A Liability Tracking Portfolio for Pension Fund Management


 William Lynch
 2 years ago
 Views:
Transcription
1 Proceedings of he 46h ISCIE Inernaional Symposium on Sochasic Sysems Theory and Is Applicaions Kyoo, Nov. 12, 214 A Liabiliy Tracking Porfolio for Pension Fund Managemen Masashi Ieda, Takashi Yamashia and Yumiharu Nakano Graduae School of Innovaion Managemen, Tokyo Insiue of Technology Ookayama, Meguroku, Tokyo, Japan The Governmen Pension Invesmen Fund, Japan Kasumigaseki, Chiyodaku, Tokyo Japan Absrac We sudy he long erm porfolio which is able o rack a liabiliy. The porfolio opimizaion problem is defined as he sochasic opimal conrol problem and he performance crierion is he lower mean square error beween he liabiliy and our wealh. We impose consrains for he porfolio weighs and obain he opimal porfolio sraegy numerically by solving he HamilonJacobiBellman equaion applying he quadraic approximaion scheme. The numerical simulaions using he empirical daa provided by Japanese organizaions are run under he wo ypes of consrains: he noshorselling consrain; he upper bound consrain for he porfolio weighs. The former demonsraes ha he liabiliy racking abiliy of our opimal porfolio sraegy does no drop if we resric he shor selling. The laer implies he imbalance beween he growh rae of he liabiliy and he profiabiliy of he asses. 1 Inroducion A porfolio sraegy aking ino accoun he liabiliy is one of he significan issues in he pension fund managemen. Due o he populaion ageing especially observed in he developed counries, i is expeced ha he benefi of he welfare pension fund increases and his or her conribuion decreases. Moreover he low birh rae implies ha his relaion is no resolved for decades. Thus he pension funds face a problem o include he mehod for hedging heir liabiliies ino heir long erm porfolio managemen. In conras, a lo of praciioners in pension funds deermine heir porfolio by he radiional single period mean variance approach. I is difficul o adop his approach o include a view of hedging he liabiliy since he single ime period mehod is unable o allow us o change he porfolio afer he iniial ime. Alhough he muli ime period mehod allows he change afer he iniial ime, we face a problem of he compuaional cos: we are usually unable o obain he opimal long erm porfolio sraegy in realisic ime. The lieraure [1] sudies he long erm pension fund managemen aking ino accoun he liabiliy employing he LQG (Linear, Quadraic cos, Gaussian) conrol problem which is a class of sochasic conrol problem. Since he analyical soluion of he LQG conrol problem is available, he opimal porfolio sraegy is obained in realisic ime. I demonsraes ha he opimal porfolio sraegy racks he liabiliy wih he low racking error by he numerical simulaion using he empirical daa provided by he Japanese organizaions. In he presen paper, we develop he porfolio sraegy from he sraegy proposed by Ieda e. al. [1] menioned above. The previous sudy remains wo poins which should be improved: (i) penalising he wealh of he invesor exceed he liabiliy; (ii) permiing he large shor selling. To resolve hese poins, we employ a lower mean square error from he racked liabiliy as he performance crierion and resric he porfolio weigh o a posiive and bounded value. In his case we are no able o obain an analyical soluion and hus we employ he numerical mehod o solve he corresponding HamilonJacobiBellman (HJB) equaion. Since we have currenly consider he porfolio consruced by muli asses which correlae each oher, i is difficul o obain a soluion in general: for insance, he finie difference mehod is failed in his case (see e.g., Kushner and Dupuis [2]). To cope wih his problem, we employ he quadraic approximaion scheme proposed by Nakano [3]. The advanage of his mehod is he low compuaional cos, and hence applying his mehod, we are able o obain a proxy of he opimal porfolio sraegy in realisic ime. Main implicaions from he presen work are as follows. The firs remarkable one is ha he liabiliy racking abiliy of our opimal porfolio sraegy does no drop if we resric he shor selling. The mean hedging error is approximaely 4.5% of he liabiliy and i is he equivalen level wihou he noshorselling consrain sudied in [1]. The hedge is realized by borrowing he money a lo and invesing he lowrisk asses. We nex noe ha resricing he large money borrowing by imposing he upper bound consrain for he porfolio weighs, we find a number of sample pahs which have a large racking error. I implies ha he imbalance beween he growh rae of he liabiliy and he profiabiliy of he asses. The presen paper is organized as follows. Secion 2 gives he mahemaical formulaion of he problem. The processes describing he asses and he liabiliy is defined as he sochasic differenial equaions, and we adop he lower mean square error beween he liabiliy and our wealh as he performance crierion. The numerical mehod is presened in Secion 3. We describe he procedure o obain he opimal porfolio sraegy according o he quadraic ap
2 proximaion scheme wih regression mehod. In Secion 4, numerical simulaions using he empirical daa provided by he Japanese organizaions are served. We regard he shorfall of income of he pension fund as he liabiliy and hedge i under he following wo ypes of consrains: (i) he noshorselling consrain in Secion 4.1; (ii) he upper bound consrain for he porfolio weighs in Secion Model Le (Ω, F, {F },P) be a filered probabiliy space and le {W } be a ndimensional Brownian moion. We denoe by S, S = (S 1,, Sn ) and Y prices of a riskfree asse, risky asses and a liabiliy a ime which are governed by he following sochasic difference equaions: ds ds i n S = r()d, S i = b i ()d + σ i j ()dw j, S j=1 = s >, S i = si >, i = 1,2,,n, dy = (A()Y + B()) d, Y = y R, where r, A, B : [,T] R, b : [,T] R n, σ : [,T] R n n are deerminisic coninuous funcions and T < sands for he mauriy. We assume ha all of he reurn rae of risky asse are larger han he riskfree rae, i.e., b i () r() >, i {1,2,,n}, [,T]. The wealh of an invesor X saisfies dx n = π i ds i n X S i + 1 π i ds i=1 S, {π } T A π, i=1 X = x = s + s 1, where π = (π 1,, π n ) sands for he porfolio weigh vecor and 1 = (1,,1) R n. A class of porfolio sraegies A π is he collecion of R n valued F adaped process {u } T which saisfies u i π, i = 1,2,,n. The hear of he definiion of he class A π is prohibiion of he shor selling. We define he performance crierion J π based on he downward side of he mean square error beween he liabiliy and our wealh