FORECASTING THE UK UNEMPLOYMENT RATE: MODEL COMPARISONS FLOROS, Christos *
|
|
- Kathleen Marsha Perry
- 7 years ago
- Views:
Transcription
1 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) FORECASTING THE UK UNEMPLOYMENT RATE: MODEL COMPARISONS FLOROS, Chrisos * Absrac This paper compares he ou-of-sample forecasing accuracy of ime series models using he Roo Mean Square, Mean Absolue and Mean Absolue Percen Errors. We evaluae he performance of he compeing models covering he period January 97 o December 00. The forecasing sample (January 99 December 00) is divided ino four sub-periods. Firs, for oal forecasing sample, we find ha MA()-ARCH() provides superior forecass of unemploymen rae. On he oher hand, wo forecasing samples show ha he MA() model performs well, while boh MA() and AR() prove o be he bes forecasing models for he oher wo forecasing periods. The empirical evidence derived from our invesigaion suggess a close relaionship beween forecasing heory and labour marke condiions. Our findings bring forecasing mehods nearer o he realiies of UK labour marke. JEL classificaion: C53, E7 Keywords: UK, Unemploymen, Forecasing, AR, MA, GARCH. Inroducion A number of research papers have used ime series models for forecasing macroeconomic variables. Predicing he unemploymen rae is of grea imporance o many economic decisions. Various echniques, from he simple OLS mehod o he GARCH models, have been used o explain he forecasing performance of US and UK unemploymen raes. Recen invesigaions of forecasing unemploymen rae are Proiei (00), Gil-Alana (00), Peel and Speigh (000), Johnes (999), Peel and Speigh (995) and Rohman (99). * Chrisos Floros is Lecurer of Finance and Banking a he Universiy of Porsmouh (UK), Chrisos.Floros@por.ac.uk 57
2 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) Rohman (99) compares he ou-of-sample forecasing accuracy of six nonlinear models, while Parker and Rohman (99) model he quarerly adjused rae wih AR() model. Koop and Poer (999) use hreshold auoregressive (TAR) for modelling and forecasing he US monhly unemploymen rae. Recenly, Proiei (00) examines he ou-of-sample forecasing for he US monhly unemploymen rae by using seven forecasing models (linear and nonlinear). The sudy shows ha linear models are characerised by higher persisence perform significanly bes. As a general conclusion, Proiei (00) argues ha srucural ime series models are more parsimonious. Research papers for modelling and forecasing he UK unemploymen rae are Johnes (999), Peel and Speigh (000) and Gil-Alana (00). Johnes (999) repors he forecasing compeiion beween AR(), AR()-GARCH(,), SETAR(3;,,), Neural nework and Naïve forecas of UK monhly unemploymen rae. The sample covers he period January 90 o Augus 99. The resuls indicae ha SETAR model dominaes he ohers for shor period forecass, while non-lineariies are presen in he daa. Peel and Speigh (000) es wheher nonlinear ime series models of simple SETAR form are able o provide superior ou-of-sample forecass of UK unemploymen daa (February 97 Sepember 99). The resuls show evidence for superior ou-of-sample SETAR forecasing performance relaive o AR models (in erms of RMSE). Furhermore, Gil-Alana (00) uses a Bloomfield exponenial specral model for modelling UK unemploymen, as an alernaive o he ARMA models. The resuls indicae ha his model is a feasible way of modelling UK unemploymen rae. In his paper, we focus on modelling unemploymen using daa from he UK. In paricular, we re-examine he evidence for forecasing by using ime-series models. We compare hese forecass o several mehods based on he work of recen sudies. The main purpose of his paper is o es and repor he forecasing compeiion beween differen models and forecasing periods (horizons). Our approach is much in he same spiri of Proiei 5
3 Floros, Ch. Forecasing he UK unemploymen rae: model comparisons (00), Peel and Speigh (000) and Johnes (999) in ha i focuses on an in-deph comparison of forecasing models. We exend heir analysis and compare he performance of weny-hree models for UK unemploymen using recen daa. Johnes (999) compares five models (linear auoregressive, GARCH, hreshold auoregressive and neural nework), while Proiei (00) applies seven forecasing models for he levels of he unemploymen rae. In his paper, we use he RMSE, MAE and MAPE crieria, and compare various models. For comparison purposes, we esimae differen ARMA and (G)ARCH models, namely AR(p), MA(q), ARMA(p,q), GARCH(p,q), EGARCH(,) and TGARCH(,). Finally, we produce dynamic and saic forecass. The paper is organised as follows: Secion provides he daa and mehodology, while Secion 3 presens he main empirical resuls from various economeric models. Finally, Secion concludes he paper and summarises our findings.. Daa and Mehodology We employ monhly observaions of UK unemploymen rae covering he period January 97 o December 00. The daa are sourced from Daasream. The firs 300 observaions (January 97 December 995) are used for parameer esimaion, while he nex observaions (January 99 December 00) are used for forecas evaluaion. Figure presens he graph of he UK monhly unemploymen rae series for he period January 97 o December 00. In he Table, summary saisics for unemploymen rae are presened. Descripive Saisics show a mean of six and posiive values of skewness and kurosis. Also, he Jarque-Bera saisics sugges ha he normaliy is rejeced a 5% level. ADF es fails o rejec he null hypohesis of a uni roo in he unemploymen rae series. 59
4 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) Figure. The UK Unemploymen Rae (January 97 December 00) U K U n e m p l o y m e n R a e Table. Descripive Saisics UK Unemploymen Rae Mean.009 Median Maximum Minimum Sd. Dev Skewness Kurosis.590 Jarque-Bera Probabiliy Observaions 3 ADF- level Probabiliy ADF- s diff. Probabiliy (0.) (0.0053) The following ime-series models are employed: AR(p) model is one where he curren value of a variable depends on he values ha he variable ook in previous periods plus an error erm. AR(p) model can be expressed as:
