for a Single Facor Model in he Mulivariae Sae Space Framework Chen C.-Y. M. Chiba and M. Kobayashi Inernaional Graduae School of Social Sciences Yokohama Naional Universiy Japan Faculy of Economics
More informationProblem Set on Differential Equations
Problem Se on Differenial Equaions 1. Solve he following differenial equaions (a) x () = e x (), x () = 3/ 4. (b) x () = e x (), x (1) =. (c) xe () = + (1 x ()) e, x () =.. (An asse marke model). Le p()
More informationVector autoregression VAR. Case 1
Vecor auoregression VAR So far we have focused mosl on models where deends onl on as. More generall we migh wan o consider oin models ha involve more han one variable. There are wo reasons: Firs, we migh
More informationPolicy regimes Theory
Advanced Moneary Theory and Policy EPOS 2012/13 Policy regimes Theory Giovanni Di Barolomeo giovanni.dibarolomeo@uniroma1.i The moneary policy regime The simple model: x = - s (i - p e ) + x e + e D p
More informationSome Basic Information about M-S-D Systems
Some Basic Informaion abou M-S-D Sysems 1 Inroducion We wan o give some summary of he facs concerning unforced (homogeneous) and forced (non-homogeneous) models for linear oscillaors governed by second-order,
More informationOur main purpose in this section is to undertake an examination of the stock
3. Caial gains ax and e sock rice volailiy Our main urose in is secion is o underake an examinaion of e sock rice volailiy by considering ow e raional seculaor s olding canges afer e ax rae on caial gains
More informationExplaining Total Factor Productivity. Ulrich Kohli University of Geneva December 2015
Explaining Toal Facor Produciviy Ulrich Kohli Universiy of Geneva December 2015 Needed: A Theory of Toal Facor Produciviy Edward C. Presco (1998) 2 1. Inroducion Toal Facor Produciviy (TFP) has become
More informationA First Course on Kinetics and Reaction Engineering. Class 19 on Unit 18
A Firs ourse on Kineics and Reacion Engineering lass 19 on Uni 18 Par I - hemical Reacions Par II - hemical Reacion Kineics Where We re Going Par III - hemical Reacion Engineering A. Ideal Reacors B. Perfecly
More informationI. Return Calculations (20 pts, 4 points each)
Universiy of Washingon Spring 015 Deparmen of Economics Eric Zivo Econ 44 Miderm Exam Soluions This is a closed book and closed noe exam. However, you are allowed one page of noes (8.5 by 11 or A4 double-sided)
More informationMath 105 Second Midterm March 16, 2017
Mah 105 Second Miderm March 16, 2017 UMID: Insrucor: Iniials: Secion: 1. Do no open his exam unil you are old o do so. 2. Do no wrie your name anywhere on his exam. 3. This exam has 9 pages including his
More informationEstimation Uncertainty
Esimaion Uncerainy The sample mean is an esimae of β = E(y +h ) The esimaion error is = + = T h y T b ( ) = = + = + = = = T T h T h e T y T y T b β β β Esimaion Variance Under classical condiions, where
More informationDealing with the Trilemma: Optimal Capital Controls with Fixed Exchange Rates
Dealing wih he Trilemma: Opimal Capial Conrols wih Fixed Exchange Raes by Emmanuel Farhi and Ivan Werning June 15 Ricardo Reis Columbia Universiy Porugal s challenge risk premium Porugal s challenge sudden
More informationu(x) = e x 2 y + 2 ) Integrate and solve for x (1 + x)y + y = cos x Answer: Divide both sides by 1 + x and solve for y. y = x y + cos x
. 1 Mah 211 Homework #3 February 2, 2001 2.4.3. y + (2/x)y = (cos x)/x 2 Answer: Compare y + (2/x) y = (cos x)/x 2 wih y = a(x)x + f(x)and noe ha a(x) = 2/x. Consequenly, an inegraing facor is found wih
More informationEnergy Storage Benchmark Problems
Energy Sorage Benchmark Problems Daniel F. Salas 1,3, Warren B. Powell 2,3 1 Deparmen of Chemical & Biological Engineering 2 Deparmen of Operaions Research & Financial Engineering 3 Princeon Laboraory
More informationState-Space Models. Initialization, Estimation and Smoothing of the Kalman Filter
Sae-Space Models Iniializaion, Esimaion and Smoohing of he Kalman Filer Iniializaion of he Kalman Filer The Kalman filer shows how o updae pas predicors and he corresponding predicion error variances when
More informationOBJECTIVES OF TIME SERIES ANALYSIS
OBJECTIVES OF TIME SERIES ANALYSIS Undersanding he dynamic or imedependen srucure of he observaions of a single series (univariae analysis) Forecasing of fuure observaions Asceraining he leading, lagging
More informationCompetitive and Cooperative Inventory Policies in a Two-Stage Supply-Chain
Compeiive and Cooperaive Invenory Policies in a Two-Sage Supply-Chain (G. P. Cachon and P. H. Zipkin) Presened by Shruivandana Sharma IOE 64, Supply Chain Managemen, Winer 2009 Universiy of Michigan, Ann
