Comparisons Between RV, ARV and WRV

Size: px
Start display at page:

Download "Comparisons Between RV, ARV and WRV"

Transcription

1 Comparisos Bewee RV, ARV ad WRV Cao Gag,Guo Migyua School of Maageme ad Ecoomics, Tiaji Uiversiy, Tiaji,30007 Absrac: Realized Volailiy (RV) have bee widely used sice i was pu forward by Aderso ad Bollerslev i 998.Some scholars pu forward heir improved form for opimizig he qualiy of RV. As a resul, i is possible o compariso bewee RV, ARV ad WRV. This paper akes a corac of sock idex fuures i Chia as sample, aimig a researchig he compariso of RV, ARV ad WRV. Key Words: Realized Volailiy (RV), Adjused Realized Volailiy (ARV), Weighed Realized Volailiy (WRV), sock idex fuures Iroducio Realized Volailiy have bee widely used sice i was pu forward by Aderso ad Bollerslev i 998[]. I esimaes he volailiy usig he sum of square of reurs.i is model free ad do' eed researchers o esimaig he parameers of he model. Some scholars foud ha here exis errors whe usig RV i some siuaios, so hey pu forward heir improved forms. Xu (004) [] pu forward "Adjused Realized Volailiy" (ARV) ad Guo (006)[3] pu forward "Weighed Realized Volailiy". Boh auhors proved heir improved form is beer ha RV. Afer ha, ARV ad WRV is widely used i empirical aalysis, while here is' a lieraure does compariso of RV,ARV ad WRV. This paper akes a corac of sock idex fuures i Chia as sample, aimig a researchig he compariso of RV, ARV ad WRV. Realized Volailiy Assume r (, = p, p, =,, L T, =,LN) p, is h logarihmic price i r h day,, is he reur of logarihmic price of fiacial asse, N is he umber of sample ake from [-,] Accordig o Aderso ad Bollerslev(998) [],realized volailiy is he sum of square of reurs i a radig day, so 99

2 N = = RV r, () 3 Adjused Realized Volailiy Xu (004)[] pu forward "Adjused Realized Volailiy (ARV)"o dealig wih he measure error of RV. Xu ook he followig formula as ARV: E( RV) E( RQ) Var( RV) E( RQ) () ARV = + RV Var( RV ) Var( RV ) 3 j, ) 4 RQ r( + Where: i= Xu (004) [] proved he mea of ARV is equal o RV ad he variace of ARV is smaller ha RV. So i his opiio, ARV is more effecive ha RV. 4 Weighed Realized Volailiy Accordig o he defiiio of RV, i gives a equal weigh o each square of iraday reur. Bu here exiss "Caledar Effecs" i sock marke which meas he iraday reur was higher i he opeig ad closig period which smaller i he mid-ime of radig day. So i's ureasoable o imposig he ideical weighs. Guo (006)[3] ook "Caledar effecs" io cosideraio ad pu forward "Weighed Realized Volailiy (WRV)",I aimed a o describig he "Caledar Effecs" beer. Defiiio Weighed Realized Volailiy (WRV) is a weighed sum of he square of iraday reur of fiacial asses WRV N = = wr, (3) w is he weigh of square of iraday reurs, i mees N = w = (4) The mos impora work o compuig WRV is o compuig he weighs. Guo pu forward codiios : ()WRV is he ubiased esimaor of IV ()WRV is he esimaor wih leas variace. I compuig of codiio, Guo brough Lagrage fucio o makig he leas variace, he resul is 00

3 N = N r, w = r, (5) 5 The compariso of saisical iformaio amog RV, ARV ad WRV 5. Daa Descripio The sample came from a corac of sock idex fuures marke of Chia. I raged from Apr.6,00-Aug 3,0 which ivolved i 330 radig day.the aalysis aims a he daily realized volailiy of IF0,we compue i wih he daa of 5-miue idex price. 5. Saisical feaures of RV, ARV ad WRV () The series of RV, ARV ad WRV Table shows he saisical iformaio of 3 origial series. From able, i is easy o realizig ha 3 origial series are all "skew o righ wih high peak" which mees he firs iem i he characer of RV; heir mea is equal which is cosise wih he coclusio of Xu (004) ad Guo (006). Table. Saisical iformaio of RV, ARV ad WRV RV ARV WRV Mea Sd.Dev skewess Kurosis Jarque-Bera () The series of l RV, l ARV ad l WRV Table shows he saisical iformaio of 3 logarihmic series. Table. Saisical iformaio of l RV,l ARV ad l WRV l RV l ARV l WRV Mea Sd.Dev skewess Kurosis Jarque-Bera

4 From able, i's clear ha 3 logarihmic series improve heir resul obviously i skewess, kurosis ad J-B saisics. They are much approximae o ormal disribuio ha heir origial series which mees he secod iem of characer of RV. (3) The series of sadardized reurs r RV Se as sadardized reurs, so as o ARV ad WRV. Table 3 shows he saisical iformaio of hree series of sadardized reurs. Table 3. Table capios should always be posiioed above he ables r RV r ARV r WRV Mea Sd.Dev skewess Kurosis Jarque-Bera From able 3, i is easily o geig he followig coclusio: he series of r RV r ARV ad is close eough o ormal disribuio while he series r WRV of feaures wih "high peak ad skew o lef". 5.3 The Auocorrelaio of RV, ARV ad WRV This par researches he auocorrelaio of RV, ARV ad WRV. Image -3 shows he series of 00-order auocorrelaio coefficies..4 RVZXGX Fig.. 00-order auocorrelaio coefficies of RV 0

