Strictly as per the compliance and regulations of:
|
|
- Raymond Erick Allen
- 8 years ago
- Views:
Transcription
1 Global Journal of Managemen and Business Research Finance Volume 3 Issue 3 Version.0 Year 03 Type: Double Blind Peer Reviewed Inernaional Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: & Prin ISSN: Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. By Dr. Jiban Chandra Paul, Md. Shahidul Hoque & Mohammad Morshedur Rahman Universiy of Chiagong, Bangladesh Absrac - This work is an aemp o examine empirically he bes ARIMA model for forecasing. Average daily share price indices of he daa series of Square Pharmaceuicals Limied (SPL) have been used for his purpose. A firs he saionariy condiion of he daa series are observed by ACF and PACF plos, hen checked using he Saisics such as Ljung-Box-Pierce Q-saisic and Dickey-Fuller es saisic. I has been found ha he average daily share price indices of he daa series of Square Pharmaceuicals Limied (SPL) are non-saionary. The average daily share price indices of SPL daa series are nonsaionary even afer log-ransformaion. Bu afer aking firs difference of logarihmic values of SPL daa series, he same ypes of plos and he same ypes of saisics show ha he daa is saionary. The bes ARIMA model have been seleced by using he crieria such as AIC, AIC c, SIC, AME, RMSE and MAPE ec. To selec he bes ARIMA model he daa spli ino wo periods, viz. esimaion period and validaion period. The model for which he values of crieria are smalles is considered as he bes model. Hence, ARIMA (,, and ) is found as he bes model for forecasing he SPL daa series. GJMBR-C Classificaion : JEL Code: F37 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh A Case Sudy on Square Pharmaceuical Ld. Sricly as per he compliance and regulaions of: 03. Dr. Jiban Chandra Paul, Md. Shahidul Hoque & Mohammad Morshedur Rahman. This is a research/review paper, disribued under he erms of he Creaive Commons Aribuion-Noncommercial 3.0 Unpored License hp://creaivecommons.org/licenses/by-nc/3.0/), permiing all non-commercial use, disribuion, and reproducion in any medium, provided he original work is properly cied.
2 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Dr. Jiban Chandra Paul α, Md. Shahidul Hoque σ & Mohammad Morshedur Rahman ρ Absrac - This work is an aemp o examine empirically he bes ARIMA model for forecasing. Average daily share price indices of he daa series of Square Pharmaceuicals Limied (SPL) have been used for his purpose. A firs he saionariy condiion of he daa series are observed by ACF and PACF plos, hen checked using he Saisics such as Ljung-Box- Pierce Q-saisic and Dickey-Fuller es saisic. I has been found ha he average daily share price indices of he daa series of Square Pharmaceuicals Limied (SPL) are nonsaionary. The average daily share price indices of SPL daa series are non-saionary even afer log-ransformaion. Bu afer aking firs difference of logarihmic values of SPL daa series, he same ypes of plos and he same ypes of saisics show ha he daa is saionary. The bes ARIMA model have been seleced by using he crieria such as AIC, AIC c, SIC, AME, RMSE and MAPE ec. To selec he bes ARIMA model he daa spli ino wo periods, viz. esimaion period and validaion period. The model for which he values of crieria are smalles is considered as he bes model. Hence, ARIMA (,, ) is found as he bes model for forecasing he SPL daa series. Then, forecass of he daa have been made using seleced ype of ARIMA model. Finally, he values of ADSPI of SPL up o February 0 are prediced and repored in he sudy. I. Inroducion S ock exchange plays a vial role in he naional economy of Bangladesh. Sock marke is an essenial par of he capial marke. The economy of a counry largely depends on capial marke. In he capial marke he invesors inves he money o ge he profi. The invesors buy he securiy bond of differen company on he prioriy basis. They choose he securiy bond of differen company on he basis of he differen facors. Some of he significan facors are Company s informaion analysis & predicion, dividend declaraion, ec. A large amoun of invesors has no knowledge abou he marke analysis and proper predicion of he fuure prices of differen ypes of shares available in he marke. So, mos of he ime hey spend he money o Auhor α : Professor, Deparmen of Saisics, Universiy of Chiagong. Auhor σ : Lecurer, Deparmen of Saisics, Universiy of Chiagong. Auhor ρ : Assisan Professor, Deparmen of Accouning and Informaion Sysem, Universiy of Chiagong, Bangladesh. mmrseu@yahoo.com buy securiy bond of differen companies on he basis of wrong and humb idea, wihou any idea abou daa analysis and predicion. For his reason here are exreme ups and downs in he daily share price indices, someimes rise very quickly and fall sharply. In his siuaion, he marke condiion becomes unpredicable. Hence, a large amoun of invesors loss heir capial in his unsable capial marke. As a resul he general invesors do no find ineres o inves he money in he capial marke. Then here arises a crisis in he capial marke which creaes problem and hampers he naional economic growh. Therefore, if i is possible o provide a beer model for he share marke which can enable he invesors o predic he prices in advance, i would help he invesors as well as keep sabiliy of he naional economy. This sudy is an effor owards ha direcion. II. Lieraure Review Conreras e al. (003) used ARIMA models o predic nex day elecriciy prices; hey have found wo ARIMA models o predic hourly prices in he elecriciy markes of Spain & California. The Spanish model needs 5 hours o predic fuure prices as opposed o he hours needed by he Californian model. Kumar e al. (004) used ARIMA model o forecas daily maximum surface ozone concenraions in Brunei Darussalam. They have found ha ARIMA (, 0, ) was suiable for he surface O 3 daa colleced a he airpor in Brunei Darussalam. Tsisika e al. (007) used ARIMA model o forecas pelagic fish producion. The final model seleced were of he form ARIMA (, 0, ) & ARIMA (0,, ). Azad e al. (0) used ARIMA model in forecasing Exchange Raes of Bangladesh. By using Box Jenkins mehodology hey ried o find ou bes model for forecasing. They have found ha ERNN (exchange rae neural nework) model shows beer performance han ARIMA. Merh (0) used ANN & ARIMA models in nex day sock marke forecasing. They used ANN (4-4-) and ARIMA (,, and ) for forecasing he fuure index value of sensex (BSE 30). Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 5
