Volume 31, Issue 4. Stock Market Anomalies in South Africa and its Neighbouring Countries
|
|
- Belinda Underwood
- 6 years ago
- Views:
Transcription
1 Volume, Issue Sock Marke Anomalies in Souh Africa and is Neighbouring Counries Chia Ricky CheeJiun Labuan school of Inernaional Business and Finance, Universii Malaysia Sabah Lim Shiok Ye Labuan School of Inernaional Business and Finance, Universii Malaysia Sabah. Absrac This sudy adoped he alernaive approach called closure es principle which is proposed by Al e al. (0) o examine he sock marke anomalies in Souh Africa and is Neighbouring Counries. Overall, Egyp is he only counry ha has a srong Monday effec. On he oher hand, weak Monday effec is found in Mauriius, Nigeria and Tunisia sock markes. When he imevarying volailiy in he marke reurns is aken ino accoun by he EGARCH M model, srong Monday volailiy is found in Egyp while Kenya and Nigeria is found o have weak Monday volailiy. Ciaion Chia Ricky CheeJiun and Lim Shiok Ye, (0) ''Sock Marke Anomalies in Souh Africa and is Neighbouring Counries'', Economics Bullein, Vol. No. pp. 7. Conac Chia Ricky CheeJiun ricky_chia8@homail.com, Lim Shiok Ye sy_lim8@homail.com. Submied Sepember 0, 0. Published November 09, 0.
2 Economics Bullein, 0, Vol. No. pp. 7. Inroducion Since Fields (9) observed ha he US sock marke consisenly experienced significan negaive Monday reurns and posiive Friday reurns, his marke anomaly remains one of he mos popular research issues in finance. This issue embraces imporan implicaions for hose paricipans who are acively rade in markes. From previous lieraure, marke anomalies are found no only in developed markes such as Unied Saes, Unied Kingdom, Germany and Japan, bu also in emerging markes like Malaysia, Taiwan and Hong Kong. (See examples, Cross, 97; French, 980; Gibbons and Hess, 98; Keim and Sambaugh, 98; Jaffe and Weserfiled, 98; Wong e al., 99; Arsad and Cous, 996; Lucey, 000; Brooks and Persand, 00; Apolonario e al., 006.) Among he emerging markes, one of he mos appealing markes which grow rapidly in recen years is Souh Africa sock marke. The sock exchange of Souh Africa is he larges marke in Africa and i is siuaed a he corner of Maude Sree and Gwen Lane in Sandon, Johannesburg, Souh Africa. In 990, his marke conribued a oal of.9% of he GDP of he counry and i archived marke capializaion of US$8 billion a he end of 99. Recenly, he marke capializaion of he Souh Africa has increased up o US$00 billion and presenly his marke is he 6h larges sock exchange worldwide. Therefore, due o he imporan role played by his marke, i is worhwhile o reexamine he exisence of Monday effec in he sock markes of Souh Africa and also is neighbouring counries. Tradiionally, he esing procedure for he Monday effec or dayofheweek effec on he sock reurns usually involves regression model wih dummy variables. This radiional model has been replaced wih he symmerical GARCH model due o few limiaions of he model. The radiional regression model wih dummy variables assumed he residual erm is consan hrough he ime period. The regression model also fails o capure he imevarying volailiy in he reurn series. To overcome he remaining ARCH effecs in he model, few sudies sared o employ he ARCH/GARCH family o examine he Monday or dayofheweek effec. Laer, Nelson (99) developed asymmeric Exponenial GARCH or EGARCH model which is able o capure asymmeric effec. Since Engle and Ng (99) observed ha marke reacion on bad and good news ends o be asymmeric in naure, he use of symmerical GARCH model in esing marke anomalies is inadequae because he GARCH model is unable o capure asymmeric effec. Therefore, o incorporae he possible asymmery effec of sock marke behavior, he asymmeric Exponenial GARCH or EGARCH model is a beer approach o examine he Monday or dayofheweek effec and is volailiy. In he speculaion of Monday effec or dayofheweek effec, he usual approach is o es he null hypohesis ha all he mean daily reurns are equal. If his null hypohesis is rejeced, he common pracice is o look a he values of he saisics for See, Appendix for deails. Among he few, Alexakis and Xanhaki (99), Chia e al. (008) and Chia and Liew (00) are able o find evidence of he asymmeric behavior in dayofheweek effec in he sock markes.
3 Economics Bullein, 0, Vol. No. pp. 7 he regression coefficiens. If he saisic of he coefficien for a given day is significan, hen his would indicae ha he mean reurns of ha given day and Monday are differen. However, his esing procedures suffer from he socalled mulipliciy effec as illusraed by Greensone and Oyer (000) and Al e al. (0). In paricular, he radiional way of esing each null hypohesis a he significance level may lead o an inflaed occurrence of muliple ype error. Greensone and Oyer (000) have suggesed using Bonferroni procedure o overcome he problem. However, Al e al. (0) proposed an alernaive approach called closure es principle which is inroduced by Marcus e al. (976) o replace he Bonferroni procedures for examine he Monday effec or dayofheweek effec. This closed es procedure is differen from he radiional approaches in he sense ha he null hypoheses are esed in such a way ha he probabiliy of commiing ype error is always kep smaller han or equal o a given significan level for each combinaion of rue null hypoheses. Referring o he Mone Carlo sudy of Al e al. (0), he power of he closed Fes is greaer and i is superior o he Bonferroni procedure. Besides, he closed es principle and is assumpions are well explained in he sudy of Al e al. (0). Therefore, his sudy aemps o fill in he gap in he lieraure of marke anomalies in Souh Africa and is neighbouring counries by using he closure es principle in examining he dayofheweek effec. Besides, his sudy also differeniaes iself from ohers as i allows for condiional imechanging variance by using he asymmeric EGARCH Mean model. Overall, his sudy considers sock marke volailiy and is asymmeric behavior which is more applicable in sock markes, wih conrol on he muliple ype error. The paper is organized as follows, Secion saes he daa and Secion discusses he empirical mehodology. Secion presens he resuls and he las secion concludes he paper.. Daa The daa of his sudy consiss of he daily closing values of he Emerging Markes (Egyp, Morocco and Souh Africa) and Fronier Markes (Kenya, Mauriius, Nigeria and Tunisia), over he period s June 00 o 8 h Augus 0. The daily reurns are calculaed as he firs difference in he naural logarihms of he sock marke index R 00 ln( I / I ) () where I and I are he values for each index for periods and, respecively. In he case of a day following a nonrading day, he reurn is calculaed using he closing price indices of he laes rading day.