which improve he negaive poin in he previous sudy [1] menioned in he inroducory secion: J π (x, y) [ 1 = E 2 (Y T X T ) T (Y s X s ) +ds ] 2 X = x,y = y. Here we use a noaion ( ) + s.. (x) + = x1 {x>}, x R. The value funcion of our problem V is defined by V (x, y) = inf π A Jπ π (x, y), wih erminal condiion V T (x, y) = 1 2 (y x)2 +, and he corresponding HJB equaion is V (x, y) + min π A π { L π V (x, y) } =, (1) where L π is an infiniesimal generaor of (X,Y ) : L π ϕ(x, y) = ( r() + (b() r()1) π ) x x ϕ(x, y) + (A()y + B()) y ϕ(x, y) x2 π σ()σ() π x x ϕ(x, y) (y x)2 +, for ϕ : R 2 R. We define he Hamilonian H as follow: H(, x, y, V, 2 V ) = min π A π { L π V (x, y) }. (2) 3 Numerical Mehod Under he curren conrol se A π, i is difficul o obain he analyical soluion for he HJB equaion (1). Hence we employ he quadraic approximaion scheme proposed by Nakano [3]. This mehod sars wih he following ime sepping V i (x, y) V i+1 + hh( i, x, y, V i+1, 2 V i+1 ), (3) and our goal is o obain V i, he approximaed value funcion around a poin ( x, ȳ) a ime i, in he following quadraic form: V i (x, y) =c 1,i + c 2,i (x x) + c 3,i (y ȳ) c 4,i (x x) 2 + c 5,i (x x)(y ȳ) c 6,i (y ȳ) 2, where i = ih, i =,1,, M, h = T/M, c j,i R, j = 1,,6. Hence he key o obain he approximae soluion is he approximaion of he Hamilonian. Before we inroduce he procedure o obain he approximae soluion, we ransform he Hamilonian (2) ino he form wihou he minimum. We assume ha x x V >, and hen he KarshKuhnTacker condiion implies ha where H(, x, y, V, 2 V ) = L ˆπ V (x, y). {( ) } x V ˆπ i = min (π ) i, π, (4) x x x V + and π () = ( σσ ) 1 (b r1) (). We now show he procedure o obain he approximae soluion. Since erminal condiion is given in he quadraic funcion of x and y, he coefficiens { } c j, M are deermined as j=1,,6 follows: c 1, M = 1 2 (ȳ x)2, c 2, M = (ȳ x), c 3, M = ȳ x c 4, M = 1,c 5, M = 1,c 6, M = 1. Nex we define q R 2 and P R 2 2 as follows: ( ) q = (c 2, M,c 3, M ) c4,, P = M c 5, M, (5) c 5, M c 6, M
3 and hen we find ha V M = P(x x, y ȳ) + q and 2 V M = P. Applying he regression mehod, we approximae he Hamilonian by he quadraic funcion: H ( n 1, x, y, P(x x, y ȳ) + q, P ) L 1 + L 2 (x x) + L 3 (y ȳ) L 4(x x) 2 + L 5 (x x)(y ȳ) (6) L 6(y ȳ) 2, where { } L j R, j = 1,,6. To deermine he coefficiens L j, we employ he leas square mehod wih K j=1,,6 sample poins (x k, y k ), k = 1,, K which are randomly chosen from he circle area wih radius r and he cenral poin ( x, ȳ). By (3) and (6), we have c j, M 1 = c j, M + hl j, j = 1,,6. Replacing M 1 ino M, going back o equaion (5) and { } repeaing his procedure, we obain he coefficiens cj,i which is equivalen o find he approximaed j=1,,6 value funcion V i. We are able o find he opimal sraegy subsiue V i ino he equaion (4). We remark ha he above procedure o obain he opimal sraegy does no conain processes which need a high compuaional cos. This is he advanage employing he quadraic approximaion mehod. Furhermore, if we only impose he noshorselling consrain, i.e., π =, we find he explici form of ˆπ, ˆπ = ( ( c2,i + c 4,i (x x) + c 5,i (y ȳ) )) + xc 4,i π 1 {( x<ȳ) (x<y)}, and he explici recursive formula for { c j,i } j=1,,6 : c 1,i =c 1,i+1 + h { r( i ) xc 2,i+1 + (ȳa( i ) + B( i )) c 3,i+1 2 (i ) c2 2,i+1 1 {c2,i+1 } + 1 (ȳ x)2 2c 4,i+1 2, c 2,i =c 2,i+1 + h { r( i )c 2,i+1 + r( i ) xc 4,i+1 + (ȳa( i ) + B( i )) c 5,i+1 2 (i )c 2,i+1 1 {c2,i+1 } (ȳ x) c 3,i =c 3,i+1 + h { r( i ) xc 5,i+1 + A( i )c 3,i+1 + (ȳa( i ) + B( i )) c 6,i+1 2 (i ) c } 2,i+1c 5,i+1 1 {c2,i+1 } + ȳ x, c 4,i+1 { c 4,i =c 4,i+1 + h 2r( i ) } 2 (i )c 4,i+1 1 {c2,i+1 } + 1, c 5,i =c 5,i+1 + h {r( i ) + A( i ) 1 2 (i )c 5,i+1 1 {c2,i+1 } }, c 6,i =c 6,i+1 + h { 2A( i )c 6,i (i ) c2 5,i+1 1 {c2,i+1 } c 4,i+1, }, under he condiion c 4,i >, i = 1,,n. Therefore we are able o obain a proxy of he opimal sraegy wih remarkable high speed. 4 Numerical Resuls In his secion, we serve numerical simulaions base on our mehod using he empirical daa provided by he Japanese organizaions. The usage of daa is he same as in he lieraure [1]. Le C and B be he esimaed income and expense of he pension fund which are deermined by he daa published by he Japanese Minisry of Healh, Labour and Welfare [4]. We se = as he year 24 when he esimaed shorfall of he pension fund sars o expand drasically (see Figure 1). We se A() = and B() as he numerical differeniaion fo B C and hen we regard he esimaed shorfall of he pension fund as he liabiliy. [rillion yen] [year] income expence Fig. 1: Esimaions of he income and expense of he Japanese welfare pensions. Red and blue lines indicae C, he esimaed income, and B, he esimaed expense respecively. We nex deermine he riskfree rae and he expeced reurn raes and volailiies of risky asses. We inves he following four asses: indices of he domesic bond, he domesic sock, he foreign bond and he foreign sock; we se n = 4 and number hem sequenially. According o he esimaions of reurn rae and volailiies by he Governmen Pension Invesmen Fund, Japan [5], we consruc b() and σ S () as follows: b 1 () = 3%, b 2 () = 4.8%, b 3 () = 3.5% and b 4 () = 5.%; σ() is he Cholesky decomposiion of he following variancecovariance marix of he asses: Σ = We choose a money marke accoun as he riskfree asse and we se r() =.%. In he following wo subsecions, we consider he cases: (i) he noshorselling consrain; (ii) he upper bound consrain for he porfolio weigh. We calculae he opimal porfolio weigh by he mehod menioned in Secion
4 and run invesmen simulaions using he EulerMaruyama scheme. We se he relaed parameers as in he Table resul Symbol Descripion Value T erminal ime 15 h ime sepping for calculaion.1 of opimal sraegy N number of sample pahs 1 ime sepping for simulaion.25 (quarerly rebalance) r radius of he regression region 2h K number of sample poins 1 for regression Table 1: Parameers Le us inroduce he mean hedging error ( ) N Y X i + E =, N i=1 o discuss he performance of he sraegy, where X i is he ih sample pah of he invesmen simulaion. 