3 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 6 The forecasing accuracy obained for ARIMA (,,) is beer han ANN(4-4-). Liv e al. (0) used ARIMA model in forecasing incidence of hemorrhagic fever wih renal syndrome in China. The goodness of fi es of he opimum ARIMA (0, 3, and ) model showed nonsignifican auocorrelaion in he residuals of he model. Daa (0) used ARIMA model in forecasing inflaion in he Bangladesh Economy. He showed ha ARIMA (, 0, ) model fis he inflaion daa of Bangladesh saisfacorily. Al-Zeaud (0) used ARIMA model in modeling &forecasing volailiy. The resul shows ha bes ARIMA models a 95% confidence inerval for banks secor is ARIMA (, 0, and ) model. Uko e al. (0) examined he relaive predicive power of ARIMA, VAR & ECM models in forecasing inflaion in Nigeria. The resul shows ha ARIMA is a good predicor of inflaion in Nigeria & serves as a benchmark model in inflaion forecasing. From he above menioned sudies i is clear ha ARIMA can be used o forecas. In very few of hem he auhors ried o find ou bes ARIMA model, bu in mos of he aricles he auhors used ARIMA o forecas. The presen sudy is designed o selec he bes ARIMA model o forecas average daily price index of lised companies in Dhaka Sock Exchange. III. Objecives of he Sudy Share price index is a ime series daa. One of he imporan objecives of he ime series analysis is o sudy he pas behavior of he available daa and hen forecas wih fiing a suiable model wih he help of economeric or saisical echniques. Thus, he specific objecives of his sudy are as follows:. To check wheher he seleced ime series daa is saionary or no. If no, he daa are o be ransformed ino saionary using suiable ransformaion.. To selec he bes ARIMA model using some selecion crieria. Then ARIMA echniques are applied o fi and forecas he average daily share price indices of DSE daa for he Square Pharmaceuicals Limied (SPL) Company. 3. Finally, o draw a conclusion for forecasing he average daily share price indices of he seleced company efficienly. IV. Daa and Mehodology The ADSPI daa recorded agains SPL have been colleced from Dhaka Sock Exchange (DSE) for he year 0. Thus we obained a oal of 36 observaions agains all working days from Square Pharmaceuicals limied. The sepwise mehodology used in his sudy is oulined below: Firsly, he daa is presened graphically o check wheher he daa series is saionary or no. For his purpose, he saisics like Ljung-Box-Pierce Q- saisic (978) based on auo correlaion; Dickey-Fuller es (DF) (979), Augmened Dickey-Fuller (ADF) es (98) based on uni roo process have been applied. To selec he bes ARIMA (p, d, q) ype of models fied for he company, heir goodness of fi have been compared using following crieria; a) The Akaike Informaion Crieria (AIC) b) The Correced Akaike Informaion Crieria (AICc) c) Schwarz Informaion Crieria (SIC) d) Mean Absolue Percen Error (MAPE) e) Roo Mean Square Error (RMSE) and f) Absolue Mean Error (AME) A brief descripion abou he crieria for he selecion of bes ARIMA model is given below: a) Akaike Informaion Crierion (AIC) AIC is an imporan and leading saisics by which we can deermine he order of an auoregressive model Mr. Akaike developed his saisics. According o his name his saisics is known as Akaike Informaion Crierion (AIC). The AIC akes ino accoun boh how well he model fis he observed series and he number of parameers o be used in he fi. AIC due o Akaike (969) is defined as AIC = N Inδ + + ( p + ) Where he parameer bears he usual meaning. Akaike also menion ha he minimum AIC crierion produced a seleced model, which is hopefully closer o he bes possible choice. b) Correced Akaike Informaion Crierion Someimes he AIC does no provide he efficien order of model selecion, which asympoic efficiency is more desirable crierion. Shibaa in 976 shown ha AIC crierion is no consisen oo. Thus Hurvich and Tsai (989) provide a crierion of AIC for bias. The correlaion is of paricular use when he sample size is small or when he number of fied parameer is a moderae o a large fracion of sample size. The crierion is defined as i.e, P + AIC N N c = lnδ + P + N P + AIC = + N c AIC = P + N ( Ρ + )( Ρ + ) ( Ν Ρ + )
4 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Thus AIC c is he sum of AIC and an addiional non-sochasic penaly erm (p+) (p+) / (N-p+), where he parameer bears he usual meaning. c) Schwaez Informaion Crieria In 978 Schwaez discussed a crierion denoed by SIC which help in deciding he order of auo regression. Iniially he developed his crierion for aking decisions abou he regress subse. Laer Engel e. al, in 99 use his crierion as a ool for deermining he order of auo regression and hey defined his crierion as below p SIC = δ N N Where, he parameers bear he usual meaning. Schwarz also shows ha his crierion is beer han AIC. The model wih minimum SIC assumes o describe he daa series adequaely. The minimum value of his crierion is desirable for he adequacy of a model. Crieria used for esing he validiy of model The crieria menioned above are compared for correc deerminaion of he order of auo regression and he degree of differencing and his crierion is compued only for esimaion period. Bu for he selecion of an ARIMA model, which adequaely describes he daa series, he values of he following crieria are compared for hree periods viz, esimaion period, validaion period and oal period. The crieria used in his sudy are as follows: a) Absolue Mean Error (AME) b) Roo Mean Square Error (RMSE) c) Mean Absolue Percen Error (MAPE) d) Absolue Mean Error (AME) The mean of he absolue deviaion of prediced and observed values is called absolue mean error and is defined as T Ζ obs Ζ pred AME = I= Τ This crierion is used for he comparison of he models in hree periods. e) Roo Mean Square Error (RMSE) The square roo of he sum of square of he deviaion of he prediced values from he observed value dividing by heir number of observaion is known as he roo mean square error. The roo mean square error is defined as RMSE = T Τ I= p N ( ) Ζ Ζ obs pred Where, T is he number of periods. This crierion is used for he comparison of he models in hree periods. f) Mean Absolue Percen Error (MAPE) The mean of he sum of absolue deviaion of prediced and observed value dividing by he observed value is called mean absolue error. For comparison we have muliplied by 00, which is called mean absolue percen error and which is defined as T Ζ MAPE = Τ = obs Ζ Ζ obs pred 00 Where, he parameers bear he usual meaning. From he above discussion i is clear ha he smaller error beer he forecasing performance of he observed variables and if he model variable perform well, so will he model as a whole do oo. For he daa series a separae ARIMA model has been used. For ha purpose, a general concep of ARIMA (p, d, and q) model is discussed below: ARIMA models are, in heory, he mos general class of models for forecasing a ime series ha can be saioneries by ransformaions such as differencing and logging. If we have o difference a ime series d imes o make i saionary and hen apply he ARMA (p, q) model o i, we can say ha he original ime series is ARIMA (p, d, q), ha is i is an auoregressive inegraed moving average ime series, where p denoes he number of auoregressive erms, d denoes he ime series have o be differenced before i becomes saionary and q denoes he number of moving average erms. Thus an ARIMA (,,) ime series has o be differenced once (d=) before i becomes saionary and he saionary ime series can be modeled as an ARMA (,) process ha is i has wo AR and wo MA erms. Of course if d=0 hen ARIMA (p, d=0,q) = ARMA (p, q). A mos general ARIMA model consiues hree ypes of process named as auoregressive (AR) process, differencing o srip of he inegraion (I) and moving average (MA) process. The goodness of fi wih respec o every crierion are examined and he model which saisfies mos of he crierion, is considered as he bes one. Auo Regressive (AR) Process In an auoregressive process each value in a series is linear funcion of he preceding value. Thus in he firs order auoregressive process only he single preceding value is used as a funcion of curren value. In he second order auoregressive process wo preceding values are used as a funcion of he curren value and so on. The firs order auoregressive is denoed by AR (), he second order auoregressive is denoed by AR () and up o he p h order auoregressive is denoed by AR (p). Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 7