5 RVPZXGXS Fig.. 00-order parial auocorrelaio coefficies of RV.4 ARVZXGXS Fig order auocorrelaio coefficies of ARV From Figure -3, all of hree series have characer of obvious auocorrelaio which mees he firs iem i characer of RV. The red of auocorrelaio coefficies bewee RV ad ARV almos he same While he red of WRV's is a lile differe from hem. The reaso is obvious: he formula of ARV ca be divided io pars, he firs par is a produc of a cosa ad RV, he secod par is a cosa. As a resul, he reds of auocorrelaio coefficies bewee RV ad ARV are almos he same. Bu he fomula of WRV break ou he srucure of RV, so is red is differe from RV ad ARV. 5.4 The Summary of Saisical Iformaio of RV,ARV ad WRV Here make a coclusio abou he compariso of RV,ARV ad WRV ()Boh hree origial series are all "skew o righ wih high peak" while heir mea is equal; 03

6 ()Three logarihmic series improve heir resul obviously i skewess, kurosis ad J-B saisics. They are much approximae o ormal disribuio ha heir origial series; (3)The hree series of sadard deviaio are all "skew o righ wih high peak", bu perform beer ha origial series; (4)The red of auocorrelaio ad parial auocorrelaio coefficies bewee RV ad ARV almos he same While he red of WRV's is a lile differe from hem. 6 Coclusios Realized Volailiy (RV) has bee widely used sice i was pu forward. Some scholars pu forward he improved form of RV aimig a beer performace i describig he volailiy. This paper selec mai form of iprovemes: Adjused Realized Volailiy (ARV) ad Weighed Realized Volailiy (WRV) ad make a compariso of RV, ARV ad WRV of a corac of sock idex of fuures i Chia. The empirical aalysis icludes saisical iformaio ad he auocorrelaio series. Refereces. Aderse T.G., Bollerslev T. Aswerig he Criics: yes, ARCH Models Do Provide Good Volailiy Forecass [J]. 997, NBER Workig Paper, No.603. Xu Z.G. Aalysis, Modelig ad Applicaio of Fiacial Marke s High FrequecyUlra High Frequecy Time Series[D].Maser Degree Paper of Tiaji Uiversiy, Guo M.Y. Aalysis, Modelig ad Applicaio of volailiy of Fiacial High Frequecy Time Series[D]. Docor Degree Paper of Tiaji Uiversiy, Aderse T. G., Bollerslev T. e.al. Modellig ad Forecasig Realized Volailiy [J]. Ecoomerics, 003, Vol.7, Issue : Aderse T.G., Bollerslev T. Exchage Rae Reurs Sadardized by Realized Volailiy are (Nearly) Gaussia [J]. 000, NBER Workig Paper, No Aderse T.G. e al. The Disribuio of Realized Exchage Rae Volailiy[J]. Joural of he America Saisical Associaio, 00, Vol.96, Issue 453: Aderse T.G., Bollerslev T. e al. The Disribuio of Realized Sock Reur Volailiy [J]. Joural of Fiacial Ecoomics, 00, Vol.6, Issue : Bamdorff-Nielse O., Shephard N. Ecoomeric aalysis of realized volailiy ad is use i esimaig sochasic volailiy models[j]. Joural of he Royal Saisical Sociey: Series B (Saisical Mehodology), 00, Vol.64, Issue :

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

F D D D D F. smoothed value of the data including Y t the most recent data.

F D D D D F. smoothed value of the data including Y t the most recent data. Module 2 Forecasig 1. Wha is forecasig? Forecasig is defied as esimaig he fuure value ha a parameer will ake. Mos scieific forecasig mehods forecas he fuure value usig pas daa. I Operaios Maageme forecasig

More information

Inference of the Second Order Autoregressive. Model with Unit Roots

Inference of the Second Order Autoregressive. Model with Unit Roots Ieraioal Mahemaical Forum Vol. 6 0 o. 5 595-604 Iferece of he Secod Order Auoregressive Model wih Ui Roos Ahmed H. Youssef Professor of Applied Saisics ad Ecoomerics Isiue of Saisical Sudies ad Research

More information

Skewness of Gaussian Mixture Absolute Value GARCH(1, 1) Model

Skewness of Gaussian Mixture Absolute Value GARCH(1, 1) Model Commuicaios for Saisical Applicaios ad Mehods 203, Vol. 20, No. 5, 395 404 DOI: hp://dx.doi.org/0.535/csam.203.20.5.395 Skewess of Gaussia Mixure Absolue Value GARCH(, Model Taewook Lee,a a Deparme of

More information

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17 OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

A Note on Prediction with Misspecified Models

A Note on Prediction with Misspecified Models ITB J. Sci., Vol. 44 A, No. 3,, 7-9 7 A Noe o Predicio wih Misspecified Models Khresha Syuhada Saisics Research Divisio, Faculy of Mahemaics ad Naural Scieces, Isiu Tekologi Badug, Jala Gaesa Badug, Jawa

More information

Application of Intelligent Systems and Econometric Models for Exchange Rate Prediction

Application of Intelligent Systems and Econometric Models for Exchange Rate Prediction 0 Ieraioal Coferece o Iovaio, Maageme ad Service IPEDR vol.4(0) (0) IACSIT Press, Sigapore Applicaio of Iellige Sysems ad Ecoomeric Models for Exchage Rae Predicio Abu Hassa Shaari Md Nor, Behrooz Gharleghi

More information

B. Maddah INDE 504 Simulation 09/02/17

B. Maddah INDE 504 Simulation 09/02/17 B. Maddah INDE 54 Simulaio 9/2/7 Queueig Primer Wha is a queueig sysem? A queueig sysem cosiss of servers (resources) ha provide service o cusomers (eiies). A Cusomer requesig service will sar service

More information

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May

Exercise 3 Stochastic Models of Manufacturing Systems 4T400, 6 May Exercise 3 Sochasic Models of Maufacurig Sysems 4T4, 6 May. Each week a very popular loery i Adorra pris 4 ickes. Each ickes has wo 4-digi umbers o i, oe visible ad he oher covered. The umbers are radomly