5 Floros, Ch. Forecasing he UK unemploymen rae: model comparisons Y + = c + a Y + ay a py p u () where u is a whie noise disurbance erm. A moving average (MA) process is one in which he sysemaic componen is a funcion of pas innovaions. MA(q) model can be expressed as: Y = c + b ε + bε bpε p + ε () By combining he AR(p) and MA(q) models, an ARMA(p,q) model is a model ha he curren value of some series depends linearly on is own previous values plus a combinaion of curren and previous values of a whie noise error erm. The ARMA(p,q) specificaion is given by equaion (3): Y = c + a Y b ε + ay a py p + ε + bε + bε q q The GARCH model of Engle (9) and Bollerslev (9) requires join esimaion of mean and variance equaions. The curren condiional variance of a ime series depends on pas squared residuals of he process and on pas he condiional variances. The equaions of GARCH(p,q) are given by: Y ε σ = µ + ε ~ N(0,) = c + P Q aiε i + i= j= β j σ j (Mean equaion) (Variance Equaion) () The Variance equaion of he Exponenial- GARCH(,) model is ε ε given by: log ( σ ) = c + a + a + a3 log( σ ) (5) σ σ
6 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) The EGARCH model of Nelson (99) capures he volailiy clusering and measures he asymmeric effec. The main advanage over he GARCH model, proposed by Bollerslev (9), is ha now he leverage a is exponenial and variances are posiive. The Threshold-GARCH(,) model of Zakoian (990) and Glosen, Jagannahan and Runkle (993) is given by: Y ε σ = µ + ε ~ N(0,) = c + a ε + aε d + a σ 3 (Mean Equaion) (Variance Equaion) () Good news ( ε <0) and bad news ( ε >0) have an impac equal o a and a + a, respecively. In oher words, a negaive innovaion has a greaer impac han a posiive innovaion. The asymmery effec is capured by he use of he dummy variable d. Furhermore, we produce boh dynamic and saic forecass using he seleced models over he sample period. Dynamic mehod calculaes muli-sep forecass saring from he firs period in he forecas sample. Saic mehod calculaes a sequence of one-sep ahead forecass, using acual raher han forecased values for lagged dependen variables. If S is he firs observaion in he forecas sample, hen he dynamic forecas is given by: y ˆ s = cˆ() + cˆ() xs + cˆ(3) zs + cˆ() ys. On he oher hand, saic forecas is calculaed using he acual value of he lagged endogenous variable as: y ˆ S+ k = cˆ() + cˆ() xs+ k + cˆ(3) z S+ k + cˆ() ys + k. Following Brailsford and Faff (99) and Johnes (999), we compare he forecas performance of each ime-series model hrough he error saisics (crieria). Three error saisics are employed o measure he performance of he forecasing models. Namely, he
7 Floros, Ch. Forecasing he UK unemploymen rae: model comparisons Roo Mean Squared Error (RMSE), he Mean Absolue Error (MAE), and he Mean Absolue Percen Error (MAPE). Suppose ha he forecas sample is = S, S +,.., S + h and denoe he acual and forecased value in period as y and ŷ, respecively. The repored forecas error saisics are compued as follows: RMSE = h S+ h ( yˆ y ) + = S MAE = h + S+ h = S yˆ y (7) MAPE = h + S+ h = S yˆ y y The RMSE and MAE error saisics depend on he scale of he dependen variable. We use hem o compare forecass for he same series and sample across differen ime series models. The beer forecasing abiliy of he model is ha wih he smaller RMSE and MAE error saisics. 3. Empirical Resuls To ge a clear view and in-deph comparison of forecasing models, we divide he forecasing period (January 99 - December 00) ino four sub-periods. The forecasing sub-periods are as follows: (i) January 99 - Sepember 997, (ii) Ocober June 999, (iii) July March 00 and (iv) April 00 December 00. We apply four forecasing models for AR(p) and MA(q) models (wih p=q=,,3,) using he Leas Squares mehod, as well as four forecasing models for ARMA(p,q) (wih p=q=,). For 3
8 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) GARCH(p,q), we esimae four models (p=0, and q=,) using he Marquard algorihm. We also apply EGARCH(,) and TGARCH(,) models. Furhermore, we esimae a fourh order auoregressive/moving average model in which he residual variance is allowed o vary over ime following ARCH, EGARCH and TGARCH. Table shows he seleced models for each of he forecasing periods. We presen he bes forecasing models for UK unemploymen rae (i.e. he model wih he smaller forecas error saisics) using boh saic and dynamic mehods. In he case of he seleced RMSE, he error saisics vary from o.. Forecasing period provides he smalles RMSE for AR() model, while he larges RMSE is from oal forecasing period for MA()-ARCH(). Hence, in erms of RMSE, AR() model is he bes forecasing model. The forecasing resuls of he seleced MAE measures show a minimum value of for forecasing period, and a maximum value of for oal forecasing period. The smalles MAE value indicaes ha AR() model is superior han he oher ime series models. On he oher hand, MA()-ARCH() model proves o be he wors forecasing model wih he larges MAE value. In he case of MAPE, we find ha forecasing period provides he smalles value ( ), while oal forecasing period shows he larges value (9.73). The resuls show ha AR() provides superior forecass of unemploymen rae, while MA()-ARCH() model shows a poor forecasing performance. Appendix presens he parameers of he seleced forecasing models. For oal forecasing period, we selec MA()-ARCH() as he bes forecas model because i provides small RMSE, MAE and MAPE. For he same reason, we selec AR() for forecasing period, and MA() for forecasing period 3 as well as forecasing period. The resuls from oher models are available upon reques.
