ПРОГНОЗИРОВАНИЕ ОБЪЕМОВ ПРОДАЖ ПРОДУКЦИИ НА ОСНОВАНИИ МНОГОФАКТОРНОЙ РЕГРЕССИОННОЙ МОДЕЛИ

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "ПРОГНОЗИРОВАНИЕ ОБЪЕМОВ ПРОДАЖ ПРОДУКЦИИ НА ОСНОВАНИИ МНОГОФАКТОРНОЙ РЕГРЕССИОННОЙ МОДЕЛИ"

Transcription

1 Kuzhda Т. Reail sales forecasing wih applicaion he muliple regression [Електронний ресурс] / Т. Kuzhda // Соціально-економічні проблеми і держава. 01. Вип. 1 (6). С Режим доступу до журн. : hp://sepd.nu.edu.ua/images/sories/pdf/01/1kibrm.pdf. УДК JEL Classificaion: C53 Тетяна Кужда Тернопільський національний технічний університет імені Івана Пулюя ПРОГНОЗУВАННЯ ОБСЯГІВ ПРОДАЖУ ПРОДУКЦІЇ НА ОСНОВІ БАГАТОФАКТОРНОЇ РЕГРЕСІЙНОЇ МОДЕЛІ Анотація. В статті описано метод багатофакторного регресійного моделювання, теоретичний підхід до побудови регресійних моделей, порядок розрахунку кількісного прогнозу залежної змінної під впливом декількох незалежних змінних. Застосовано теоретичний матеріал до прогнозування обсягів продажу продукції під впливом очікуваного доходу споживачів та витрат на рекламну діяльність. Здійснено перевірку отриманої багатофакторної регресійної моделі на статистичну надійність та значущість та розраховано прогноз обсягів продажу продукції на наступний період. Ключові слова: регресійний аналіз, залежна та незалежна змінні, багатофакторна регресійна модель, статистична надійність та значущість, екстраполяція трендів, прогноз обсягів продажу продукції. Татьяна Кужда ПРОГНОЗИРОВАНИЕ ОБЪЕМОВ ПРОДАЖ ПРОДУКЦИИ НА ОСНОВАНИИ МНОГОФАКТОРНОЙ РЕГРЕССИОННОЙ МОДЕЛИ Аннотация. В статье описано метод многофакторного регрессионного моделирования, теоретический подход к построению регрессионных моделей, порядок расчета количественного прогноза зависимой переменной под влиянием нескольких независимых переменных. Использовано теоретический материал к прогнозированию объемов продаж продукции под влиянием ожидаемого дохода потребителей и затрат на рекламную деятельность. Осуществлена проверка полученной многофакторной регрессионной модели на статистическую надежность и значимость, рассчитан прогноз объемов продаж продукции на следующий период. Ключевые слова: регрессионный анализ, зависимая и независимая переменные, многофакторная регрессионная модель, статистическая надежность и значимость, экстраполяция трендов, прогноз объемов продаж продукции. Teyana Kuzhda RETAIL SALES FORECASTI G WITH APPLICATIO THE MULTIPLE REGRESSIO Absrac. The aricle begins wih a formulaion for predicive learning called muliple regression model. Theoreical approach on consrucion of he regression models is described. The Kuzhda, T. (01). Reail sales forecasing wih applicaion he muliple regression. Sosial'no-ekonomichni problemy i derzhava - Socio-Economic Problems and he Sae [online]. 6 (1), p [Accessed May 01]. Available from: <hp://sepd.nu.edu.ua/images/sories/pdf/01/1kibrm.pdf>. 91

2 ISSN 3-38 Socio-Economic Problems and he Sae, Vol. 6, No. 1, 01 key informaion of he aricle is he mahemaical formulaion for he forecas linear equaion ha esimaes he muliple regression model. Calculaion he quaniaive value of dependen variable forecas under influence of independen variables is explained. This paper presens he reail sales forecasing wih muliple model esimaion. One of he mos imporan decisions a reailer can make wih informaion obained by he muliple regression. Recenly, a changing reail environmen is causing by an expeced consumer s income and adverising coss. Checking model on he goodness of fi and saisical significance are explored in he aricle. Finally, he quaniaive value of reail sales forecas based on muliple regression model is calculaed. Keywords: regression analysis, dependen and independen variables, muliple regression model, goodness of fi and he saisical significance, rend exrapolaion, reail sales forecas. Inroducion. Regression analysis includes many echniques for modeling and analyzing several variables, when he focus is on he relaionship beween a dependen variable and one or more independen variables. Regression analysis is also used o undersand which among he independen variables are relaed o he dependen variable, and o explore he forms of hese relaionships. Regression analysis can be used o infer causal relaionships beween he independen and dependen variables. Variables which are used o explain oher variables are called explanaory (or independen) variables. A dependen variable is wha you measure in he forecas. The dependen variable responds o he independen variable. I is called dependen because i depends on he independen variable [1]. Regression modeling is he process of consrucion forecasing models based on he relaionship beween a dependen variable and independen variables o make he fuure forecas. Regression modeling is a kind of mulifacor forecasing. The basis of regression modeling is he consrucion of regression models. Regression models are used o predic one variable from one or more oher variables. Regression models provide he scienis wih a powerful ool, allowing predicions abou fuure evens o be made wih informaion abou pas or presen evens. In order o consruc a regression model, boh he informaion which is going o be used o make he predicion and he informaion which is o be prediced mus be obained from a sample of objecs or individuals. The relaionship beween he wo pieces of informaion is hen modeled wih a linear ransformaion. Then in he fuure, only he firs informaion is necessary, and he regression model is used o ransform his informaion ino he prediced. In oher words, i is necessary o have informaion on boh variables before he model can be consruced [1, 6]. Regression models are one of he mos famous examples of economic and saisical models used in he forecasing of socio-economic processes. Consrucion of he regression models includes he following sages: 1) Selecion of an objec o forecas. The objecs of socio-economic forecasing are he economic processes (for example, inflaion, demand, supply, exchange rae, ec.), any indicaor describing he company aciviy (for example, producion, price, profi, income, sales, coss, ec.), any indicaor describing he naional economics (for example, gross domesic produc, gross invesmen, naional income, governmen spending, expor, impor, exernal deb, ec.), any indicaor describing he social processes (for example, wage, bonus fund, incenive fund, overime paymens, employmen and unemploymen, emigraion and immigraion, ec.). An objec of forecasing is a dependen variable. ) Selecion of he facors (independen variables) ha explains he changes in he socioeconomic processes. The facors should be in he causal link o he objec of forecasing and all facors mus be quaniaively measured and significan. For example, company s reail sales depend on expeced consumer s income and adverising coss. In his example, company s reail sales are he objec of forecasing (or dependen variable); he expeced consumer s income and adverising coss are facors or independen variables. 3) Daa collecion is he process of obaining useful informaion on key quaniaive characerisics of socio-economic processes. Saisical informaion necessary o forecasing can be obained from primary and secondary daa sources. Daa processing is any process ha summarizes, 9