More informationStationary Time Series
3-Jul-3 Time Series Analysis Assoc. Prof. Dr. Sevap Kesel July 03 Saionary Time Series Sricly saionary process: If he oin dis. of is he same as he oin dis. of ( X,... X n) ( X h,... X nh) Weakly Saionary
More informationMath 333 Problem Set #2 Solution 14 February 2003
Mah 333 Problem Se #2 Soluion 14 February 2003 A1. Solve he iniial value problem dy dx = x2 + e 3x ; 2y 4 y(0) = 1. Soluion: This is separable; we wrie 2y 4 dy = x 2 + e x dx and inegrae o ge The iniial
More informationInnova Junior College H2 Mathematics JC2 Preliminary Examinations Paper 2 Solutions 0 (*)
Soluion 3 x 4x3 x 3 x 0 4x3 x 4x3 x 4x3 x 4x3 x x 3x 3 4x3 x Innova Junior College H Mahemaics JC Preliminary Examinaions Paper Soluions 3x 3 4x 3x 0 4x 3 4x 3 0 (*) 0 0 + + + - 3 3 4 3 3 3 3 Hence x or
More informationKINEMATICS IN ONE DIMENSION
KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec
More informationUnemployment and Mismatch in the UK
Unemploymen and Mismach in he UK Jennifer C. Smih Universiy of Warwick, UK CAGE (Cenre for Compeiive Advanage in he Global Economy) BoE/LSE Conference on Macroeconomics and Moneary Policy: Unemploymen,
More informationPhys1112: DC and RC circuits
Name: Group Members: Dae: TA s Name: Phys1112: DC and RC circuis Objecives: 1. To undersand curren and volage characerisics of a DC RC discharging circui. 2. To undersand he effec of he RC ime consan.
More informationAnnouncements: Warm-up Exercise:
Fri Apr 13 7.1 Sysems of differenial equaions - o model muli-componen sysems via comparmenal analysis hp//en.wikipedia.org/wiki/muli-comparmen_model Announcemens Warm-up Exercise Here's a relaively simple
More informationThe Real Exchange Rate, Real Interest Rates, and the Risk Premium. Charles Engel University of Wisconsin
The Real Exchange Rae, Real Ineres Raes, and he Risk Premium Charles Engel Universiy of Wisconsin How does exchange rae respond o ineres rae changes? In sandard open economy New Keynesian model, increase
More informationDerived Short-Run and Long-Run Softwood Lumber Demand and Supply
Derived Shor-Run and Long-Run Sofwood Lumber Demand and Supply Nianfu Song and Sun Joseph Chang School of Renewable Naural Resources Louisiana Sae Universiy Ouline Shor-run run and long-run implied by
More informationReading from Young & Freedman: For this topic, read sections 25.4 & 25.5, the introduction to chapter 26 and sections 26.1 to 26.2 & 26.4.
PHY1 Elecriciy Topic 7 (Lecures 1 & 11) Elecric Circuis n his opic, we will cover: 1) Elecromoive Force (EMF) ) Series and parallel resisor combinaions 3) Kirchhoff s rules for circuis 4) Time dependence
More information(10) (a) Derive and plot the spectrum of y. Discuss how the seasonality in the process is evident in spectrum.
January 01 Final Exam Quesions: Mark W. Wason (Poins/Minues are given in Parenheses) (15) 1. Suppose ha y follows he saionary AR(1) process y = y 1 +, where = 0.5 and ~ iid(0,1). Le x = (y + y 1 )/. (11)
More informationProblem 1 / 25 Problem 2 / 20 Problem 3 / 10 Problem 4 / 15 Problem 5 / 30 TOTAL / 100
eparmen of Applied Economics Johns Hopkins Universiy Economics 602 Macroeconomic Theory and Policy Miderm Exam Suggesed Soluions Professor Sanjay hugh Fall 2008 NAME: The Exam has a oal of five (5) problems
More informationLinear Combinations of Volatility Forecasts for the WIG20 and Polish Exchange Rates
Eliza Buszkowska Universiy of Poznań, Poland Linear Combinaions of Volailiy Forecass for he WIG0 and Polish Exchange Raes Absrak. As is known forecas combinaions may be beer forecass hen forecass obained
More informationFinancial Econometrics Introduction to Realized Variance
Financial Economerics Inroducion o Realized Variance Eric Zivo May 16, 2011 Ouline Inroducion Realized Variance Defined Quadraic Variaion and Realized Variance Asympoic Disribuion Theory for Realized Variance
More informationKriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Kriging Models Predicing Arazine Concenraions in Surface Waer Draining Agriculural Waersheds Paul L. Mosquin, Jeremy Aldworh, Wenlin Chen Supplemenal Maerial Number
More informationLiquidity and Bank Capital Requirements
Liquidiy and Bank Capial Requiremens Hajime Tomura Bank of Canada November 3, 2009 Preliminary draf Absrac A dynamic compeiive equilibrium model in his paper incorporaes illiquidiy of asses due o asymmeric
More informationKalman filtering for maximum likelihood estimation given corrupted observations.
