Government Revenue Forecasting in Nepal

Size: px
Start display at page:

Download "Government Revenue Forecasting in Nepal"

Transcription

1 Governmen Revenue Forecasing in Nepal T. P. Koirala, Ph.D.* Absrac This paper aemps o idenify appropriae mehods for governmen revenues forecasing based on ime series forecasing. I have uilized level daa of monhly revenue series including 92 observaions saring from 997 o 202 for he analysis. Among he five compeiive mehods under scruiny, Winer mehod and Seasonal ARIMA mehod are found in racking he acual Daa Generaing Process (DGP) of monhly revenue series of he governmen of Nepal. Ou of wo seleced mehods, seasonal ARIMA mehod albei superior in erms of minimum MPE and MAPE crieria. However, he resuls of forecased revenues in his paper may vary depending on he applicaion of more sophisicaed mehods of forecasing which capure cyclical componens of he revenue series. The prevailing forecasing mehod based paricularly on growh rae mehod exended wih discreionary adjusmen of a number of updaed assumpions and personal judgmen can creae uncerainy in revenue forecasing pracice. Therefore, he mehods recommended here in his paper help in reducing forecasing error of he governmen revenue in Nepal. Key words: Daa generaing process, forecas bias, seasonal paern, under-or-over esimaion, governmen revenue, seasonaliy JEL Classificaion: H2, O23 * Assisan Direcor, Research Deparmen, Nepal Rasra Bank. Acknowledgemen: I would like o hank Ediorial Board of NRB Economic Review and anonymous referees for heir consrucive commens on he paper.

2 48 NRB ECONOMIC REVIEW I. INTRODUCTION The revenue forecass by he naional governmen are carried ou in course of budge preparaion. An accuracy of revenue forecass is one key issue in he design and execuion of fiscal policies (IMF, 200). Under or over-predicion of revenue forecas creaes budge planning vulnerable. Revenue forecas provides necessary discipline for negoiaions beween he execuive and legislaive branches of he governmen. I helps in seing up performance arges for revenue deparmens and agencies (Auerbach, 999, Danninger, 2005). One of he major sources of error (or bias) in revenue forecasing is he mehods adoped in forecasing revenue in addiion o variey of poliical and insiuional facors deermining such bias (Golosov and Kind, 2002, Kyobe and Danninger, 2005). In Nepal, revenue forecass is an imporan ask of Minisry of Finance (MOF) in he course of budge preparaion and specifying performance arges of revenue collecion offices. Major insiuions involved in forecasing revenue in he counry are MOF and Nepal Rasra Bank (NRB) as heir work of forecasing is an essenial par of he budgeary process. The IMF, especially is Fiscal Affairs Deparmen (FAD) ofen gives advice for a sysemaic analysis of forecasing in low-income counries in he conex of reforms on he budge planning process (Kyobe and Danninger, 2005). However, forecasing echniques are generally no pu down in formal documens, and counry pracices are ofen a mix of idiosyncraic budge pracices and influences from legacy sysems. Too much reliance on few mehods in forecasing revenue of he governmen of Nepal is considered o be less efficien in capuring rue DGP of revenue sequence. No a remarkable exercise has been carried ou in idenifying appropriae mehodology of revenue forecasing from hose insiuions involved in revenue forecasing a presen and here is a lack of privae insiuional forecaser of revenue in he economy. As a resul, here is an over-esimaion or underesimaion of he revenue of he governmen. The forecas error as percenage of GDP shows downward rend wih erraic movemen as represened by forecas error or bias as shown in Diagram. Revenue forecas shows upward biased before FY 200/02 and downward biased hereafer in Nepal. Realizing he facs ha any misspecificaion of appropriae forecasing echniques ha leads o much error in revenue forecasing as moivaing facor of his sudy. In ligh of his fac, he objecives of his paper is o ideniy appropriae mehods for revenue forecasing using monhly oal revenue sequence and rank he mehods under scruiny based on some saisical crieria. Following five imporan mehods of forecasing under consideraion, his sudy found wo mehods namely SARIMA and Winer as he represenaive mehods of revenue forecasing in Nepal. The res of he paper is organized as follows. Nex secion presens