More information

Online Supplement to Reactive Tabu Search in a Team-Learning Problem

Online Supplement to Reactive Tabu Search in a Team-Learning Problem Olie Suppleme o Reacive abu Search i a eam-learig Problem Yueli She School of Ieraioal Busiess Admiisraio, Shaghai Uiversiy of Fiace ad Ecoomics, Shaghai 00433, People s Republic of Chia, she.yueli@mail.shufe.edu.c

More information

A Bayesian Approach for Detecting Outliers in ARMA Time Series

A Bayesian Approach for Detecting Outliers in ARMA Time Series WSEAS RASACS o MAEMAICS Guochao Zhag Qigmig Gui A Bayesia Approach for Deecig Ouliers i ARMA ime Series GUOC ZAG Isiue of Sciece Iformaio Egieerig Uiversiy 45 Zhegzhou CIA 94587@qqcom QIGMIG GUI Isiue

More information

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP)

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP) ENGG450 Probabiliy ad Saisics for Egieers Iroducio 3 Probabiliy 4 Probabiliy disribuios 5 Probabiliy Desiies Orgaizaio ad descripio of daa 6 Samplig disribuios 7 Ifereces cocerig a mea 8 Comparig wo reames

More information

Additional Tables of Simulation Results

Additional Tables of Simulation Results Saisica Siica: Suppleme REGULARIZING LASSO: A CONSISTENT VARIABLE SELECTION METHOD Quefeg Li ad Ju Shao Uiversiy of Wiscosi, Madiso, Eas Chia Normal Uiversiy ad Uiversiy of Wiscosi, Madiso Supplemeary

More information

Time Series, Part 1 Content Literature

Time Series, Part 1 Content Literature Time Series, Par Coe - Saioariy, auocorrelaio, parial auocorrelaio, removal of osaioary compoes, idepedece es for ime series - Liear Sochasic Processes: auoregressive (AR), movig average (MA), auoregressive

More information

Modeling volatility with Range-based Heterogeneous Autoregressive Conditional Heteroskedasticity model

Modeling volatility with Range-based Heterogeneous Autoregressive Conditional Heteroskedasticity model Workig Papers No. 6/04 (3 TOMASZ SKOCZYLAS Modelig volailiy wi Rage-based Heerogeeous Auoregressive Codiioal Heeroskedasiciy model Warsaw 04 Modelig volailiy wi Rage-based Heerogeeous Auoregressive Codiioal

More information

Mean Square Convergent Finite Difference Scheme for Stochastic Parabolic PDEs

Mean Square Convergent Finite Difference Scheme for Stochastic Parabolic PDEs America Joural of Compuaioal Mahemaics, 04, 4, 80-88 Published Olie Sepember 04 i SciRes. hp://www.scirp.org/joural/ajcm hp://dx.doi.org/0.436/ajcm.04.4404 Mea Square Coverge Fiie Differece Scheme for

More information

Detection of Level Change (LC) Outlier in GARCH (1, 1) Processes

Detection of Level Change (LC) Outlier in GARCH (1, 1) Processes Proceedigs of he 8h WSEAS I. Cof. o NON-LINEAR ANALYSIS, NON-LINEAR SYSTEMS AND CHAOS Deecio of Level Chage () Oulier i GARCH (, ) Processes AZAMI ZAHARIM, SITI MERIAM ZAHID, MOHAMMAD SAID ZAINOL AND K.

More information

STK4080/9080 Survival and event history analysis

STK4080/9080 Survival and event history analysis STK48/98 Survival ad eve hisory aalysis Marigales i discree ime Cosider a sochasic process The process M is a marigale if Lecure 3: Marigales ad oher sochasic processes i discree ime (recap) where (formally

More information

The analysis of the method on the one variable function s limit Ke Wu

The analysis of the method on the one variable function s limit Ke Wu Ieraioal Coferece o Advaces i Mechaical Egieerig ad Idusrial Iformaics (AMEII 5) The aalysis of he mehod o he oe variable fucio s i Ke Wu Deparme of Mahemaics ad Saisics Zaozhuag Uiversiy Zaozhuag 776

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

Effect of Heat Exchangers Connection on Effectiveness

Effect of Heat Exchangers Connection on Effectiveness Joural of Roboics ad Mechaical Egieerig Research Effec of Hea Exchagers oecio o Effeciveess Voio W Koiaho Maru J Lampie ad M El Haj Assad * Aalo Uiversiy School of Sciece ad echology P O Box 00 FIN-00076

More information

Fresnel Dragging Explained

Fresnel Dragging Explained Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field

More information

Stationarity and Unit Root tests

Stationarity and Unit Root tests Saioari ad Ui Roo ess Saioari ad Ui Roo ess. Saioar ad Nosaioar Series. Sprios Regressio 3. Ui Roo ad Nosaioari 4. Ui Roo ess Dicke-Fller es Agmeed Dicke-Fller es KPSS es Phillips-Perro Tes 5. Resolvig

More information

Modelling Overnight and Daytime Returns Using a Multivariate GARCH-Copula Model

Modelling Overnight and Daytime Returns Using a Multivariate GARCH-Copula Model CAEPR Workig Paper #8- Modellig Overigh ad Dayime Reurs Usig a Mulivariae GARCH-Copula Model Log Kag HE OPIONS CLEARING CORPORAION (email: lkag@heocc.com) Simo H Babbs HE OPIONS CLEARING CORPORAION (email:

More information

State and Parameter Estimation of The Lorenz System In Existence of Colored Noise

State and Parameter Estimation of The Lorenz System In Existence of Colored Noise Sae ad Parameer Esimaio of he Lorez Sysem I Eisece of Colored Noise Mozhga Mombeii a Hamid Khaloozadeh b a Elecrical Corol ad Sysem Egieerig Researcher of Isiue for Research i Fudameal Scieces (IPM ehra