3 ISSN 3-38 Соціально-економічні проблеми і держава. Вип. 1 (6). 01 analyzes or oherwise convers daa ino usable informaion. Informaion base of forecasing based on regression models is he several inerrelaed ime series wih a feedback relaionship. 4) Selecion of he mahemaical dependence beween he facors or independen variables and dependen variable. Regression models can be described by he following ypes of dependencies: linear, power, logarihmic, ec. In linear regression, daa are modeled using linear funcions, and unknown model parameers are esimaed from he daa. Linear regression is an approach o modeling he relaionship beween wo or more independen variables () and a single dependen variable (Y). The case of one explanaory variable is called simple regression model. More han one explanaory variable is muliple regression models. On pracice is widely used he more general muliple regression model. General muliple regression model can have muliple explanaory variables. Muliple regression model is a flexible mehod of daa analysis ha may be appropriae whenever a quaniaive variable (he dependen variable) is o be examined in relaionship o any oher facors (expressed as independen variables). For example, a muliple regression model migh examine average salaries (dependen variable) as a funcion of age, educaion, gender and experience (independen variables). Muliple regression requires a large number of observaions. The number of periods mus subsanially exceed he number of independen variables you are using in regression. The absolue minimum is ha you have five periods [1, 6]. The forecas linear equaion ha esimaes he muliple regression model look like (1): Y = b0 + b1 1 + b bm m ; (1) where Y is called he exogenous variable, response variable, measured variable, or dependen variable. The decision as o which variable in a daa se is modeled as he dependen variable and which are modeled as he independen variables may be based on a presumpion ha he value of one of he variables is caused by, or direcly influenced by he oher variables; 1,,... m are called endogenous variables, explanaory variables, inpu variables, predicor variables, or independen variables a period ; b 0, b1, b... b m b are he regression coefficiens; 0 measures he changes in Y wih respec o random facors ha are no included in he regression model; b 1 measures he changes in Y wih respec o 1 ; b measures he changes in Y wih respec o ; b т measures he changes in Y wih respec o т. To find he regression coefficiens (b 0, b 1, b, b m ) need o calculae he sysem of normal equaions. The calculaion formulas are complex. For muliple regression, i is almos imperaive o use compuer sofware (Daa Analysis) o he predicion equaion. Corresponding o he muliple regression equaion, sofware finds a forecas equaion by esimaing he model parameers using sample daa. 5) Checking he model on he goodness of fi and he saisical significance based on saisical coefficiens. If a model is reliable and saisical significan, he forecas will be accurae. 6) Calculaion of he independen variables forecass is he process of predicion he independen variables under influence of a ime facor. To find he quaniaive values of independen variables forecass we can use he forecasing based on rend exrapolaion. 7) Calculaion of he forecas based on regression modeling is he process of predicion he quaniaive value of dependen variable under influence of independen variables. Muliple Model Esimaion in Pracice. Applicaion above heoreical informaion for forecasing based on muliple regression is described in example below. Saisical daa on reail sales, expeced consumer s income and adverising coss wihin 10 monhs are given on able 1. We wan o explain how o calculae he reail sales forecas for January based on muliple regression model. 93

4 ISSN 3-38 Socio-Economic Problems and he Sae, Vol. 6, No. 1, 01 Table 1 Saisics on reail sales, expeced consumer s income and adverising coss Monhs Reail sales, Expeced consumer s Adverising housand dollars income, housand dollars housand dollars Mar 15 0,0 1,5 April 16 0, 1,7 May 18 0,5 1,8 June 130 0,7 13,0 July 131 0,9 13, Augus 133 1, 13,5 Sepember 139 1,5 13,7 Ocober 14,1 13,8 November 145,7 14,0 December 150 3,5 14,4 coss, In his example, company s reail sales are dependen variable Y; he expeced consumer s income and adverising coss are facors or independen variables. To find he reail sales forecas based on regression modeling we need o use he muliple regression model (): Y = b0 + b1 1 + b, () where Y is he forecas of company s reail sales, housand dollars; 1 is he expeced consumer s income a period ; is he adverising coss a period ; b 0 b1,, b are he regression coefficiens. b, b 0 b, The calculaion of coefficiens 1 is long and laborious process. Microsof Excel provides a lo of possibiliies o forecasing based on regression modeling. Saisical daa on reails sales, expeced consumer s income and adverising coss wihin 10 monhs should be presened on Excel spreadshee. Firsly, selec he Daa menu / Daa Analysis / Regression (Figure 1). Fig. 1. Daa menu / Daa Analysis / Regression 94