3 Governmen Revenue Forecasing in Nepal 49 explanaion of each of he five mehods under he heading mehodology. Secion III provides resuls and analysis. Finally, he las secion draws he conclusion. II. METHODOLOGY In caegorizing forecasing mehodologies, wo broad approaches can be disinguished. Time series forecasing aemps in predicing he values of a variable from he pas values of he same variable. In conras o he ime series approach economeric forecasing is based on a regression model ha relaes one or more dependen variables o a number of independen variables. The ime series approach has generally been found o be superior o he economeric approach when shor-run predicions are made (Ramanahan, 2002). In his paper, use is made of ime series forecasing approach uilizing level daa of monhly oal revenue series saring from 997 o 202. Las 24 ou of oal 92 observaions are aken o check he accuracy of he forecasing mehods employed in his paper. An ex-ane forecass of 24 observaions are presened in he Appendix. Boh he cumulaive as well as ne monhly forecass are presened uilizing each of he mehods of forecasing employed. The iniial period of sample in FY 997/98 has been chosen based on he year when he governmen of Nepal adoped Value Added Tax as a landmark reform in revenue srucure in Nepal. Followings are he explanaion of basic characerisics of each of he seleced se of mehods ha are used for forecasing in his paper. Hol Mehod : The forecasing mehod developed by Hol (957) is one popular smoohing echnique of forecasing. The wo-parameer exponenial smoohing echnique developed by Hol is a modified mehod of simple exponenial smoohing formula of ~ y ( ) ~ = α y + α y ; where > α > 0 incorporaing average changes in he long-run rend (increase or decline) of he sequence y }. Here, ~ y } is smoohed sequence. Hol { mehods is superior o exponenial smoohing echnique ha former mehod incorporaes rend in he smoohing series. The smoohed or esimaed series is derived by using wo recursive equaions as given in equaion () and (2). The smoohness of he series depends on wo smoohing parameers, α and β boh of which mus lie beween 0 and, ha is, he smaller are α and β he heavier is he smoohing (Makridikis, Wheelwrigh and Hyndman, 998). ~ y = + ( )( ~ + α y α y r ) ;where, > α > 0 () r ~ ~ = β ( y y ) + ( β ) r ; where, > β > 0 (2) y ˆ = ~ y + lr (3) + l T T Here, y~ denoes an esimae of he level of he series a ime and r denoes an esimae of he slope of he series a ime. Equaion (2) adjuss y~ direcly for he rend of he previous period, r by adding i o he las smoohed value ~ y. Equaion (3) is used o forecas l. {

4 50 NRB ECONOMIC REVIEW Winer Mehod: Winer (960) exended Hol mehod by reaing seasonal effec in he forecasing equaion. Winer mehod is based on hree smoohing equaions- one for he level, one for rend, and one for seasonaliy as. ~ y y ( )( ~ = α + α y + r ) ; where, > α > 0 (4) s s r ~ ~ = β ( y y ) + ( β ) r ; where, > β > 0 (5) y s = γ ~ + ( γ ) s s where, > γ > 0 (6) y ˆ = ( ~ y r ) s (7) y + m T + Tm s+ m Where, s is he lengh of seasonaliy, y~ represens he level of he series, r denoes he rend, s is he seasonal componen, and y ˆ + m is he forecas for m periods ahead. Decomposiion Mehod: Classical decomposiion is one of he oldes commonly used forecasing mehods. This mehod is used o decompose a ime series ino rend, cyclical and seasonal componens presened in a ime series. An essenial par of his mehod includes he concep of seasonal index. The srong seasonaliy of some series makes i difficul o measure heir rend and cyclical movemens (Gujarai, 2004). In he regression decomposiion mehod, dummy (dichoomous) variables are uilized o measure seasonal influences on high frequency daa. The seasonal influence can be modeled using eiher an addiive model, or a muliplicaive model. The selecion of eiher model depends on he magniudes of he seasonal peaks and roughs of he level of he series. The formulas for addiive and muliplicaive models are represened in equaions (8) and (9) respecively. y = α + β T + β2 X 2 + β3x β n Xnn (8) ln( y ) = α + β T + β2 X 2 + β3x β n Xnn (9) Where, y is level of he series, T ime value in period, X 2, X Xnn are dummy variables for each period (i.e. monhly), ln( y ) is he logarihm of he series o he base of he naural number e. The ani-log form of he muliplicaive model of equaion (9) is used o ransform logarihmic values o level values using formula represened in equaion (0). yˆ α β = e + e + e + e e T β2x 2 β3x 3 βn Xnn (0) Seasonal Auoregressive Inegraed Moving Average (SARIMA) Mehod: Popularly known as Box-Jenkin (994) mehodology, he ARIMA model building mehod consiss of four seps: idenificaion, esimaion, diagnosic checking and forecasing (Gujarai, 2004). ARIMA model conains he use of simple and versaile model noaion designaed by he level of Auoregressive (AR), Inegraion (I), and Moving Averages (MA)

5 Governmen Revenue Forecasing in Nepal 5 (DeLurgio, 998). The sandard noaion idenifies he order of AR by p, I by d and MA by q. An exension of seasonal influence in ARIMA model is represened by SARIMA specificaion. A mixure of AR, I and MA formulaion is known as ARMA (p,d,q) where difference (d) is done before ARMA is specified. The general form of ARIMA model is: y = β y + β2 y β p y p + ε φε φε 2... φqε q () Seasonaliy in a ime series is a regular paern of changes ha repeas over S ime periods, where S defines he number of ime periods unil he paern repeas again. The ARIMA noaion can be exended readily o handle seasonal aspecs, and he general shorhand noaion is ARIMA (p,d,q) (P,D.Q)s (Pindyck and Rubinfeld (997). In a seasonal ARIMA model, seasonal AR and MA erms predic y using daa values and errors a imes wih lags ha are muliples of S (he span of he seasonaliy). Wih monhly daa (S=2), and seasonal firs order auoregressive model would use y 2 o predic y. Variance nonsainary in he ime series is handled by logarihmic ransformaion before SARIMA mehod is adoped. Growh Rae Mehod: Revenue forecasing based on year-on-year growh rae is supposed o capure seasonal influence. The forecas of period +s is calculaed based on average of pas five years (year-on-year) growh raes from period. The increase/decrease of he forecased revenue deermines increase/decrease of forecas revenue from period, ha is, condiional forecass. The formulas for growh mehod are presened in equaion () and (2). r = n n y y ( y i= yˆ = y + ( y * n )*00 (2) + r) /00 (3) Measures of accuracy for forecasing which are free of scale of he daa are adoped in his paper. Two popular relaive measures as frequenly used in measuring accuracy of forecas are Mean Percenage Error (MPE) and Mean Absolue Percenage Error (MAPE) where Percenage Error (PE) is calculaed using he formula Y F PE = *00 (4) Y n MPE = PE (5) n = n MAPE = PE (6) n = The mehods explained above are considered appropriae o capure daa generaing process of oal hisorical revenue series in his paper. While selecing appropriae mehods, due emphasis will be given o hose mehods ha incorporae rend and seasonal