alman filering maimum likelihood esimaion given corruped observaions... Holmes Naional Marine isheries Service Inroducion he alman filer is used o eend likelihood esimaion o cases wih hidden saes such
More informationGeneralized Least Squares
Generalized Leas Squares Augus 006 1 Modified Model Original assumpions: 1 Specificaion: y = Xβ + ε (1) Eε =0 3 EX 0 ε =0 4 Eεε 0 = σ I In his secion, we consider relaxing assumpion (4) Insead, assume
More informationSimulating models with heterogeneous agents
Simulaing models wih heerogeneous agens Wouer J. Den Haan London School of Economics c by Wouer J. Den Haan Individual agen Subjec o employmen shocks (ε i, {0, 1}) Incomplee markes only way o save is hrough
More informationMath 111 Midterm I, Lecture A, version 1 -- Solutions January 30 th, 2007
NAME: Suden ID #: QUIZ SECTION: Mah 111 Miderm I, Lecure A, version 1 -- Soluions January 30 h, 2007 Problem 1 4 Problem 2 6 Problem 3 20 Problem 4 20 Toal: 50 You are allowed o use a calculaor, a ruler,
More informationOutline. lse-logo. Outline. Outline. 1 Wald Test. 2 The Likelihood Ratio Test. 3 Lagrange Multiplier Tests
Ouline Ouline Hypohesis Tes wihin he Maximum Likelihood Framework There are hree main frequenis approaches o inference wihin he Maximum Likelihood framework: he Wald es, he Likelihood Raio es and he Lagrange
More informationECON 482 / WH Hong Time Series Data Analysis 1. The Nature of Time Series Data. Example of time series data (inflation and unemployment rates)
ECON 48 / WH Hong Time Series Daa Analysis. The Naure of Time Series Daa Example of ime series daa (inflaion and unemploymen raes) ECON 48 / WH Hong Time Series Daa Analysis The naure of ime series daa
More informationMath 10C: Relations and Functions PRACTICE EXAM
Mah C: Relaions and Funcions PRACTICE EXAM. Cailin rides her bike o school every day. The able of values shows her disance from home as ime passes. An equaion ha describes he daa is: ime (minues) disance
More informationHomework 4 (Stats 620, Winter 2017) Due Tuesday Feb 14, in class Questions are derived from problems in Stochastic Processes by S. Ross.
Homework 4 (Sas 62, Winer 217) Due Tuesday Feb 14, in class Quesions are derived from problems in Sochasic Processes by S. Ross. 1. Le A() and Y () denoe respecively he age and excess a. Find: (a) P{Y
More informationRice Futures Trading Activity & Spot Price Volatility
Rice Fuures Trading Aciviy & So Price Volailiy Theory Emirical Work Rice Fuures Marke Economerics Resuls Theory Why should he level of fuures rading aciviy affec so rice volailiy? Maniulaion and echnical
More informationTypes of Exponential Smoothing Methods. Simple Exponential Smoothing. Simple Exponential Smoothing
M Business Forecasing Mehods Exponenial moohing Mehods ecurer : Dr Iris Yeung Room No : P79 Tel No : 788 8 Types of Exponenial moohing Mehods imple Exponenial moohing Double Exponenial moohing Brown s
More informationReserves measures have an economic component eg. what could be extracted at current prices?
3.2 Non-renewable esources A. Are socks of non-renewable resources fixed? eserves measures have an economic componen eg. wha could be exraced a curren prices? - Locaion and quaniies of reserves of resources
More informationA Shooting Method for A Node Generation Algorithm
A Shooing Mehod for A Node Generaion Algorihm Hiroaki Nishikawa W.M.Keck Foundaion Laboraory for Compuaional Fluid Dynamics Deparmen of Aerospace Engineering, Universiy of Michigan, Ann Arbor, Michigan
More information1. VELOCITY AND ACCELERATION
1. VELOCITY AND ACCELERATION 1.1 Kinemaics Equaions s = u + 1 a and s = v 1 a s = 1 (u + v) v = u + as 1. Displacemen-Time Graph Gradien = speed 1.3 Velociy-Time Graph Gradien = acceleraion Area under
More informationPrecalculus An Investigation of Functions
Precalculus An Invesigaion of Funcions David Lippman Melonie Rasmussen Ediion.3 This book is also available o read free online a hp://www.openexbooksore.com/precalc/ If you wan a prined copy, buying from
More informationInventory Analysis and Management. Multi-Period Stochastic Models: Optimality of (s, S) Policy for K-Convex Objective Functions
Muli-Period Sochasic Models: Opimali of (s, S) Polic for -Convex Objecive Funcions Consider a seing similar o he N-sage newsvendor problem excep ha now here is a fixed re-ordering cos (> 0) for each (re-)order.