4 Economics Bullein, 0, Vol. No. pp. 7. Empirical Mehod This sudy iniially employs he following EGARCH Mean specificaion on he condiional volailiy o es he daily seasonaliy in sock marke R + iδ i + 6R + 7σ ε () i + p q ξ i ξ i logσ β j logσ j i i + βiδ i () j i σ i σ i i In Equaion (), or known as reurn equaion, R is he logarihmic reurn of he marke index a day ; R is he logarihmic reurn of he marke index a day i; δ, δ δ i, and δ are dummy variables which ake on he value if he corresponding reurn for day is a Tuesday, Wednesday, Thursday and Friday respecively and 0 oherwise. Meanwhile,,,..., 7 are he parameers o be esimaed in Equaion (). Among hem, measures he mean reurn (in percenage) on Monday; whereas,..., capure he difference of average reurn of he sock index for Tuesday, Wednesday, Thursday and Friday respecively as compared o he Monday s mean reurn. A lagged value of he reurn variable wih is coefficien, 6, is inroduced in Equaion () o avoid serial correlaion error erms in he model, which may yield misleading inferences. In he equaion, Monday dummy variable is excluded o avoid dummy variable rap. Besides, 7 measures he reward o risk raio, ε is he error erm wih zero mean and condiional variance σ. Equaion () is he variance equaion where lefhand side of he equaion is he logarihm of he condiional variance. This implies ha he leverage effec is exponenial, raher han quadraic, and ha forecass of he condiional variance are guaraneed o be nonnegaive. In his case, he presence of leverage effecs can be esed by he hypohesis ha > 0, whereas he impac is asymmeric if 0. i Referring o our case, he closure principle is applied o he mean equaion and variance equaion and i works as follows. Apar from he global and primary null hypoheses, we have o add all possible inersecions ino he se of hypoheses. The complee se of hypoheses is lised in Table as below. i 6
5 Economics Bullein, 0, Vol. No. pp. 7 Table All primary, inersecion and global hypoheses Primary Inersecion Global H H 0 i H 0 ij H 0 ijk Source Al e al. (0) 0 The closed es procedure sared wih he esing for he global null hypohesis. The closure principle saes ha if he global null hypohesis canno be rejeced, hen none of he null hypoheses saing pairwise equaliy can be rejeced. On he oher hand, if he global null hypohesis is rejeced, hen here is a need o check all he primary null hypoheses, H 0 i, and he corresponding ses of inersecion hypoheses, H 0 ij and H 0 ijk. An adjused pvalue for H 0 i is hen inroduced, which is defined as he maximum of all p values corresponding o all he hypoheses conained in he given primary hypohesis. A given primary hypohesis is rejeced if he adjused pvalue is smaller han 0%.. Empirical Resuls and Discussions A number of poins emerge from he analysis of descripive saisics. Firs, i shows he endency of he lowes mean reurn in he Nigeria marke and he highes mean reurn in Egyp marke. Follow by he mean reurn, he descripive saisics provide he sandard deviaion (Sd. Dev.), skewness and kurosis for respecive sock markes. In addiion, JarqueBera normaliy es saisics, ogeher wih is corresponding pvalue are also presened in Table. From he descripive saisics, we are able o observe he naure of he volailiy and he disribuion of he reurns. In finance, sandard deviaion is a represenaion of he risk associaed wih sock price variaion. The sock marke of Souh Africa has he highes value of sandard deviaion among ohers, follow by Egyp sock marke. This simply means ha invesing in hese wo markes has a higher risk han ohers. Moreover, he null hypohesis of he JarqueBera normaliy es is rejeced implies ha daily reurns are no normally disribued. Table Descripive Saisics for Daily Reurns Egyp Morocco Souh Kenya Mauriius Nigeria Tunisia Africa Mean Sd. Dev Skewness Kurosis JarqueBera Probabiliy
6 Economics Bullein, 0, Vol. No. pp. 7 The resuls of he mean reurns and variance equaions of he asymmeric Exponenial GARCH Mean (EGARCH M) model for he dayofheweek effec are presened in Table. The esimaed value of 7 shows negaive risk premium in Egyp, Morocco, Nigeria and Tunisia sock markes, bu only Egyp marke has a significan negaive risk premium. On he oher hand, he risk premium is posiive in Souh Africa, Kenya and Mauriius sock markes. The leverage effec erms, i, are all saisically differen from zero. Thus, his amouns o he evidence of asymmerical marke reacions owards posiive and negaive news, which reflecs he presence of asymmerical sock marke in his sudy. Moreover, he diagnosic es resuls show ha here is no remaining ARCH effec in all he esimaed EGARCH M models. Table Esimaed Exponenial GARCH Mean Resuls EGARCH Mean Resuls For DayofheWeek Effec (Reurn Equaion) Parameer Egyp Morocco Souh Africa ( p, q ) (, ) (, ) (, ) Consan, (0.6) (0.07)*** (0.0079)* (0.869) (0.0)** (0.00)* (0.688) (0.) (0.0)*** (0.6) (0.8786) Friday, (0.960) (0.6) Reurn (), 6 (0.88) (0.06)** (0.0)** (0.778) (0.907) EGARCH Mean Resuls For DayofheWeek Effec (Variance Equaion) Parameer Egyp Morocco Souh Africa Consan, β (0.96) (0.) (0.089)*** (0.00)* (0.60) (0.000) Tuesday, β (0.0) (0.60) Wednesday, β (0.86) (0.008)** Tuesday, Wednesday, Thursday, Thursday, β (0.6) 0.0 (0.90) 8
7 Economics Bullein, 0, Vol. No. pp Friday, β (0.6) (0.00)* EGARCH Mean Resuls For DayofheWeek Effec (Diagnosic Checking) ARCH LM Saisic (pvalue) Lags Lags LjungBox Q Saisic (pvalue) Lags Lags Noe *, ** and *** denoe significance a, and 0% levels respecively. Numbers in parenheses depic pvalue. The highes order of p and q considered in his sudy is. The selecion of appropriae orders of p and q in his sudy o be deermined by he smalles Schwarz Informaion Crierion (SIC) (Clare e al., 998). Table Esimaed Exponenial GARCH Mean Resuls (Con.) EGARCH Mean Resuls For DayofheWeek Effec (Reurn Equaion) Parameer Kenya Mauriius Nigeria Tunisia ( p, q ) (, ) (, ) (, ) (, ) Consan, (0.99) (0.) (0.7) (0.0) Tuesday, (0.706) (0.9880) (0.79) (0.77) Wednesday, (0.77) (0.960) (0.7) (0.60) Thursday, (0.) (0.000)** (0.8) (0.08)** Friday, (0.6) (0.867) (0.0008)* (0.0009)* Reurn (), 6 (0.079)*** (0.888) (0.9) (0.) (0.06) EGARCH Mean Resuls For DayofheWeek Effec (Variance Equaion) Parameer Kenya Mauriius Nigeria Tunisia Consan, β (0.009)** (0.09)*** (0.999) (0.0067)* (0.0) (0.67) (0.7) (0.009)* (0.76) (0.098)** (0.8) (0.0076)* (0.00)** (0.66) (0.0)** (0.608) (0.60) (0.000)* (0.0008)* 9