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 4 9/16/2013. Applications of the large deviation technique

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 4 9/16/2013. Applications of the large deviation technique MASSACHUSETTS ISTITUTE OF TECHOLOGY 6.265/5.070J Fall 203 Lecure 4 9/6/203 Applicaios of he large deviaio echique Coe.. Isurace problem 2. Queueig problem 3. Buffer overflow probabiliy Safey capial for

More information

Dynamic h-index: the Hirsch index in function of time

Dynamic h-index: the Hirsch index in function of time Dyamic h-idex: he Hirsch idex i fucio of ime by L. Egghe Uiversiei Hassel (UHassel), Campus Diepebeek, Agoralaa, B-3590 Diepebeek, Belgium ad Uiversiei Awerpe (UA), Campus Drie Eike, Uiversieisplei, B-260

More information

Samuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3

Samuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3 Ieraioal Joural of Saisics ad Aalysis. ISSN 48-9959 Volume 6, Number (6, pp. -8 Research Idia Publicaios hp://www.ripublicaio.com The Populaio Mea ad is Variace i he Presece of Geocide for a Simple Birh-Deah-

More information

Unit Root Time Series. Univariate random walk

Unit 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 information

Institute of Actuaries of India

Institute of Actuaries of India Isiue of cuaries of Idia Subjec CT3-robabiliy ad Mahemaical Saisics May 008 Eamiaio INDICTIVE SOLUTION Iroducio The idicaive soluio has bee wrie by he Eamiers wih he aim of helig cadidaes. The soluios

More information

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics

F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mathematics F.Y. Diploma : Sem. II [AE/CH/FG/ME/PT/PG] Applied Mahemaics Prelim Quesio Paper Soluio Q. Aemp ay FIVE of he followig : [0] Q.(a) Defie Eve ad odd fucios. [] As.: A fucio f() is said o be eve fucio if

More information

Comparison between Fourier and Corrected Fourier Series Methods

Comparison between Fourier and Corrected Fourier Series Methods Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1

More information

CLOSED FORM EVALUATION OF RESTRICTED SUMS CONTAINING SQUARES OF FIBONOMIAL COEFFICIENTS

CLOSED FORM EVALUATION OF RESTRICTED SUMS CONTAINING SQUARES OF FIBONOMIAL COEFFICIENTS PB Sci Bull, Series A, Vol 78, Iss 4, 2016 ISSN 1223-7027 CLOSED FORM EVALATION OF RESTRICTED SMS CONTAINING SQARES OF FIBONOMIAL COEFFICIENTS Emrah Kılıc 1, Helmu Prodiger 2 We give a sysemaic approach

More information

INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA

INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA Volume 8 No. 8, 45-54 ISSN: 34-3395 (o-lie versio) url: hp://www.ijpam.eu ijpam.eu INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA A.Arul dass M.Dhaapal

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017 Deparme of Ecoomics The Ohio Sae Uiversiy Ecoomics 8723 Macroecoomic Theory Problem Se 2 Professor Sajay Chugh Sprig 207 Labor Icome Taxes, Nash-Bargaied Wages, ad Proporioally-Bargaied Wages. I a ecoomy

More information

Affine term structure models

Affine term structure models /5/07 Affie erm srucure models A. Iro o Gaussia affie erm srucure models B. Esimaio by miimum chi square (Hamilo ad Wu) C. Esimaio by OLS (Adria, Moech, ad Crump) D. Dyamic Nelso-Siegel model (Chrisese,

More information

Some Properties of Semi-E-Convex Function and Semi-E-Convex Programming*

Some Properties of Semi-E-Convex Function and Semi-E-Convex Programming* The Eighh Ieraioal Symposium o Operaios esearch ad Is Applicaios (ISOA 9) Zhagjiajie Chia Sepember 2 22 29 Copyrigh 29 OSC & APOC pp 33 39 Some Properies of Semi-E-Covex Fucio ad Semi-E-Covex Programmig*

More information

A Comparative Analysis of Value at Risk Measurement on Emerging Stock Markets: Case of Montenegro

A Comparative Analysis of Value at Risk Measurement on Emerging Stock Markets: Case of Montenegro Busiess Sysems Research Vol. 6 No. 1 / March 2015 A Comparaive Aalysis of Value a Risk Measureme o Emergig Sock Markes: Case of Moeegro Julija Cerović, Milea Lipovia-Božović, Saša Vujošević Uiversiy of

More information

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i) Mah PracTes Be sure o review Lab (ad all labs) There are los of good quesios o i a) Sae he Mea Value Theorem ad draw a graph ha illusraes b) Name a impora heorem where he Mea Value Theorem was used i he

More information

Chapter Chapter 10 Two-Sample Tests X 1 X 2. Difference Between Two Means: Different data sources Unrelated. Learning Objectives

Chapter Chapter 10 Two-Sample Tests X 1 X 2. Difference Between Two Means: Different data sources Unrelated. Learning Objectives Chaper 0 0- Learig Objecives I his chaper, you lear how o use hypohesis esig for comparig he differece bewee: Chaper 0 Two-ample Tess The meas of wo idepede populaios The meas of wo relaed populaios The

More information

CSE 241 Algorithms and Data Structures 10/14/2015. Skip Lists

CSE 241 Algorithms and Data Structures 10/14/2015. Skip Lists CSE 41 Algorihms ad Daa Srucures 10/14/015 Skip Liss This hadou gives he skip lis mehods ha we discussed i class. A skip lis is a ordered, doublyliked lis wih some exra poiers ha allow us o jump over muliple

More information

Solutions to selected problems from the midterm exam Math 222 Winter 2015

Solutions to selected problems from the midterm exam Math 222 Winter 2015 Soluios o seleced problems from he miderm eam Mah Wier 5. Derive he Maclauri series for he followig fucios. (cf. Pracice Problem 4 log( + (a L( d. Soluio: We have he Maclauri series log( + + 3 3 4 4 +...,