5 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 8 Le us suppose ha he variable is a linear funcion of he preceding variable. Therefore he model can be wrien as Where u IN( 0, σ ) ~ u The model () is known as AR () model. Bu if we consider he model = θ + φ Where u IN( 0, σ ) ~ u + ϕ The model () is known as AR () model. In general we can wrie = θ + φ + u = θ + φ + ϕ φ + u + u () () p p (3) Where φ is known as he firs order auoregressive coefficien, φ is known as he second order auoregressive coefficien and so on The model (3) is known as AR (p) model. Differencing Differencing is a comparaively simple operaion ha involves calculaing consecuive changes in he values of he daa series. Differencing is used when he mean of a series is changing over ime o ime. A consciousness ha is homogeneously non-saionary can be ransform ino saionary by differencing. Differencing is no dealing wih non-saionary variance. To difference a series once (d=) we have o calculae he period o period change, o difference a series wice (d=) we have o calculae he period o period changes in he firs difference series and so on for furher differences. Moving Average In Saisics, a moving average or rolling average is one of a family of similar echniques used o analyze ime series daa. I is applied in finance and especially in echnical analysis. I can also be used as a generic smoohing operaion, in which case he raw daa need no be a ime series. A moving average series can be calculaed for any ime series. In finance i is mos ofen applied o sock prices, reurns or rading volumes. Moving averages are used o smooh ou shor-erm flucuaions, hus highlighing longer-erm rends or cycles. The hreshold beween shor-erm and long-erm depends on he applicaion, and he parameers of he moving average will be se accordingly. Mahemaically, each of hese moving averages is an example of a convoluion. These averages are also similar o he low-pass filers used in signal processing. In moving average process, each value is deermined by he average of he curren disurbance and one or more previous disurbances. Suppose he model Y as follows: = θ + u + β u (4) Where θ is consan and u is he whie noise error erm i.e., u~n ( 0, σ ). Here Y a ime is equal o a consan plus a moving average of he curren and pas error erms. In his case, we say ha Y follows a firs order moving average or MA () process. Bu if Y follows he expression = θ + u + β u + β u (5) Then we say ha Y follows a second order moving average or MA () process. In general, = θ + u + β u + β u β q u q Then we say ha Y follows a q h order moving average or MA (q) process. In shor, a moving average process is simply a linear combinaion of whie noise error erms. Characerisics of a good ARIMA model Our main moivaion is o build up a good ARIMA model in his sudy. The Characerisics of a good ARIMA model are as follows:. A good model is saionary, ha is, i has an AR coefficien ha saisfies some mahemaical inequaliies.. A good model is inverible, ha is, i has MA coefficien, which saisfies some mahemaical inequaliies. 3. A good model is parsimonious i.e., uses he small number of coefficiens needed o explain he available daa. 4. A good model has saisically independen residuals. 5. A good model has high-equaliy esimaed coefficien a he esimaion sage. 6. A good model fis he available daa sufficienly well a he esimaion sage. 7. Roo-Mean Squared Error (RMSE) is accepable. 8. Mean-Absolue percen error (MAPE) is accepable. 9. A good model has sufficienly small forecas errors i.e., i forecass he fuure saisfacory. Selecion of ARIMA models for ADSPI of SPL daa series In order o idenify he enaive ARIMA model for he ADSPI of SPL, he seps described by Box and Jenkins have been followed. For his purpose he daa (6)
6 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. are pariioned ino wo sages. The firs sage is known as he esimaion sage and second is known as he validaion sage. The sample of observaions o 6 has been used in esimaion sage and he res has been used for esing he validiy of model. Ten ARIMA models wih enaively seleced various values of p, d and q are esimaed by using compuer sofware SHAZAM versions 8.0 for windows. The en enaively seleced models are ARIMA (,,), ARIMA (,,), ARIMA (,,), ARIMA (,,), ARIMA (,,3), ARIMA (,,3), ARIMA (3,,), ARIMA (3,,), ARIMA (3,,3) and ARIMA (,,4). Among he models only five comparaively well performed models are displayed in he able -c. Table- c discloses ha ARIMA wih p=, d = and q= process has maximum number of lowes values of all he seleced crieria AIC, AICc, SIC, and AME, RMSE, MAPE in he hree periods i.e., esimaion period, validaion period and oal period Hence, ARIMA (,,) model has been seleced for forecasing he ADSPI of SPL daa series. The fied ARIMA (,, and ) model seleced for SPL daa series is given by ( *B *B ) SPL - = (-0.449*B-0.489*B ) a (0.95) (0.737) (0.3036) (0.97) (Values in he parenhesis are corresponding -values and * means saisical significance p<0.0) v. Resuls and Discussion The major findings of he sudy are as follows:. The upward rends of plos of he daa series are visualized alhough he overall rends are no smooh.. The ACF and PACF plos of original daa series show ha he Average Daily Share Price Indices (ADSPI) of Square Pharmaceuicals Limied (SPL) are non-saionary, ha is, mos of he ACF and PACF plos are beyond he confidence limis shown in Figure- a. 3. From ACF and PACF plos of logarihmic ransformaion daa series has been found ha he ADSPI of SPL daa series is sill non-saionary, ha is, all he ACF & PACF plos are ou of he confidence limis.shown in figure-b. Bu afer aking firs difference of logarihmic values of SPL daa series, he same plos shows ha he daa is saionary shown in Figure- c. 4. The Dickey-Fuller uni roo es saisic and he Ljung-Box-pierce Q-Saisic also indicae ha he Average Daily Share Price Indices (ADSPI) of SPL daa series is non-saionary. The compued absolue values of he τ-saisic for SPL is found as τ =.733, none of which exceeds he DF or Mackinnon DF absolue criical τ values (o be noed ha %, 5% and 0% level of significance he absolue DF values are 4.047, 3.46 & 3.3 respecively) shown in Table- a. 5. Afer aking firs difference of logarihmic values of SPL daa series, he same es saisic shows ha he daa is saionary, because hence he compued absolue value of he τ-saisic is τ = 4.65 which exceeds he DF or Mackinnon DF absolue criical τ values shown in Table- b. 6. For SPL daa series en ypes of enaively ARIMA models wih varied values of p, d & q are seleced of which five-performed model for he daa series are esimaed and he validiy of he model is esed by using AME, RMSE & MAPE for hree differen period shown in Tabil -c. 7. I is found ha ARIMA (,, ) is he bes model for forecasing he SPL daa series. 8. Finally, he Average Daily Share Price Indices (ADSPI) for Square Pharmaceuicals Limied (SPL) daa series have been forecased by using he seleced model and repored in able- d. Table (a) : The values of he various saionary ess of he company for average daily share price indices of DSE daa series Tes Saisic Ljung-Box-Pierce Q- saisic SPL Time lag Time lag Dickey-Fuller es A %, 5% and 0% level of significance he DF values are 4.047, and 3.3 respecively. Table (b) : Values of Dickey-Fuller es saisic for differen values of differencing of Logarihmic Transformaion SPL daa series Difference SPL Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 9