8 Economics Bullein, 0, Vol. No. pp Tuesday, β (0.096) (0.7) (0.00)* (0.69) Wednesday, β (0.069)** (0.8) (0.07)** (0.9) Thursday, β (0.6) (0.97) (0.000)** (0.86) Friday, β (0.66) (0.) (0.8) (0.8077) EGARCH Mean Resuls For DayofheWeek Effec (Diagnosic Checking) ARCH LM Saisic (pvalue) Lags Lags LjungBox Q Saisic (pvalue) Lags Lags Noe *, ** and *** denoe significance a, and 0% levels respecively. Numbers in parenheses depic pvalue. The highes order of p and q considered in his sudy is. The selecion of appropriae orders of p and q in his sudy o be deermined by he smalles Schwarz Informaion Crierion (SIC) (Clare e al., 998). For all he markes in his sudy, he closure es principle is applied. For he mean equaion, pvalues for hypoheses of pairwise equaliy of Monday reurns wih oher day of he week reurns are presened in Table. For each primary hypohesis, he firs value is he adjused pvalue, while he oher values are radiional pvalues. For example, in esing he hypoheses of Monday reurns are equal o Tuesday reurns (MONTUE) in Egyp sock marke, he adjused pvalue is 0.096, which is he maximum of he p values for all he hypoheses of H 0. Then, he adjused pvalues for all he hypoheses H 0 i are summarized in Table. In deermining Monday effec from he closed es resuls, we followed he definiion of Monday effec as defined by Al e al. (0). The erm weak Monday effec is used when Monday reurns are saisically differen from a leas one oher day of he week, while srong Monday effec refers o he case where Monday reurns are saisically differen from all oher days of he week. Referring o Table, Egyp is he only counry ha has a srong Monday effec as he Monday reurns differ from all oher weekdays. Besides ha, hree ou of four of he Fronier Markes (Mauriius, Nigeria and Tunisia) have a weak Monday effec. 0
9 Economics Bullein, 0, Vol. No. pp. 7 Table Adjused pvalue and radiional pvalues for he complee se of hypoheses of EGARCHMean mean equaion Egyp Morocco Souh Africa Kenya Mauriius Nigeria Tunisia MON TUE Adjused H 0 pvalue Tradiional H pvalues H Adjused pvalue Tradiional pvalues Adjused pvalue Tradiional pvalues Adjused pvalue Tradiional pvalues H H H H H H MON WED H H H H H H H H H MON THU H H H H H H H H H MON FRI H H H H H H H H H
10 Economics Bullein, 0, Vol. No. pp. 7 Table Closed FTes resuls (Mean Equaion) Primary hypohesis MONTUE MON WED MON TUE MON FRI (adjused pvalue) (adjused pvalue) (adjused pvalue) (adjused pvalue) Egyp 0.096** 0.09** 0.0*** 0.000* Morocco Souh Africa Kenya Mauriius *** Nigeria * Tunisia ** 0.008* *, ** and *** indicae significance a he %, % and 0% level. If he Monday reurn is differen from all oher days of he week, i can be concluded as srong Monday effec. If he Monday reurn is differen from a leas one oher day of he week, i can be concluded as weak Monday effec. Table 6 and Table 7 presen he closure es resuls for he variance equaion of he EGARCHMean model. In a similar manner, we deermine he adjused pvalues for he complee se of hypoheses for he variance equaion. In he following, we will use he erm weak Monday volailiy when Monday volailiy is disinguishable from a leas one oher days of he week. On he oher hand, if Monday volailiy is saisically differen from all oher days of he week, hen his can be concluded as srong Monday volailiy. Referring o Table 7, Egyp has a srong Monday volailiy while Kenya and Nigeria have a weak Monday volailiy. No significan resul is found for he oher counries.
11 Economics Bullein, 0, Vol. No. pp. 7 Table 6 Adjused pvalue and radiional pvalues for he complee se of hypoheses of EGARCHMean variance equaion Egyp Morocco Souh Africa Kenya Mauriius Nigeria Tunisia MON TUE Adjused H 0 β pvalue Tradiional H 0 β pvalues H β β Adjused pvalue Tradiional pvalues Adjused pvalue Tradiional pvalues Adjused pvalue Tradiional pvalues 0 0 β β 0 β β 0 β β β 0 β β β 0 β β β 0 β β β β H H H H H H MON WED H β H β β β 0 β β 0 β β 0 β β β 0 β β β 0 β β β 0 β β β β H H H H H H H MON THU H β H β β β 0 β β 0 β β 0 β β β 0 β β β 0 β β β 0 β β β β H H H H H H H MON FRI H β H β β β 0 β β 0 β β 0 β β β 0 β β β 0 β β β 0 β β β β H H H H H H H
12 Economics Bullein, 0, Vol. No. pp. 7 Table 8 Closed FTes resuls (Variance Equaion) Primary hypohesis MONTUE MON WED MON TUE MON FRI (adjused pvalue) (adjused pvalue) (adjused pvalue) (adjused pvalue) Egyp * * * * Morocco Souh Africa Kenya ** Mauriius Nigeria 0.068** 0.08** 0.0** 0.86 Tunisia *, ** and *** indicae significance a he %, % and 0% level. If he Monday volailiy is differen from all oher days of he week, i can be concluded as srong Monday volailiy. If he Monday volailiy is differen from a leas one oher day of he week, i can be concluded as weak Monday volailiy.. Conclusion This sudy examined he exisence of a daily paern of dayofheweek effec in he Souh Africa and is neighbouring counries sock markes by using he asymmeric Exponenial GARCH Mean model wih closure es principle which proposed by Al e al. (0). Generally, Egyp is he only counry ha has a srong Monday effec as he Monday reurns differ from all oher weekdays. Besides, weak Monday effec is found in Mauriius, Nigeria and Tunisia sock markes. When he imevarying volailiy in he marke reurns is aken ino accoun by he EGARCH M model, srong Monday volailiy is found in Egyp while Kenya and Nigeria is found o have weak Monday volailiy. However, here is no significan Monday volailiy is found in he oher markes. Lasly, he leverage effec erms are all significan, which reflecs he presence of asymmerical behavior in hese markes.