5 ISSN 3-38 Соціально-економічні проблеми і держава. Вип. 1 (6). 01 The following window appears (Figure ). The firs box is he Inpu Y Range. Here, we ell Excel abou our dependen variable (reail sales). The dependen variable mus be a column. To fill Inpu Y Range need click here and ener he cell reference for he range of daa on reail sales. The nex sage is o inpu independen variables. The independen variables mus be a block of daa, if he independen variables are several, or column of daa, if he independen variable is one. In he daase we are using we have wo independen variables: he expeced consumer s income and adverising coss. To fill Inpu Range need click here and ener he cell reference for he block of daa on expeced consumer s income and adverising coss. If he Confidence Level equals o 95%, you can say ha you are 95% sure ha he reail sales forecas will be accurae. Nex we ell Excel where we wan he resuls o be wrien. To fill Oupu range ener he reference for he cell (B13) of he oupu able. So, finally, we click OK. Fig.. Regression window And we ge a lo of oupu. The regression oupu has hree componens: Regression saisics able, ANOVA able, Regression coefficiens able (Figure 3). Figure 3 conains he informaion need o ge he muliple regression model. b Quaniaive values of he coefficiens: 0 b is opposie Inercep ( 0 = 33,96); b1 is opposie Variable 1 ( b 1 = 5,04); b is opposie Variable ( b = 4,38). The muliple regression model need o forecas he reail sales (Y) for January is: Y = 33,96+ 5, , 38. (3) We have he muliple regression model (3) need o forecas he reail sales, bu quaniaive value of he forecas using Daa Analysis we can no ge. The nex sage is checking he muliple regression model (3) on he goodness of fi and he saisical significance. And afer checking he model, we can calculae quaniaive value of he reail sales forecas. Saisical goodness of fi for he muliple regression model can be deermined by he following saisical coefficiens: he correlaion coefficien (r), he coefficien of deerminaion (R ) and adjused coefficien of deerminaion (AR ). 95

6 ISSN 3-38 Socio-Economic Problems and he Sae, Vol. 6, No. 1, 01 Fig. 3. The regression oupu: Regression saisics able, A OVA able, Regression coefficiens able Coefficien of deerminaion (R ) is a measure o assess how well he muliple regression model explains and predics fuure oucomes. I is expressed as a value beween 0 and 1. A value of one indicaes a perfec fi, and herefore, a very reliable muliple regression model for fuure forecass. A value of zero, on he oher hand, would indicae ha he muliple regression model fails o accuraely forecas he daase [3, 5]. The following poins are acceped guidelines for inerpreing he coefficien of deerminaion: values beween 0 and 0,3 indicae a weak posiive linear relaionship; values beween 0,3 and 0,7 indicae a moderae posiive linear relaionship; values beween 0,7 and 1 indicae a srong posiive linear relaionship. The correlaion coefficien (r), is a measure of he srengh of he relaionship beween wo or more independen variables () and a single dependen variable (Y). One of ways o find his coefficien is he following: correlaion coefficien (r) is he square roo of he coefficien of deerminaion (4): r = R (4) The correlaion coefficien akes on values ranging beween +1 and -1. The following poins are acceped guidelines for inerpreing he correlaion coefficien: 0 indicaes no linear relaionship; +1 indicaes a perfec posiive linear relaionship; -1 indicaes a perfec negaive linear relaionship; values beween 0 and 0,3 (0 and -0,3) indicae a weak posiive (negaive) linear relaionship; values beween 0,3 and 0,7 (-0,3 and -0,7) indicae a moderae posiive (negaive) linear relaionship; values beween 0,7 and 1 (-0,7 and -1) indicae a srong posiive (negaive) linear relaionship [4, 5]. In a muliple linear regression model, adjused coefficien of deerminaion (AR ) measures he share of he variaion in he dependen variable accouned by he explanaory variables. Adjused coefficien of deerminaion is generally considered o be a more accurae goodness-of-fi measure han he coefficien of deerminaion. The adjused R will always be less han or equal o he coefficien of deerminaion (R ). Adjused coefficien of deerminaion is paricularly useful in he feaure selecion sage of model building [4, 5]. 96

7 ISSN 3-38 Соціально-економічні проблеми і держава. Вип. 1 (6). 01 Adjused coefficien of deerminaion (R-Square) is compued using he following formula (5): (1 R ) ( n 1) Adjused R = 1 ( n k 1), (5) where R is he coefficien of deerminaion; n is he number of observaions (or periods); k is he number of independen variables. To find he correlaion coefficien and coefficien of deerminaion we need o inerpre Regression saisics able (Figure 3). Table Regression saisics Explanaion Muliple R 0, Correlaion coefficien R Square 0, Coefficien of deerminaion Adjused R Square 0, Adjused coefficien of deerminaion Sandard Error 1, Sandard Error is a measure of error in predicion Observaion 10 Number of observaions used in he regression Correlaion coefficien can be calculaed by he formula (4): r = 0, ,99 Correlaion coefficien r=0,99 may be inerpreed as follows: approximaely 99% (0,99*100%) of he variaion in he dependen variable (reail sales) can be explained by he muliple regression model (3). If he coefficien of deerminaion is greaer han 0,7, as i is in his case, here is a good fi o he daa. The coefficien of deerminaion 0,985 means approximaely 98,5% (0,985*100%) of he variaion in he dependen variable (reail sales) can be explained by he independen variables (he expeced consumer s income and adverising coss). Adjused coefficien of deerminaion by he following formula (5): Adjused R (1 0, ) (10 1) = 1 (10 1) 0,981 Adjused coefficien of deerminaion 0,981 means approximaely 98,1% (0,981*100%) of he variaion in he dependen variable (reail sales) can be explained by he independen variables (he expeced consumer s income and adverising coss). Checking he model on he saisical significance based on ANOVA able (Figure 3), where (SS is he sum of squares, he numeraor of he variance; DF is he denominaor; MS is he mean square of variance; Significance F means he saisical significance of he muliple regression model). ANOVA means an analysis of variance ha consiss of calculaions ha provide informaion abou levels of variabiliy wihin a regression model and form a basis for ess of significance. Significance F means he saisical significance of he muliple regression model. In his example (Figure 3), he value of Significance F is lower han 0,05, hen we can say he muliple regression model is generally accepable and saisical significan o forecas of he reail sales (3,98*10-7 <0,05). Checking of each coefficien on he saisical significance based on Regression coefficiens able (Figure 3), where column Coefficien gives he quaniaive values of regression coefficiens 97