More information

Page 1. Before-After Control-Impact (BACI) Power Analysis For Several Related Populations. Richard A. Hinrichsen. March 3, 2010

Page 1. Before-After Control-Impact (BACI) Power Analysis For Several Related Populations. Richard A. Hinrichsen. March 3, 2010 Page Before-Afer Corol-Impac BACI Power Aalysis For Several Relaed Populaios Richard A. Hirichse March 3, Cavea: This eperimeal desig ool is for a idealized power aalysis buil upo several simplifyig assumpios

More information

A Generalized Cost Malmquist Index to the Productivities of Units with Negative Data in DEA

A Generalized Cost Malmquist Index to the Productivities of Units with Negative Data in DEA Proceedigs of he 202 Ieraioal Coferece o Idusrial Egieerig ad Operaios Maageme Isabul, urey, July 3 6, 202 A eeralized Cos Malmquis Ide o he Produciviies of Uis wih Negaive Daa i DEA Shabam Razavya Deparme

More information

Local Influence Diagnostics of Replicated Data with Measurement Errors

Local Influence Diagnostics of Replicated Data with Measurement Errors ISSN 76-7659 Eglad UK Joural of Iformaio ad Compuig Sciece Vol. No. 8 pp.7-8 Local Ifluece Diagosics of Replicaed Daa wih Measureme Errors Jigig Lu Hairog Li Chuzheg Cao School of Mahemaics ad Saisics

More information

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.

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 information

Maximum Likelihood Estimation for Allpass Time Series Models

Maximum Likelihood Estimation for Allpass Time Series Models Maximum Likelihood Esimaio or Allass Time Series Models Richard A. Davis Dearme o Saisics Colorado Sae Uiversiy h://www.sa.colosae.edu/~rdavis/lecures/magdeburg.d Joi work wih Jay Breid, Colorado Sae Uiversiy

More information

On the Validity of the Pairs Bootstrap for Lasso Estimators

On the Validity of the Pairs Bootstrap for Lasso Estimators O he Validiy of he Pairs Boosrap for Lasso Esimaors Lorezo Campoovo Uiversiy of S.Galle Ocober 2014 Absrac We sudy he validiy of he pairs boosrap for Lasso esimaors i liear regressio models wih radom covariaes

More information

BAYESIAN ESTIMATION METHOD FOR PARAMETER OF EPIDEMIC SIR REED-FROST MODEL. Puji Kurniawan M

BAYESIAN ESTIMATION METHOD FOR PARAMETER OF EPIDEMIC SIR REED-FROST MODEL. Puji Kurniawan M BAYESAN ESTMATON METHOD FOR PARAMETER OF EPDEMC SR REED-FROST MODEL Puji Kuriawa M447 ABSTRACT. fecious diseases is a impora healh problem i he mos of couries, belogig o doesia. Some of ifecious diseases

More information

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE Mohia & Samaa, Vol. 1, No. II, December, 016, pp 34-49. ORIGINAL RESEARCH ARTICLE OPEN ACCESS FIED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE 1 Mohia S. *, Samaa T. K. 1 Deparme of Mahemaics, Sudhir Memorial

More information

A Note on Random k-sat for Moderately Growing k

A Note on Random k-sat for Moderately Growing k A Noe o Radom k-sat for Moderaely Growig k Ju Liu LMIB ad School of Mahemaics ad Sysems Sciece, Beihag Uiversiy, Beijig, 100191, P.R. Chia juliu@smss.buaa.edu.c Zogsheg Gao LMIB ad School of Mahemaics

More information

Procedia - Social and Behavioral Sciences 230 ( 2016 ) Joint Probability Distribution and the Minimum of a Set of Normalized Random Variables

Procedia - Social and Behavioral Sciences 230 ( 2016 ) Joint Probability Distribution and the Minimum of a Set of Normalized Random Variables Available olie a wwwsciecedireccom ScieceDirec Procedia - Social ad Behavioral Scieces 30 ( 016 ) 35 39 3 rd Ieraioal Coferece o New Challeges i Maageme ad Orgaizaio: Orgaizaio ad Leadership, May 016,

More information

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY U.P.B. Sci. Bull., Series A, Vol. 78, Iss. 2, 206 ISSN 223-7027 A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY İbrahim Çaak I his paper we obai a Tauberia codiio i erms of he weighed classical

More information

The Solution of the One Species Lotka-Volterra Equation Using Variational Iteration Method ABSTRACT INTRODUCTION

The Solution of the One Species Lotka-Volterra Equation Using Variational Iteration Method ABSTRACT INTRODUCTION Malaysia Joural of Mahemaical Scieces 2(2): 55-6 (28) The Soluio of he Oe Species Loka-Volerra Equaio Usig Variaioal Ieraio Mehod B. Baiha, M.S.M. Noorai, I. Hashim School of Mahemaical Scieces, Uiversii

More information

CSE 202: Design and Analysis of Algorithms Lecture 16

CSE 202: Design and Analysis of Algorithms Lecture 16 CSE 202: Desig ad Aalysis of Algorihms Lecure 16 Isrucor: Kamalia Chaudhuri Iequaliy 1: Marov s Iequaliy Pr(X=x) Pr(X >= a) 0 x a If X is a radom variable which aes o-egaive values, ad a > 0, he Pr[X a]

More information

Lecture 15 First Properties of the Brownian Motion

Lecture 15 First Properties of the Brownian Motion Lecure 15: Firs Properies 1 of 8 Course: Theory of Probabiliy II Term: Sprig 2015 Isrucor: Gorda Zikovic Lecure 15 Firs Properies of he Browia Moio This lecure deals wih some of he more immediae properies