13 Economics Bullein, 0, Vol. No. pp. 7 References Alexakis, P. and Xanhakis, M. (99) Day of he week effec on he Greek sock marke Applied Financial Economics,, 0. Al, R., Forin, I. and Weinberger, S. (0) The Monday effec revisied An alernaive esing approach Journal of Empirical Finance, 8, Apolinario, R. M. C., Sanana, O. M., Sales, L. J. and Caro, A. R. (006) Day of he week effec on European sock markes Inernaional Research Journal of Finance and Economics,, 70. Arsad, Z. and Cous, J. A. (996) The weekend effec, good news, bad news and he Financial Times Indusrial Ordinary Shares Index 9 9 Applied Economics Leers,, Brooks, C. and Persand, G. (00) Seasonaliy in Souheas Asian sock markes some new evidence on Dayofheweek effecs Applied Economics Leers, 8, 8. Chia, R. C. J., Liew, V. K. S. and Syed Azizi Wafa S. K. W. (008) Dayofheweek effecs in Seleced Eas Asian sock markes Economics Bullein, 7, 8. Chia, R. C. J. and Liew, V. K. S. (00) Evidence on he Dayofheweek Effec and Asymmeric Behavior in he Bombay Sock Exchange The IUP Journal of Applied Finance, 6, 7 9. Clare, A. D., Ibrahim, M. S. B. and Thomas, S. H. (998) The impac of selemen procedures on Dayofheweek effecs Evidence from he Kuala Lumpur sock exchange Journal of Business Finance and Accouning,, 0 8. Cross, F. (97) The behavior of sock prices on Fridays and Mondays Financial Analyss Journal, 9, Engle, R. F. and Ng, V. K. (99) Measuring and Tesing he impac of News on volailiy Journal of Finance, 8, Field, M. J. (9) Sock prices a problem in verificaion Journal of Business, 7, 8. French, K. R. (980) Sock reurns and he weekend effec Journal of Financial Economics, 8, 70. Gibbons, M. R. and Hess, P. (98) Day of he week effec and Asses Reurns The Journal of Business,,
14 Economics Bullein, 0, Vol. No. pp. 7 Greensone, M. and Oyer, P. (000) Are here secoral anomalies oo? The pifalls of unrepored muliple hypohesis esing and a simple soluion Review of Quaniaive Finance and Accouning,, 7. Jaffe, J. and Weserfield, R. (98) The weekend effec in common sock reurns The inernaional evidence Journal of Finance, 0,. Keim, D. B. and Sambaugh, R. F. (98) A furher invesigaion of he weekend effec in sock reurns Journal of Finance, 9, Lucey, B. M. (000) Anomalous Daily Seasonaliy in Ireland? Applied Economics Leers, 7, Marcus, R., Periz, E. and Gabriel, K. R. (976) On closed esing procedures wih special reference o ordered analysis of variance Biomerika, 6, Nelson, D. B. (99) Condiional Heeroskedasiciy in Asse Reurns A new approach Economerica, 9, Wong, K. A., Hui, T. K. and Chan, C. Y. (99) Dayofheweek effec Evidence from developing sock markes Applied Financial Economics,,
15 Economics Bullein, 0, Vol. No. pp. 7 Appendix Marke capializaion of lised companies (% of GDP) Counry Name Counry Code Egyp, Arab Rep. EGY Morocco MAR Souh Africa ZAF Kenya KEN Mauriius MUS Nigeria NGA Tunisia TUN Toal Unied Saes USA Marke capializaion of lised companies (curren US$) Egyp, Arab Rep. EGY.76E E+09.87E E+0 8.E+0 Morocco MAR 9.66E+08.9E+09.09E+0.7E+0 6.9E+0 Souh Africa ZAF.8E+.8E+.0E+.6E+.0E+ Kenya KEN.E+08.89E+09.8E E+09.E+0 Mauriius MUS.68E+08.E+09.E+09.6E+09 6.E+09 Nigeria NGA.7E+09.0E+09.E+09.9E+0.09E+0 Tunisia TUN.E+08.9E+09.8E+09.88E+09.07E+0 Toal.E+.0E+.E+ 7.0E+.E+ Unied Saes USA.06E+ 6.86E+.E+.70E+.7E+ Source The World Bank World Developmen Indicaors. 7
DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND
DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND Asymmery and Leverage in Condiional Volailiy Models Michael McAleer WORKING PAPER
More informationAsymmetry and Leverage in Conditional Volatility Models*
Asymmery and Leverage in Condiional Volailiy Models* Micael McAleer Deparmen of Quaniaive Finance Naional Tsing Hua Universiy Taiwan and Economeric Insiue Erasmus Scool of Economics Erasmus Universiy Roerdam
More informationSolutions to Odd Number Exercises in Chapter 6
1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b
More informationTourism forecasting using conditional volatility models
Tourism forecasing using condiional volailiy models ABSTRACT Condiional volailiy models are used in ourism demand sudies o model he effecs of shocks on demand volailiy, which arise from changes in poliical,
More informationRegression with Time Series Data
Regression wih Time Series Daa y = β 0 + β 1 x 1 +...+ β k x k + u Serial Correlaion and Heeroskedasiciy Time Series - Serial Correlaion and Heeroskedasiciy 1 Serially Correlaed Errors: Consequences Wih
More informationChapter 5. Heterocedastic Models. Introduction to time series (2008) 1
Chaper 5 Heerocedasic Models Inroducion o ime series (2008) 1 Chaper 5. Conens. 5.1. The ARCH model. 5.2. The GARCH model. 5.3. The exponenial GARCH model. 5.4. The CHARMA model. 5.5. Random coefficien
More informationGranger Causality Among Pre-Crisis East Asian Exchange Rates. (Running Title: Granger Causality Among Pre-Crisis East Asian Exchange Rates)
Granger Causaliy Among PreCrisis Eas Asian Exchange Raes (Running Tile: Granger Causaliy Among PreCrisis Eas Asian Exchange Raes) Joseph D. ALBA and Donghyun PARK *, School of Humaniies and Social Sciences
More informationVectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1
Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies
More informationTime series Decomposition method
Time series Decomposiion mehod A ime series is described using a mulifacor model such as = f (rend, cyclical, seasonal, error) = f (T, C, S, e) Long- Iner-mediaed Seasonal Irregular erm erm effec, effec,
More informationDEPARTMENT OF STATISTICS
A Tes for Mulivariae ARCH Effecs R. Sco Hacker and Abdulnasser Haemi-J 004: DEPARTMENT OF STATISTICS S-0 07 LUND SWEDEN A Tes for Mulivariae ARCH Effecs R. Sco Hacker Jönköping Inernaional Business School
More informationTesting for a Single Factor Model in the Multivariate State Space Framework
esing for a Single Facor Model in he Mulivariae Sae Space Framework Chen C.-Y. M. Chiba and M. Kobayashi Inernaional Graduae School of Social Sciences Yokohama Naional Universiy Japan Faculy of Economics
More informationLinden, Mikael Louhelainen, Mika
Tesing for weekday anomaly in inernaional sock index reurns wih non-normal errors Linden, Mikael Louhelainen, Mika ISBN 95-458-44-5 ISSN 458-686X no 4 Tesing for Weekday Anomaly in Inernaional Sock Index