8 ISSN 3-38 Socio-Economic Problems and he Sae, Vol. 6, No. 1, 01 b0, b1, b ; column Sandard error gives he sandard errors (i.e. he esimaed sandard deviaion) of regression coefficiens; column Sa gives he compued -saisic (is a raio of he deparure of an esimaed parameer from is noional value and is sandard error); column Pvalue gives he probabiliy value for each regression coefficien. If P-value is less han 0,05 (5% misake probabiliy), hen he coefficien is saisical significan (95 % probabiliy means he forecas based on muliple regression model is accurae), and if P-value is more han 0,05; he coefficien is saisical insignifican. In his example, P-value for coefficien b 0 is 0,008 (lower han 0,05), P-value for coefficien b 1 is 0,01 (lower han 0,05), P-value for coefficien b is 0,17 (higher han 0,05), hen we can say he muliple regression model in generally is saisical significan. Thus, he muliple regression model (3) is saisical significan, he model is useful and reliable o forecas. To find he forecas of he reail sales for January, a firs, we need o calculae he quaniaive values of expeced consumer s income forecas and adverising coss forecas for January. Calculaion of he expeced consumer s income forecas and adverising coss forecas for January is possible using he forecasing based on rend exrapolaion. To do his we need o find he forecas of expeced consumer s income depending on ime () and he forecas of adverising coss depending on ime (). Firsly, we need o calculae he expeced consumer s income forecas based on rend exrapolaion (using a linear equaion). Linear equaion looks like (6): х = а+ b, (6) where х is he expeced consumer s income forecas based on rend exrapolaion (or adverising coss forecas based on rend exrapolaion); a and b are he designae coefficiens; is he ime uni. Coefficien b can be calculaed by he formula (7): b х n х = n _, (7) where n number of periods; is he average value of variable (ime or independen variable); _ х is he average value of dependen variable x (average value of expeced consumer s income or average value of adverising coss). Average value of variable can be calculaed by he formula (8): = n, (8) where n is he number of periods; - is he sum of numbers from 1 o n; Average value of variable x can be calculaed by he formula (9): _ х х = n ; (9) where n is he number of periods; х - is he sum of saisical daa for n periods. Coefficien a can be calculaed by he formula (10): a = х b. (10) 98

9 ISSN 3-38 Соціально-економічні проблеми і держава. Вип. 1 (6). 01 To wrie down a linear equaion х1 = а+ b (where х 1 is he expeced consumer s income forecas) and calculae he coefficiens b and a need o find:, x 1 * on able 3. Resuls of calculaions Monhs Expeced consumer s income ( х 1), housand dollars x 1 * Mar 0, April 0, 4 40,4 May 0, ,5 June 0, ,8 July 0, ,5 Augus 1, , Sepember 1, ,5 Ocober, ,8 November, ,3 December 3, , Table 3 Average value of ime () by he formula (8): = n 55 = = 5,5 10. Average expeced consumer s income (x 1 ) by he formula (9): Coefficien b by he formula (7): 13,3 10 х1 = = 1, ,5 1,33 b= (5,5) 0,361 Coefficien a by he formula (10): a= 1,33 0,361 5,5= 19,34 Linear equaion looks like: х1 = a+ b = 19,34+ 0, 361 Forecas of expeced consumer s income for January based on rend exrapolaion: х = 19,34+ 0, ,311 housand dollars. To wrie down a linear equaion х = а+ b (where х is adverising coss forecas) and calculae he coefficiens b and a need o find:, x * on able 4. 99

10 ISSN 3-38 Socio-Economic Problems and he Sae, Vol. 6, No. 1, 01 Resuls of calculaions Monhs Adverising coss ( х ), housand dollars x * Mar 1, ,5 April 1,7 4 5,4 May 1, ,4 June 13, July 13, Augus 13, Sepember 13, ,9 Ocober 13, ,4 November 14, December 14, , ,6 Table 4 Average value of ime () by he formula (8): = n 55 = = 5,5 10 Average adverising coss (x ) by he formula (9): Coefficien b by he formula (7): Coefficien a by he formula (10): Linear equaion looks like: 133,6 10 х = = 13,36 751,6 10 5,5 13,36 b= (5,5) 0,03 a = 13,36 0,03 5,5= 1,4 х = a+ b = 1,4+ 0, 03 Forecas of adverising coss for January based on rend exrapolaion: х = 1,4+ 0,03 11= 14,473 housand dollars. Reail sales forecas for January based on muliple regression model (formula 3): Y = 33,96+ 5, ,38 = = 33,96+ 5,04 3,311+ 4,38 14, ,04 housand dollars. 100

11 ISSN 3-38 Соціально-економічні проблеми і держава. Вип. 1 (6). 01 Thus, he reail sales forecas for January based on muliple regression model equals o 150,04 housand dollars. Conclusion. The muliple regression model was effecive for forecasing reail sales under influence of expeced consumer s income and adverising coss. I can be applied for forecasing oher business daa. Using such models for forecasing reail sales can assis company managers in planning and making decisions more effecively. References: 1. Cohen, J., Cohen, P., Wes, S. G., & Aiken, L. S. (003). Applied muliple regression/correlaion analysis for he behavioral sciences, 3rd Ed. Mahwah, NJ: Lawrence Erlbaum Associaes.. Rogers, David S. A Review of Sales Forecasing Models, Inernaional Journal of Reail and Disribuion Managemen, MCB Universiy Press, Vol. 0, Issue 4, Mining-Long Lee & R. Kelley Pace Spaial Disribuion of Reail Sales, The Journal of Reail Esae Finance and Economics, Springer, Vol. 31(1), pages 53-69, Augus, Lundholm, Russell J. and McVay, Sarah E., Forecasing Sales: A model and some evidence from he reail indusry (January, 004). 5. Wassana Suwanviji, Chamnein Choonpradub, Niaya McNeil Saisical Model For Shor-Term Forecasing Sparkling Beverage Sales In Souhern Thailand, Inernaional Business & Economics Research Journal, Vol.8, 9, Sepember Samawi, H.M., Ababneh, F.M., On regression analysis using ranked se sample, Journal of Saisical Research. 35 (001), Рецензія: д.е.н., проф. Кирич Н. Б. Received: March, 01 1s Revision: April, 01 Acceped: May,

Chapter 8: Regression with Lagged Explanatory Variables

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 information

Duration 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.