9 Floros, Ch. Forecasing he UK unemploymen rae: model comparisons For forecasing period, we selec he MA() model because i provides he smalles RMSE. Since mos of he parameers are significan a 5% level, we believe ha hese models can be used for poenial forecasing resuls. Table : Forecasing Performance for Seleced ARMA and GARCH models Model RMSE MAE MAPE Forecasing Period : January 99-Sepember 997 MA()- Dynamic Saic MA()- Dynamic Saic Forecasing Period : Ocober 997-June 999 AR()- Dynamic Saic Forecasing Period 3: July 999-March 00 MA()- Dynamic Saic Forecasing Period : April 00-December 00 MA()- Dynamic Saic Forecasing Period: January 99-December 00 MA()-ARCH()- Dynamic Saic Furhermore, we produce saic and dynamic forecass using he seleced models over he sample. Dynamic forecasing performs a muli-sep forecas of unemploymen rae Y, while saic forecasing performs a series of one-sep ahead forecass of he dependen variable. Appendix shows graphs of dynamic and saic forecass for UK unemploymen rae.. Conclusions Macroeconomic modelling, and forecasing, is a widely researched area in he applied economic lieraure. Predicing he unemploymen rae is one of he mos imporan applicaions for economiss and
10 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) policymakers. The accuracy of differen forecasing mehods is a opic of coninuing ineres and research. In his paper we repor he forecasing compeiion beween Auoregressive (AR), Moving Average (MA), GARCH, EGARCH and TGARCH models of he UK monhly unemploymen rae series. We es he ou-of-sample forecasing accuracy of weny-hree models. Specifically, we compare he forecasing echniques based on he following symmeric error saisics: Roo Mean Square Error (RMSE), Mean Absolue Error (MAE) and Mean Absolue Percen Error (MAPE). The resuls from he comparisons of saic and dynamic forecass by he ime series models show ha he simples models are he mos appropriae for forecasing. Our findings are basically as follows: (i) For oal forecasing period (January 99 December 00) he MA()-ARCH() model is a more appropriae approach han he oher models, (ii) For forecasing period January 99 - Sepember 997, he simple moving average MA() model produces he lowes RMSE. However, boh MAE and MAPE sugges ha MA() is he mos appropriae model, (iii) For forecasing period Ocober June 999, a fourh order linear auoregressive AR() is he seleced forecasing model, (iv) For forecasing periods July March 00 and April 00 - December 00, we find ha a fourh order moving average MA() model shows a good fi in our daa. The above resuls sugges ha AR and MA models perform well in erms of forecasing, in conras wih oher research papers, see Johnes (999). One possible explanaion for he forecasing superioriy of hese models is ha radiional, simple ime-series models capure he dynamical srucure generaing he unemploymen levels. However, a highly daa se is possible o affec he qualiy of he forecass. In addiion, forecasing resuls may change due o he forecasing periods (horizon) as well as he selecion of in-sample and forecas daa. Our findings bring economeric heory nearer o he realiies of UK labour marke. Addiional research is required o explain he forecasing superioriy of simple and highly approaches The selecion of he sar of he forecas sample is imporan for dynamic forecasing.
11 Floros, Ch. Forecasing he UK unemploymen rae: model comparisons using monhly and quarerly daa. Fuure research should seek o invesigae more complex forecasing mehods o predic European and Asian unemploymen series. References Bollerslev, T. (9): Generalized Auoregressive Condiional Heeroskedasiciy, Journal of Economerics, 3, Brailsford, T. J. and Faff, R.W. (99): An Evaluaion of Volailiy Forecasing Techniques, Journal of Banking and Finance, 0, 9-3. Engle, R. F. (9): Auoregressive Condiional Heeroscedasiciy wih Esimaes of he Variance of Unied Kingdom Inflaion, Economerica, 50, Gil-Alana, L. A. (00): A fracionally Inegraed Exponenial Model for UK Unemploymen, Journal of Forecasing, 0, Glosen, L.R., Jagannahan, R., and Runkle, D.E. (993): On he Relaion Beween he Expeced Value and he Volailiy of he Nominal Excess Reurn on Socks, The Journal of Finance,, Johnes, G. (999): Forecasing Unemploymen, Applied Financial Economics,, Koop, G. and Poer, S. M. (999): Dynamic Asymmeries in U.S. Unemploymen, Journal of Business and Economic Saisics, 7, 9-3. Nelson, D. B. (99): Condiional Heeroskedasiciy in Asse Reurns: A New Approach, Economerica, 59, Parker, R. E. and Rohman, P. (99): The Curren Deph-of- Recession and Unemploymen Rae Forecass, Sudies in Nonlinear Dynamics and Economerics,,
12 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) Peel, D. A. and Speigh, A. E. H. (995): Nonlinear Dependence in Unemploymen, Oupu and Inflaion: Empirical Evidence for he UK, in The Naural Rae of Unemploymen: Reflecions on 5 Years of he Hypohesis, (Ed.) R. Cross, Cambridge Universiy Press, Cambridge. Peel, D. A. and Speigh, A. E. H. (000): Threshold Nonlineariies in Unemploymen Raes: Furher Evidence for he UK and G3 Economies, Applied Economics, 3, Proiei, T. (00): Forecasing he US Unemploymen Rae, Working Paper, Diparimeno di Scienze Saisiche, Universia di Udine, Udine, Ialy. Rohman, P. (99): Forecasing Asymmeric Unemploymen Raes, Review of Economics and Saisics, 0, -. Zakoian, J. M. (990): Threshold heeroskedasic models, Manuscrip, CREST, INSEE, Paris. Appendix. I.Toal Forecasing Period: January 99 December 00 MA()-ARCH() Mehod: ML - ARCH (Marquard) Mean Equaion Coefficien Sd. Error z-saisic Prob. c * MA() * MA() * MA(3) * MA() * Variance Equaion c * ARCH() * * Significan a he 5% level