More information

6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend

6/10/2014. Definition. Time series Data. Time series Graph. Components of time series. Time series Seasonal. Time series Trend 6//4 Defiiio Time series Daa A ime series Measures he same pheomeo a equal iervals of ime Time series Graph Compoes of ime series 5 5 5-5 7 Q 7 Q 7 Q 3 7 Q 4 8 Q 8 Q 8 Q 3 8 Q 4 9 Q 9 Q 9 Q 3 9 Q 4 Q Q

More information

in insurance : IFRS / Solvency II

in insurance : IFRS / Solvency II Impac es of ormes he IFRS asse jumps e assurace i isurace : IFRS / Solvecy II 15 h Ieraioal FIR Colloquium Zürich Sepember 9, 005 Frédéric PNCHET Pierre THEROND ISF Uiversié yo 1 Wier & ssociés Sepember

More information

Available online at J. Math. Comput. Sci. 4 (2014), No. 4, ISSN:

Available online at   J. Math. Comput. Sci. 4 (2014), No. 4, ISSN: Available olie a hp://sci.org J. Mah. Compu. Sci. 4 (2014), No. 4, 716-727 ISSN: 1927-5307 ON ITERATIVE TECHNIQUES FOR NUMERICAL SOLUTIONS OF LINEAR AND NONLINEAR DIFFERENTIAL EQUATIONS S.O. EDEKI *, A.A.

More information

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws.

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws. Limi of a fucio.. Oe-Sided..... Ifiie limis Verical Asympoes... Calculaig Usig he Limi Laws.5 The Squeeze Theorem.6 The Precise Defiiio of a Limi......7 Coiuiy.8 Iermediae Value Theorem..9 Refereces..

More information

Clock Skew and Signal Representation

Clock Skew and Signal Representation Clock Skew ad Sigal Represeaio Ch. 7 IBM Power 4 Chip 0/7/004 08 frequecy domai Program Iroducio ad moivaio Sequeial circuis, clock imig, Basic ools for frequecy domai aalysis Fourier series sigal represeaio

More information

Semiparametric and Nonparametric Methods in Political Science Lecture 1: Semiparametric Estimation

Semiparametric and Nonparametric Methods in Political Science Lecture 1: Semiparametric Estimation Semiparameric ad Noparameric Mehods i Poliical Sciece Lecure : Semiparameric Esimaio Michael Peress, Uiversiy of Rocheser ad Yale Uiversiy Lecure : Semiparameric Mehods Page 2 Overview of Semi ad Noparameric

More information

Math 6710, Fall 2016 Final Exam Solutions

Math 6710, Fall 2016 Final Exam Solutions Mah 67, Fall 6 Fial Exam Soluios. Firs, a sude poied ou a suble hig: if P (X i p >, he X + + X (X + + X / ( evaluaes o / wih probabiliy p >. This is roublesome because a radom variable is supposed o be

More information

An economic and actuarial analysis of death bonds

An economic and actuarial analysis of death bonds w w w. I C A 2 1 4. o r g A ecoomic ad acuarial aalysis of deah bods JOÃO VINÍCIUS DE FRANÇA CARVALHO UNIVERSITY OF SAO PAULO, BRAZIL LUÍS EDUARDO AFONSO UNIVERSITY OF SAO PAULO, BRAZIL Ageda Iroducio

More information

David Randall. ( )e ikx. k = u x,t. u( x,t)e ikx dx L. x L /2. Recall that the proof of (1) and (2) involves use of the orthogonality condition.

David Randall. ( )e ikx. k = u x,t. u( x,t)e ikx dx L. x L /2. Recall that the proof of (1) and (2) involves use of the orthogonality condition. ! Revised April 21, 2010 1:27 P! 1 Fourier Series David Radall Assume ha u( x,) is real ad iegrable If he domai is periodic, wih period L, we ca express u( x,) exacly by a Fourier series expasio: ( ) =

More information

COS 522: Complexity Theory : Boaz Barak Handout 10: Parallel Repetition Lemma

COS 522: Complexity Theory : Boaz Barak Handout 10: Parallel Repetition Lemma COS 522: Complexiy Theory : Boaz Barak Hadou 0: Parallel Repeiio Lemma Readig: () A Parallel Repeiio Theorem / Ra Raz (available o his websie) (2) Parallel Repeiio: Simplificaios ad he No-Sigallig Case

More information

Analysis of Using a Hybrid Neural Network Forecast Model to Study Annual Precipitation

Analysis of Using a Hybrid Neural Network Forecast Model to Study Annual Precipitation Aalysis of Usig a Hybrid Neural Nework Forecas Model o Sudy Aual Precipiaio Li MA, 2, 3, Xuelia LI, 2, Ji Wag, 2 Jiagsu Egieerig Ceer of Nework Moiorig, Najig Uiversiy of Iformaio Sciece & Techology, Najig

More information

INVESTMENT PROJECT EFFICIENCY EVALUATION

INVESTMENT PROJECT EFFICIENCY EVALUATION 368 Miljeko Crjac Domiika Crjac INVESTMENT PROJECT EFFICIENCY EVALUATION Miljeko Crjac Professor Faculy of Ecoomics Drsc Domiika Crjac Faculy of Elecrical Egieerig Osijek Summary Fiacial efficiecy of ivesme

More information

DEPARTMENT 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 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 information

Parameter Estimation of a Class of Hidden Markov Model with Diagnostics

Parameter Estimation of a Class of Hidden Markov Model with Diagnostics Joural of oder Applied Saisical ehods Volume Issue Aricle 5--3 Parameer Esimaio of a Class of Hidde arkov odel wih Diagosics E. B. Nkemole Uiversiy of Lagos, Nigeria, Africa O. Abass Uiversiy of Lagos,

More information

Approximating Solutions for Ginzburg Landau Equation by HPM and ADM

Approximating Solutions for Ginzburg Landau Equation by HPM and ADM Available a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 193-9466 Vol. 5, No. Issue (December 1), pp. 575 584 (Previously, Vol. 5, Issue 1, pp. 167 1681) Applicaios ad Applied Mahemaics: A Ieraioal Joural