4.1 Case of noshorselling consrain In his subsecion, le us consider he case of noshor selling consrain only, i.e., π =. Figure 2 displays a sample pah of a invesmen simulaion. We find ha our opimal porfolio is mainly consruced by he domesic bond, he mos lowrisk asse. The foreign bond is he second weighed asse and i is included in he opimal porfolio wih a considerable weigh. The weigh of domesic and foreign socks are lile and hus he highrisk asses do no play an imporan role in our hedging sraegy. These facs imply ha he opimal hedging mehod for he liabiliy is borrowing he money a lo and invesing he bonds, he lowrisk asses. Since here does no exis he limi for he amoun of money borrowing, we are able o choose he sraegy ha invesing he lowrisk asses a lo and earning he desired profi wih lowrisk. Anoher feaure of our opimal sraegy is ha he large hedging error occurred in earlier sage of invesmen is quickly hedged. In Figure 2, we observe ha he porfolio weighs near he sar and erminal imes are differen despie he racking errors are no much differen. I is quie reasonable behavior: he racking error which is occurred in earlier ime and is no hedged expands by he reinvesmen effec, and hus we need o cover i quickly. Figure 3 describes he saisical resuls of he invesmen simulaion. We observe ha he mean wealh indicaed by he blue line racks he liabiliy indicaed by he red line well. The mean hedging error E described in he lower panel of Figure 3 is approximaely 4.5 % of he liabiliy and i is he equivalen level wihou he noshorselling consrain sudied in [1]. Hence we find ha he liabiliy racking abiliy of our opimal porfolio sraegy does no drop if we resric he shor selling weigh ime X B C asse_ype domesic bond domesic sock foreign bond foreign sock money marke accoun Fig. 2: A sample of he invesmen simulaion in he case of π =. Red and blue lines in he upper panel indicae he ime evoluion of X, our wealh, and B C, he liabiliy, respecively. Red, yellow, green, blue and purple bars in he lower panel display he porfolio weighs of he domesic bond, he domesic sock, he foreign bond, he foreign sock and money marke accoun respecively mean pah error ime E B C mean(x) Fig. 3: Saisical resuls of he invesmen simulaion in he case of π =. Red and blue lines in he upper panel represen he ime evoluion of mean(x ) = N X i i=1 N, he mean wealh, and B C, he liabiliy, respecively. Red bar in he lower panel indicae he ime evoluion E, he mean hedging error. 4.2 Cases of upper bound consrain for he porfolio weighs The opimal porfolio invesigaed in he previous subsecion is hard o apply he real business since he large money borrowing is quie difficul o realize. Therefore we sudy he case ha we impose he upper bound for he porfolio weighs. We se π = 1, i.e., he weigh of each asse is bounded by 1% and hence he minimum weigh of he money marke accoun is 3%. Alhough he bound of he money borrowing is no lile, resuls of invesmen simulaions imply ha he pension fund faces he severe siuaion under he curren resricion. Figure 4 displays a sample pah invesmen simulaion which is a case ha he liabiliy is well hedged. We find ha he porfolio weighs are no concenraed on he domesic bond: all of he weighs closes up he upper bound around = 1. We also noe ha he weigh rises up in he increasing
5 order of he risk, i.e, he domesic bond is he mos weighed asse, he foreign bond is second one, and he domesic and foreign socks follows sequenially resul weigh ime X B C asse_ype domesic bond domesic sock foreign bond foreign sock money marke accoun Fig. 4: A sample of he invesmen simulaion under he consrain π = 1: he well hedged case. Red and blue lines in he upper panel indicae he ime evoluion of X, our wealh, and B C, he liabiliy, respecively. Red, yellow, green, blue and purple bars in he lower panel display he porfolio weighs of he domesic bond, he domesic sock, he foreign bond, he foreign sock and money marke accoun respecively. Under he consrain π = 1, he poor hedged resuls as shown in Figure 5 appear frequenly, hough hey are rarely found in he case of π =. Figure 6 implies ha he resul displayed in Figure 5 is no a special case. The mean hedging error E described in he lower panel of Figure 6 is approximaely 21% of he liabiliy and which is much higher han 4.5% sudied in he previous subsecion. Almos all of he porfolio weighs in Figure 5 reach he upper limi however he profi earned by hose asses is no enough o recover he racking error resul weigh ime X B C asse_ype domesic bond domesic sock foreign bond foreign sock money marke accoun Fig. 5: A sample of he invesmen simulaion under he consrain π = 1: he poor hedged case. Red and blue lines in he upper panel indicae he ime evoluion of X, our wealh, and B C, he liabiliy, respecively. Red, yellow, green, blue and purple bars in he lower panel display he porfolio weighs of he domesic bond, he domesic sock, he foreign bond, he foreign sock and money marke accoun respecively. Le us discuss he implicaion of hese resuls. Our simulaions imply he imbalance beween he growh rae of he liabiliy and he profiabiliy of he asses. We denoe by α he growh rae of he liabiliy and i is 7.6% which is higher han all of b i. Since our purpose is minimising he downside racking error, i is no enough ha he expeced reurn raes of he asses close o α: he high volailiy asses possibly disurb he racking. Furhermore he curren resricion which arrows he 3% money borrowing is even weak for he pracice. Therefore he implicaion is summarized ha he imbalance of he growh rae of he liabiliy and he profiabiliy of he invesed asses should be resolved mean pah error ime E B C mean(x) Fig. 6: Saisical resuls of he invesmen simulaion in he case of π = 1. Red and blue lines in he upper panel represen he ime evoluion of mean(x ) = N X i i=1 N, he mean wealh, and B C, he liabiliy, respecively. Red bar in he lower panel indicae he ime evoluion E, he mean hedging error. 5 Summary In he presen paper, we develop he porfolio sraegy from he sraegy proposed by Ieda e. al. [1]. The previous sudy remains wo poins which should be improved: (i) penalising he wealh of he invesor exceed he liabiliy; (ii) permiing he large shor selling. To resolve hese poins, we employ a lower mean square error from he racked liabiliy as he performance crierion and resric he porfolio weigh o a posiive and bounded value. In his case we are no able o obain an analyical soluion and hus we employ he numerical scheme proposed by Nakano [3] o solve he corresponding HJB equaion. We run numerical simulaions using he empirical daa provided by Japanese organizaions under he wo ypes of consrains: (i) he noshorselling consrain; (ii) he upper bound consrain for he porfolio weighs. The implicaions obained by hese simulaions are summarized as follows. We find ha he liabiliy racking abiliy of our opimal porfolio sraegy does no drop if we resric he shor selling. The resuls sudied in Secion 4.1 indicaes ha he mean hedging error is approximaely 4.5% of he liabiliy and i is he equivalen level wihou he noshorselling consrain sudied in [1]. Under he noshorselling consrain, he hedge is realized by borrowing he money a lo and invesing