13 Floros, Ch. Forecasing he UK unemploymen rae: model comparisons II. Forecasing Period : January 99-Sepember 997 MA() Mehod: Leas Squares Variable Coefficien Sd. Error -Saisic Prob. C * MA() * MA() Mehod: Leas Squares Variable Coefficien Sd. Error -Saisic Prob. C * MA() * MA() * * Significan a he 5% level III. Forecasing Period : Ocober 997-June 999 AR() Mehod: Leas Squares Variable Coefficien Sd. Error -Saisic Prob. c * AR() * AR() AR(3) AR() * * Significan a he 5% level IV: Forecasing Periods: July 999-March 00, April 00- December 00 MA() Mehod: Leas Squares Variable Coefficien Sd. Error -Saisic Prob. C * MA() * MA() * MA(3) * MA() * * Significan a he 5% level 9
14 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) Appendix. Graphs: Dynamic and Saic Forecass Forecasing Period M A( )- D Y N AM IC F O R EC A S T M A ( )- ST A T I C F O R EC A S T M A ( )- D Y N A M I C F O R E C A S T M A ( )- S T A T I C F O R E C A S T Forecasing Period A R ( )- D YN A MIC F O R E C AS T AR ( )- ST A T I C F O R EC A S T Forecasing Period M A ( )- D Y N A M I C F O R E C A S T M A ( )- S T A T I C F O R E C A S T 70
15 Floros, Ch. Forecasing he UK unemploymen rae: model comparisons Forecasing Period M A( )- D Y N AM IC F O R EC A S T M A ( )- S T A T I C F O R E C A S T Toal Forecasing Period M A ( )-A R C H ( )- D Y N A M I C F O R E C A S T.E+07.E+07.0E+07.E+07.E+07.0E+0.0E+0 0.0E F orec a s o f V a rian c e 7
16 Inernaional Journal of Applied Economerics and Quaniaive Sudies. Vol.-(005) M A ( )-A R C H ( ) - S T A T I C F O R E C A S T F o re c a s o f V a ria n c e Journal published by he Euro-American Associaion of Economic Developmen Sudies: hp:/ 7
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 informationTHE EFFECTS OF INTERNATIONAL ACCOUNTING STANDARDS ON STOCK MARKET VOLATILITY: THE CASE OF GREECE
Invesmen Managemen and Financial Innovaions, Volume 4, Issue 1, 007 61 THE EFFECTS OF INTERNATIONAL ACCOUNTING STANDARDS ON STOCK MARKET VOLATILITY: THE CASE OF GREECE Chrisos Floros * Absrac The adopion
More informationChapter 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 informationVolatility in Returns of Islamic and Commercial Banks in Pakistan
Volailiy in Reurns of Islamic and Commercial Banks in Pakisan Muhammad Iqbal Non-Linear Time Series Analysis Prof. Rober Kuns Deparmen of Economic, Universiy of Vienna, Vienna, Ausria Inroducion Islamic
More informationVector Autoregressions (VARs): Operational Perspectives
Vecor Auoregressions (VARs): Operaional Perspecives Primary Source: Sock, James H., and Mark W. Wason, Vecor Auoregressions, Journal of Economic Perspecives, Vol. 15 No. 4 (Fall 2001), 101-115. Macroeconomericians
More informationSAMUELSON S HYPOTHESIS IN GREEK STOCK INDEX FUTURES MARKET
154 Invesmen Managemen and Financial Innovaions, Volume 3, Issue 2, 2006 SAMUELSON S HYPOTHESIS IN GREEK STOCK INDEX FUTURES MARKET Chrisos Floros, Dimirios V. Vougas Absrac Samuelson (1965) argues ha
More informationThe naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1
Business Condiions & Forecasing Exponenial Smoohing LECTURE 2 MOVING AVERAGES AND EXPONENTIAL SMOOTHING OVERVIEW This lecure inroduces ime-series smoohing forecasing mehods. Various models are discussed,
More informationChapter 8 Student Lecture Notes 8-1
Chaper Suden Lecure Noes - Chaper Goals QM: Business Saisics Chaper Analyzing and Forecasing -Series Daa Afer compleing his chaper, you should be able o: Idenify he componens presen in a ime series Develop
More informationMeasuring the Downside Risk of the Exchange-Traded Funds: Do the Volatility Estimators Matter?
Proceedings of he Firs European Academic Research Conference on Global Business, Economics, Finance and Social Sciences (EAR5Ialy Conference) ISBN: 978--6345-028-6 Milan-Ialy, June 30-July -2, 205, Paper
More informationGOOD NEWS, BAD NEWS AND GARCH EFFECTS IN STOCK RETURN DATA
Journal of Applied Economics, Vol. IV, No. (Nov 001), 313-37 GOOD NEWS, BAD NEWS AND GARCH EFFECTS 313 GOOD NEWS, BAD NEWS AND GARCH EFFECTS IN STOCK RETURN DATA CRAIG A. DEPKEN II * The Universiy of Texas
More informationJournal Of Business & Economics Research September 2005 Volume 3, Number 9
Opion Pricing And Mone Carlo Simulaions George M. Jabbour, (Email: jabbour@gwu.edu), George Washingon Universiy Yi-Kang Liu, (yikang@gwu.edu), George Washingon Universiy ABSTRACT The advanage of Mone Carlo
More informationHow Useful are the Various Volatility Estimators for Improving GARCH-based Volatility Forecasts? Evidence from the Nasdaq-100 Stock Index
Inernaional Journal of Economics and Financial Issues Vol. 4, No. 3, 04, pp.65-656 ISSN: 46-438 www.econjournals.com How Useful are he Various Volailiy Esimaors for Improving GARCH-based Volailiy Forecass?