7 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 0 VI. Conclusion This sudy made he bes endeavor o develop he bes ARIMA model o efficienly forecasing he Average Daily Share Price Indices (ADSPI) of he Square Pharmaceuicals Limied (SPL), because if i is possible o provide a beer model for he share marke which can enable he invesors o predic he prices in advance, i would help he invesors as well as sabiliy of he naional economy. The empirical analysis indicaed ha he ARIMA (,,) model is bes for forecasing he Average Daily Share Price Indices (ADSPI) of he Square Pharmaceuicals Limied (SPL) daa series so far he diagnosic crieria are concerned. Finally, he Average Daily Share Price Indies (ADSPI) for Square Pharmaceuicals Limied (SPL) daa series is forecased up o February, 0 by using he seleced model. References Références Referencias. Al-Zeaud, H.A. (0) Modeling &Forecasing Volailiy using ARIMA model, European Journal of Economics, Finance & Adminisraive Science, Issue 35, pp Azad, A.K. & Mahsin, M. (0) Forecasing Exchange Raes of Bangladesh using ANN & ARIMA models: A comparaive sudy, Inernaional Journal of Advanced Engineering Science & Technologies, Vol. No. 0, Issue No., pp Conreras, J., Espinola, R., Nogales, F.J. and Conejo, A.J. (003) ARIMA models o predic Nex Day Elecriciy Prices, IFEE Transacions on power sysem, Vol. 8, No. 3, pp Daa, K. (0) ARIMA Forecasing of Inflaion in he Bangladesh Economy, The IUP Journal of Bank Managemen, Vol. X, No. 4, pp Kumar, K.; Yadav, A.K.; Singh, M.P.; Hassan, H. and Jain, V.K. (004) Forecasing Daily Maximum Surface Ozone." 6. Concenraions in Brunei Darussalam- An ARIMA Modeling Approach, Journal of Air & Wase Managemen and Associaion, Volume 54, pp Liv, Q.; Liu, X.; Jiang, B. & Yang, W. (0) Forecasing incidence of hemorrhagic fever wih renal syndrome in China using ARIMA model, Biomed Cenral, pp Merh, N.; Saxena, V.P. & Pardasani, K.R. (0) Nex Day Sock marke Forecasing: An Applicaion of ANN & ARIMA, The IUP Journal of Applied Science, Vol. 7 No., pp Tsisika, E.V.; Maravelias, C.D & Haralaous, J. (007) Modeling & forecasing pelagic fish producion using univariae and mulivariae ARIMA models, Fisheries Science, Volume 73 pp Uko, A.K; Nkoro, E. (0) Inflaion Forecass wih ARIMA, Vecor Auoregressive & Error Correcion Models in Nigeria, European Journal of Economics, Finance & Adminisraive Science, Issue 50, pp
8 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Appendix Figure (a) : The ACF and PACF plos of original daa for average daily share price indices of SPL daa series AUTOCORRELATION FUNCTION OF THE SERIES (-B) (-B) AVG RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRR +. PARTIAL AUTOCORRELATION FUNCTION OF THE SERIES (-B)(-B) AVG RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRR RR RRRRR RR RRR RRRRR RRRRR R RRRRR RRRR RRRR+. Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03
9 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Figure (b) : The ACF and PACF plos of Logarihmic Transformaion daa for average daily share price indices of SPL daa series AUTOCORRELATION FUNCTION OF THE SERIES (-B) (-B) X Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR +. PARTIAL AUTOCORRELATION FUNCTION OF THE SERIES (-B) (-B) X RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR RRRRRR RR RRRRR R RRR RRRR RRRR R RRRRR RR RRRR+.
10 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Figure (c) : The ACF and PACF plos of Logarihmic Transformaion daa for average daily share price indices of SPL daa series wih difference one AUTOCORRELATION FUNCTION OF THE SERIES 0 0 (-B) (-B) X -.3. RRRRRRRRR R RRRRR RR RRR RRRR RRRR RRR RRRRRR RRRR RR RR RRR RR RRRRR RRRRR RRRR RR RR RR RRRR RRRRRRR RRR RRRR +. PARTIAL AUTOCORRELATION FUNCTION OF THE SERIES Table (c) : The values of diagnosic crieria for ARIMA model for logarihmic ransformaion difference series of average daily share price indices of DSE daa of Square Pharmaceuicals limied Transformaion Difference= 0 0 (-B) (-B) X -.3. RRRRRRRRR RRR RRRRR RR RRRR RRRRR RRRRRR R RRRRR RRR RRRR RRR +. Validaion of diagnosic crieria for he model Crieria Period ARIMA (,,) ARIMA (,,) ARIMA (,,3) ARIMA (,,) ARIMA (,,) AIC Esimaion * AICc Esimaion * SIC Esimaion * Esimaion * AME Validaion * Toal * Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 3
11 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 4 Esimaion * RMSE Validaion * Toal * Esimaion * MAPE Validaion * Toal * No. Of lowes values Noe: A * (sarle) indicae he lowes value in each row. Table (d) : The observed and forecased values wih is lowes and highes values obained by ARIMA (,,) model for ADSPI of SPL daa series Fuure Dae Lower Forecas Upper Acual Error E E E E E E E E E E E E E E E E E
12 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 5
13 Selecion of Bes ARIMA Model for Forecasing Average Daily Share Price Index of Pharmaceuical Companies in Bangladesh: A Case Sudy on Square Pharmaceuical Ld. Global Journal of Managemen and Business Research ( C ) Volume XIII Issue III Version I Y ear 03 6 This page is inenionally lef blank
Chapter 8: Regression with Lagged Explanatory Variables
Chaper 8: Regression wih Lagged Explanaory Variables Time series daa: Y for =1,..,T End goal: Regression model relaing a dependen variable o explanaory variables. Wih ime series new issues arise: 1. One
More informationStock Price Prediction Using the ARIMA Model
2014 UKSim-AMSS 16h Inernaional Conference on Compuer Modelling and Simulaion Sock Price Predicion Using he ARIMA Model 1 Ayodele A. Adebiyi., 2 Aderemi O. Adewumi 1,2 School of Mahemaic, Saisics & Compuer
More informationDOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR
Invesmen Managemen and Financial Innovaions, Volume 4, Issue 3, 7 33 DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR Ahanasios
More informationUsefulness of the Forward Curve in Forecasting Oil Prices
Usefulness of he Forward Curve in Forecasing Oil Prices Akira Yanagisawa Leader Energy Demand, Supply and Forecas Analysis Group The Energy Daa and Modelling Cener Summary When people analyse oil prices,
More informationTime Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test
ABSTRACT Time Series Analysis Using SAS R Par I The Augmened Dickey-Fuller (ADF) Tes By Ismail E. Mohamed The purpose of his series of aricles is o discuss SAS programming echniques specifically designed
More informationPredicting Stock Market Index Trading Signals Using Neural Networks
Predicing Sock Marke Index Trading Using Neural Neworks C. D. Tilakarane, S. A. Morris, M. A. Mammadov, C. P. Hurs Cenre for Informaics and Applied Opimizaion School of Informaion Technology and Mahemaical
More informationAnalysis of I-Series, An Appraisal and Its Models
Vol. No.2, pp.-, June 203 MODELING TO ANTICIPATE WORLD PRICE OF EACH OUNCE OF GOLD IN INTERNATIONAL MARKETS Mohammad Rikhegar Business Managemen, MA Suden Islamic Azad Universiy, a Souh Tehran Branch 009893632406
More informationSPEC model selection algorithm for ARCH models: an options pricing evaluation framework
Applied Financial Economics Leers, 2008, 4, 419 423 SEC model selecion algorihm for ARCH models: an opions pricing evaluaion framework Savros Degiannakis a, * and Evdokia Xekalaki a,b a Deparmen of Saisics,
More informationMorningstar Investor Return
Morningsar Invesor Reurn Morningsar Mehodology Paper Augus 31, 2010 2010 Morningsar, Inc. All righs reserved. The informaion in his documen is he propery of Morningsar, Inc. Reproducion or ranscripion
More informationCointegration: The Engle and Granger approach
Coinegraion: The Engle and Granger approach Inroducion Generally one would find mos of he economic variables o be non-saionary I(1) variables. Hence, any equilibrium heories ha involve hese variables require
More informationTEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS
TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS RICHARD J. POVINELLI AND XIN FENG Deparmen of Elecrical and Compuer Engineering Marquee Universiy, P.O.