More information

UNIVERSITY OF NOTTINGHAM. Discussion Papers in Economics

UNIVERSITY OF NOTTINGHAM. Discussion Papers in Economics UNIVERSITY OF NOTTINGHAM Discussio Papers i Ecoomics Discussio Paper No. /9 JAMES-STEIN TYPE ESTIMATORS IN LARGE SAMPLES WITH APPLICATION TO THE LEAST ABSOLUTE DEVIATION ESTIMATOR by Tae-Hwa Kim ad Halber

More information

Homotopy Analysis Method for Solving Fractional Sturm-Liouville Problems

Homotopy Analysis Method for Solving Fractional Sturm-Liouville Problems Ausralia Joural of Basic ad Applied Scieces, 4(1): 518-57, 1 ISSN 1991-8178 Homoopy Aalysis Mehod for Solvig Fracioal Surm-Liouville Problems 1 A Neamay, R Darzi, A Dabbaghia 1 Deparme of Mahemaics, Uiversiy

More information

Using Autoregressive Logit Models to Forecast the Exceedance Probability. for Financial Risk Management

Using Autoregressive Logit Models to Forecast the Exceedance Probability. for Financial Risk Management Usig Auoregressive Logi Models o Forecas he Exceedace Probabiliy for Fiacial Risk Maageme James W. Taylor * Saïd Busiess School, Uiversiy of Oxford, Park Ed Sree, Oxford, OX HP, UK. (james.aylor@sbs.ox.ac.uk)

More information

Actuarial Society of India

Actuarial Society of India Acuarial Sociey of Idia EXAMINAIONS Jue 5 C4 (3) Models oal Marks - 5 Idicaive Soluio Q. (i) a) Le U deoe he process described by 3 ad V deoe he process described by 4. he 5 e 5 PU [ ] PV [ ] ( e ).538!

More information

Academic Forum Cauchy Confers with Weierstrass. Lloyd Edgar S. Moyo, Ph.D. Associate Professor of Mathematics

Academic Forum Cauchy Confers with Weierstrass. Lloyd Edgar S. Moyo, Ph.D. Associate Professor of Mathematics Academic Forum - Cauchy Cofers wih Weiersrass Lloyd Edgar S Moyo PhD Associae Professor of Mahemaics Absrac We poi ou wo limiaios of usig he Cauchy Residue Theorem o evaluae a defiie iegral of a real raioal

More information

Asian Economic and Financial Review THE CORRELATION AND CONTAGION EFFECT BETWEEN US REITS AND JAPAN REITS - BASED ON THE ARMAX-GJR-GARCH-COPULA MODEL

Asian Economic and Financial Review THE CORRELATION AND CONTAGION EFFECT BETWEEN US REITS AND JAPAN REITS - BASED ON THE ARMAX-GJR-GARCH-COPULA MODEL Asia Ecoomic ad Fiacial Review, 3, 3():69-69 Asia Ecoomic ad Fiacial Review joural homepage: hp://aessweb.com/joural-deail.php?id=5 THE CORREATION AND CONTAGION EFFECT BETWEEN US REITS AND JAPAN REITS

More information

Forecasting the Stock Indexes of Fragile Five Countries through Box-Jenkins Methods

Forecasting the Stock Indexes of Fragile Five Countries through Box-Jenkins Methods Ieraioal Joural of Busiess ad Social Sciece Vol. 6, No. 8; Augus 2015 Forecasig he Sock Idexes of Fragile Five Couries hrough Box-Jekis Mehods Seda Yeice, PhD Assis. Prof. Gazi Uiversiy Vocaioal School

More information

July 24-25, Overview. Why the Reliability Issue is Important? Some Well-known Reliability Measures. Weibull and lognormal Probability Plots

July 24-25, Overview. Why the Reliability Issue is Important? Some Well-known Reliability Measures. Weibull and lognormal Probability Plots Par I: July 24-25, 204 Overview Why he Reliabiliy Issue is Impora? Reliabiliy Daa Paer Some Well-kow Reliabiliy Measures Weibull ad logormal Probabiliy Plos Maximum Likelihood Esimaor 2 Wha is Reliabiliy?

More information

Modified Ratio and Product Estimators for Estimating Population Mean in Two-Phase Sampling

Modified Ratio and Product Estimators for Estimating Population Mean in Two-Phase Sampling America Joural of Operaioal esearch 06, 6(3): 6-68 DOI: 0.593/j.ajor.060603.0 Moifie aio a Prouc Esimaors for Esimaig Populaio Mea i Two-Phase Samplig Subhash Kumar Yaav, Sa Gupa, S. S. Mishra 3,, Alok

More information

Supplement for SADAGRAD: Strongly Adaptive Stochastic Gradient Methods"

Supplement for SADAGRAD: Strongly Adaptive Stochastic Gradient Methods Suppleme for SADAGRAD: Srogly Adapive Sochasic Gradie Mehods" Zaiyi Che * 1 Yi Xu * Ehog Che 1 iabao Yag 1. Proof of Proposiio 1 Proposiio 1. Le ɛ > 0 be fixed, H 0 γi, γ g, EF (w 1 ) F (w ) ɛ 0 ad ieraio

More information

Estimation Uncertainty

Estimation 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 information

Using GLS to generate forecasts in regression models with auto-correlated disturbances with simulation and Palestinian market index data

Using GLS to generate forecasts in regression models with auto-correlated disturbances with simulation and Palestinian market index data America Joural of Theoreical ad Applied Saisics 04; 3(: 6-7 Published olie December 30, 03 (hp://www.sciecepublishiggroup.com//aas doi: 0.648/.aas.04030. Usig o geerae forecass i regressio models wih auo-correlaed