6 52 NRB ECONOMIC REVIEW componen in a ime series analysis. The cyclical componen has no been decomposed from rend componen in his analysis. III. RESULTS AND ANALYSIS In he presen paper, I have conduced shor-erm revenue forecas of he governmen of Nepal for 24 monhs saring from Augus 202 o Augus 204 (i.e. wo years) based on 92 hisorical monhly revenue series beginning from Augus 997 o July 202. A visual inspecion of revenue series in Figure 2 reveals some sylized facs ha he daa generaing process of monhly revenue series clearly shows upward rend accompanied wih monhly seasonal paern. Revenue series shows also ime varying variance over he period. The seasonal paern is no clearly visible in he Diagram 2 as he diagram covers whole sample period. In order o be more specific, he seasonal paern is displayed by he monhly daa for he FY 20/2 reveals clear seasonal paern in he monh of January (six monh), April (nine monh) and July (welve monh). Such seasonal paern can be applicable for he inference of monhly seasonal paern for oher FYs as depiced in Figure 3. The reason for such seasonal influence of revenue mobilizaion in hose monhs is ha he corporae eniies in Nepal are direced o pay declared ax ino hree-insallmen each year including 40% ill mid-january (six monh), 70% ill mid-april (nine monh) and 00% ill mid-july (Twelve monh). I have uilized five equally compeiive mehods ha are applicable for forecasing in case of a ime series daa characerizing period-o-period upward rend, seasonal paern and ime-varying variance. Those mehods include (a) Hol mehod, (b) Winer mehod, (c) decomposiion mehod (d) SARIMA mehod, and (e) growh rae mehod. Among hose mehods, Hol and Winer are he smoohing mehods of forecasing ime series. The esimaed values of he smoohing parameers of α, β and γ deermine he forecas values in hese mehods. As α, β and γ represen smoohing, rend and seasonal parameers respecively, he esimaed values of hose parameers uilizing whole sample daa of presen analysis are presened in Table. The crierion for he selecion of hose parameers is he minimum mean sum of squared error.

7 Governmen Revenue Forecasing in Nepal 53 Table : Esimaed Smoohing and Seasonal Parameers (996 Augus o 202 July) Smoohing and Seasonal Hol's wo parameer Winer's Three Parameer Parameers (no seasonal) (Seasonal) α β γ Subsiuing he values of smoohing parameers in he corresponding equaions of Hol and Winer mehods for revenue forecasing looks like: Hol Mehod: ~ y 0.04 ( 0.04)( ~ = y + y + r ) ; where, > α > 0 (7) r.5( ~ ~ = 0 y y ) + ( 0.5) r ; where, > β > 0 (8) Forecasing for l period ahead as: y ˆ l = ~ + yt + lrt (9) Winer Mehod: ~ y y 0.8 ( 0.8)( ~ = + y + r ) ; where, > α > 0 (20) s s r.8( ~ ~ = 0 y y ) + ( 0.8) r ; where, > β > 0 (2) y s 0 s where, > γ > 0 (22) =.65 ~ + ( 0.65) y s Forecasing for m period ahead as: ˆ ( ~ (23) y + m = yt + rtm ) s s+ m The monhly revenue forecass using Hol and Winer mehods for he FY 202/3 and he FY 203/4 are presened in Table of Appendix I. As shown in Diagram of Appendix II, Hol mehod could no capure rue daa generaing process of governmen of Nepal as revenue series of he governmen characerizes seasonal effec. Winer mehod includes seasonal parameer γ, he noable seasonal picks are found in he monhs of January, April and July as shown in Diagram 4. The ex-ane revenue forecass under his mehod are found more accurae han ha of Hol mehod as displayed in Diagram 2 of Appendix II. Under decomposiion mehod, regression mehod of decomposiion has been used o decompose revenue series ino rend and seasonal componen in his paper. The varian of muliple decomposiion formula is considered plausible here because he revenue series under review characerizes variance non-saionariy. Twelve monhly dummies are inroduced o capure seasonal influence. Excep he dummy for he second monh, all