More informationVector Autoregressions (VARs): Operational Perspectives
Vecor Auoregressions (VARs): Operaional Perspecives Primary Source: Sock, James H., and Mark W. Wason, Vecor Auoregressions, Journal of Economic Perspecives, Vol. 15 No. 4 (Fall 2001), 101-115. Macroeconomericians
More informationChapter 8 Student Lecture Notes 8-1
Chaper Suden Lecure Noes - Chaper Goals QM: Business Saisics Chaper Analyzing and Forecasing -Series Daa Afer compleing his chaper, you should be able o: Idenify he componens presen in a ime series Develop
More informationForecasting Stock Market Series. with ARIMA Model
Journal of Statistical and Econometric Methods, vol.3, no.3, 2014, 65-77 ISSN: 2241-0384 (print), 2241-0376 (online) Scienpress Ltd, 2014 Forecasting Stock Market Series with ARIMA Model Fatai Adewole
More informationDuration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613.
Graduae School of Business Adminisraion Universiy of Virginia UVA-F-38 Duraion and Convexiy he price of a bond is a funcion of he promised paymens and he marke required rae of reurn. Since he promised
More informationHotel Room Demand Forecasting via Observed Reservation Information
Proceedings of he Asia Pacific Indusrial Engineering & Managemen Sysems Conference 0 V. Kachivichyanuul, H.T. Luong, and R. Piaaso Eds. Hoel Room Demand Forecasing via Observed Reservaion Informaion aragain
More informationMarket Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand
36 Invesmen Managemen and Financial Innovaions, 4/4 Marke Liquidiy and he Impacs of he Compuerized Trading Sysem: Evidence from he Sock Exchange of Thailand Sorasar Sukcharoensin 1, Pariyada Srisopisawa,
More informationThe Relationship between Stock Return Volatility and. Trading Volume: The case of The Philippines*
The Relaionship beween Sock Reurn Volailiy and Trading Volume: The case of The Philippines* Manabu Asai Faculy of Economics Soka Universiy Angelo Unie Economics Deparmen De La Salle Universiy Manila May
More informationModelling and Forecasting Volatility of Gold Price with Other Precious Metals Prices by Univariate GARCH Models
Deparmen of Saisics Maser's Thesis Modelling and Forecasing Volailiy of Gold Price wih Oher Precious Meals Prices by Univariae GARCH Models Yuchen Du 1 Supervisor: Lars Forsberg 1 Yuchen.Du.84@suden.uu.se
More informationHow To Write A Demand And Price Model For A Supply Chain
Proc. Schl. ITE Tokai Univ. vol.3,no,,pp.37-4 Vol.,No.,,pp. - Paper Demand and Price Forecasing Models for Sraegic and Planning Decisions in a Supply Chain by Vichuda WATTANARAT *, Phounsakda PHIMPHAVONG
More informationFORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA
Inernaional Review of Business Research Papers Vol.4 No. 5 Ocober-November 8 Pp. 31-48 FORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA Fahis F. Lawgali* This paper examines
More informationImprovement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network
American Journal of Inelligen Sysems 2012, 2(2): 12-17 DOI: 10.5923/j.ajis.20120202.02 Improvemen in Forecasing Accuracy Using he Hybrid Model of ARFIMA and Feed Forward Neural Nework Cagdas Hakan Aladag
More informationMathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)
Mahemaics in Pharmacokineics Wha and Why (A second aemp o make i clearer) We have used equaions for concenraion () as a funcion of ime (). We will coninue o use hese equaions since he plasma concenraions
More informationHow Useful are the Various Volatility Estimators for Improving GARCH-based Volatility Forecasts? Evidence from the Nasdaq-100 Stock Index
Inernaional Journal of Economics and Financial Issues Vol. 4, No. 3, 04, pp.65-656 ISSN: 46-438 www.econjournals.com How Useful are he Various Volailiy Esimaors for Improving GARCH-based Volailiy Forecass?
More informationA New Type of Combination Forecasting Method Based on PLS
American Journal of Operaions Research, 2012, 2, 408-416 hp://dx.doi.org/10.4236/ajor.2012.23049 Published Online Sepember 2012 (hp://www.scirp.org/journal/ajor) A New Type of Combinaion Forecasing Mehod
More informationHow To Calculate Price Elasiciy Per Capia Per Capi
Price elasiciy of demand for crude oil: esimaes for 23 counries John C.B. Cooper Absrac This paper uses a muliple regression model derived from an adapaion of Nerlove s parial adjusmen model o esimae boh
More informationCausal Relationship between Macro-Economic Indicators and Stock Market in India
Asian Journal of Finance & Accouning Causal Relaionship beween Macro-Economic Indicaors and Sock Marke in India Dr. Naliniprava ripahy Associae Professor (Finance), Indian Insiue of Managemen Shillong
More informationARCH 2013.1 Proceedings
Aricle from: ARCH 213.1 Proceedings Augus 1-4, 212 Ghislain Leveille, Emmanuel Hamel A renewal model for medical malpracice Ghislain Léveillé École d acuaria Universié Laval, Québec, Canada 47h ARC Conference
More informationDYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS
DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS Hong Mao, Shanghai Second Polyechnic Universiy Krzyszof M. Osaszewski, Illinois Sae Universiy Youyu Zhang, Fudan Universiy ABSTRACT Liigaion, exper
More informationThe Real Business Cycle paradigm. The RBC model emphasizes supply (technology) disturbances as the main source of
Prof. Harris Dellas Advanced Macroeconomics Winer 2001/01 The Real Business Cycle paradigm The RBC model emphasizes supply (echnology) disurbances as he main source of macroeconomic flucuaions in a world
More information4. International Parity Conditions
4. Inernaional ariy ondiions 4.1 urchasing ower ariy he urchasing ower ariy ( heory is one of he early heories of exchange rae deerminaion. his heory is based on he concep ha he demand for a counry's currency
More informationJournal Of Business & Economics Research September 2005 Volume 3, Number 9
Opion Pricing And Mone Carlo Simulaions George M. Jabbour, (Email: jabbour@gwu.edu), George Washingon Universiy Yi-Kang Liu, (yikang@gwu.edu), George Washingon Universiy ABSTRACT The advanage of Mone Carlo
More informationThe Kinetics of the Stock Markets
Asia Pacific Managemen Review (00) 7(1), 1-4 The Kineics of he Sock Markes Hsinan Hsu * and Bin-Juin Lin ** (received July 001; revision received Ocober 001;acceped November 001) This paper applies he
More informationBALANCE OF PAYMENTS. First quarter 2008. Balance of payments
BALANCE OF PAYMENTS DATE: 2008-05-30 PUBLISHER: Balance of Paymens and Financial Markes (BFM) Lena Finn + 46 8 506 944 09, lena.finn@scb.se Camilla Bergeling +46 8 506 942 06, camilla.bergeling@scb.se
More informationA Note on Using the Svensson procedure to estimate the risk free rate in corporate valuation
A Noe on Using he Svensson procedure o esimae he risk free rae in corporae valuaion By Sven Arnold, Alexander Lahmann and Bernhard Schwezler Ocober 2011 1. The risk free ineres rae in corporae valuaion
More informationPrincipal components of stock market dynamics. Methodology and applications in brief (to be updated ) Andrei Bouzaev, bouzaev@ya.