More informationVehicle Arrival Models : Headway
Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where
More information6. Stochastic calculus with jump processes
A) Trading sraegies (1/3) Marke wih d asses S = (S 1,, S d ) A rading sraegy can be modelled wih a vecor φ describing he quaniies invesed in each asse a each insan : φ = (φ 1,, φ d ) The value a of a porfolio
More information5.1 - Logarithms and Their Properties
Chaper 5 Logarihmic Funcions 5.1 - Logarihms and Their Properies Suppose ha a populaion grows according o he formula P 10, where P is he colony size a ime, in hours. When will he populaion be 2500? We
More informationSolutions to Odd Number Exercises in Chapter 6
1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b
More informationNotes on cointegration of real interest rates and real exchange rates. ρ (2)
Noe on coinegraion of real inere rae and real exchange rae Charle ngel, Univeriy of Wiconin Le me ar wih he obervaion ha while he lieraure (mo prominenly Meee and Rogoff (988) and dion and Paul (993))
More informationSeminar 4: Hotelling 2
Seminar 4: Hoelling 2 November 3, 211 1 Exercise Par 1 Iso-elasic demand A non renewable resource of a known sock S can be exraced a zero cos. Demand for he resource is of he form: D(p ) = p ε ε > A a
More informationChapter 13 Homework Answers
Chaper 3 Homework Answers 3.. The answer is c, doubling he [C] o while keeping he [A] o and [B] o consan. 3.2. a. Since he graph is no linear, here is no way o deermine he reacion order by inspecion. A
More informationExcel-Based Solution Method For The Optimal Policy Of The Hadley And Whittin s Exact Model With Arma Demand
Excel-Based Soluion Mehod For The Opimal Policy Of The Hadley And Whiin s Exac Model Wih Arma Demand Kal Nami School of Business and Economics Winson Salem Sae Universiy Winson Salem, NC 27110 Phone: (336)750-2338
More information(a) Set up the least squares estimation procedure for this problem, which will consist in minimizing the sum of squared residuals. 2 t.
Insrucions: The goal of he problem se is o undersand wha you are doing raher han jus geing he correc resul. Please show your work clearly and nealy. No credi will be given o lae homework, regardless of
More informationExponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3 Control Limits
DOI: 0.545/mjis.07.5009 Exponenial Weighed Moving Average (EWMA) Char Under The Assumpion of Moderaeness And Is 3 Conrol Limis KALPESH S TAILOR Assisan Professor, Deparmen of Saisics, M. K. Bhavnagar Universiy,
More informationStochastic Modelling in Finance - Solutions to sheet 8
Sochasic Modelling in Finance - Soluions o shee 8 8.1 The price of a defaulable asse can be modeled as ds S = µ d + σ dw dn where µ, σ are consans, (W ) is a sandard Brownian moion and (N ) is a one jump
More informationWeek 1 Lecture 2 Problems 2, 5. What if something oscillates with no obvious spring? What is ω? (problem set problem)
Week 1 Lecure Problems, 5 Wha if somehing oscillaes wih no obvious spring? Wha is ω? (problem se problem) Sar wih Try and ge o SHM form E. Full beer can in lake, oscillaing F = m & = ge rearrange: F =
More informationA unit root test based on smooth transitions and nonlinear adjustment
MPRA Munich Personal RePEc Archive A uni roo es based on smooh ransiions and nonlinear adjusmen Aycan Hepsag Isanbul Universiy 5 Ocober 2017 Online a hps://mpra.ub.uni-muenchen.de/81788/ MPRA Paper No.
More informationRandom Processes 1/24
Random Processes 1/24 Random Process Oher Names : Random Signal Sochasic Process A Random Process is an exension of he concep of a Random variable (RV) Simples View : A Random Process is a RV ha is a Funcion
More informationWhy is Chinese Provincial Output Diverging? Joakim Westerlund, University of Gothenburg David Edgerton, Lund University Sonja Opper, Lund University
Why is Chinese Provincial Oupu Diverging? Joakim Weserlund, Universiy of Gohenburg David Edgeron, Lund Universiy Sonja Opper, Lund Universiy Purpose of his paper. We re-examine he resul of Pedroni and
More information