More informationBias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé
Bias in Condiional and Uncondiional Fixed Effecs Logi Esimaion: a Correcion * Tom Coupé Economics Educaion and Research Consorium, Naional Universiy of Kyiv Mohyla Academy Address: Vul Voloska 10, 04070
More informationLicenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A
Licenciaura de ADE y Licenciaura conjuna Derecho y ADE Hoja de ejercicios PARTE A 1. Consider he following models Δy = 0.8 + ε (1 + 0.8L) Δ 1 y = ε where ε and ε are independen whie noise processes. In
More informationAsymmetry and Leverage in Conditional Volatility Models
Economerics 04,, 45-50; doi:0.3390/economerics03045 OPEN ACCESS economerics ISSN 5-46 www.mdpi.com/journal/economerics Aricle Asymmery and Leverage in Condiional Volailiy Models Micael McAleer,,3,4 Deparmen
More informationFinancial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS
Name SOLUTIONS Financial Economerics Jeffrey R. Russell Miderm Winer 009 SOLUTIONS You have 80 minues o complee he exam. Use can use a calculaor and noes. Try o fi all your work in he space provided. If
More informationChapter 15. Time Series: Descriptive Analyses, Models, and Forecasting
Chaper 15 Time Series: Descripive Analyses, Models, and Forecasing Descripive Analysis: Index Numbers Index Number a number ha measures he change in a variable over ime relaive o he value of he variable
More informationACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin
ACE 56 Fall 005 Lecure 4: Simple Linear Regression Model: Specificaion and Esimaion by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Simple Regression: Economic and Saisical Model
More informationRobust estimation based on the first- and third-moment restrictions of the power transformation model
h Inernaional Congress on Modelling and Simulaion, Adelaide, Ausralia, 6 December 3 www.mssanz.org.au/modsim3 Robus esimaion based on he firs- and hird-momen resricions of he power ransformaion Nawaa,
More informationA Specification Test for Linear Dynamic Stochastic General Equilibrium Models
Journal of Saisical and Economeric Mehods, vol.1, no.2, 2012, 65-70 ISSN: 2241-0384 (prin), 2241-0376 (online) Scienpress Ld, 2012 A Specificaion Tes for Linear Dynamic Sochasic General Equilibrium Models
More informationComparing Means: t-tests for One Sample & Two Related Samples
Comparing Means: -Tess for One Sample & Two Relaed Samples Using he z-tes: Assumpions -Tess for One Sample & Two Relaed Samples The z-es (of a sample mean agains a populaion mean) is based on he assumpion
More informationForecasting optimally
I) ile: Forecas Evaluaion II) Conens: Evaluaing forecass, properies of opimal forecass, esing properies of opimal forecass, saisical comparison of forecas accuracy III) Documenaion: - Diebold, Francis
More informationDepartment of Economics East Carolina University Greenville, NC Phone: Fax:
March 3, 999 Time Series Evidence on Wheher Adjusmen o Long-Run Equilibrium is Asymmeric Philip Rohman Eas Carolina Universiy Absrac The Enders and Granger (998) uni-roo es agains saionary alernaives wih
More informationModeling and Forecasting Volatility Autoregressive Conditional Heteroskedasticity Models. Economic Forecasting Anthony Tay Slide 1
Modeling and Forecasing Volailiy Auoregressive Condiional Heeroskedasiciy Models Anhony Tay Slide 1 smpl @all line(m) sii dl_sii S TII D L _ S TII 4,000. 3,000.1.0,000 -.1 1,000 -. 0 86 88 90 9 94 96 98
More informationVehicle Arrival Models : Headway
Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where
More informationHow to Deal with Structural Breaks in Practical Cointegration Analysis
How o Deal wih Srucural Breaks in Pracical Coinegraion Analysis Roselyne Joyeux * School of Economic and Financial Sudies Macquarie Universiy December 00 ABSTRACT In his noe we consider he reamen of srucural
More informationExponential Weighted Moving Average (EWMA) Chart Under The Assumption of Moderateness And Its 3 Control Limits
DOI: 0.545/mjis.07.5009 Exponenial Weighed Moving Average (EWMA) Char Under The Assumpion of Moderaeness And Is 3 Conrol Limis KALPESH S TAILOR Assisan Professor, Deparmen of Saisics, M. K. Bhavnagar Universiy,
More informationHypothesis Testing in the Classical Normal Linear Regression Model. 1. Components of Hypothesis Tests
ECONOMICS 35* -- NOTE 8 M.G. Abbo ECON 35* -- NOTE 8 Hypohesis Tesing in he Classical Normal Linear Regression Model. Componens of Hypohesis Tess. A esable hypohesis, which consiss of wo pars: Par : a
More informationMean Reversion of Balance of Payments GEvidence from Sequential Trend Break Unit Root Tests. Abstract
Mean Reversion of Balance of Paymens GEvidence from Sequenial Trend Brea Uni Roo Tess Mei-Yin Lin Deparmen of Economics, Shih Hsin Universiy Jue-Shyan Wang Deparmen of Public Finance, Naional Chengchi
More informationTime Series Test of Nonlinear Convergence and Transitional Dynamics. Terence Tai-Leung Chong
Time Series Tes of Nonlinear Convergence and Transiional Dynamics Terence Tai-Leung Chong Deparmen of Economics, The Chinese Universiy of Hong Kong Melvin J. Hinich Signal and Informaion Sciences Laboraory
More informationCALENDAR ANOMALIES AND CAPITAL MARKET EFFICIENCY: LISTED PROPERTY TRUST INVESTMENT STRATEGIES
CALENDAR ANOMALIES AND CAPITAL MARKET EFFICIENCY: LISTED PROPERTY TRUST INVESTMENT STRATEGIES ABSTRACT VINCENT PENG Universiy of Wesern Sydney This sudy focuses on he efficien marke hypohesis (EMH) and
More informationR t. C t P t. + u t. C t = αp t + βr t + v t. + β + w t
Exercise 7 C P = α + β R P + u C = αp + βr + v (a) (b) C R = α P R + β + w (c) Assumpions abou he disurbances u, v, w : Classical assumions on he disurbance of one of he equaions, eg. on (b): E(v v s P,
More informationECON 482 / WH Hong Time Series Data Analysis 1. The Nature of Time Series Data. Example of time series data (inflation and unemployment rates)
ECON 48 / WH Hong Time Series Daa Analysis. The Naure of Time Series Daa Example of ime series daa (inflaion and unemploymen raes) ECON 48 / WH Hong Time Series Daa Analysis The naure of ime series daa
More informationACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.