Principal componens of sock marke dynamics Mehodology and applicaions in brief o be updaed Andrei Bouzaev, bouzaev@ya.ru Why principal componens are needed Objecives undersand he evidence of more han one
More informationThe naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1
Business Condiions & Forecasing Exponenial Smoohing LECTURE 2 MOVING AVERAGES AND EXPONENTIAL SMOOTHING OVERVIEW This lecure inroduces ime-series smoohing forecasing mehods. Various models are discussed,
More informationTime Series Analysis for Predicting the Occurrences of Large Scale Earthquakes
Inernaional Journal of Applied Science and Technology Vol. No. 7; Augus 01 Time Series Analysis for Predicing he Occurrences of Large Scale Earhquakes Amei Amei* Wandong Fu** Chih-Hsiang Ho*** Absrac Earhquakes
More informationForecasting Electricity Consumption: A Comparison of Models for New Zealand
Paper Tile: Forecasing Elecriciy Consumpion: A Comparison of Models for New Zealand Auhors: Zaid Mohamed and Pa Bodger,* Affiliaions:. Mohamed, Z., B.E (Hons), is a Ph.D. suden in he Deparmen of Elecrical
More informationA comparison of the Lee-Carter model and AR-ARCH model for forecasting mortality rates
A comparison of he Lee-Carer model and AR-ARCH model for forecasing moraliy raes Rosella Giacomei a, Marida Berocchi b, Svelozar T. Rachev c, Frank J. Fabozzi d,e a Rosella Giacomei Deparmen of Mahemaics,
More informationThe Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas
The Greek financial crisis: growing imbalances and sovereign spreads Heaher D. Gibson, Sephan G. Hall and George S. Tavlas The enry The enry of Greece ino he Eurozone in 2001 produced a dividend in he
More informationTitle: Who Influences Latin American Stock Market Returns? China versus USA
Cenre for Global Finance Working Paper Series (ISSN 2041-1596) Paper Number: 05/10 Tile: Who Influences Lain American Sock Marke Reurns? China versus USA Auhor(s): J.G. Garza-García; M.E. Vera-Juárez Cenre
More informationSupply chain management of consumer goods based on linear forecasting models
Supply chain managemen of consumer goods based on linear forecasing models Parícia Ramos (paricia.ramos@inescporo.p) INESC TEC, ISCAP, Insiuo Poliécnico do Poro Rua Dr. Robero Frias, 378 4200-465, Poro,
More informationSingle-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1
Absrac number: 05-0407 Single-machine Scheduling wih Periodic Mainenance and boh Preempive and Non-preempive jobs in Remanufacuring Sysem Liu Biyu hen Weida (School of Economics and Managemen Souheas Universiy
More informationChapter 1.6 Financial Management
Chaper 1.6 Financial Managemen Par I: Objecive ype quesions and answers 1. Simple pay back period is equal o: a) Raio of Firs cos/ne yearly savings b) Raio of Annual gross cash flow/capial cos n c) = (1
More informationMeasuring macroeconomic volatility Applications to export revenue data, 1970-2005
FONDATION POUR LES ETUDES ET RERS LE DEVELOPPEMENT INTERNATIONAL Measuring macroeconomic volailiy Applicaions o expor revenue daa, 1970-005 by Joël Cariolle Policy brief no. 47 March 01 The FERDI is a
More informationStochastic Optimal Control Problem for Life Insurance
Sochasic Opimal Conrol Problem for Life Insurance s. Basukh 1, D. Nyamsuren 2 1 Deparmen of Economics and Economerics, Insiue of Finance and Economics, Ulaanbaaar, Mongolia 2 School of Mahemaics, Mongolian
More informationWhy Did the Demand for Cash Decrease Recently in Korea?
Why Did he Demand for Cash Decrease Recenly in Korea? Byoung Hark Yoo Bank of Korea 26. 5 Absrac We explores why cash demand have decreased recenly in Korea. The raio of cash o consumpion fell o 4.7% in
More informationCan Individual Investors Use Technical Trading Rules to Beat the Asian Markets?
Can Individual Invesors Use Technical Trading Rules o Bea he Asian Markes? INTRODUCTION In radiional ess of he weak-form of he Efficien Markes Hypohesis, price reurn differences are found o be insufficien
More informationTime-Expanded Sampling (TES) For Ensemble-based Data Assimilation Applied To Conventional And Satellite Observations
27 h WAF/23 rd NWP, 29 June 3 July 2015, Chicago IL. 1 Time-Expanded Sampling (TES) For Ensemble-based Daa Assimilaion Applied To Convenional And Saellie Observaions Allen Zhao 1, Qin Xu 2, Yi Jin 1, Jusin
More informationEstimating Time-Varying Equity Risk Premium The Japanese Stock Market 1980-2012
Norhfield Asia Research Seminar Hong Kong, November 19, 2013 Esimaing Time-Varying Equiy Risk Premium The Japanese Sock Marke 1980-2012 Ibboson Associaes Japan Presiden Kasunari Yamaguchi, PhD/CFA/CMA
More informationSkewness and Kurtosis Adjusted Black-Scholes Model: A Note on Hedging Performance
Finance Leers, 003, (5), 6- Skewness and Kurosis Adjused Black-Scholes Model: A Noe on Hedging Performance Sami Vähämaa * Universiy of Vaasa, Finland Absrac his aricle invesigaes he dela hedging performance
More informationAppendix D Flexibility Factor/Margin of Choice Desktop Research
Appendix D Flexibiliy Facor/Margin of Choice Deskop Research Cheshire Eas Council Cheshire Eas Employmen Land Review Conens D1 Flexibiliy Facor/Margin of Choice Deskop Research 2 Final Ocober 2012 \\GLOBAL.ARUP.COM\EUROPE\MANCHESTER\JOBS\200000\223489-00\4
More informationNikkei Stock Average Volatility Index Real-time Version Index Guidebook
Nikkei Sock Average Volailiy Index Real-ime Version Index Guidebook Nikkei Inc. Wih he modificaion of he mehodology of he Nikkei Sock Average Volailiy Index as Nikkei Inc. (Nikkei) sars calculaing and
More informationCAUSAL RELATIONSHIP BETWEEN STOCK MARKET AND EXCHANGE RATE, FOREIGN EXCHANGE RESERVES AND VALUE OF TRADE BALANCE: A CASE STUDY FOR INDIA