More information

The Moment Approximation of the First Passage Time for the Birth Death Diffusion Process with Immigraton to a Moving Linear Barrier

The Moment Approximation of the First Passage Time for the Birth Death Diffusion Process with Immigraton to a Moving Linear Barrier America Joural of Applied Mahemaics ad Saisics, 015, Vol. 3, No. 5, 184-189 Available olie a hp://pubs.sciepub.com/ajams/3/5/ Sciece ad Educaio Publishig DOI:10.1691/ajams-3-5- The Mome Approximaio of

More information

ASSESSING GOODNESS OF FIT

ASSESSING GOODNESS OF FIT ASSESSING GOODNESS OF FIT 1. Iroducio Ofe imes we have some daa ad wa o es if a paricular model (or model class) is a good fi. For isace, i is commo o make ormaliy assumpios for simpliciy, bu ofe i is

More information

Common Fixed Point Theorem in Intuitionistic Fuzzy Metric Space via Compatible Mappings of Type (K)

Common Fixed Point Theorem in Intuitionistic Fuzzy Metric Space via Compatible Mappings of Type (K) Ieraioal Joural of ahemaics Treds ad Techology (IJTT) Volume 35 umber 4- July 016 Commo Fixed Poi Theorem i Iuiioisic Fuzzy eric Sace via Comaible aigs of Tye (K) Dr. Ramaa Reddy Assisa Professor De. of

More information

High Watermarks of Market Risks

High Watermarks of Market Risks High Waermarks of Marke Risks Berrad Maille Thierry Michel - February 007 - Prelimiary versio Absrac The volailiy has log bee used as a auxiliary variable i he processes explaiig he reurs o risky asses.

More information

Conditional Probability and Conditional Expectation

Conditional Probability and Conditional Expectation Hadou #8 for B902308 prig 2002 lecure dae: 3/06/2002 Codiioal Probabiliy ad Codiioal Epecaio uppose X ad Y are wo radom variables The codiioal probabiliy of Y y give X is } { }, { } { X P X y Y P X y Y

More information

Introduction: Review of Literature: NSE/DEAP/155. Proposal No. 155

Introduction: Review of Literature: NSE/DEAP/155. Proposal No. 155 NSE/DEAP/155 Proposal No. 155 Lead-Lag elaioship bewee Equiies ad Sock Idex Fuures Marke ad is Variaio aroud Iformaio elease: Empirical Evidece from Idia. Iroducio: The Idia capial marke has wiessed a

More information

λiv Av = 0 or ( λi Av ) = 0. In order for a vector v to be an eigenvector, it must be in the kernel of λi

λiv Av = 0 or ( λi Av ) = 0. In order for a vector v to be an eigenvector, it must be in the kernel of λi Liear lgebra Lecure #9 Noes This week s lecure focuses o wha migh be called he srucural aalysis of liear rasformaios Wha are he irisic properies of a liear rasformaio? re here ay fixed direcios? The discussio

More information

O & M Cost O & M Cost

O & M Cost O & M Cost 5/5/008 Turbie Reliabiliy, Maieace ad Faul Deecio Zhe Sog, Adrew Kusiak 39 Seamas Ceer Iowa Ciy, Iowa 54-57 adrew-kusiak@uiowa.edu Tel: 39-335-5934 Fax: 39-335-5669 hp://www.icae.uiowa.edu/~akusiak Oulie

More information

Extended Laguerre Polynomials

Extended Laguerre Polynomials I J Coemp Mah Scieces, Vol 7, 1, o, 189 194 Exeded Laguerre Polyomials Ada Kha Naioal College of Busiess Admiisraio ad Ecoomics Gulberg-III, Lahore, Pakisa adakhaariq@gmailcom G M Habibullah Naioal College

More information

Theoretical Physics Prof. Ruiz, UNC Asheville, doctorphys on YouTube Chapter R Notes. Convolution

Theoretical Physics Prof. Ruiz, UNC Asheville, doctorphys on YouTube Chapter R Notes. Convolution Theoreical Physics Prof Ruiz, UNC Asheville, docorphys o YouTube Chaper R Noes Covoluio R1 Review of he RC Circui The covoluio is a "difficul" cocep o grasp So we will begi his chaper wih a review of he

More information

NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE

NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE Yugoslav Joural of Operaios Research 8 (2008, Number, 53-6 DOI: 02298/YUJOR080053W NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE Jeff Kuo-Jug WU, Hsui-Li

More information

Statistical Estimation

Statistical Estimation Learig Objecives Cofidece Levels, Iervals ad T-es Kow he differece bewee poi ad ierval esimaio. Esimae a populaio mea from a sample mea f large sample sizes. Esimae a populaio mea from a sample mea f small

More information

Calculus BC 2015 Scoring Guidelines

Calculus BC 2015 Scoring Guidelines AP Calculus BC 5 Scorig Guidelies 5 The College Board. College Board, Advaced Placeme Program, AP, AP Ceral, ad he acor logo are regisered rademarks of he College Board. AP Ceral is he official olie home

More information

A note on deviation inequalities on {0, 1} n. by Julio Bernués*

A note on deviation inequalities on {0, 1} n. by Julio Bernués* A oe o deviaio iequaliies o {0, 1}. by Julio Berués* Deparameo de Maemáicas. Faculad de Ciecias Uiversidad de Zaragoza 50009-Zaragoza (Spai) I. Iroducio. Le f: (Ω, Σ, ) IR be a radom variable. Roughly

More information

Introduction to the Mathematics of Lévy Processes

Introduction to the Mathematics of Lévy Processes Iroducio o he Mahemaics of Lévy Processes Kazuhisa Masuda Deparme of Ecoomics The Graduae Ceer, The Ciy Uiversiy of New York, 365 Fifh Aveue, New York, NY 10016-4309 Email: maxmasuda@maxmasudacom hp://wwwmaxmasudacom/

More information