ACE 564 Spring 2006 Lecure 7 Exensions of The Muliple Regression Model: Dumm Independen Variables b Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Dumm Variables and Varing Coefficien Models
More informationOn Measuring Pro-Poor Growth. 1. On Various Ways of Measuring Pro-Poor Growth: A Short Review of the Literature
On Measuring Pro-Poor Growh 1. On Various Ways of Measuring Pro-Poor Growh: A Shor eview of he Lieraure During he pas en years or so here have been various suggesions concerning he way one should check
More informationSTRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN
Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.1-3(004) STRUCTURAL CHANGE IN TIME SERIES OF THE EXCHANGE RATES BETWEEN YEN-DOLLAR AND YEN-EURO IN 001-004 OBARA, Takashi * Absrac The
More informationTypes of Exponential Smoothing Methods. Simple Exponential Smoothing. Simple Exponential Smoothing
M Business Forecasing Mehods Exponenial moohing Mehods ecurer : Dr Iris Yeung Room No : P79 Tel No : 788 8 Types of Exponenial moohing Mehods imple Exponenial moohing Double Exponenial moohing Brown s
More informationInnova Junior College H2 Mathematics JC2 Preliminary Examinations Paper 2 Solutions 0 (*)
Soluion 3 x 4x3 x 3 x 0 4x3 x 4x3 x 4x3 x 4x3 x x 3x 3 4x3 x Innova Junior College H Mahemaics JC Preliminary Examinaions Paper Soluions 3x 3 4x 3x 0 4x 3 4x 3 0 (*) 0 0 + + + - 3 3 4 3 3 3 3 Hence x or
More information20. Applications of the Genetic-Drift Model
0. Applicaions of he Geneic-Drif Model 1) Deermining he probabiliy of forming any paricular combinaion of genoypes in he nex generaion: Example: If he parenal allele frequencies are p 0 = 0.35 and q 0
More informationEcon107 Applied Econometrics Topic 7: Multicollinearity (Studenmund, Chapter 8)
I. Definiions and Problems A. Perfec Mulicollineariy Econ7 Applied Economerics Topic 7: Mulicollineariy (Sudenmund, Chaper 8) Definiion: Perfec mulicollineariy exiss in a following K-variable regression
More informationIntroduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.
Inroducion Gordon Model (1962): D P = r g r = consan discoun rae, g = consan dividend growh rae. If raional expecaions of fuure discoun raes and dividend growh vary over ime, so should he D/P raio. Since
More information2017 3rd International Conference on E-commerce and Contemporary Economic Development (ECED 2017) ISBN:
7 3rd Inernaional Conference on E-commerce and Conemporary Economic Developmen (ECED 7) ISBN: 978--6595-446- Fuures Arbirage of Differen Varieies and based on he Coinegraion Which is under he Framework
More informationSolutions: Wednesday, November 14
Amhers College Deparmen of Economics Economics 360 Fall 2012 Soluions: Wednesday, November 14 Judicial Daa: Cross secion daa of judicial and economic saisics for he fify saes in 2000. JudExp CrimesAll
More information1. Diagnostic (Misspeci cation) Tests: Testing the Assumptions
Business School, Brunel Universiy MSc. EC5501/5509 Modelling Financial Decisions and Markes/Inroducion o Quaniaive Mehods Prof. Menelaos Karanasos (Room SS269, el. 01895265284) Lecure Noes 6 1. Diagnosic
More informationMethodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size.
Mehodology. Uni Roo Tess A ime series is inegraed when i has a mean revering propery and a finie variance. I is only emporarily ou of equilibrium and is called saionary in I(0). However a ime series ha
More informationOBJECTIVES OF TIME SERIES ANALYSIS
OBJECTIVES OF TIME SERIES ANALYSIS Undersanding he dynamic or imedependen srucure of he observaions of a single series (univariae analysis) Forecasing of fuure observaions Asceraining he leading, lagging
More informationVolatility. Many economic series, and most financial series, display conditional volatility
Volailiy Many economic series, and mos financial series, display condiional volailiy The condiional variance changes over ime There are periods of high volailiy When large changes frequenly occur And periods
More informationPolitical Elections and Stock Price Volatility: The Case of Greece. Str., Chios, Greece. Thessaloniki, Greece.
Poliical Elecions and Sock Price Volailiy: The Case of Greece Ahanasios Koulakiois a, Aposolos Dasilas b and Konsaninos Tolikas c a Universiy of he Aegean, Deparmen of Financial and Managemen Engineering,
More informationESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING
Inernaional Journal of Social Science and Economic Research Volume:02 Issue:0 ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING Chung-ki Min Professor
More informationModeling the Volatility of Shanghai Composite Index
Modeling he Volailiy of Shanghai Composie Index wih GARCH Family Models Auhor: Yuchen Du Supervisor: Changli He Essay in Saisics, Advanced Level Dalarna Universiy Sweden Modeling he volailiy of Shanghai
More informationA Dynamic Model of Economic Fluctuations
CHAPTER 15 A Dynamic Model of Economic Flucuaions Modified for ECON 2204 by Bob Murphy 2016 Worh Publishers, all righs reserved IN THIS CHAPTER, OU WILL LEARN: how o incorporae dynamics ino he AD-AS model
More informationRobust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation
WORKING PAPER 01: Robus criical values for uni roo ess for series wih condiional heeroscedasiciy errors: An applicaion of he simple NoVaS ransformaion Panagiois Manalos ECONOMETRICS AND STATISTICS ISSN
More informationA unit root test based on smooth transitions and nonlinear adjustment
MPRA Munich Personal RePEc Archive A uni roo es based on smooh ransiions and nonlinear adjusmen Aycan Hepsag Isanbul Universiy 5 Ocober 2017 Online a hps://mpra.ub.uni-muenchen.de/81788/ MPRA Paper No.
More informationChoice of Spectral Density Estimator in Ng-Perron Test: A Comparative Analysis
Inernaional Economeric Review (IER) Choice of Specral Densiy Esimaor in Ng-Perron Tes: A Comparaive Analysis Muhammad Irfan Malik and Aiq-ur-Rehman Inernaional Islamic Universiy Islamabad and Inernaional
More informationReading from Young & Freedman: For this topic, read sections 25.4 & 25.5, the introduction to chapter 26 and sections 26.1 to 26.2 & 26.4.