6 he lowrisk asses. According o his resul, we resric he large money borrowing by imposing he upper bound consrain for he porfolio weighs in Secion 4.2. Then we find a number of sample pahs which have a large racking error. I implies ha he imbalance beween he growh rae of he liabiliy and he profiabiliy of he asses. Acknowledgemens This work is suppored by a collaboraion research projec wih he Governmen Pension Invesmen Fund in Japan in References [1] Masashi Ieda, Takashi Yamashia, and Yumiharu Nakano. A Liabiliy Tracking Approach o Long Term Managemen of Pension Funds. Journal of Mahemaical Finance, 3(3):392 4, 213. [2] Harold J Kushner and Paul G Dupuis. Numerical mehods for sochasic conrol problems in coninuous ime, volume 24. Springer, 2. [3] Yumiharu Nakano. A quadraic approximaion scheme for HamilonJacobiBellman equaions. Working Paper, Tokyo Insiue of Technology, Graduae School of Innovaion Managemen, pages 1 23, 213. [4] The Japanese Minisry of Healh, Labour and Welfare. hp:// bunya/nenkin/nenkin/zaiseikensyo/index. hml [accessed 1 Ocober 214]. [5] The Governmen Pension Invesmen Fund in Japan. hp:// pdf/h1911_appendix_5.pdf [accessed 1 Ocober 214]
Lifecycle Funds. T. Rowe Price Target Retirement Fund. Lifecycle Asset Allocation
Lifecycle Funds Towards a Dynamic Asse Allocaion Framework for Targe Reiremen Funds: Geing Rid of he Dogma in Lifecycle Invesing Anup K. Basu Queensland Universiy of Technology The findings of he Mercer
More informationStrategic Decision Making in Portfolio Management with Goal Programming Model
American Journal of Operaions Managemen and Informaion Sysems 06; (): 3438 hp://www.sciencepublishinggroup.com//aomis doi: 0.648/.aomis.0600.4 Sraegic Decision Making in Porfolio Managemen wih Goal Programming
More informationOptimal Portfolio Strategy with Discounted Stochastic Cash Inflows
Journal of Mahemaical Finance 3 3 337 hp://dxdoiorg/436/jmf33 Published Online February 3 (hp://wwwscirporg/journal/jmf) Opimal Porfolio raegy wih iscouned ochasic Cash nflows Charles Nkeki eparmen of
More informationEconomics 487. Homework #4 Solution Key Portfolio Calculations and the Markowitz Algorithm
Economics 87 Homework # Soluion Key Porfolio Calculaions and he Markowiz Algorihm A. Excel Exercises: (10 poins) 1. Download he Excel file hw.xls from he class websie. This file conains monhly closing
More informationEvaluating Portfolio Policies: A Duality Approach
OPERATIONS RESEARCH Vol. 54, No. 3, May June 26, pp. 45 418 issn 3364X eissn 15265463 6 543 45 informs doi 1.1287/opre.16.279 26 INFORMS Evaluaing Porfolio Policies: A Dualiy Approach Marin B. Haugh
More informationUsing Rates of Change to Create a Graphical Model. LEARN ABOUT the Math. Create a speed versus time graph for Steve s walk to work.
2.4 Using Raes of Change o Creae a Graphical Model YOU WILL NEED graphing calculaor or graphing sofware GOAL Represen verbal descripions of raes of change using graphs. LEARN ABOUT he Mah Today Seve walked
More informationA Probabilistic Approach to Worst Case Scenarios
A Probabilisic Approach o Wors Case Scenarios A Probabilisic Approach o Wors Case Scenarios By Giovanni BaroneAdesi Universiy of Albera, Canada and Ciy Universiy Business School, London Frederick Bourgoin
More informationMorningstar Investor Return
Morningsar Invesor Reurn Morningsar Mehodology Paper March 3, 2009 2009 Morningsar, Inc. All righs reserved. The informaion in his documen is he propery of Morningsar, Inc. Reproducion or ranscripion by
More informationEvaluating the Performance of Forecasting Models for Portfolio Allocation Purposes with Generalized GRACH Method
Advances in mahemaical finance & applicaions, 2 (1), (2017), 17 Published by IA Universiy of Arak, Iran Homepage: www.amfa.iauarak.ac.ir Evaluaing he Performance of Forecasing Models for Porfolio Allocaion
More informationQUANTITATIVE FINANCE RESEARCH CENTRE. Optimal Time Series Momentum QUANTITATIVE FINANCE RESEARCH CENTRE QUANTITATIVE F INANCE RESEARCH CENTRE
QUANTITATIVE FINANCE RESEARCH CENTRE QUANTITATIVE F INANCE RESEARCH CENTRE QUANTITATIVE FINANCE RESEARCH CENTRE Research Paper 353 January 15 Opimal Time Series Momenum XueZhong He, Kai Li and Youwei
More informationDYNAMIC portfolio optimization is one of the important
, July 24, 2014, London, U.K. A Simulaionbased Porfolio Opimizaion Approach wih Leas Squares Learning Chenming Bao, Geoffrey Lee, and Zili Zhu Absrac This paper inroduces a simulaionbased numerical
More informationAsset Allocation with Higher Order Moments and Factor Models
Asse Allocaion wih Higher Order Momens and Facor Models Kris Boud (VU Brussel, Amserdam) Based on join research wih: Wanbo Lu (SWUFE) and Benedic Peeers (Finvex Group) 1 The world of asse reurns is nonnormal.
More informationMarket Timing with GEYR in Emerging Stock Market: The Evidence from Stock Exchange of Thailand
Journal of Finance and Invesmen Analysis, vol. 1, no. 4, 2012, 5365 ISSN: 22410998 (prin version), 22410996(online) Scienpress Ld, 2012 Marke Timing wih GEYR in Emerging Sock Marke: The Evidence from
More informationReceived August 16, 2013; revised September 27, 2013; accepted October 26, 2013
Journal of Mahemaical Finance 78 Published Online November (hp://wwwscirporg/journal/jmf) hp://dxdoiorg//jmf Opimal Variaional Porfolios wih Inflaion Proecion raegy and Efficien Fronier of Expeced Value
More informationThe safe ships trajectory in a restricted area
Scienific Journals Mariime Universiy of Szczecin Zeszyy Naukowe Akademia Morska w Szczecinie 214, 39(111) pp. 122 127 214, 39(111) s. 122 127 ISSN 1733867 The safe ships rajecory in a resriced area Zbigniew
More informationTimeVariation in Diversification Benefits of Commodity, REITs, and TIPS 1
TimeVariaion in Diversificaion Benefis of Commodiy, REITs, and TIPS 1 Jingzhi Huang 2 and Zhaodong Zhong 3 This Draf: July 11, 2006 Absrac Diversificaion benefis of hree ho asse classes, Commodiy, Real
More informationThe Current Account as A Dynamic Portfolio Choice Problem
Public Disclosure Auhorized Policy Research Working Paper 486 WPS486 Public Disclosure Auhorized Public Disclosure Auhorized The Curren Accoun as A Dynamic Porfolio Choice Problem Taiana Didier Alexandre
More informationDo Competitive Advantages Lead to Higher Future Rates of Return?