Duration 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 information

Vector Autoregressions (VARs): Operational Perspectives

Vector 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 information

Hedging with Forwards and Futures

Hedging 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 information

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR The firs experimenal publicaion, which summarised pas and expeced fuure developmen of basic economic indicaors, was published by he Minisry

More information

SPEC model selection algorithm for ARCH models: an options pricing evaluation framework

SPEC 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 information

INTRODUCTION TO FORECASTING

INTRODUCTION 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 information

ARCH 2013.1 Proceedings

ARCH 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 information

Price elasticity of demand for crude oil: estimates for 23 countries

Price elasticity of demand for crude oil: estimates for 23 countries 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 information

Forecasting Malaysian Gold Using. GARCH Model

Forecasting Malaysian Gold Using. GARCH Model Applied Mahemaical Sciences, Vol. 7, 2013, no. 58, 2879-2884 HIKARI Ld, www.m-hikari.com Forecasing Malaysian Gold Using GARCH Model Pung Yean Ping 1, Nor Hamizah Miswan 2 and Maizah Hura Ahmad 3 Deparmen

More information

Chapter 8 Student Lecture Notes 8-1

Chapter 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 information

Chapter 6: Business Valuation (Income Approach)

Chapter 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 information

Measuring macroeconomic volatility Applications to export revenue data, 1970-2005

Measuring 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 information

DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS

DYNAMIC 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 information

Principal components of stock market dynamics. Methodology and applications in brief (to be updated ) Andrei Bouzaev, bouzaev@ya.

Principal 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 information

Consumer sentiment is arguably the

Consumer 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

TEMPORAL 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 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 information

4. International Parity Conditions

4. 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 information

Segmentation, Probability of Default and Basel II Capital Measures. for Credit Card Portfolios

Segmentation, Probability of Default and Basel II Capital Measures. for Credit Card Portfolios Segmenaion, Probabiliy of Defaul and Basel II Capial Measures for Credi Card Porfolios Draf: Aug 3, 2007 *Work compleed while a Federal Reserve Bank of Philadelphia Dennis Ash Federal Reserve Bank of Philadelphia

More information

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES

USE 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 information

A New Type of Combination Forecasting Method Based on PLS

A 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 information

The Real Business Cycle paradigm. The RBC model emphasizes supply (technology) disturbances as the main source of

The 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 information

Forecasting Sales: A Model and Some Evidence from the Retail Industry. Russell Lundholm Sarah McVay Taylor Randall

Forecasting 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 information

Predicting Stock Market Index Trading Signals Using Neural Networks

Predicting 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 information

Time Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test

Time 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 information

TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK

TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK Inernaional Journal of Innovaive Managemen, Informaion & Producion ISME Inernaionalc2011 ISSN 2185-5439 Volume 2, Number 1, June 2011 PP. 57-67 TOOL OUTSOURCING RISK RESEARCH BASED ON BP NEURAL NETWORK

More information

Chapter 1 Overview of Time Series

Chapter 1 Overview of Time Series Chaper 1 Overview of Time Series 1.1 Inroducion 1 1.2 Analysis Mehods and SAS/ETS Sofware 2 1.2.1 Opions 2 1.2.2 How SAS/ETS Sofware Procedures Inerrelae 4 1.3 Simple Models: Regression 6 1.3.1 Linear

More information

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE

PROFIT 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 information

Measuring the Effects of Exchange Rate Changes on Investment. in Australian Manufacturing Industry

Measuring the Effects of Exchange Rate Changes on Investment. in Australian Manufacturing Industry Measuring he Effecs of Exchange Rae Changes on Invesmen in Ausralian Manufacuring Indusry Robyn Swif Economics and Business Saisics Deparmen of Accouning, Finance and Economics Griffih Universiy Nahan

More information

Nikkei Stock Average Volatility Index Real-time Version Index Guidebook

Nikkei 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 information

SURVEYING THE RELATIONSHIP BETWEEN STOCK MARKET MAKER AND LIQUIDITY IN TEHRAN STOCK EXCHANGE COMPANIES

SURVEYING THE RELATIONSHIP BETWEEN STOCK MARKET MAKER AND LIQUIDITY IN TEHRAN STOCK EXCHANGE COMPANIES Inernaional Journal of Accouning Research Vol., No. 7, 4 SURVEYING THE RELATIONSHIP BETWEEN STOCK MARKET MAKER AND LIQUIDITY IN TEHRAN STOCK EXCHANGE COMPANIES Mohammad Ebrahimi Erdi, Dr. Azim Aslani,

More information

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)

Mathematics 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 information

Morningstar Investor Return

Morningstar 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 information

A Note on Using the Svensson procedure to estimate the risk free rate in corporate valuation

A 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 information

The 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. 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 information

Cointegration: The Engle and Granger approach

Cointegration: 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 information

Fakultet for informasjonsteknologi, Institutt for matematiske fag

Fakultet for informasjonsteknologi, Institutt for matematiske fag Page 1 of 5 NTNU Noregs eknisk-naurviskaplege universie Fakule for informasjonseknologi, maemaikk og elekroeknikk Insiu for maemaiske fag - English Conac during exam: John Tyssedal 73593534/41645376 Exam

More information

The Effect of Working Capital Management on Reducing the Stock Price Crash Risk(Case Study: Companies Listed in Tehran Stock Exchange)

The Effect of Working Capital Management on Reducing the Stock Price Crash Risk(Case Study: Companies Listed in Tehran Stock Exchange) Inernaional Research Journal of Applied and Basic Sciences 2013 Available online a www.irjabs.com ISSN 2251-838X / Vol, 6 (9): 1222-1228 Science Explorer Publicaions The Effec of Working Capial Managemen