PHY1 Elecriciy Topic 7 (Lecures 1 & 11) Elecric Circuis n his opic, we will cover: 1) Elecromoive Force (EMF) ) Series and parallel resisor combinaions 3) Kirchhoff s rules for circuis 4) Time dependence
More informationExponentially Weighted Moving Average (EWMA) Chart Based on Six Delta Initiatives
hps://doi.org/0.545/mjis.08.600 Exponenially Weighed Moving Average (EWMA) Char Based on Six Dela Iniiaives KALPESH S. TAILOR Deparmen of Saisics, M. K. Bhavnagar Universiy, Bhavnagar-36400 E-mail: kalpesh_lr@yahoo.co.in
More informationDiebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles
Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance
More informationLinear Combinations of Volatility Forecasts for the WIG20 and Polish Exchange Rates
Eliza Buszkowska Universiy of Poznań, Poland Linear Combinaions of Volailiy Forecass for he WIG0 and Polish Exchange Raes Absrak. As is known forecas combinaions may be beer forecass hen forecass obained
More informationA New Unit Root Test against Asymmetric ESTAR Nonlinearity with Smooth Breaks
Iran. Econ. Rev. Vol., No., 08. pp. 5-6 A New Uni Roo es agains Asymmeric ESAR Nonlineariy wih Smooh Breaks Omid Ranjbar*, sangyao Chang, Zahra (Mila) Elmi 3, Chien-Chiang Lee 4 Received: December 7, 06
More informationT. J. HOLMES AND T. J. KEHOE INTERNATIONAL TRADE AND PAYMENTS THEORY FALL 2011 EXAMINATION
ECON 841 T. J. HOLMES AND T. J. KEHOE INTERNATIONAL TRADE AND PAYMENTS THEORY FALL 211 EXAMINATION This exam has wo pars. Each par has wo quesions. Please answer one of he wo quesions in each par for a
More informationProblem Set 5. Graduate Macro II, Spring 2017 The University of Notre Dame Professor Sims
Problem Se 5 Graduae Macro II, Spring 2017 The Universiy of Nore Dame Professor Sims Insrucions: You may consul wih oher members of he class, bu please make sure o urn in your own work. Where applicable,
More informationEcon Autocorrelation. Sanjaya DeSilva
Econ 39 - Auocorrelaion Sanjaya DeSilva Ocober 3, 008 1 Definiion Auocorrelaion (or serial correlaion) occurs when he error erm of one observaion is correlaed wih he error erm of any oher observaion. This
More informationFinancial crises and stock market volatility transmission: evidence from Australia, Singapore, the UK, and the US
Universiy of Wollongong Research Online Faculy of Commerce - Papers (Archive) Faculy of Business 2009 Financial crises and sock marke volailiy ransmission: evidence from Ausralia, Singapore, he UK, and
More informationA New Fama-French 5-Factor Model Based on SSAEPD Error and GARCH-Type Volatility
Journal of Mahemaical Finance, 06, 6, 7-77 hp://www.scirp.org/journal/jmf ISSN Online: 6-44 ISSN Prin: 6-434 A New Fama-French 5-Facor Model Based on SSAEPD Error and GARCH-Type Volailiy Wenao Zhou, Liuling
More informationGMM - Generalized Method of Moments
GMM - Generalized Mehod of Momens Conens GMM esimaion, shor inroducion 2 GMM inuiion: Maching momens 2 3 General overview of GMM esimaion. 3 3. Weighing marix...........................................
More informationOn a Discrete-In-Time Order Level Inventory Model for Items with Random Deterioration
Journal of Agriculure and Life Sciences Vol., No. ; June 4 On a Discree-In-Time Order Level Invenory Model for Iems wih Random Deerioraion Dr Biswaranjan Mandal Associae Professor of Mahemaics Acharya
More informationThe general Solow model
The general Solow model Back o a closed economy In he basic Solow model: no growh in GDP per worker in seady sae This conradics he empirics for he Wesern world (sylized fac #5) In he general Solow model:
More informationComputer Simulates the Effect of Internal Restriction on Residuals in Linear Regression Model with First-order Autoregressive Procedures
MPRA Munich Personal RePEc Archive Compuer Simulaes he Effec of Inernal Resricion on Residuals in Linear Regression Model wih Firs-order Auoregressive Procedures Mei-Yu Lee Deparmen of Applied Finance,
More informationACE 562 Fall Lecture 8: The Simple Linear Regression Model: R 2, Reporting the Results and Prediction. by Professor Scott H.
ACE 56 Fall 5 Lecure 8: The Simple Linear Regression Model: R, Reporing he Resuls and Predicion by Professor Sco H. Irwin Required Readings: Griffihs, Hill and Judge. "Explaining Variaion in he Dependen
More informationSummer Term Albert-Ludwigs-Universität Freiburg Empirische Forschung und Okonometrie. Time Series Analysis
Summer Term 2009 Alber-Ludwigs-Universiä Freiburg Empirische Forschung und Okonomerie Time Series Analysis Classical Time Series Models Time Series Analysis Dr. Sevap Kesel 2 Componens Hourly earnings:
More informationAN APPLICATION OF THE GARCH-t MODEL ON CENTRAL EUROPEAN STOCK RETURNS
AN APPLICATION OF THE GARCH- MODEL ON CENTRAL EUROPEAN STOCK RETURNS Miloslav VOŠVRDA, Filip ŽIKEŠ * Absrac: The purpose of his paper is o invesigae he ime-series and disribuional properies of Cenral European
More informationProperties of Autocorrelated Processes Economics 30331
Properies of Auocorrelaed Processes Economics 3033 Bill Evans Fall 05 Suppose we have ime series daa series labeled as where =,,3, T (he final period) Some examples are he dail closing price of he S&500,
More informationPhysics 127b: Statistical Mechanics. Fokker-Planck Equation. Time Evolution
Physics 7b: Saisical Mechanics Fokker-Planck Equaion The Langevin equaion approach o he evoluion of he velociy disribuion for he Brownian paricle migh leave you uncomforable. A more formal reamen of his
More informationMathematical Theory and Modeling ISSN (Paper) ISSN (Online) Vol 3, No.3, 2013
Mahemaical Theory and Modeling ISSN -580 (Paper) ISSN 5-05 (Online) Vol, No., 0 www.iise.org The ffec of Inverse Transformaion on he Uni Mean and Consan Variance Assumpions of a Muliplicaive rror Model
More informationACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H.
ACE 56 Fall 005 Lecure 5: he Simple Linear Regression Model: Sampling Properies of he Leas Squares Esimaors by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Inference in he Simple
More informationNavneet Saini, Mayank Goyal, Vishal Bansal (2013); Term Project AML310; Indian Institute of Technology Delhi
Creep in Viscoelasic Subsances Numerical mehods o calculae he coefficiens of he Prony equaion using creep es daa and Herediary Inegrals Mehod Navnee Saini, Mayank Goyal, Vishal Bansal (23); Term Projec
More informationLecture 5. Time series: ECM. Bernardina Algieri Department Economics, Statistics and Finance
Lecure 5 Time series: ECM Bernardina Algieri Deparmen Economics, Saisics and Finance Conens Time Series Modelling Coinegraion Error Correcion Model Two Seps, Engle-Granger procedure Error Correcion Model
More informationA Hybrid Model for Improving. Malaysian Gold Forecast Accuracy
In. Journal of Mah. Analysis, Vol. 8, 2014, no. 28, 1377-1387 HIKARI Ld, www.m-hikari.com hp://dx.doi.org/10.12988/ijma.2014.45139 A Hybrid Model for Improving Malaysian Gold Forecas Accuracy Maizah Hura
More informationThe Validity of the Tourism-Led Growth Hypothesis for Thailand
MPRA Munich Personal RePEc Archive The Validiy of he Tourism-Led Growh Hypohesis for Thailand Komain Jiranyakul Naional Insiue of Developmen Adminisraion Augus 206 Online a hps://mpra.ub.uni-muenchen.de/72806/
More information10. State Space Methods
. Sae Space Mehods. Inroducion Sae space modelling was briefly inroduced in chaper. Here more coverage is provided of sae space mehods before some of heir uses in conrol sysem design are covered in he
More informationt is a basis for the solution space to this system, then the matrix having these solutions as columns, t x 1 t, x 2 t,... x n t x 2 t...