CAUSAL RELATIONSHIP BETWEEN STOCK MARKET AND EXCHANGE RATE, FOREIGN EXCHANGE RESERVES AND VALUE OF TRADE BALANCE: A CASE STUDY FOR INDIA BASABI BHATTACHARYA & JAYDEEP MUKHERJEE Reader, Deparmen of Economics,
More informationINTRODUCTION TO FORECASTING
INTRODUCTION TO FORECASTING INTRODUCTION: Wha is a forecas? Why do managers need o forecas? A forecas is an esimae of uncerain fuure evens (lierally, o "cas forward" by exrapolaing from pas and curren
More informationChapter 7. Response of First-Order RL and RC Circuits
Chaper 7. esponse of Firs-Order L and C Circuis 7.1. The Naural esponse of an L Circui 7.2. The Naural esponse of an C Circui 7.3. The ep esponse of L and C Circuis 7.4. A General oluion for ep and Naural
More informationMarket Efficiency or Not? The Behaviour of China s Stock Prices in Response to the Announcement of Bonus Issues
Discussion Paper No. 0120 Marke Efficiency or No? The Behaviour of China s Sock Prices in Response o he Announcemen of Bonus Issues Michelle L. Barnes and Shiguang Ma May 2001 Adelaide Universiy SA 5005,
More informationTime Series Analysis using In a Nutshell
1 Time Series Analysis using In a Nushell dr. JJM J.J.M. Rijpkema Eindhoven Universiy of Technology, dep. Mahemaics & Compuer Science P.O.Box 513, 5600 MB Eindhoven, NL 2012 j.j.m.rijpkema@ue.nl Sochasic
More informationBid-ask Spread and Order Size in the Foreign Exchange Market: An Empirical Investigation
Bid-ask Spread and Order Size in he Foreign Exchange Marke: An Empirical Invesigaion Liang Ding* Deparmen of Economics, Macaleser College, 1600 Grand Avenue, S. Paul, MN55105, U.S.A. Shor Tile: Bid-ask
More informationGovernment Revenue Forecasting in Nepal
Governmen Revenue Forecasing in Nepal T. P. Koirala, Ph.D.* Absrac This paper aemps o idenify appropriae mehods for governmen revenues forecasing based on ime series forecasing. I have uilized level daa
More informationContrarian insider trading and earnings management around seasoned equity offerings; SEOs
Journal of Finance and Accounancy Conrarian insider rading and earnings managemen around seasoned equiy offerings; SEOs ABSTRACT Lorea Baryeh Towson Universiy This sudy aemps o resolve he differences in
More informationA DCC Analysis of Two Stock Market Returns Volatility with an Oil Price Factor: An Evidence Study of Singapore and Thailand s Stock Markets
Journal of Convergence Informaion Technology Volume 4, Number 1, March 9 A DCC Analysis of Two Sock Marke Reurns Volailiy wih an Oil Price Facor: An Evidence Sudy of Singapore and Thailand s Sock Markes
More informationPerformance of combined double seasonal univariate time series models for forecasting water demand
1 Performance of combined double seasonal univariae ime series models for forecasing waer demand Jorge Caiado a a Cener for Applied Mahemaics and Economics (CEMAPRE), Insiuo Superior de Economia e Gesão,
More informationForecasting Sales: A Model and Some Evidence from the Retail Industry. Russell Lundholm Sarah McVay Taylor Randall
Forecasing Sales: A odel and Some Evidence from he eail Indusry ussell Lundholm Sarah cvay aylor andall Why forecas financial saemens? Seems obvious, bu wo common criicisms: Who cares, can we can look
More informationA PROPOSAL TO OBTAIN A LONG QUARTERLY CHILEAN GDP SERIES *
CUADERNOS DE ECONOMÍA, VOL. 43 (NOVIEMBRE), PP. 285-299, 2006 A PROPOSAL TO OBTAIN A LONG QUARTERLY CHILEAN GDP SERIES * JUAN DE DIOS TENA Universidad de Concepción y Universidad Carlos III, España MIGUEL
More informationMarkit Excess Return Credit Indices Guide for price based indices
Marki Excess Reurn Credi Indices Guide for price based indices Sepember 2011 Marki Excess Reurn Credi Indices Guide for price based indices Conens Inroducion...3 Index Calculaion Mehodology...4 Semi-annual
More informationInternational Business & Economics Research Journal March 2007 Volume 6, Number 3
Weak Form Efficiency In Indian Sock Markes Rakesh Gupa, (E-mail: r.gupa@cqu.edu.au), Cenral Queensland Universiy, Ausralia Parikshi K. Basu, (E-mail: pbasu@csu.edu.au), Charles Sur Universiy, Ausralia
More informationKey Words: Steel Modelling, ARMA, GARCH, COGARCH, Lévy Processes, Discrete Time Models, Continuous Time Models, Stochastic Modelling
Vol 4, No, 01 ISSN: 1309-8055 (Online STEEL PRICE MODELLING WITH LEVY PROCESS Emre Kahraman Türk Ekonomi Bankası (TEB A.Ş. Direcor / Risk Capial Markes Deparmen emre.kahraman@eb.com.r Gazanfer Unal Yediepe
More informationLead Lag Relationships between Futures and Spot Prices
Working Paper No. 2/02 Lead Lag Relaionships beween Fuures and Spo Prices by Frank Asche Ale G. Guormsen SNF-projec No. 7220: Gassmarkeder, menneskelig kapial og selskapssraegier The projec is financed
More informationPurchasing Power Parity (PPP), Sweden before and after EURO times
School of Economics and Managemen Purchasing Power Pariy (PPP), Sweden before and afer EURO imes - Uni Roo Tes - Coinegraion Tes Masers hesis in Saisics - Spring 2008 Auhors: Mansoor, Rashid Smora, Ami
More informationMATERIALS AND METHODS
Amin e al., The Journal of Animal & Plan Sciences, 24(5): 204, Page: J. 444-45 Anim. Plan Sci. 24(5):204 ISSN: 08-708 TIME SERIES MODELING FOR FORECASTING WHEAT PRODUCTION OF PAKISTAN M. Amin, M. Amanullah
More informationForecasting, Ordering and Stock- Holding for Erratic Demand
ISF 2002 23 rd o 26 h June 2002 Forecasing, Ordering and Sock- Holding for Erraic Demand Andrew Eaves Lancaser Universiy / Andalus Soluions Limied Inroducion Erraic and slow-moving demand Demand classificaion
More informationWorking Paper A fractionally integrated exponential model for UK unemployment
econsor www.econsor.eu Der Open-Access-Publikaionsserver der ZBW Leibniz-Informaionszenrum Wirschaf The Open Access Publicaion Server of he ZBW Leibniz Informaion Cenre for Economics Gil-Alaña, Luis A.
More informationStatistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt
Saisical Analysis wih Lile s Law Supplemenary Maerial: More on he Call Cener Daa by Song-Hee Kim and Ward Whi Deparmen of Indusrial Engineering and Operaions Research Columbia Universiy, New York, NY 17-99
More informationAsian Economic and Financial Review VOLATILITY MEAN REVERSION AND STOCK MARKET EFFICIENCY. Hojatallah Goudarzi
Asian Economic and Financial Review journal homepage: hp://aessweb.com/journal-deail.php?id=500 VOLATILITY MEAN REVERSION AND STOCK MARKET EFFICIENCY Hojaallah Goudarzi Deparmen of Finance and Insurance,
More informationTime-Series Forecasting Model for Automobile Sales in Thailand
การประช มว ชาการด านการว จ ยด าเน นงานแห งชาต ประจ าป 255 ว นท 24 25 กรกฎาคม พ.ศ. 255 Time-Series Forecasing Model for Auomobile Sales in Thailand Taweesin Apiwaanachai and Jua Pichilamken 2 Absrac Invenory
More informationIdealistic characteristics of Islamic Azad University masters - Islamshahr Branch from Students Perspective
Available online a www.pelagiaresearchlibrary.com European Journal Experimenal Biology, 202, 2 (5):88789 ISSN: 2248 925 CODEN (USA): EJEBAU Idealisic characerisics Islamic Azad Universiy masers Islamshahr
More informationUSE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES
USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES Mehme Nuri GÖMLEKSİZ Absrac Using educaion echnology in classes helps eachers realize a beer and more effecive learning. In his sudy 150 English eachers were
More informationForecasting and Information Sharing in Supply Chains Under Quasi-ARMA Demand
Forecasing and Informaion Sharing in Supply Chains Under Quasi-ARMA Demand Avi Giloni, Clifford Hurvich, Sridhar Seshadri July 9, 2009 Absrac In his paper, we revisi he problem of demand propagaion in
More informationMarket Overreaction and Under reaction for Currency Futures Prices. Stephen J. Larson *, Associate Professor of Finance Ramapo College of New Jersey
Marke Overreacion and Under reacion for Currency Fuures Prices Sephen J. Larson *, Associae Professor of Finance Ramapo College of New Jersey Sephen E. Wilcox, Professor of Finance Minnesoa Sae Universiy,
More informationInventory Management and Demand Prediction System for Reagents and Consumables
Invenory Managemen and Demand Predicion Sysem for Reagens and Consumables Tzu-Chuen Lu, Shih-Chieh Lai, 3 Chun-Ya Tseng *, Firs Auhor, Corresponding Auhor Deparmen of Informaion Managemen, Chaoyang Universiy
More informationThe Transport Equation
The Transpor Equaion Consider a fluid, flowing wih velociy, V, in a hin sraigh ube whose cross secion will be denoed by A. Suppose he fluid conains a conaminan whose concenraion a posiion a ime will be
More informationPROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE
Profi Tes Modelling in Life Assurance Using Spreadshees PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Erik Alm Peer Millingon 2004 Profi Tes Modelling in Life Assurance Using Spreadshees
More informationDistributing Human Resources among Software Development Projects 1
Disribuing Human Resources among Sofware Developmen Proecs Macario Polo, María Dolores Maeos, Mario Piaini and rancisco Ruiz Summary This paper presens a mehod for esimaing he disribuion of human resources
More informationHedging with Forwards and Futures
Hedging wih orwards and uures Hedging in mos cases is sraighforward. You plan o buy 10,000 barrels of oil in six monhs and you wish o eliminae he price risk. If you ake he buy-side of a forward/fuures
More informationThe Grantor Retained Annuity Trust (GRAT)
WEALTH ADVISORY Esae Planning Sraegies for closely-held, family businesses The Granor Reained Annuiy Trus (GRAT) An efficien wealh ransfer sraegy, paricularly in a low ineres rae environmen Family business
More informationForecasting and Forecast Combination in Airline Revenue Management Applications
Forecasing and Forecas Combinaion in Airline Revenue Managemen Applicaions Chrisiane Lemke 1, Bogdan Gabrys 1 1 School of Design, Engineering & Compuing, Bournemouh Universiy, Unied Kingdom. E-mail: {clemke,
More informationForecasting stock indices: a comparison of classification and level estimation models
Inernaional Journal of Forecasing 16 (2000) 173 190 www.elsevier.com/ locae/ ijforecas Forecasing sock indices: a comparison of classificaion and level esimaion models Mark T. Leung *, Hazem Daouk, An-Sing
More informationTHE RELATIONSHIPS AMONG PETROLEUM PRICES. Abstract
Inernaional Conference On Applied Economics ICOAE 2010 459 THE RELATIONSHIPS AMONG PETROLEUM PRICES RAYMOND LI 1 Absrac This paper evaluaes in a mulivariae framework he relaionship among he spo prices
More informationChapter 6: Business Valuation (Income Approach)
Chaper 6: Business Valuaion (Income Approach) Cash flow deerminaion is one of he mos criical elemens o a business valuaion. Everyhing may be secondary. If cash flow is high, hen he value is high; if he
More informationModelling and forecasting the volatility of petroleum futures prices
Modelling and forecasing he volailiy of peroleum fuures prices Sang Hoon Kang a, Seong-Min Yoon b, * a Deparmen of Business Adminisraion, Pusan Naional Universiy, Busan 609-735, Korea b Deparmen of Economics,
More informationOption Put-Call Parity Relations When the Underlying Security Pays Dividends
Inernaional Journal of Business and conomics, 26, Vol. 5, No. 3, 225-23 Opion Pu-all Pariy Relaions When he Underlying Securiy Pays Dividends Weiyu Guo Deparmen of Finance, Universiy of Nebraska Omaha,
More informationForecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices
(IJCSIS) ernaional Journal of Compuer Science and formaion Securiy, Forecasing Model for Crude Oil Price Using Arificial Neural Neworks and Commodiy Fuures Prices Siddhivinayak Kulkarni Graduae School
More informationTHE NEW MARKET EFFECT ON RETURN AND VOLATILITY OF SPANISH STOCK SECTOR INDEXES
THE NEW MARKET EFFECT ON RETURN AND VOLATILITY OF SPANISH STOCK SECTOR INDEXES Juan Ángel Lafuene Universidad Jaume I Unidad Predeparamenal de Finanzas y Conabilidad Campus del Riu Sec. 1080, Casellón
More informationMeasuring the Services of Property-Casualty Insurance in the NIPAs
1 Ocober 23 Measuring he Services of Propery-Casualy Insurance in he IPAs Changes in Conceps and Mehods By Baoline Chen and Dennis J. Fixler A S par of he comprehensive revision of he naional income and
More informationANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS
ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS R. Caballero, E. Cerdá, M. M. Muñoz and L. Rey () Deparmen of Applied Economics (Mahemaics), Universiy of Málaga,
More informationSEASONAL ADJUSTMENT. 1 Introduction. 2 Methodology. 3 X-11-ARIMA and X-12-ARIMA Methods
SEASONAL ADJUSTMENT 1 Inroducion 2 Mehodology 2.1 Time Series and Is Componens 2.1.1 Seasonaliy 2.1.2 Trend-Cycle 2.1.3 Irregulariy 2.1.4 Trading Day and Fesival Effecs 3 X-11-ARIMA and X-12-ARIMA Mehods
More informationINTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES
INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES OPENGAMMA QUANTITATIVE RESEARCH Absrac. Exchange-raded ineres rae fuures and heir opions are described. The fuure opions include hose paying
More informationConsumer sentiment is arguably the
Does Consumer Senimen Predic Regional Consumpion? Thomas A. Garre, Rubén Hernández-Murillo, and Michael T. Owyang This paper ess he abiliy of consumer senimen o predic reail spending a he sae level. The
More information