More information

Hotel Room Demand Forecasting via Observed Reservation Information

Hotel 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 information

The naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1

The 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 information

Revisions to Nonfarm Payroll Employment: 1964 to 2011

Revisions to Nonfarm Payroll Employment: 1964 to 2011 Revisions o Nonfarm Payroll Employmen: 1964 o 2011 Tom Sark December 2011 Summary Over recen monhs, he Bureau of Labor Saisics (BLS) has revised upward is iniial esimaes of he monhly change in nonfarm

More information

House Price Index (HPI)

House Price Index (HPI) House Price Index (HPI) The price index of second hand houses in Colombia (HPI), regisers annually and quarerly he evoluion of prices of his ype of dwelling. The calculaion is based on he repeaed sales

More information

Chabot College Physics Lab RC Circuits Scott Hildreth

Chabot College Physics Lab RC Circuits Scott Hildreth Chabo College Physics Lab Circuis Sco Hildreh Goals: Coninue o advance your undersanding of circuis, measuring resisances, currens, and volages across muliple componens. Exend your skills in making breadboard

More information

BALANCE OF PAYMENTS. First quarter 2008. Balance of payments

BALANCE 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 information

Why Did the Demand for Cash Decrease Recently in Korea?

Why 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 information

Forecasting. Including an Introduction to Forecasting using the SAP R/3 System

Forecasting. Including an Introduction to Forecasting using the SAP R/3 System Forecasing Including an Inroducion o Forecasing using he SAP R/3 Sysem by James D. Blocher Vincen A. Maber Ashok K. Soni Munirpallam A. Venkaaramanan Indiana Universiy Kelley School of Business February

More information

Market Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand

Market 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 information

Small and Large Trades Around Earnings Announcements: Does Trading Behavior Explain Post-Earnings-Announcement Drift?

Small and Large Trades Around Earnings Announcements: Does Trading Behavior Explain Post-Earnings-Announcement Drift? Small and Large Trades Around Earnings Announcemens: Does Trading Behavior Explain Pos-Earnings-Announcemen Drif? Devin Shanhikumar * Firs Draf: Ocober, 2002 This Version: Augus 19, 2004 Absrac This paper

More information

Measuring the Services of Property-Casualty Insurance in the NIPAs

Measuring 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 information

Journal Of Business & Economics Research September 2005 Volume 3, Number 9

Journal 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 information

SEASONAL ADJUSTMENT. 1 Introduction. 2 Methodology. 3 X-11-ARIMA and X-12-ARIMA Methods

SEASONAL 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 information

Estimating Time-Varying Equity Risk Premium The Japanese Stock Market 1980-2012

Estimating 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 information

Risk Modelling of Collateralised Lending

Risk Modelling of Collateralised Lending Risk Modelling of Collaeralised Lending Dae: 4-11-2008 Number: 8/18 Inroducion This noe explains how i is possible o handle collaeralised lending wihin Risk Conroller. The approach draws on he faciliies

More information

Journal of Business & Economics Research Volume 1, Number 10

Journal of Business & Economics Research Volume 1, Number 10 Annualized Invenory/Sales Journal of Business & Economics Research Volume 1, Number 1 A Macroeconomic Analysis Of Invenory/Sales Raios William M. Bassin, Shippensburg Universiy Michael T. Marsh (E-mail:

More information

ANALYSIS FOR FINDING AN EFFICIENT SALES FORECASTING METHOD IN THE PROCESS OF PRODUCTION PLANNING, OPERATION AND OTHER AREAS OF DECISION MAKING

ANALYSIS FOR FINDING AN EFFICIENT SALES FORECASTING METHOD IN THE PROCESS OF PRODUCTION PLANNING, OPERATION AND OTHER AREAS OF DECISION MAKING Inernaional Journal of Mechanical and Producion Engineering Research and Developmen (IJMPERD ) Vol.1, Issue 2 Dec 2011 1-36 TJPRC Pv. Ld., ANALYSIS FOR FINDING AN EFFICIENT SALES FORECASTING METHOD IN

More information

Modeling Tourist Arrivals Using Time Series Analysis: Evidence From Australia

Modeling Tourist Arrivals Using Time Series Analysis: Evidence From Australia Journal of Mahemaics and Saisics 8 (3): 348-360, 2012 ISSN 1549-3644 2012 Science Publicaions Modeling Touris Arrivals Using Time Series Analysis: Evidence From Ausralia 1 Gurudeo AnandTularam, 2 Vicor

More information

Recovering Market Expectations of FOMC Rate Changes with Options on Federal Funds Futures

Recovering Market Expectations of FOMC Rate Changes with Options on Federal Funds Futures w o r k i n g p a p e r 5 7 Recovering Marke Expecaions of FOMC Rae Changes wih Opions on Federal Funds Fuures by John B. Carlson, Ben R. Craig, and William R. Melick FEDERAL RESERVE BANK OF CLEVELAND

More information

Usefulness of the Forward Curve in Forecasting Oil Prices

Usefulness 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 information

Bid-ask Spread and Order Size in the Foreign Exchange Market: An Empirical Investigation

Bid-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 information

Individual Health Insurance April 30, 2008 Pages 167-170

Individual Health Insurance April 30, 2008 Pages 167-170 Individual Healh Insurance April 30, 2008 Pages 167-170 We have received feedback ha his secion of he e is confusing because some of he defined noaion is inconsisen wih comparable life insurance reserve

More information

The Kinetics of the Stock Markets

The 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 information

DEMAND FORECASTING MODELS

DEMAND FORECASTING MODELS DEMAND FORECASTING MODELS Conens E-2. ELECTRIC BILLED SALES AND CUSTOMER COUNTS Sysem-level Model Couny-level Model Easside King Couny-level Model E-6. ELECTRIC PEAK HOUR LOAD FORECASTING Sysem-level Forecas

More information

Implied Equity Duration: A New Measure of Equity Risk *

Implied Equity Duration: A New Measure of Equity Risk * Implied Equiy Duraion: A New Measure of Equiy Risk * Paricia M. Dechow The Carleon H. Griffin Deloie & Touche LLP Collegiae Professor of Accouning, Universiy of Michigan Business School Richard G. Sloan

More information

SPECIAL REPORT May 4, Shifting Drivers of Inflation Canada versus the U.S.