Mah 228- Fri Mar 24 5.6 Marix exponenials and linear sysems: The analogy beween firs order sysems of linear differenial equaions (Chaper 5) and scalar linear differenial equaions (Chaper ) is much sronger
More informationTesting the Random Walk Model. i.i.d. ( ) r
he random walk heory saes: esing he Random Walk Model µ ε () np = + np + Momen Condiions where where ε ~ i.i.d he idea here is o es direcly he resricions imposed by momen condiions. lnp lnp µ ( lnp lnp
More informationDynamic relationship between stock return, trading volume, and volatility in the Stock Exchange of Thailand: does the US subprime crisis matter?
MPRA Munich Personal RePEc Archive Dynamic relaionship beween sock reurn, rading volume, and volailiy in he Sock Exchange of Thailand: does he US subprime crisis maer? Komain Jiranyakul Naional Insiue
More informationInstitutional Assessment Report Texas Southern University College of Pharmacy and Health Sciences "P1-Aggregate Analyses of 6 cohorts ( )
Insiuional Assessmen Repor Texas Souhern Universiy College of Pharmacy and Healh Sciences "P1-Aggregae Analyses of 6 cohors (2009-14) The following analysis illusraes relaionships beween PCAT Composie
More informationWednesday, November 7 Handout: Heteroskedasticity
Amhers College Deparmen of Economics Economics 360 Fall 202 Wednesday, November 7 Handou: Heeroskedasiciy Preview Review o Regression Model o Sandard Ordinary Leas Squares (OLS) Premises o Esimaion Procedures
More informationEstimation Uncertainty
Esimaion Uncerainy The sample mean is an esimae of β = E(y +h ) The esimaion error is = + = T h y T b ( ) = = + = + = = = T T h T h e T y T y T b β β β Esimaion Variance Under classical condiions, where
More informationStability and Bifurcation in a Neural Network Model with Two Delays
Inernaional Mahemaical Forum, Vol. 6, 11, no. 35, 175-1731 Sabiliy and Bifurcaion in a Neural Nework Model wih Two Delays GuangPing Hu and XiaoLing Li School of Mahemaics and Physics, Nanjing Universiy
More informationDistribution of Estimates
Disribuion of Esimaes From Economerics (40) Linear Regression Model Assume (y,x ) is iid and E(x e )0 Esimaion Consisency y α + βx + he esimaes approach he rue values as he sample size increases Esimaion
More informationForecasting Volatility of Returns for Corn using GARCH Models
he exas Journal of Agriculure and Naural Resources 6:4-55 (03) 4 Agriculural Consorium of exas Forecasing Volailiy of Reurns for Corn using GARCH Models Naveen Musunuru * Mark Yu Arley Larson Deparmen
More informationKINEMATICS IN ONE DIMENSION
KINEMATICS IN ONE DIMENSION PREVIEW Kinemaics is he sudy of how hings move how far (disance and displacemen), how fas (speed and velociy), and how fas ha how fas changes (acceleraion). We say ha an objec
More informationNature Neuroscience: doi: /nn Supplementary Figure 1. Spike-count autocorrelations in time.
Supplemenary Figure 1 Spike-coun auocorrelaions in ime. Normalized auocorrelaion marices are shown for each area in a daase. The marix shows he mean correlaion of he spike coun in each ime bin wih he spike
More information3.1.3 INTRODUCTION TO DYNAMIC OPTIMIZATION: DISCRETE TIME PROBLEMS. A. The Hamiltonian and First-Order Conditions in a Finite Time Horizon
3..3 INRODUCION O DYNAMIC OPIMIZAION: DISCREE IME PROBLEMS A. he Hamilonian and Firs-Order Condiions in a Finie ime Horizon Define a new funcion, he Hamilonian funcion, H. H he change in he oal value of
More informationStock Prices and Dividends in Taiwan's Stock Market: Evidence Based on Time-Varying Present Value Model. Abstract
Sock Prices and Dividends in Taiwan's Sock Marke: Evidence Based on Time-Varying Presen Value Model Chi-Wei Su Deparmen of Finance, Providence Universiy, Taichung, Taiwan Hsu-Ling Chang Deparmen of Accouning
More informationStationary Time Series
3-Jul-3 Time Series Analysis Assoc. Prof. Dr. Sevap Kesel July 03 Saionary Time Series Sricly saionary process: If he oin dis. of is he same as he oin dis. of ( X,... X n) ( X h,... X nh) Weakly Saionary
More informationForecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models
Forecasing Sock Exchange Movemens Using Arificial Neural Nework Models and Hybrid Models Erkam GÜREEN and Gülgün KAYAKUTLU Isanbul Technical Universiy, Deparmen of Indusrial Engineering, Maçka, 34367 Isanbul,
More informationChapter 11. Heteroskedasticity The Nature of Heteroskedasticity. In Chapter 3 we introduced the linear model (11.1.1)
Chaper 11 Heeroskedasiciy 11.1 The Naure of Heeroskedasiciy In Chaper 3 we inroduced he linear model y = β+β x (11.1.1) 1 o explain household expendiure on food (y) as a funcion of household income (x).
More informationStat 601 The Design of Experiments
Sa 601 The Design of Experimens Yuqing Xu Deparmen of Saisics Universiy of Wisconsin Madison, WI 53706, USA December 1, 2016 Yuqing Xu (UW-Madison) Sa 601 Week 12 December 1, 2016 1 / 17 Lain Squares Definiion
More informationAnalysing Trends and Volatility in Atmospheric Carbon Dioxide Concentration Levels
Inernaional Congress on Environmenal Modelling and Sofware Brigham Young Universiy BYU ScholarsArchive 2nd Inernaional Congress on Environmenal Modelling and Sofware - Osnabrück, Germany - June 2004 Jul
More informationMath 10B: Mock Mid II. April 13, 2016
Name: Soluions Mah 10B: Mock Mid II April 13, 016 1. ( poins) Sae, wih jusificaion, wheher he following saemens are rue or false. (a) If a 3 3 marix A saisfies A 3 A = 0, hen i canno be inverible. True.
More information( ) (, ) F K L = F, Y K N N N N. 8. Economic growth 8.1. Production function: Capital as production factor
8. Economic growh 8.. Producion funcion: Capial as producion facor Y = α N Y (, ) = F K N Diminishing marginal produciviy of capial and labor: (, ) F K L F K 2 ( K, L) K 2 (, ) F K L F L 2 ( K, L) L 2
More information