More informationMACROECONOMIC 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 informationSPEC model selection algorithm for ARCH models: an options pricing evaluation framework
Applied Financial Economics Leers, 2008, 4, 419 423 SEC model selecion algorihm for ARCH models: an opions pricing evaluaion framework Savros Degiannakis a, * and Evdokia Xekalaki a,b a Deparmen of Saisics,
More informationThe Relationship between Stock Return Volatility and. Trading Volume: The case of The Philippines*
The Relaionship beween Sock Reurn Volailiy and Trading Volume: The case of The Philippines* Manabu Asai Faculy of Economics Soka Universiy Angelo Unie Economics Deparmen De La Salle Universiy Manila May
More information4. International Parity Conditions
4. Inernaional ariy ondiions 4.1 urchasing ower ariy he urchasing ower ariy ( heory is one of he early heories of exchange rae deerminaion. his heory is based on he concep ha he demand for a counry's currency
More informationCointegration: The Engle and Granger approach
Coinegraion: The Engle and Granger approach Inroducion Generally one would find mos of he economic variables o be non-saionary I(1) variables. Hence, any equilibrium heories ha involve hese variables require
More informationUsefulness of the Forward Curve in Forecasting Oil Prices
Usefulness of he Forward Curve in Forecasing Oil Prices Akira Yanagisawa Leader Energy Demand, Supply and Forecas Analysis Group The Energy Daa and Modelling Cener Summary When people analyse oil prices,
More informationA DCC Analysis of Two Stock Market Returns Volatility with an Oil Price Factor: An Evidence Study of Singapore and Thailand s Stock Markets
Journal of Convergence Informaion Technology Volume 4, Number 1, March 9 A DCC Analysis of Two Sock Marke Reurns Volailiy wih an Oil Price Facor: An Evidence Sudy of Singapore and Thailand s Sock Markes
More informationCentral Bank Communication and Exchange Rate Volatility: A GARCH Analysis
Cenral Bank Communicaion and Exchange Rae Volailiy: A GARCH Analysis Radovan Fišer Insiue of Economic Sudies, Charles Universiy, Prague Roman Horváh* Czech Naional Bank and Insiue of Economic Sudies, Charles
More informationA COMPARISON OF FORECASTING MODELS FOR ASEAN EQUITY MARKETS
Sunway Academic Journal, 1 1 (005) A COMPARISON OF FORECASTING MODELS FOR ASEAN EQUITY MARKETS WONG YOKE CHEN a Sunway Universiy College KOK KIM LIAN b Universiy of Malaya ABSTRACT This paper compares
More informationMeasuring macroeconomic volatility Applications to export revenue data, 1970-2005
FONDATION POUR LES ETUDES ET RERS LE DEVELOPPEMENT INTERNATIONAL Measuring macroeconomic volailiy Applicaions o expor revenue daa, 1970-005 by Joël Cariolle Policy brief no. 47 March 01 The FERDI is a
More informationInvestor sentiment of lottery stock evidence from the Taiwan stock market
Invesmen Managemen and Financial Innovaions Volume 9 Issue 1 Yu-Min Wang (Taiwan) Chun-An Li (Taiwan) Chia-Fei Lin (Taiwan) Invesor senimen of loery sock evidence from he Taiwan sock marke Absrac This
More informationOil Price Fluctuations and Firm Performance in an Emerging Market: Assessing Volatility and Asymmetric Effect
Journal of Economics, Business and Managemen, Vol., No. 4, November 203 Oil Price Flucuaions and Firm Performance in an Emerging Marke: Assessing Volailiy and Asymmeric Effec Hawai Janor, Aisyah Abdul-Rahman,
More informationDay Trading Index Research - He Ingeria and Sock Marke
Influence of he Dow reurns on he inraday Spanish sock marke behavior José Luis Miralles Marcelo, José Luis Miralles Quirós, María del Mar Miralles Quirós Deparmen of Financial Economics, Universiy of Exremadura
More informationThe predictive power of volatility models: evidence from the ETF market
Invesmen Managemen and Financial Innovaions, Volume, Issue, 4 Chang-Wen Duan (Taiwan), Jung-Chu Lin (Taiwan) The predicive power of volailiy models: evidence from he ETF marke Absrac This sudy uses exchange-raded
More informationTHE NEW MARKET EFFECT ON RETURN AND VOLATILITY OF SPANISH STOCK SECTOR INDEXES
THE NEW MARKET EFFECT ON RETURN AND VOLATILITY OF SPANISH STOCK SECTOR INDEXES Juan Ángel Lafuene Universidad Jaume I Unidad Predeparamenal de Finanzas y Conabilidad Campus del Riu Sec. 1080, Casellón
More information11/6/2013. 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 informationTime Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test
ABSTRACT Time Series Analysis Using SAS R Par I The Augmened Dickey-Fuller (ADF) Tes By Ismail E. Mohamed The purpose of his series of aricles is o discuss SAS programming echniques specifically designed
More informationVIX, Gold, Silver, and Oil: How do Commodities React to Financial Market Volatility?
VIX, Gold, Silver, and Oil: How do Commodiies Reac o Financial Marke Volailiy? Daniel Jubinski Sain Joseph s Universiy Amy F. Lipon Sain Joseph s Universiy We examine how implied and conemporaneous equiy
More informationThe transitory and permanent components of return volatility in Asian stock markets
Invesmen Managemen and Financial Innovaions, Volume 10, Issue 4, 013 Yung-Shi Liau (Taiwan), Chun-Fan You (Taiwan) The ransiory and permanen componens of reurn volailiy in Asian sock markes Absrac Moivaed
More informationDeterminants of Capital Structure: Comparison of Empirical Evidence from the Use of Different Estimators
Serrasqueiro and Nunes, Inernaional Journal of Applied Economics, 5(1), 14-29 14 Deerminans of Capial Srucure: Comparison of Empirical Evidence from he Use of Differen Esimaors Zélia Serrasqueiro * and
More informationImprovement in Forecasting Accuracy Using the Hybrid Model of ARFIMA and Feed Forward Neural Network
American Journal of Inelligen Sysems 2012, 2(2): 12-17 DOI: 10.5923/j.ajis.20120202.02 Improvemen in Forecasing Accuracy Using he Hybrid Model of ARFIMA and Feed Forward Neural Nework Cagdas Hakan Aladag
More informationThe Asymmetric Effects of Oil Shocks on an Oil-exporting Economy*
CUADERNOS DE ECONOMÍA, VOL. 47 (MAYO), PP. 3-13, 2010 The Asymmeric Effecs of Oil Shocks on an Oil-exporing Economy* Omar Mendoza Cenral Bank of Venezuela David Vera Ken Sae Universiy We esimae he effecs
More informationReal Estate Investment Trusts and Seasonal Volatility: A Periodic GARCH Model
Real Esae Invesmen Truss and Seasonal Volailiy: A Periodic GARCH Model Marc Winniford * Duke Universiy Durham, NC Spring 2003 * Marc Winniford will graduae from Duke Universiy in Spring 2004 wih a Bachelor
More informationCALENDAR ANOMALIES IN EMERGING BALKAN EQUITY MARKETS
INTERNATIONAL ECONOMICS & FINANCE JOURNAL Vol. 6, No. 1, January-June (2011) : 67-82 CALENDAR ANOMALIES IN EMERGING BALKAN EQUITY MARKETS Andreas G. Georganopoulos *, Dimiris F. Kenourgios ** and Anasasios
More informationModelling and Forecasting Volatility of Gold Price with Other Precious Metals Prices by Univariate GARCH Models
Deparmen of Saisics Maser's Thesis Modelling and Forecasing Volailiy of Gold Price wih Oher Precious Meals Prices by Univariae GARCH Models Yuchen Du 1 Supervisor: Lars Forsberg 1 Yuchen.Du.84@suden.uu.se
More informationThe Economic Value of Volatility Timing Using a Range-based Volatility Model
The Economic Value of Volailiy Timing Using a Range-based Volailiy Model Ray Yeuien Chou * Insiue of Economics, Academia Sinica & Insiue of Business Managemen, Naional Chiao Tung Universiy Nahan Liu Deparmen
More informationHow To Calculate Price Elasiciy Per Capia Per Capi
Price elasiciy of demand for crude oil: esimaes for 23 counries John C.B. Cooper Absrac This paper uses a muliple regression model derived from an adapaion of Nerlove s parial adjusmen model o esimae boh
More informationA Note on Using the Svensson procedure to estimate the risk free rate in corporate valuation
A Noe on Using he Svensson procedure o esimae he risk free rae in corporae valuaion By Sven Arnold, Alexander Lahmann and Bernhard Schwezler Ocober 2011 1. The risk free ineres rae in corporae valuaion
More informationJEL classifications: Q43;E44 Keywords: Oil shocks, Stock market reaction.