Do Compeiive Advanages Lead o Higher Fuure Raes of Reurn? Vicki Dickinson Universiy of Florida Greg Sommers Souhern Mehodis Universiy 2010 CARE Conference Forecasing and Indusry Fundamenals April 9, 2010
More informationMarket timing and statistical arbitrage: Which market timing opportunities arise from equity price busts coinciding with recessions?
Journal of Applied Finance & Banking, vol.1, no.1, 2011, 5381 ISSN: 17926580 (prin version), 17926599 (online) Inernaional Scienific Press, 2011 Marke iming and saisical arbirage: Which marke iming
More informationConstructing Absolute Return Funds with ETFs: A Dynamic RiskBudgeting Approach. July 2008
Consrucing Absolue Reurn Funds wih ETFs: A Dynamic RiskBudgeing Approach July 2008 Noël Amenc Direcor, EDHEC Risk & Asse Managemen Research Cenre Professor of Finance, EDHEC Business School noel.amenc@edhecrisk.com
More informationPaul M. Sommers David U. Cha And Daniel P. Glatt. March 2010 MIDDLEBURY COLLEGE ECONOMICS DISCUSSION PAPER NO
AN EMPIRICAL TEST OF BILL JAMES S PYTHAGOREAN FORMULA by Paul M. Sommers David U. Cha And Daniel P. Gla March 2010 MIDDLEBURY COLLEGE ECONOMICS DISCUSSION PAPER NO. 1006 DEPARTMENT OF ECONOMICS MIDDLEBURY
More informationMethods for Estimating Term Structure of Interest Rates
Mehods for Esimaing Term Srucure of Ineres Raes Iskander Karibzhanov Absrac This paper compares differen inerpolaion algorihms for consrucing yield curves: cubic splines, linear and quadraic programming,
More informationThe APT with Lagged, ValueatRisk and Asset Allocations by Using Econometric Approach
Proceedings of he 16 Inernaional Conference on Indusrial Engineering and Operaions Managemen Deroi, USA, Sepember 35, 16 he AP wih Lagged, ValueaRisk and Asse Allocaions by Using Economeric Approach
More informationStock Return Expectations in the Credit Market
Sock Reurn Expecaions in he Credi Marke Hans Bysröm * Sepember 016 In his paper we compue longerm sock reurn expecaions (across he business cycle) for individual firms using informaion backed ou from
More informationIdiosyncratic Volatility, Stock Returns and Economy Conditions: The Role of Idiosyncratic Volatility in the Australian Stock Market
Idiosyncraic Volailiy, Sock Reurns and Economy Condiions: The Role of Idiosyncraic Volailiy in he Ausralian Sock Marke Bin Liu Amalia Di Iorio RMIT Universiy Melbourne Ausralia Absrac This sudy examines
More informationTesting Portfolio Efficiency with NonTraded Assets: Taking into Account Labor Income, Housing and Liabilities
Tesing Porfolio Efficiency wih NonTraded Asses: Taking ino Accoun Labor Income, Housing and Liabiliies Roy Kouwenberg Mahidol Universiy and Erasmus Universiy Roerdam Thierry Pos Erasmus Universiy Roerdam
More information296 Finance a úvěrczech Journal of Economics and Finance, 64, 2014, no. 4
JEL Classificaion: C32, F31, G11 Keywords: Emerging Easern Europe, sock and currency markes, porfolio, VaR Effeciveness of Porfolio Diversificaion and he Dynamic Relaionship beween Sock and Currency Markes
More informationThe Effects of Systemic Risk on the Allocation between Value and Growth Portfolios
Journal of Mahemaical Finance, 013, 3, 165180 hp://x.oi.org/10.436/mf.013.31a016 Publishe Online March 013 (hp://www.scirp.org/ournal/mf) The Effecs of Sysemic Risk on he Allocaion beween Value an Growh
More informationKEY CONCEPTS AND PROCESS SKILLS. 1. An allele is one of the two or more forms of a gene present in a population. MATERIALS AND ADVANCE PREPARATION
Gene Squares 61 40 o 2 3 50minue sessions ACIVIY OVERVIEW P R O B L E M S O LV I N G SUMMARY Sudens use Punne squares o predic he approximae frequencies of rais among he offspring of specific crier crosses.
More informationReliability Design Technology for Power Semiconductor Modules
Reliabiliy Design Technology for Power Semiconducor Modules Akira Morozumi Kasumi Yamada Tadashi Miyasaka 1. Inroducion The marke for power semiconducor modules is spreading no only o generalpurpose inverers,
More informationCALCULATION OF EXPECTED SLIDING DISTANCE OF BREAKWATER CAISSON CONSIDERING VARIABILITY IN WAVE DIRECTION
CALCULATION OF EXPECTED SLIDING DISTANCE OF BREAKWATER CAISSON CONSIDERING VARIABILITY IN WAVE DIRECTION SU YOUNG HONG School of Civil, Urban, and Geosysem Engineering, Seoul Naional Universiy, San 561,
More informationSimulation based approach for measuring concentration risk
MPRA Munich Personal RePEc Archive Simulaion based approach for measuring concenraion risk Kim, Joocheol and Lee, Duyeol UNSPECIFIED February 27 Online a hp://mpra.ub.unimuenchen.de/2968/ MPRA Paper No.
More informationPortfolio Efficiency: Traditional MeanVariance Analysis versus Linear Programming
Porfolio Efficiency: Tradiional MeanVariance Analysis versus Linear Programming Seve Eli Ahiabu Universiy of Torono Spring 003 Please send commens o Sephen.ahiabu@uorono.ca I hank Prof. Adonis Yachew
More informationFINVEX WHITE PAPER ON ASSET ALLOCATION WITH RISK FACTORS
FINVEX WHITE PAPER ON AET ALLOCATION WITH RIK FACTOR By Dr Kris Boud PhD Professor of Finance & Research Parner a Finvex Group Benedic Peeers CoFounder Finvex Group July 3 Execuive ummary In his paper,
More informationThe Construction of a Bioeconomic Model of the Indonesian Flying Fish Fishery
Marine Resource Economics, Volume 0, pp. 357372 0738360/95 $3.00 +.00 Prined in he U.S.A. All righs reserved. Copyrigh 995 Marine Resources Foundaion The Consrucion of a Bioeconomic Model of he Indonesian
More informationJames Sefton and Sylvain Champonnois London Quant Conference September 2012
Dynamic Porfolio Opimisaion wih Trading Coss James Sefon and Sylvain Champonnois London Quan Conference Sepember 2012 Tracabiliy and Transparency Imporan Quans have needed o upgrade heir approach To rebalance
More informationCALCULATORS: Casio: ClassPad 300 ClassPad 300 Plus ClassPad Manager TI: TI89, TI89 Titanium Voyage 200. The Casio ClassPad 300
The Casio ClassPad 300 CC Edwards 1950 1955 1960 1965 1970 23.0 23.8 24.4 24.5 24.2 1975 1980 1985 1990 1995 24.7 25.2 25.5 25.9 26.3 The able shows how he average age of he firs marriage of Japanese women
More informationThe ttest. What We Will Cover in This Section. A Research Situation
The es 1//008 P331 ess 1 Wha We Will Cover in This Secion Inroducion Onesample es. Power and effec size. Independen samples es. Dependen samples es. Key learning poins. 1//008 P331 ess A Research
More informationFORECASTING TECHNIQUES ADE 2013 Prof Antoni Espasa TOPIC 1 PART 2 TRENDS AND ACCUMULATION OF KNOWLEDGE. SEASONALITY HANDOUT
FORECASTING TECHNIQUES ADE 2013 Prof Anoni Espasa TOPIC 1 PART 2 TRENDS AND ACCUMULATION OF KNOWLEDGE. SEASONALITY HANDOUT February 2013 MAIN FACTORS CAUSING TRENDS Increases in populaion. Seady inflaion.