SPECIAL REPORT May 4, Shifting Drivers of Inflation Canada versus the U.S. Paul Ferley Assisan Chief Economis 416-974-7231 paul.ferley@rbc.com Nahan Janzen Economis 416-974-0579 nahan.janzen@rbc.com SPECIAL REPORT May 4, 2010 Shifing Drivers of Inflaion Canada versus he U.S.

More information

Appendix D Flexibility Factor/Margin of Choice Desktop Research

Appendix 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 information

YEN FUTURES: EXAMINING HEDGING EFFECTIVENESS BIAS AND CROSS-CURRENCY HEDGING RESULTS ROBERT T. DAIGLER FLORIDA INTERNATIONAL UNIVERSITY SUBMITTED FOR

YEN FUTURES: EXAMINING HEDGING EFFECTIVENESS BIAS AND CROSS-CURRENCY HEDGING RESULTS ROBERT T. DAIGLER FLORIDA INTERNATIONAL UNIVERSITY SUBMITTED FOR YEN FUTURES: EXAMINING HEDGING EFFECTIVENESS BIAS AND CROSS-CURRENCY HEDGING RESULTS ROBERT T. DAIGLER FLORIDA INTERNATIONAL UNIVERSITY SUBMITTED FOR THE FIRST ANNUAL PACIFIC-BASIN FINANCE CONFERENCE The

More information

Time-Series Forecasting Model for Automobile Sales in Thailand

Time-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 information

Investor sentiment of lottery stock evidence from the Taiwan stock market

Investor sentiment of lottery stock evidence from the Taiwan stock market Invesmen Managemen and Financial Innovaions Volume 9 Issue 1 Yu-Min Wang (Taiwan) Chun-An Li (Taiwan) Chia-Fei Lin (Taiwan) Invesor senimen of loery sock evidence from he Taiwan sock marke Absrac This

More information

Default Risk in Equity Returns

Default Risk in Equity Returns Defaul Risk in Equiy Reurns MRI VSSLOU and YUHNG XING * BSTRCT This is he firs sudy ha uses Meron s (1974) opion pricing model o compue defaul measures for individual firms and assess he effec of defaul

More information

COMPARISON OF AIR TRAVEL DEMAND FORECASTING METHODS

COMPARISON OF AIR TRAVEL DEMAND FORECASTING METHODS COMPARISON OF AIR RAVE DEMAND FORECASING MEHODS Ružica Škurla Babić, M.Sc. Ivan Grgurević, B.Eng. Universiy of Zagreb Faculy of ranspor and raffic Sciences Vukelićeva 4, HR- Zagreb, Croaia skurla@fpz.hr,

More information

DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR

DOES 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 information

Chapter 7. Response of First-Order RL and RC Circuits

Chapter 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 information

Term Structure of Prices of Asian Options

Term Structure of Prices of Asian Options Term Srucure of Prices of Asian Opions Jirô Akahori, Tsuomu Mikami, Kenji Yasuomi and Teruo Yokoa Dep. of Mahemaical Sciences, Risumeikan Universiy 1-1-1 Nojihigashi, Kusasu, Shiga 525-8577, Japan E-mail:

More information

Benefit-Cost Analysis

Benefit-Cost Analysis Slide 1 Benefi-Cos Analysis Sco Pearson Sanford Universiy Sco Pearson is Professor Emerius of Agriculural Economics a he Food Research Insiue, Sanford Universiy. He has paricipaed in projecs ha combined

More information

JEL classifications: Q43;E44 Keywords: Oil shocks, Stock market reaction.

JEL classifications: Q43;E44 Keywords: Oil shocks, Stock market reaction. Applied Economerics and Inernaional Developmen. AEID.Vol. 5-3 (5) EFFECT OF OIL PRICE SHOCKS IN THE U.S. FOR 1985-4 USING VAR, MIXED DYNAMIC AND GRANGER CAUSALITY APPROACHES AL-RJOUB, Samer AM * Absrac

More information

An Investigation into the Interdependency of the Volatility of Technology Stocks

An Investigation into the Interdependency of the Volatility of Technology Stocks An Invesigaion ino he Inerdependency of he Volailiy of Technology Socks Zoravar Lamba Adviser: Prof. George Tauchen Spring 009, Duke Universiy The Duke Communiy Sandard was upheld in he compleion of his

More information

Distributing Human Resources among Software Development Projects 1

Distributing 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 information

Chapter 4: Exponential and Logarithmic Functions

Chapter 4: Exponential and Logarithmic Functions Chaper 4: Eponenial and Logarihmic Funcions Secion 4.1 Eponenial Funcions... 15 Secion 4. Graphs of Eponenial Funcions... 3 Secion 4.3 Logarihmic Funcions... 4 Secion 4.4 Logarihmic Properies... 53 Secion

More information

LEASING VERSUSBUYING

LEASING VERSUSBUYING LEASNG VERSUSBUYNG Conribued by James D. Blum and LeRoy D. Brooks Assisan Professors of Business Adminisraion Deparmen of Business Adminisraion Universiy of Delaware Newark, Delaware The auhors discuss

More information

Stochastic Optimal Control Problem for Life Insurance

Stochastic 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 information

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment Vol. 7, No. 6 (04), pp. 365-374 hp://dx.doi.org/0.457/ijhi.04.7.6.3 Research on Invenory Sharing and Pricing Sraegy of Mulichannel Reailer wih Channel Preference in Inerne Environmen Hanzong Li College

More information

Does Option Trading Have a Pervasive Impact on Underlying Stock Prices? *

Does Option Trading Have a Pervasive Impact on Underlying Stock Prices? * Does Opion Trading Have a Pervasive Impac on Underlying Sock Prices? * Neil D. Pearson Universiy of Illinois a Urbana-Champaign Allen M. Poeshman Universiy of Illinois a Urbana-Champaign Joshua Whie Universiy

More information

An Empirical Comparison of Asset Pricing Models for the Tokyo Stock Exchange

An Empirical Comparison of Asset Pricing Models for the Tokyo Stock Exchange An Empirical Comparison of Asse Pricing Models for he Tokyo Sock Exchange Absrac In his sudy we compare he performance of he hree kinds of asse pricing models proposed by Fama and French (1993), Carhar

More information

ACTUARIAL FUNCTIONS 1_05

ACTUARIAL FUNCTIONS 1_05 ACTUARIAL FUNCTIONS _05 User Guide for MS Office 2007 or laer CONTENT Inroducion... 3 2 Insallaion procedure... 3 3 Demo Version and Acivaion... 5 4 Using formulas and synax... 7 5 Using he help... 6 Noaion...