Applied Economerics and Inernaional Developmen. AEID.Vol. 5-3 (5) EFFECT OF OIL PRICE SHOCKS IN THE U.S. FOR 1985-4 USING VAR, MIXED DYNAMIC AND GRANGER CAUSALITY APPROACHES AL-RJOUB, Samer AM * Absrac
More informationModeling Long Memory in The Indian Stock Market using Fractionally Integrated Egarch Model
Inernaional Journal of Trade, Economics and Finance, Vol., No.3, Ocober, 00 ing Long Memory in The Indian Sock Marke using Fracionally Inegraed Egarch Hojaallah Goudarzi Absrac The weak form of marke efficiency
More informationNONLINEAR MARKET DYNAMICS BETWEEN STOCK RETURNS AND TRADING VOLUME: EMPIRICAL EVIDENCES FROM ASIAN STOCK MARKETS
ANALELE ŞTIINłIFICE ALE UNIVERSITĂłII ALEXANDRU IOAN CUZA DIN IAŞI Tomul LVI ŞiinŃe Economice 009 NONLINEAR MARKET DYNAMICS BETWEEN STOCK RETURNS AND TRADING VOLUME: EMPIRICAL EVIDENCES FROM ASIAN STOCK
More informationMonetary Policy & Real Estate Investment Trusts *
Moneary Policy & Real Esae Invesmen Truss * Don Bredin, Universiy College Dublin, Gerard O Reilly, Cenral Bank and Financial Services Auhoriy of Ireland & Simon Sevenson, Cass Business School, Ciy Universiy
More informationWhy does the correlation between stock and bond returns vary over time?
Why does he correlaion beween sock and bond reurns vary over ime? Magnus Andersson a,*, Elizavea Krylova b,**, Sami Vähämaa c,*** a European Cenral Bank, Capial Markes and Financial Srucure Division b
More informationWhy Did the Demand for Cash Decrease Recently in Korea?
Why Did he Demand for Cash Decrease Recenly in Korea? Byoung Hark Yoo Bank of Korea 26. 5 Absrac We explores why cash demand have decreased recenly in Korea. The raio of cash o consumpion fell o 4.7% in
More informationMarket Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand
36 Invesmen Managemen and Financial Innovaions, 4/4 Marke Liquidiy and he Impacs of he Compuerized Trading Sysem: Evidence from he Sock Exchange of Thailand Sorasar Sukcharoensin 1, Pariyada Srisopisawa,
More informationThe Influence of Positive Feedback Trading on Return Autocorrelation: Evidence for the German Stock Market
The Influence of Posiive Feedback Trading on Reurn Auocorrelaion: Evidence for he German Sock Marke Absrac: In his paper we provide empirical findings on he significance of posiive feedback rading for
More informationModelling the dependence of the UK stock market on the US stock market: A need for multiple regimes
Modelling he dependence of he UK sock marke on he US sock marke: A need for muliple regimes A J Khadaroo Deparmen of Economics and Saisics Universiy of Mauriius Redui Mauriius Email: j.khadaroo@uom.ac.mu
More informationDynamic co-movement and correlations in fixed income markets: Evidence from selected emerging market bond yields
P Thupayagale* and I Molalapaa Dynamic co-movemen and correlaions in fixed income markes: Evidence from seleced emerging marke bond yield Dynamic co-movemen and correlaions in fixed income markes: Evidence
More informationStock market returns and volatility in the BRVM
African Journal of Business Managemen Vol. (5) pp. 07-, Augus 007 Available online hp://www.academicjournals.org/ajbm ISSN 993-833 007 Academic Journals Full Lengh esearch Paper Sock marke reurns and volailiy
More informationModelling the Growth and Volatility in Daily International Mass Tourism to Peru
Modelling he Growh and Volailiy in Daily Inernaional Mass Tourism o Peru Jose Angelo Divino Deparmen of Economics Caholic Universiy of Brasilia Michael McAleer Deparmen of Quaniaive Economics Compluense
More informationGovernment Revenue Forecasting in Nepal
Governmen Revenue Forecasing in Nepal T. P. Koirala, Ph.D.* Absrac This paper aemps o idenify appropriae mehods for governmen revenues forecasing based on ime series forecasing. I have uilized level daa
More informationApplied Econometrics and International Development Vol.7-1 (2007)
Applied Economerics and Inernaional Developmen Vol.7- (7) THE INFLUENCE OF INTERNATIONAL STOCK MARKETS AND MACROECONOMIC VARIABLES ON THE THAI STOCK MARKET CHANCHARAT, Surachai *, VALADKHANI, Abbas HAVIE,
More informationMODELING SPILLOVERS BETWEEN STOCK MARKET AND MONEY MARKET IN NIGERIA
Working Paper Series: 16 Jan/2015 MODELING SPILLOVERS BETWEEN STOCK MARKET AND MONEY MARKET IN NIGERIA Afees A. Salisu and Kazeem O. Isah MODELING SPILLOVERS BETWEEN STOCK MARKET AND MONEY MARKET IN NIGERIA
More informationPrincipal components of stock market dynamics. Methodology and applications in brief (to be updated ) Andrei Bouzaev, bouzaev@ya.