More informationAsset and Liability Management, Caisse. a manager of public debt
Asse and Liabiliy Managemen by CADES, a manager of public deb Name Deparmen & affiliaion Mailing Address email address(es) Phone number 331 55 78 58 19, 331 55 78 58 00 Fax number 331 55 78 58 02 Eric
More informationProportional Reasoning
Proporional Reasoning Focus on Afer his lesson, you will be able o... solve problems using proporional reasoning use more han one mehod o solve proporional reasoning problems When you go snowboarding or
More informationCapacity Utilization Metrics Revisited: Delay Weighting vs Demand Weighting. Mark Hansen ChiehYu Hsiao University of California, Berkeley 01/29/04
Capaciy Uilizaion Merics Revisied: Delay Weighing vs Demand Weighing Mark Hansen ChiehYu Hsiao Universiy of California, Berkeley 01/29/04 1 Ouline Inroducion Exising merics examinaion Proposed merics
More informationBootstrapping Multilayer Neural Networks for Portfolio Construction
Asia Pacific Managemen Review 17(2) (2012) 113126 Boosrapping Mulilayer Neural Neworks for Porfolio Consrucion ChinSheng Huang a*, ZhengWei Lin b, ChengWei Chen c www.apmr.managemen.ncku.edu.w a Deparmen
More informationAn Alternative Mathematical Model for Oxygen Transfer Evaluation in Clean Water
An Alernaive Mahemaical Model for Oxygen Transfer Evaluaion in Clean Waer Yanjun (John) He 1, PE, BCEE 1 Kruger Inc., 41 Weson Parkway, Cary, NC 27513 Email: john.he@veolia.com ABSTRACT Energy consumpion
More informationHomework 2. is unbiased if. Y is consistent if. c. in real life you typically get to sample many times.
Econ526 Mulile Choice. Homework 2 Choose he one ha bes comlees he saemen or answers he quesion. (1) An esimaor ˆ µ of he oulaion value µ is unbiased if a. ˆ µ = µ. b. has he smalles variance of all esimaors.
More informationITG Dynamic Daily Risk Model for Europe
December 2010 Version 1 ITG Dynamic Daily Risk Model for Europe 2010 All righs reserved. No o be reproduced or reransmied wihou permission. 121610 29140 These maerials are for informaional purposes only,
More informationRevisiting the Growth of Hong Kong, Singapore, South Korea, and Taiwan, From the Perspective of a Neoclassical Model
Revisiing he Growh of Hong Kong, Singapore, Souh Korea, and Taiwan, 9782006 From he Perspecive of a Neoclassical Model Shushiuan Lu * Naional Tsing Hua Univereseiy December, 2008 Absrac This paper sudies
More informationBetting Against Beta
Being Agains Bea Andrea Frazzini and Lasse H. Pedersen * This draf: Ocober 5, 2010 Absrac. We presen a model in which some invesors are prohibied from using leverage and oher invesors leverage is limied
More informationTime & Distance SAKSHI If an object travels the same distance (D) with two different speeds S 1 taking different times t 1
www.sakshieducaion.com Time & isance The raio beween disance () ravelled by an objec and he ime () aken by ha o ravel he disance is called he speed (S) of he objec. S = = S = Generally if he disance ()
More informationSources of OverPerformance in Equity Markets: Mean Reversion, Common Trends and Herding
The Universiy of Reading THE BUSINESS SCHOOL FOR FINANCIAL MARKETS Sources of OverPerformance in Equiy Markes: Mean Reversion, Common Trends and Herding ISMA Cenre Discussion Papers in Finance 200308
More informationCan Optimized Portfolios Beat 1/N?
Can Opimized Porfolios Bea 1/N? This disseraion is presened in par fulfillmen of he requiremen for he compleion of an MSc in Economics in he Deparmen of Economics, Universiy of Konsanz, and an MSc in Economics
More informationSURFACE PAVEMENT CHARACTERISTICS AND ACCIDENT RATE
The 10 h Inernaional Conference RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION 2010 Proceedings of he 10h Inernaional Conference Reliabiliy and Saisics in Transporaion and Communicaion
More informationMeasuring dynamics of risk and performance of sector indices on Zagreb Stock Exchange
Measuring dynamics of risk and performance of secor indices on Zagreb Sock Exchange Tihana Škrinjarić Faculy of Economics and Business, Universiy of Zagreb, Zagreb, Croaia skrinjaric@efzg.hr Absrac Invesors
More informationSan Francisco State University ECON 560 Fall Midterm Exam 2. Tuesday, October hour, 15 minutes
San Francisco Sae Universiy Micael Bar ECON 560 Fall 207 Miderm Exam 2 Tuesday, Ocober 3 our, 5 minues Name: Insrucions. Tis is closed book, closed noes exam. 2. No calculaors or elecronic devices of any
More informationMODEL SELECTION FOR VALUEATRISK: UNIVARIATE AND MULTIVARIATE APPROACHES SANG JIN LEE
MODEL SELECTION FOR VALUEATRISK: UNIVARIATE AND MULTIVARIATE APPROACHES By SANG JIN LEE Bachelor of Science in Mahemaics Yonsei Universiy Seoul, Republic of Korea 999 Maser of Business Adminisraion Yonsei
More informationWhat the Puck? an exploration of TwoDimensional collisions
Wha he Puck? an exploraion of TwoDimensional collisions 1) Have you ever played 8Ball pool and los he game because you scrached while aemping o sink he 8Ball in a corner pocke? Skech he sho below: Each
More informationRolling ADF Tests: Detecting Rational Bubbles in Greater China Stock Markets
Singapore Managemen Universiy Insiuional Knowledge a Singapore Managemen Universiy Disseraions and Theses Collecion (Open Access) Disseraions and Theses 2008 Rolling ADF Tess: Deecing Raional Bubbles in
More informationKeywords: overfishing, voluntary vessel buy back programs, backward bending supply curve, offshore fisheries in Taiwan
EVALUATION AND SIMULATION OF FISHING CAPACITY AND BACKWARD BENDING SUPPLY OF THE OFFSHORE FISHERY IN TAIWAN ChinHwa Sun, Insiue of Applied Economics, Naional Taiwan Ocean Universiy, jsun@mail.nou.edu.w
More informationOverview. Do whitetailed tailed and mule deer compete? Ecological Definitions (Birch 1957): Mule and whitetailed tailed deer potentially compete.