8 54 NRB ECONOMIC REVIEW oher coefficiens of monhly dummies including consan erm and rend componen are found saisically significanly differen from zero. Subsiuing he values of esimaed parameers in he corresponding regression equaon looks like: ln(rev ) = T + 0.0D S2 + 0.D D D D7 = (45.9) (47.38) (0.7) (.54) (4.8) (2.80) (9.6) (2.) 0.3D D D D+ 0.89D2 (24) (.98) (6.26) (3.63) (5.30) (3.8) As shown in Diagram 3 of Appendix II, ex-pos forecass of revenue are racking well o he acual revenues from his mehod oo. The revenue forecasing under his mehod are presened in Table of Appendix I. The ARIMA mehod exended wih seasonal componens represened by SARIMA has been uilized in his paper by assuming ha he revenue series under review characerizes seasonal influence. While using SARIMA mehod, revenue series has been convered ino logarihm o he base e before is use in he analysis o capure variance nonsaionary. The SARIMA (0,0,0)(,0,0) 2 is he final specificaion based on idenificaion and diagnosic checking of he mehod. As such he logarihmic 2 h order difference wihou consan erm is he robus represenaion of he model as: ln(rev ) =.02*ln(Rev) 2 (25) = (688.55) Above specificaion yields very good racking of revenue forecass o acual revenue as shown in Diagram 4 of Appendix II. Applying he growh rae mehod, revenue forecasing is deermined by he increase/decrease of five years average of year-on-year growh raes of monhly revenue. The ex-ane forecas for consecuive monhs in he fuure dae are considered condiional forecas based on he forecased revenue a he same monh las year, ha is, i is he ieraive process. Based on his mehod, he forecased revenue during he in-sample period is found saisfacory as depiced by he forecas revenues ha are well racking he acual revenues as shown in he Diagram 5 of Appendix II. The basis for he selecion of appropriae mehods of revenue forecasing in his paper, as quaniaive measure, is he minimum values of MPE and MAPE saisics for each mehod. For his purpose, MPE and MAPE have been calculaed based on laes 24 observaions of forecas errors derived from he difference of acual and esimaed values. The mehod ha obains values of MPE and MAPE close o zero is considered he bes mehod. The MPE and MAPE for each mehod are presened in Table 2.

9 Governmen Revenue Forecasing in Nepal 55 Table 2: Saisical Measures of Model Accuracy (996 Augus o 202 July) S. No. Saisical mehods Mean Percenage Error (MPE) Mean Absolue Percenage Error (MAPE). Hol Mehod Winer Mehod Regression Decomposiion Mehod 4. SARIMA Mehod Growh Rae Mehod Ou of five compeing mehods, wo mehods including Hol mehod, Decomposiion mehod are found less saisfacory mehods in erms of minimum MPE and MAPE crieria. On he remaining hree mehods, growh rae mehod is ranked hird. SARIMA mehod and Winer mehods rank firs and second posiion respecively. The MPE and MAPE for SARIMA mehod are and 6.22 respecively whereas for Winer mehod hey are -.27 and 6.24 respecively. As boh he Winer and SARIMA mehods have buil-in characer o capure he seasonal influence in forecasing, hese mehods can be he represenaive mehods of forecasing governmen revenue in Nepal. Boh he monhly ne and monhly cumulaive forecass for he FY 202/3 and FY 203/4 are presened in Table and 2 of Appendix I. The cumulaive forecas revenues for he FY 202/3 and FY203/4 incorporaing all he five mehods are dragged in Table 3 from Table 2 of Appendix I o inerpree some ineresing conclusions. As SARIMA mehod is ranked firs among he five alernaive mehods under rial, he cumulaive revenue forecass accouns o Rs billion and Rs billion respecively in he FY202/3 and FY203/4. I yields year-on-year growh raes of 4.8 percen and 5.7 percen respecively in FY 202/3 and FY203/4. Table 3: Cumulaive Revenue Foecass and Growh Raes FY Cumulaive Forecas (Rs in Million Percenage Change Decomposiion Growh Decomp- Growh Hol Winer ARIMA Rae Hol Winer osiion ARIMA Rae 20/ / / Similarly, he cumulaive forecas revenues using Winer mehod, as i is found second bes mehod in his paper, are Rs billion and billion for he FY202/3 and FY203/4 respecively. The growh rae is projeced o be increased by 4.8 percen in FY 202/3 and 5.7 percen in FY203/4 according o his mehod. Las bu no he leas, wha i can be concluded in his paper is ha Growh rae mehod is found overly opimisic whereas Decomposiion mehod underesimaes he forecass. Hol mehod is ruled-ou because i does no capure seasonal influence. Therefore, among he five compeiive mehods under scruiny in his paper, Winer and ARIMA are found suiable for revenue projecion based on saisical crieria specified in his paper.