Principal componens of sock marke dynamics Mehodology and applicaions in brief o be updaed Andrei Bouzaev, bouzaev@ya.ru Why principal componens are needed Objecives undersand he evidence of more han one
More informationModelling and forecasting the volatility of petroleum futures prices
Modelling and forecasing he volailiy of peroleum fuures prices Sang Hoon Kang a, Seong-Min Yoon b, * a Deparmen of Business Adminisraion, Pusan Naional Universiy, Busan 609-735, Korea b Deparmen of Economics,
More informationHotel Room Demand Forecasting via Observed Reservation Information
Proceedings of he Asia Pacific Indusrial Engineering & Managemen Sysems Conference 0 V. Kachivichyanuul, H.T. Luong, and R. Piaaso Eds. Hoel Room Demand Forecasing via Observed Reservaion Informaion aragain
More informationTEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS
TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS RICHARD J. POVINELLI AND XIN FENG Deparmen of Elecrical and Compuer Engineering Marquee Universiy, P.O.
More informationThe Effectiveness of Reputation as a Disciplinary Mechanism in Sell-side Research
The Effeciveness of Repuaion as a Disciplinary Mechanism in Sell-side Research Lily Fang INSEAD Ayako Yasuda The Wharon School, Universiy of Pennsylvania We hank Franklin Allen, Gary Goron, Pierre Hillion,
More informationDEMAND 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 informationWhen Do TIPS Prices Adjust to Inflation Information?
When Do TIPS Prices Adjus o Inflaion Informaion? Quenin C. Chu a, *, Deborah N. Piman b, Linda Q. Yu c Augus 15, 2009 a Deparmen of Finance, Insurance, and Real Esae. The Fogelman College of Business and
More informationContrarian insider trading and earnings management around seasoned equity offerings; SEOs
Journal of Finance and Accounancy Conrarian insider rading and earnings managemen around seasoned equiy offerings; SEOs ABSTRACT Lorea Baryeh Towson Universiy This sudy aemps o resolve he differences in
More informationDeterminants of the asymmetric gold market
Invesmen Managemen and Financial Innovaions, Volume 7, Issue 4, 00 Aposolos Kiohos (Greece), Nikolaos Sariannidis (Greece) Deerminans of he asymmeric gold marke Absrac The purpose of his paper is o explore
More informationBid-ask Spread and Order Size in the Foreign Exchange Market: An Empirical Investigation
Bid-ask Spread and Order Size in he Foreign Exchange Marke: An Empirical Invesigaion Liang Ding* Deparmen of Economics, Macaleser College, 1600 Grand Avenue, S. Paul, MN55105, U.S.A. Shor Tile: Bid-ask
More informationSupply chain management of consumer goods based on linear forecasting models
Supply chain managemen of consumer goods based on linear forecasing models Parícia Ramos (paricia.ramos@inescporo.p) INESC TEC, ISCAP, Insiuo Poliécnico do Poro Rua Dr. Robero Frias, 378 4200-465, Poro,
More informationThe stock index futures hedge ratio with structural changes
Invesmen Managemen and Financial Innovaions Volume 11 Issue 1 2014 Po-Kai Huang (Taiwan) The sock index fuures hedge raio wih srucural changes Absrac This paper esimaes he opimal sock index fuures hedge
More informationAn Empirical Study on Capital Structure and Financing Decision- Evidences from East Asian Tigers
An Empirical Sudy on Capial Srucure and Financing Decision- Evidences from Eas Asian Tigers Dr. Jung-Lieh Hsiao and Ching-Yu Hsu, Naional Taipei Universiy, Taiwan Dr. Kuang-Hua Hsu, Chaoyang Universiy
More informationInvestment Management and Financial Innovations, 3/2005
46 Invesmen Managemen and Financial Innovaions, 3/5 The Relaionship beween Trading Volume, Volailiy and Sock Marke Reurns: A es of Mixed Disribuion Hypohesis for A Pre- and Pos Crisis on Kuala Lumpur Sock
More informationStability. Coefficients may change over time. Evolution of the economy Policy changes
Sabiliy Coefficiens may change over ime Evoluion of he economy Policy changes Time Varying Parameers y = α + x β + Coefficiens depend on he ime period If he coefficiens vary randomly and are unpredicable,
More informationAN INVESTIGATION INTO THE LINKAGES BETWEEN EURO AND STERLING SWAP SPREADS. Somnath Chatterjee* Department of Economics University of Glasgow
AN INVESTIGATION INTO THE LINKAGES BETWEEN EURO AND STERLING SWAP SPREADS Somnah Chaerjee* Deparmen of Economics Universiy of Glasgow January, 2005 Absrac This paper examines he causal relaionship beween
More informationRelationship between Stock Returns and Trading Volume: Domestic and Cross-Country Evidence in Asian Stock Markets
Proceedings of he 2013 Inernaional Conference on Economics and Business Adminisraion Relaionship beween Sock Reurns and Trading olume: Domesic and Cross-Counry Evidence in Asian Sock Markes Ki-Hong Choi
More informationAsymmetric Information, Perceived Risk and Trading Patterns: The Options Market
Asymmeric Informaion, Perceived Risk and Trading Paerns: The Opions Marke Guy Kaplanski * Haim Levy** March 01 * Bar-Ilan Universiy, Israel, Tel: 97 50 696, Fax: 97 153 50 696, email: guykap@biu.ac.il.