More information

What does the Bank of Russia target?

What does the Bank of Russia target? SBERBANK OF RUSSIA CENTRE FOR MACROECONOMIC RESEARCH, SBERBANK 5 Augus 2010 Wha does he Bank of Russia arge? The crisis has promped he Russian Cenral Bank (CBR) o review is policies drasically. New frameworks

More information

is a random vector with zero mean and Var(e

is a random vector with zero mean and Var(e Economics Leers 73 (2001) 147 153 www.elsevier.com/ locae/ econbase Esimaion of direc and indirec impac of oil price on growh Tilak Abeysinghe* Deparmen of Economics, Naional Universiy of Singapore, 10Ken

More information

Working Paper No. 482. Net Intergenerational Transfers from an Increase in Social Security Benefits

Working Paper No. 482. Net Intergenerational Transfers from an Increase in Social Security Benefits Working Paper No. 482 Ne Inergeneraional Transfers from an Increase in Social Securiy Benefis By Li Gan Texas A&M and NBER Guan Gong Shanghai Universiy of Finance and Economics Michael Hurd RAND Corporaion

More information

Pavel Trunin. nestabilnosti v razvivayuschikhsya ekonomikakh (na primere Rossii). Nauchnye trudy IEPP. M., 2007, 111

Pavel Trunin. nestabilnosti v razvivayuschikhsya ekonomikakh (na primere Rossii). Nauchnye trudy IEPP. M., 2007, 111 Pavel Trunin The Use of he Signal Approach o Developmen of Early Warning Indicaors of Financial Turmoil in Russia 1 A number of serious imbalances have emerged in he global economy o dae. These are, primarily:

More information

CLASSICAL TIME SERIES DECOMPOSITION

CLASSICAL TIME SERIES DECOMPOSITION Time Series Lecure Noes, MSc in Operaional Research Lecure CLASSICAL TIME SERIES DECOMPOSITION Inroducion We menioned in lecure ha afer we calculaed he rend, everyhing else ha remained (according o ha

More information

Cable & Wireless Jamaica s Price Cap Plan

Cable & Wireless Jamaica s Price Cap Plan Office of Uiliies Regulaion Cable & Wireless Jamaica s Price Cap Plan Deerminaion Noice 2001 Augus 1 Absrac Cable and Wireless Jamaica (C&WJ) has radiionally been regulaed under a rae of reurn regime.

More information

Oil Price Fluctuations and Firm Performance in an Emerging Market: Assessing Volatility and Asymmetric Effect

Oil Price Fluctuations and Firm Performance in an Emerging Market: Assessing Volatility and Asymmetric Effect Journal of Economics, Business and Managemen, Vol., No. 4, November 203 Oil Price Flucuaions and Firm Performance in an Emerging Marke: Assessing Volailiy and Asymmeric Effec Hawai Janor, Aisyah Abdul-Rahman,

More information

Issues Using OLS with Time Series Data. Time series data NOT randomly sampled in same way as cross sectional each obs not i.i.d

Issues Using OLS with Time Series Data. Time series data NOT randomly sampled in same way as cross sectional each obs not i.i.d These noes largely concern auocorrelaion Issues Using OLS wih Time Series Daa Recall main poins from Chaper 10: Time series daa NOT randomly sampled in same way as cross secional each obs no i.i.d Why?

More information

William E. Simon Graduate School of Business Administration. IPO Market Cycles: Bubbles or Sequential Learning?

William E. Simon Graduate School of Business Administration. IPO Market Cycles: Bubbles or Sequential Learning? Universiy of Rocheser William E. Simon Graduae School of Business Adminisraion The Bradley Policy Research Cener Financial Research and Policy Working Paper No. FR 00-21 January 2000 Revised: June 2001

More information

VALUE BASED FINANCIAL PERFORMANCE MEASURES: AN EVALUATION OF RELATIVE AND INCREMENTAL INFORMATION CONTENT

VALUE BASED FINANCIAL PERFORMANCE MEASURES: AN EVALUATION OF RELATIVE AND INCREMENTAL INFORMATION CONTENT VALUE BASED FINANCIAL PERFORMANCE MEASURES: AN EVALUATION OF RELATIVE AND INCREMENTAL INFORMATION CONTENT Pierre Erasmus Absrac Value-based (VB) financial performance measures are ofen advanced as improvemens

More information

Market Efficiency or Not? The Behaviour of China s Stock Prices in Response to the Announcement of Bonus Issues

Market 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 information

ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS

ANALYSIS 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 information

µ r of the ferrite amounts to 1000...4000. It should be noted that the magnetic length of the + δ

µ r of the ferrite amounts to 1000...4000. It should be noted that the magnetic length of the + δ Page 9 Design of Inducors and High Frequency Transformers Inducors sore energy, ransformers ransfer energy. This is he prime difference. The magneic cores are significanly differen for inducors and high

More information

Chapter 1.6 Financial Management

Chapter 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 information

When Is Growth Pro-Poor? Evidence from a Panel of Countries

When Is Growth Pro-Poor? Evidence from a Panel of Countries Forhcoming, Journal of Developmen Economics When Is Growh Pro-Poor? Evidence from a Panel of Counries Aar Kraay The World Bank Firs Draf: December 2003 Revised: December 2004 Absrac: Growh is pro-poor

More information

Supply chain management of consumer goods based on linear forecasting models

Supply 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 information