10 56 NRB ECONOMIC REVIEW However, he conclusion drawn in his paper depends on he use of five mehods of forecasing only. Complex forecasing mehods which capure cyclical influence in revenue mobiliaion are ou of perview in his paper. Since he moivaion of he sudy is o use ime series analysis in revenue forecsing as agains he condiional foecass mehod, laer mehod may obain differen resuls. IV. CONCLUSION Governmen revenue forecasing is an imporan aspec in he design and execuion of sound fiscal policies. The forecas error as percen of GDP over he sudy period is downward rending. As a consequence, here is an over-esimaion of revenue followed by under-esimaion. Furher, here is an erraic movemen of forecas error oo. As he exising mehods of revenue forecasing in Nepal is limied o growh rae basis and hence miss he arge, he objecive of he paper is o idenify appropriae mehodology of revenue forecasing. This paper uilizes monhly revenue series including 92 observaions saring from 997 o 202 or he analysis. Ou of he five popular echniques scruinized in his paper, wo compeing mehods including Winer and SARIMA mehods are found o be appropriae for he revenue forecasing in Nepal. However, SARIMA mehod is found albei superior han Winer mehod in erm of minimum MPE and MAPE crierion. Using SARIMA mehod, oal revenue is forecased o be increase by 5.7 in FY 202/3 and 4.8 percen in FY 203/4. The resuls of revenue forecasing in his paper may vary depending on he use of mehods ha capure cyclical componen of revenue series. Furher, he mehods of condiional forecasing are no applied here and hence may give differen resuls. Therefore, in ligh of hese limiaions, he forecasing aemps in his paper have opened an avenue for he sysemaic analysis of revenue forecasing using several mehods raher han depending on exising growh rae mehod. REFERENCES Auerbach, A J "On he Performance and Use of Governmen Revenue Forecass." Universiy of California, Berkeley and NBER, USA. Box, G.P.E. and G. M. Jenkins Time Series Analysis, Forecasing and Conrol. 3 rd ed. Englewood Cliffs, NJ: Prenice-Hall. Danninger, S "Revenue Forecass as Performance Targes." IMF Working Paper No. WP/05/4, IMF, Washingon, D.C. DeLurgio, S.A Forecasing Principles and Applicaions. Irwin McGraw-Hill Book Co., Singapore. Gujarai, D.N Basic Economerics 4 h ediion, Taa McGraw-Hill Publishing Company Ld., New Delhi. Hol, C.C "Forecasing Seasonal and Trends by Exponenially Weighed Moving Averages, Office of Naval Research, Research Memorandum No.52.

11 Governmen Revenue Forecasing in Nepal 57 Golosov, M. and J. King "The Revenue Forecass in IMF-Suppored Programs." IMF Working Paper No.WP/02/236, Washingon D.C., USA. Inernaional Moneary Fund Revised Manual of Fiscal Transparency. Washingon D.C. USA. Kyobe A, and S. Danninger "Revenue Forecasing-How is i Done? Resuls from a Survey of Low-Income Counries." IMF working paper No.WP/05/24, IMF, Washingon D.C., USA Makridikis, S., Wheelwrigh, S.C., and R.J. Hyndman Forecasing Mehods and Applicaions. John Wiley and Sons (Asia) Pe.Ld. Singapore. Pindyck, R.S. and D.L. Rubinfeld. 997., Economeric Models and Economic Forecasings. 4 h Ediion, McGraw-Hill, Inernaional Ediion, Singapore. Ramanahan R Inroducory Economerics wih Applicaions. 5 h Ediion, Thomson Asia Pe Ld., Singapore. Winers, P.R "Forecasing sales by exponenially weighed moving averages. Managemen Science- 6, p.p

12 58 NRB ECONOMIC REVIEW Appendix I: Tables Table : Monhly Revenue Forecass for 202/3 and 203/4 Mid-Monhs Hol Mehod Winer Mehod Decomposiion Mehod ARIMA Mehod (Rs in million) Growh Rae Mehod Ne Forecased Revenue for he FY 202/3 Augus Sepember Ocober November December January February March April May June July Ne Forecased Revenue for he FY 203/4 Augus Sepember Ocober November December January February March April May June July

13 Governmen Revenue Forecasing in Nepal 59 Table 2: Monhly Revenue Forecas (Cumulaive) for 202/3 and 203/4 Rs in million Mid-Monhs Hol Mehod Winer Mehod Decomposiion Mehod ARIMA Mehod Growh Rae Mehod Monhly Revenue Forecas (Cumulaive) for 202/3 Augus Sepember Ocober November December January February March April May June July Monhly Revenue Forecas (Cumulaive) for 203/4 Augus Sepember Ocober November December January February March April May June July

14 60 NRB ECONOMIC REVIEW Appendix II: Diagrams

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

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

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 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

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 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

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

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

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

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

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

Stock Price Prediction Using the ARIMA Model

Stock Price Prediction Using the ARIMA Model 2014 UKSim-AMSS 16h Inernaional Conference on Compuer Modelling and Simulaion Sock Price Predicion Using he ARIMA Model 1 Ayodele A. Adebiyi., 2 Aderemi O. Adewumi 1,2 School of Mahemaic, Saisics & Compuer

More 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

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

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

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

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

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

Demand and Price Forecasting Models for Strategic and Planning Decisions in a Supply Chain

Demand and Price Forecasting Models for Strategic and Planning Decisions in a Supply Chain Proc. Schl. ITE Tokai Univ. vol.3,no,,pp.37-4 Vol.,No.,,pp. - Paper Demand and Price Forecasing Models for Sraegic and Planning Decisions in a Supply Chain by Vichuda WATTANARAT *, Phounsakda PHIMPHAVONG

More 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

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

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

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

Time Series Analysis using In a Nutshell

Time Series Analysis using In a Nutshell 1 Time Series Analysis using In a Nushell dr. JJM J.J.M. Rijpkema Eindhoven Universiy of Technology, dep. Mahemaics & Compuer Science P.O.Box 513, 5600 MB Eindhoven, NL 2012 j.j.m.rijpkema@ue.nl Sochasic

More 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

Forecasting, Ordering and Stock- Holding for Erratic Demand

Forecasting, Ordering and Stock- Holding for Erratic Demand ISF 2002 23 rd o 26 h June 2002 Forecasing, Ordering and Sock- Holding for Erraic Demand Andrew Eaves Lancaser Universiy / Andalus Soluions Limied Inroducion Erraic and slow-moving demand Demand classificaion

More 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

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

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

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

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

How Useful are the Various Volatility Estimators for Improving GARCH-based Volatility Forecasts? Evidence from the Nasdaq-100 Stock Index

How Useful are the Various Volatility Estimators for Improving GARCH-based Volatility Forecasts? Evidence from the Nasdaq-100 Stock Index Inernaional Journal of Economics and Financial Issues Vol. 4, No. 3, 04, pp.65-656 ISSN: 46-438 www.econjournals.com How Useful are he Various Volailiy Esimaors for Improving GARCH-based Volailiy Forecass?