COMPETITION BETWEEN MULE AND WHITE TAILED DEER METAPOPULATIONS IN NORTHCENTRAL WASHINGTON E. O. Garon, Kris Hennings : Fish and Wildlife Dep., Univ. of Idaho, Moscow, ID 83844 Maureen Murphy, and Seve
More informationMeasuring Potential Output and Output Gap and Macroeconomic Policy: The Case of Kenya
Universiy of Connecicu DigialCommons@UConn Economics Working Papers Deparmen of Economics Ocober 2005 Measuring Poenial Oupu and Oupu Gap and Macroeconomic Policy: The Case of Kenya Angelica E. Njuguna
More informationMacro Sensitive Portfolio Strategies
Marke Insigh Macro Sensiive Porfolio Sraegies Marke Insigh Macro Sensiive Porfolio Sraegies Macroeconomic Risk and Asse Cash Flows Kur Winkelmann, Raghu Suryanarayanan, Ludger Henschel, and Kaalin Varga
More informationGuidance Statement on Calculation Methodology
Guidance Saemen on Calculaion Mehodology Adopion Dae: 28 Sepember 200 Effecive Dae: January 20 Reroacive Applicaion: No Required www.gipssandards.org 200 CFA Insiue Guidance Saemen on Calculaion Mehodology
More informationANALYSIS OF RELIABILITY, MAINTENANCE AND RISK BASED INSPECTION OF PRESSURE SAFETY VALVES
ANALYSIS OF RELIABILITY, MAINTENANCE AND RISK BASED INSPECTION OF PRESSURE SAFETY VALVES Venilon Forunao Francisco Machado Mechanical Engineering Dep, Insiuo Superior Técnico, Av. Rovisco Pais, 04900,
More informationOn convexity of SD efficiency sets  no short sales case
4. mezinárodní konference Řízení a modelování finančních rizik Osrava VŠBU Osrava Ekonomická fakula kaedra Financí.. září 008 On conveiy of SD efficiency ses  no shor sales case Miloš Kopa Absrac his
More informationThe credit portfolio management by the econometric models: A theoretical analysis
The credi porfolio managemen by he economeric models: A heoreical analysis Abdelkader Derbali To cie his version: Abdelkader Derbali. The credi porfolio managemen by he economeric models: A heoreical analysis.
More informationPerformance Optimization of Markov Models in Simulating Computer Networks
Proceedings of he World Congress on Engineering and Copuer Science 9 Vol I WCECS 9, Ocober , 9, San Francisco, USA Perforance Opiizaion of Marov Models in Siulaing Copuer Newors Nisrine Sinno, Hussein
More informationThe Economic Costs of Vetoes: Evidence from NATO
The Economic Coss of Veoes: Evidence from NATO Kyriakos Drivas PhD Candidae Deparmen of Agriculural Economics Universiy of CaliforniaBerkeley 207 Giannini Hall # 3310 Universiy of California Berkeley,
More informationEXAMINING THE FEASIBILITY OF PAIRED CLOSELYSPACED PARALLEL APPROACHES
EXAMINING THE FEASIBILITY OF PAIRED CLOSELYSPACED PARALLEL APPROACHES Seven J. Landry and Amy R. Priche Georgia Insiue of Technology Alana GA 303320205 ABSTRACT Paired closelyspaced parallel approaches
More informationInterpreting Sinusoidal Functions
6.3 Inerpreing Sinusoidal Funcions GOAL Relae deails of sinusoidal phenomena o heir graphs. LEARN ABOUT he Mah Two sudens are riding heir bikes. A pebble is suck in he ire of each bike. The wo graphs show
More informationCOVER S UNIVERSAL PORTFOLIO, STOCHASTIC PORTFOLIO THEORY AND THE NUMÉRAIRE PORTFOLIO
COVER S UNIVERSAL PORFOLIO, SOCHASIC PORFOLIO HEORY AND HE NUMÉRAIRE PORFOLIO CHRISA CUCHIERO, WALER SCHACHERMAYER AND INGKAM LEONARD WONG Absrac. Cover s celebraed heorem saes ha he long run yield of
More informationWhat should investors know about the stability of momentum investing and its riskiness? The case of the Australian Security Exchange
Wha should invesors know abou he sabiliy of momenum invesing and is riskiness? The case of he Ausralian Securiy Exchange Emilios C. Galariois To cie his version: Emilios C. Galariois. Wha should invesors
More informationProtecting the African Elephant: A Dynamic Bioeconomic Model of. Ivory Trade
Proecing he African Elephan: A Dynamic Bioeconomic Model of Ivory Trade G. Cornelis van Kooen Deparmen of Economics Universiy of Vicoria P.O. Box 1700, Sn CSC Vicoria, BC V8W 2Y2 Canada Email: kooen@uvic.ca
More informationMonte Carlo simulation modelling of aircraft dispatch with known faults
Loughborough Universiy Insiuional Reposiory Mone Carlo simulaion modelling of aircraf dispach wih known fauls This iem was submied o Loughborough Universiy's Insiuional Reposiory by he/an auhor. Ciaion:
More informationPerformance Attribution for Equity Portfolios
PERFORMACE ATTRIBUTIO FOR EQUITY PORTFOLIOS Performance Aribuion for Equiy Porfolios Yang Lu and David Kane Inroducion Many porfolio managers measure performance wih reference o a benchmark. The difference
More informationAs time goes by  Using time series based decision tree induction to analyze the behaviour of opponent players
As ime goes by  Using ime series based decision ree inducion o analyze he behaviour of opponen players Chrisian Drücker, Sebasian Hübner, Ubbo Visser, HansGeorg Weland TZI  Cener for Compuing Technologies
More informationPortfolio Strategies Based on Analysts Consensus
Porfolio Sraegies Based on Analyss Consensus Enrico Maria Cervellai Deparmen of Managemen Faculy of Economics Universiy of Bologna Piazza Scaravilli, 1 40126 Bologna Tel: +39 (0)51 2098087 Fax: +39 (0)51
More informationReproducing laboratoryscale rip currents on a barred beach by a Boussinesq wave model
See discussions, sas, and auhor profiles for his publicaion a: hps://www.researchgae.ne/publicaion/9977 Reproducing laboraoryscale rip currens on a barred beach by a Boussinesq wave model Aricle in Journal
More informationValuing Volatility Spillovers
Valuing Volailiy Spillovers George Milunovich Division of Economic and Financial Sudies Macquarie Universiy Sydney Susan Thorp School of Finance and Economics Universiy of Technology Sydney March 2006
More information2. JOMON WARE ROPE STYLES
Proceedings of he IIEEJ Image Elecronics and Visual Compuing Workshop 2012 Kuching, Malaysia, November 2124, 2012 A SIMULATION SYSTEM TO SYNTHESIZE ROPE ROLLING PATTERNS IN A VIRTUAL SPACE FOR RESEARCH
More informationDynamics of market correlations: Taxonomy and portfolio analysis