More 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

Improvement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network

Improvement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network American Journal of Inelligen Sysems 2012, 2(2): 12-17 DOI: 10.5923/j.ajis.20120202.02 Improvemen in Forecasing Accuracy Using he Hybrid Model of ARFIMA and Feed Forward Neural Nework Cagdas Hakan Aladag

More 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

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

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

Premium Income of Indian Life Insurance Industry

Premium Income of Indian Life Insurance Industry Premium Income of Indian Life Insurance Indusry A Toal Facor Produciviy Approach Ram Praap Sinha* Subsequen o he passage of he Insurance Regulaory and Developmen Auhoriy (IRDA) Ac, 1999, he life insurance

More information

II.1. Debt reduction and fiscal multipliers. dbt da dpbal da dg. bal

II.1. Debt reduction and fiscal multipliers. dbt da dpbal da dg. bal Quarerly Repor on he Euro Area 3/202 II.. Deb reducion and fiscal mulipliers The deerioraion of public finances in he firs years of he crisis has led mos Member Saes o adop sizeable consolidaion packages.

More information

UPDATE OF QUARTERLY NATIONAL ACCOUNTS MANUAL: CONCEPTS, DATA SOURCES AND COMPILATION 1 CHAPTER 7. SEASONAL ADJUSTMENT 2

UPDATE OF QUARTERLY NATIONAL ACCOUNTS MANUAL: CONCEPTS, DATA SOURCES AND COMPILATION 1 CHAPTER 7. SEASONAL ADJUSTMENT 2 UPDATE OF QUARTERLY NATIONAL ACCOUNTS MANUAL: CONCEPTS, DATA SOURCES AND COMPILATION 1 CHAPTER 7. SEASONAL ADJUSTMENT 2 Table of Conens 1. Inroducion... 3 2. Main Principles of Seasonal Adjusmen... 6 3.

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

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

Forecasting Daily Supermarket Sales Using Exponentially Weighted Quantile Regression

Forecasting Daily Supermarket Sales Using Exponentially Weighted Quantile Regression Forecasing Daily Supermarke Sales Using Exponenially Weighed Quanile Regression James W. Taylor Saïd Business School Universiy of Oxford European Journal of Operaional Research, 2007, Vol. 178, pp. 154-167.

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

Forecasting tourist arrivals using time-varying parameter structural time series models

Forecasting tourist arrivals using time-varying parameter structural time series models Forecasing ouris arrivals using ime-varying parameer srucural ime series models HAIYAN SONG 1, GANG LI*, STEPHEN F. WITT* AND GEORGE ATHANASOPOULOS School of Hoel and Tourism Managemen The Hong Kong Polyechnic

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

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

The Interest Rate Risk of Mortgage Loan Portfolio of Banks

The Interest Rate Risk of Mortgage Loan Portfolio of Banks The Ineres Rae Risk of Morgage Loan Porfolio of Banks A Case Sudy of he Hong Kong Marke Jim Wong Hong Kong Moneary Auhoriy Paper presened a he Exper Forum on Advanced Techniques on Sress Tesing: Applicaions

More information

Multiprocessor Systems-on-Chips

Multiprocessor Systems-on-Chips Par of: Muliprocessor Sysems-on-Chips Edied by: Ahmed Amine Jerraya and Wayne Wolf Morgan Kaufmann Publishers, 2005 2 Modeling Shared Resources Conex swiching implies overhead. On a processing elemen,

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

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

Explaining long-term trends in groundwater hydrographs

Explaining long-term trends in groundwater hydrographs 18 h World IMACS / MODSIM Congress, Cairns, Ausralia 13-17 July 2009 hp://mssanz.org.au/modsim09 Explaining long-erm rends in groundwaer hydrographs Ferdowsian, R. 1 and D.J. Pannell 2 1 Deparmen of Agriculure

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

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

WATER MIST FIRE PROTECTION RELIABILITY ANALYSIS

WATER MIST FIRE PROTECTION RELIABILITY ANALYSIS WATER MIST FIRE PROTECTION RELIABILITY ANALYSIS Shuzhen Xu Research Risk and Reliabiliy Area FM Global Norwood, Massachuses 262, USA David Fuller Engineering Sandards FM Global Norwood, Massachuses 262,

More information

INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES

INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES OPENGAMMA QUANTITATIVE RESEARCH Absrac. Exchange-raded ineres rae fuures and heir opions are described. The fuure opions include hose paying

More 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

Contrarian insider trading and earnings management around seasoned equity offerings; SEOs

Contrarian insider trading and earnings management around seasoned equity offerings; SEOs Journal of Finance and Accounancy Conrarian insider rading and earnings managemen around seasoned equiy offerings; SEOs ABSTRACT Lorea Baryeh Towson Universiy This sudy aemps o resolve he differences in

More 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

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

An Analysis of Tax Revenue Forecast Errors

An Analysis of Tax Revenue Forecast Errors An Analysis of Tax Revenue Forecas Errors Marin Keene and Peer Thomson N EW Z EALAND T REASURY W ORKING P APER 07/02 M ARCH 2007 NZ TREASURY WORKING PAPER 07/02 An Analysis of Tax Revenue Forecas Errors

More information

An Optimal Strategy of Natural Hedging for. a General Portfolio of Insurance Companies

An Optimal Strategy of Natural Hedging for. a General Portfolio of Insurance Companies An Opimal Sraegy of Naural Hedging for a General Porfolio of Insurance Companies Hong-Chih Huang 1 Chou-Wen Wang 2 De-Chuan Hong 3 ABSTRACT Wih he improvemen of medical and hygienic echniques, life insurers

More information

Relationships between Stock Prices and Accounting Information: A Review of the Residual Income and Ohlson Models. Scott Pirie* and Malcolm Smith**

Relationships between Stock Prices and Accounting Information: A Review of the Residual Income and Ohlson Models. Scott Pirie* and Malcolm Smith** Relaionships beween Sock Prices and Accouning Informaion: A Review of he Residual Income and Ohlson Models Sco Pirie* and Malcolm Smih** * Inernaional Graduae School of Managemen, Universiy of Souh Ausralia

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

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

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

The Grantor Retained Annuity Trust (GRAT)

The Grantor Retained Annuity Trust (GRAT) WEALTH ADVISORY Esae Planning Sraegies for closely-held, family businesses The Granor Reained Annuiy Trus (GRAT) An efficien wealh ransfer sraegy, paricularly in a low ineres rae environmen Family business

More 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

The Relationship between Stock Return Volatility and. Trading Volume: The case of The Philippines*

The Relationship between Stock Return Volatility and. Trading Volume: The case of The Philippines* The Relaionship beween Sock Reurn Volailiy and Trading Volume: The case of The Philippines* Manabu Asai Faculy of Economics Soka Universiy Angelo Unie Economics Deparmen De La Salle Universiy Manila May

More information

The Application of Multi Shifts and Break Windows in Employees Scheduling

The Application of Multi Shifts and Break Windows in Employees Scheduling The Applicaion of Muli Shifs and Brea Windows in Employees Scheduling Evy Herowai Indusrial Engineering Deparmen, Universiy of Surabaya, Indonesia Absrac. One mehod for increasing company s performance

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

11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements

11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements Inroducion Chaper 14: Dynamic D-S dynamic model of aggregae and aggregae supply gives us more insigh ino how he economy works in he shor run. I is a simplified version of a DSGE model, used in cuing-edge

More information

Supplementary Appendix for Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking?

Supplementary Appendix for Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking? Supplemenary Appendix for Depression Babies: Do Macroeconomic Experiences Affec Risk-Taking? Ulrike Malmendier UC Berkeley and NBER Sefan Nagel Sanford Universiy and NBER Sepember 2009 A. Deails on SCF

More information

Option Put-Call Parity Relations When the Underlying Security Pays Dividends

Option Put-Call Parity Relations When the Underlying Security Pays Dividends Inernaional Journal of Business and conomics, 26, Vol. 5, No. 3, 225-23 Opion Pu-all Pariy Relaions When he Underlying Securiy Pays Dividends Weiyu Guo Deparmen of Finance, Universiy of Nebraska Omaha,

More information

Working paper No.3 Cyclically adjusting the public finances

Working paper No.3 Cyclically adjusting the public finances Working paper No.3 Cyclically adjusing he public finances Thora Helgadoir, Graeme Chamberlin, Pavandeep Dhami, Sephen Farringon and Joe Robins June 2012 Crown copyrigh 2012 You may re-use his informaion

More information

CHARGE AND DISCHARGE OF A CAPACITOR

CHARGE AND DISCHARGE OF A CAPACITOR REFERENCES RC Circuis: Elecrical Insrumens: Mos Inroducory Physics exs (e.g. A. Halliday and Resnick, Physics ; M. Sernheim and J. Kane, General Physics.) This Laboraory Manual: Commonly Used Insrumens:

More information

Strictly as per the compliance and regulations of:

Strictly as per the compliance and regulations of: Global Journal of Managemen and Business Research Finance Volume 3 Issue 3 Version.0 Year 03 Type: Double Blind Peer Reviewed Inernaional Research Journal Publisher: Global Journals Inc. (USA) Online ISSN:

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

Common Risk Factors in the US Treasury and Corporate Bond Markets: An Arbitrage-free Dynamic Nelson-Siegel Modeling Approach

Common Risk Factors in the US Treasury and Corporate Bond Markets: An Arbitrage-free Dynamic Nelson-Siegel Modeling Approach Common Risk Facors in he US Treasury and Corporae Bond Markes: An Arbirage-free Dynamic Nelson-Siegel Modeling Approach Jens H E Chrisensen and Jose A Lopez Federal Reserve Bank of San Francisco 101 Marke

More information

Forecasting and Forecast Combination in Airline Revenue Management Applications

Forecasting and Forecast Combination in Airline Revenue Management Applications Forecasing and Forecas Combinaion in Airline Revenue Managemen Applicaions Chrisiane Lemke 1, Bogdan Gabrys 1 1 School of Design, Engineering & Compuing, Bournemouh Universiy, Unied Kingdom. E-mail: {clemke,