Dynamics of marke correlaions: Taxonomy and porfolio analysis J.P. Onnela, A. Chakrabori, and K. Kaski Laboraory of Compuaional Engineering, Helsinki Universiy of Technology, P.O. Box 9203, FIN02015
More informationTransit Priority Strategies for Multiple Routes Under HeadwayBased Operations
Transi Prioriy Sraegies for Muliple Roues Under HeadwayBased Operaions Yongjie Lin, Xianfeng Yang, GangLen Chang, and Nan Zou This paper presens a ransi signal prioriy (TSP) model designed o consider
More informationMULTIVARIATE RISKRETURN DECISION MAKING WITHIN DYNAMIC ESTIMATION
Economic Analysis Working Papers. 7h Volume Number 11 MULIVARIAE RISKREURN DECISION MAKING WIHIN DYNAMIC ESIMAION Josip Arnerić 1, Elza Jurun, and Snježana Pivac, 3 Universiy of Spli, Faculy of Economics,
More informationA Study on the Powering Performance of MultiAxes Propulsion Ships with Wing Pods
Second Inernaional Symposium on Marine Propulsors smp amburg Germany une A Sudy on he Powering Performance of MuliAxes Propulsion Ships wih Wing Pods eungwon Seo Seokcheon Go Sangbong Lee and ungil Kwon
More informationMachine Learning for Stock Selection
Machine Learning for Sock Selecion Rober J. Yan Compuer Science Dep., The Uniersiy of Wesern Onario jyan@csd.uwo.ca Charles X. Ling Compuer Science Dep., The Uniersiy of Wesern Onario cling@csd.uwo.ca
More information2017 MCM/ICM Merging Area Designing Model for A Highway Toll Plaza Summary Sheet
Team#55307 Page 1 of 25 For office use only T1 T2 T3 T4 Team Conrol Number 55307 Problem Chosen B For office use only F1 F2 F3 F4 2017 MCM/ICM Merging Area Designing Model for A Highway Toll Plaza Summary
More informationSemiFixedPriority Scheduling: New Priority Assignment Policy for Practical Imprecise Computation
SemiFixedPrioriy Scheduling: New Prioriy Assignmen Policy for Pracical Imprecise Compuaion Hiroyuki Chishiro, Akira Takeda 2, Kenji Funaoka 2 and Nobuyuki Yamasaki School of Science for Open and Environmen
More informationProceedings of the ASME 28th International Conference on Ocean, Offshore and Arctic Engineering OMAE2009 May 31  June 5, 2009, Honolulu, Hawaii
Proceedings of he ASME 28h Inernaional Conference on Ocean, Offshore and Arcic Engineering OMAE29 May 31  June 5, 29, Honolulu, Hawaii OMAE2979385 ANALYSIS OF THE TUNNEL IMMERSION FOR THE BUSANGEOJE
More informationCHARACTERIZATION AND MODELING OF A PROPORTIONAL VALVE FOR CONTROL SYNTHESIS
CHARACTERIZATION AND MODELING OF A PROPORTIONAL VALVE FOR CONTROL SYNTHESIS Osama. OLABY, Xavier. BRN, Sylvie. SESMAT, Tanneguy. REDARCE and Eric. BIDEAX Laboraoire d Auomaique Indusrielle  hp://wwwlai.insalyon.fr
More informationStraight Leg ged Walking of a Biped Robot
Sraigh Leg ged Walking of a Biped Robo Ryo Kurazume, Shunaro Tanaka, Masahiro Yamashia Tsuomu Hasegawa Kyushu Universiy 6101, Hakozaki, Higashiku, Fukuoka, Japan Email: kurazume @ is.kyushuu.ac.jp
More informationThe design of courier transportation networks with a nonlinear zeroone programming model
The design of courier ransporaion newors wih a nonlinear zeroone programming model Boliang Lin School of Traffic and Transporaion, Being Jiaoong Universiy, Being 100044, People s Republic of China (A
More informationName Class Date. Step 2: Rearrange the acceleration equation to solve for final speed. a v final v initial v. final v initial v.
Skills Workshee Mah Skills Acceleraion Afer you sudy each sample problem and soluion, work ou he pracice problems on a separae shee of paper. Wrie your answers in he spaces provided. In 1970, Don Big Daddy
More informationArbitrage pricing theorybased Gaussian temporal factor analysis for adaptive portfolio management
Decision Suppor Sysems 37 (24) 485 5 www.elsevier.com/locae/dsw rbirage pricing heorybased Gaussian emporal facor analysis for adapive porfolio managemen Kaihun hiu*, Lei Xu Deparmen of ompuer Science
More informationEconomic Growth with Bubbles
Economic Growh wih Bubbles AlberoMarin,andJaumeVenura March 2010 Absrac We develop a sylized model of economic growh wih bubbles. In his model, financial fricions lead o equilibrium dispersion in he raes
More information3.00 m. 8. At La Ronde, the freefall ride called the Orbit" causes a 60.0 kg person to accelerate at a rate of 9.81 m/s 2 down.
Physics Prees: Torque 1. Newon s 2 nd Law 2. Kinemaics (Free fall, graphs, and combined wih F R = ma) Pracice Quesions/Problems 1. Wha is Newon s 2 nd Law? Name and explain i. 2. Prove ha acceleraion for
More informationKINEMATICS IN ONE DIMENSION
chaper KINEMATICS IN ONE DIMENSION Secion 2.1 Displacemen Secion 2.2 Speed and Velociy 1. A paricle ravels along a curved pah beween wo poins P and Q as shown. The displacemen of he paricle does no depend
More informationExamining the limitations for visual anglecar following models
Examining he limiaions for visual anglecar following models Al Obaedi, JTS and Yousif, S Tile Auhors Type URL Published Dae 009 Examining he limiaions for visual angle car following models Al Obaedi, JTS
More informationAMURE PUBLICATIONS. Working Papers Series
AMURE PUBLICATIONS Working Papers Series N D202006 < A CosBenefi Analysis of Improving Trawl Seleciviy: he Nephrops norvegicus Fishery in he Bay of Biscay > Claire MACHER */** Olivier GUYADER * Caherine
More information1. The value of the digit 4 in the number 42,780 is 10 times the value of the digit 4 in which number?
Mahemaics. he value of he digi in he number 70 is 0 imes he value of he digi in which number?. Mike is years old. Joe is imes as old as Mike. Which equaion shows how o find Joe s age? 70 000 = = = + =
More informationAutomatic airmain charging and pressure control system for compressed air supplies
Auomaic airmain charging and pressure conrol sysem for compressed air supplies Type PCS A module from he sysem vacorol Swiching onoff a compressed air uni in a compressed air supply generally akes place
More informationFIVE RISK FACTORS MODEL: PRICING SECTORAL PORTFOLIOS IN THE BRAZILIAN STOCK MARKET
Revisa Caarinense da Ciência Conábil, ISSN 18083781  eissn 22377662, Florianópolis, SC, Brazil, v. 16, n. 48, p. 8198, May/Aug. 2017 doi: 10.16930/22377662/rccc.v16n48.2376 Available a hp://revisa.crcsc.org.br
More information