More information

FORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA

FORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA Inernaional Review of Business Research Papers Vol.4 No. 5 Ocober-November 8 Pp. 31-48 FORECASTING WATER DEMAND FOR AGRICULTURAL, INDUSTRIAL AND DOMESTIC USE IN LIBYA Fahis F. Lawgali* This paper examines

More 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

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

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

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software Informaion Theoreic Evaluaion of Change Predicion Models for Large-Scale Sofware Mina Askari School of Compuer Science Universiy of Waerloo Waerloo, Canada maskari@uwaerloo.ca Ric Hol School of Compuer

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

I. Basic Concepts (Ch. 1-4)

I. Basic Concepts (Ch. 1-4) (Ch. 1-4) A. Real vs. Financial Asses (Ch 1.2) Real asses (buildings, machinery, ec.) appear on he asse side of he balance shee. Financial asses (bonds, socks) appear on boh sides of he balance shee. Creaing

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

NATIONAL BANK OF POLAND WORKING PAPER No. 120

NATIONAL BANK OF POLAND WORKING PAPER No. 120 NATIONAL BANK OF POLAND WORKING PAPER No. 120 Large capial inflows and sock reurns in a hin marke Janusz Brzeszczyński, Marin T. Bohl, Dobromił Serwa Warsaw 2012 Acknowledgemens: We would like o hank Ludwig

More information

ANALYSIS OF ECONOMIC CYCLES USING UNOBSERVED COMPONENTS MODELS

ANALYSIS OF ECONOMIC CYCLES USING UNOBSERVED COMPONENTS MODELS ANALYSIS OF ECONOMIC CYCLES USING UNOBSERVED COMPONENTS MODELS Diego J. Pedregal Escuela Técnica Superior de Ingenieros Indusriales Universidad de Casilla-La Mancha Avda. Camilo José Cela, 3 13071 Ciudad

More information

Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach * Ben S. Bernanke, Federal Reserve Board

Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach * Ben S. Bernanke, Federal Reserve Board Measuring he Effecs of Moneary Policy: A acor-augmened Vecor Auoregressive (AVAR) Approach * Ben S. Bernanke, ederal Reserve Board Jean Boivin, Columbia Universiy and NBER Pior Eliasz, Princeon Universiy

More information

Yale ICF Working Paper No. 00-44 March 2002

Yale ICF Working Paper No. 00-44 March 2002 Yale ICF Working Paper No. 00-44 March 2002 STOCK MARKET RETURNS IN THE LONG RUN: PARTICIPATING IN THE REAL ECONOMY Roger G. Ibboson Yale School of Managemen Peng Chen Ibboson Associaes, Inc. This paper

More information

Real-time Particle Filters

Real-time Particle Filters Real-ime Paricle Filers Cody Kwok Dieer Fox Marina Meilă Dep. of Compuer Science & Engineering, Dep. of Saisics Universiy of Washingon Seale, WA 9895 ckwok,fox @cs.washingon.edu, mmp@sa.washingon.edu Absrac

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

AN ECONOMETRIC CHARACTERIZATION OF BUSINESS CYCLE DYNAMICS WITH FACTOR STRUCTURE AND REGIME SWITCHING * Marcelle Chauvet 1

AN ECONOMETRIC CHARACTERIZATION OF BUSINESS CYCLE DYNAMICS WITH FACTOR STRUCTURE AND REGIME SWITCHING * Marcelle Chauvet 1 AN ECONOMETRIC CHARACTERIZATION OF BUSINESS CYCLE DYNAMICS WITH FACTOR STRUCTURE AND REGIME SWITCHING * Marcelle Chauve Deparmen of Economics Universiy of California, Riverside 5 Universiy Avenue Riverside,

More information

Using Weather Ensemble Predictions in Electricity Demand Forecasting

Using Weather Ensemble Predictions in Electricity Demand Forecasting Using Weaher Ensemble Predicions in Elecriciy Demand Forecasing James W. Taylor Saïd Business School Universiy of Oxford 59 George Sree Oxford OX1 2BE, UK Tel: +44 (0)1865 288678 Fax: +44 (0)1865 288651

More information

Occasional Paper series

Occasional Paper series Occasional Paper series No 84 / Shor-erm forecasing of GDP using large monhly daases a pseudo real-ime forecas evaluaion exercise by Karim Barhoumi, Szilard Benk, Riccardo Crisadoro, Ard Den Reijer, Audronė

More information

Time Series Modeling for Risk of Stock. Price with Value at Risk Computation

Time Series Modeling for Risk of Stock. Price with Value at Risk Computation Applied Mahemaical Sciences, Vol 9, 015, no 56, 779-787 HIKARI Ld, wwwm-hikaricom hp://dxdoiorg/101988/ams0155144 Time Series Modeling for Risk of Sock Price wih Value a Risk Compuaion Dodi Deviano, Maiyasri

More information

Present Value Methodology

Present Value Methodology Presen Value Mehodology Econ 422 Invesmen, Capial & Finance Universiy of Washingon Eric Zivo Las updaed: April 11, 2010 Presen Value Concep Wealh in Fisher Model: W = Y 0 + Y 1 /(1+r) The consumer/producer

More information