More informationThe impact of the trading systems development on bid-ask spreads
Chun-An Li (Taiwan), Hung-Cheng Lai (Taiwan)* The impac of he rading sysems developmen on bid-ask spreads Absrac Following he closure, on 30 June 2005, of he open oucry sysem on he Singapore Exchange (SGX),
More informationDoes Skewness Matter? Evidence from the Index Options Market
Does Skewness Maer? Evidence from he Index Opions Marke Madhu Kalimipalli School of Business and Economics Wilfrid Laurier Universiy Waerloo, Onario, Canada NL 3C5 Tel: 59-884-070 (ex. 87) mkalimip@wlu.ca
More informationA New Type of Combination Forecasting Method Based on PLS
American Journal of Operaions Research, 2012, 2, 408-416 hp://dx.doi.org/10.4236/ajor.2012.23049 Published Online Sepember 2012 (hp://www.scirp.org/journal/ajor) A New Type of Combinaion Forecasing Mehod
More informationDYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus University Toruń 2006. Ryszard Doman Adam Mickiewicz University in Poznań
DYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus Universiy Toruń 26 1. Inroducion Adam Mickiewicz Universiy in Poznań Measuring Condiional Dependence of Polish Financial Reurns Idenificaion of condiional
More informationForecasting and Forecast Combination in Airline Revenue Management Applications
Forecasing and Forecas Combinaion in Airline Revenue Managemen Applicaions Chrisiane Lemke 1, Bogdan Gabrys 1 1 School of Design, Engineering & Compuing, Bournemouh Universiy, Unied Kingdom. E-mail: {clemke,
More informationPredicting Stock Market Index Trading Signals Using Neural Networks
Predicing Sock Marke Index Trading Using Neural Neworks C. D. Tilakarane, S. A. Morris, M. A. Mammadov, C. P. Hurs Cenre for Informaics and Applied Opimizaion School of Informaion Technology and Mahemaical
More informationStock Price Prediction Using the ARIMA Model
2014 UKSim-AMSS 16h Inernaional Conference on Compuer Modelling and Simulaion Sock Price Predicion Using he ARIMA Model 1 Ayodele A. Adebiyi., 2 Aderemi O. Adewumi 1,2 School of Mahemaic, Saisics & Compuer
More informationSupplementary 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 informationPERFORMANCE OF VAR IN DEVELOPED AND CEE COUNTRIES
5. PERFORMANCE OF VAR IN DEVELOPED AND CEE COUNTRIES DURING THE GLOBAL FINANCIAL CRISIS Mirjana MILETIĆ 1 Siniša MILETIĆ Absrac The aim of his paper is o compare performance of Value a Risk (VaR) models
More informationCausal Relationship between Macro-Economic Indicators and Stock Market in India
Asian Journal of Finance & Accouning Causal Relaionship beween Macro-Economic Indicaors and Sock Marke in India Dr. Naliniprava ripahy Associae Professor (Finance), Indian Insiue of Managemen Shillong
More informationThe Economic Value of Volatility Transmission between the Stock and Bond Markets
The Economic Value of Volailiy Transmission beween he Sock and Bond Markes Helena Chuliá * Hipòli Torró Sepember 006 Keywords: Volailiy Spillovers, GARCH, Trading Rules JEL Classificaion: C3, C53, G11
More informationDetecting Earnings Management Using Cross-Sectional Abnormal Accruals Models *
Deecing Earnings Managemen Using Cross-Secional Abnormal Accruals Models * K. V. Peasnell, P. F. Pope and S. Young Lancaser Universiy Draf: December 1999 * We are graeful for helpful commens and suggesions
More informationWorking 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 informationAn asymmetric process between initial margin requirements and volatility: New evidence from Japanese stock market
African Journal of Business Managemen Vol.6 (9), pp. 870-8736, 5 July, 0 Available online a hp://www.academicjournals.org/ajbm DOI: 0.5897/AJBM.88 ISSN 993-833 0 Academic Journals Full Lengh Research Paper
More informationAn Econometric Analysis of Market Anomaly - Day of the Week Effect on a Small Emerging Market
Inernaional Journal of Academic Research in Accouning, Finance and Managemen Sciences Vol., No., January 0, pp. 4 ISSN: 5-89 0 HRMARS www.hrmars.com An Economeric Analysis of Marke Anomaly - Day of he
More informationFlorida State University Libraries
Florida Sae Universiy Libraries Elecronic Theses, Treaises and Disseraions The Graduae School 2008 Two Essays on he Predicive Abiliy of Implied Volailiy Consanine Diavaopoulos Follow his and addiional
More informationEarnings Timeliness and Seasoned Equity Offering Announcement Effect
Inernaional Journal of Humaniies and Social Science Vol. 1 No. 0; December 011 Earnings Timeliness and Seasoned Equiy Offering Announcemen Effec Yuequan Wang School of Accouning and Finance The Hong Kong
More informationAnalysis of Calendar Effects: Day-of-the-Week Effect on the Stock Exchange of Thailand (SET)
200-023X Analysis of Calendar Effecs: Day-of-he-Week Effec on he Sock Echange of Thailand () Phaisarn Suheebanjard and Wichian Premchaiswadi Absrac According o he Efficien Marke Hypohesis (EMH), a sock
More informationPurchasing Power Parity (PPP), Sweden before and after EURO times
School of Economics and Managemen Purchasing Power Pariy (PPP), Sweden before and afer EURO imes - Uni Roo Tes - Coinegraion Tes Masers hesis in Saisics - Spring 2008 Auhors: Mansoor, Rashid Smora, Ami
More informationexpressed here and the approaches suggested are of the author and not necessarily of NSEIL.
I. Inroducion Do Fuures and Opions rading increase sock marke volailiy Dr. Premalaa Shenbagaraman * In he las decade, many emerging and ransiion economies have sared inroducing derivaive conracs. As was
More informationThe Real Business Cycle paradigm. The RBC model emphasizes supply (technology) disturbances as the main source of
Prof. Harris Dellas Advanced Macroeconomics Winer 2001/01 The Real Business Cycle paradigm The RBC model emphasizes supply (echnology) disurbances as he main source of macroeconomic flucuaions in a world
More informationKey Words: Steel Modelling, ARMA, GARCH, COGARCH, Lévy Processes, Discrete Time Models, Continuous Time Models, Stochastic Modelling
Vol 4, No, 01 ISSN: 1309-8055 (Online STEEL PRICE MODELLING WITH LEVY PROCESS Emre Kahraman Türk Ekonomi Bankası (TEB A.Ş. Direcor / Risk Capial Markes Deparmen emre.kahraman@eb.com.r Gazanfer Unal Yediepe
More informationSkewness and Kurtosis Adjusted Black-Scholes Model: A Note on Hedging Performance
Finance Leers, 003, (5), 6- Skewness and Kurosis Adjused Black-Scholes Model: A Noe on Hedging Performance Sami Vähämaa * Universiy of Vaasa, Finland Absrac his aricle invesigaes he dela hedging performance
More informationThe Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas
The Greek financial crisis: growing imbalances and sovereign spreads Heaher D. Gibson, Sephan G. Hall and George S. Tavlas The enry The enry of Greece ino he Eurozone in 2001 produced a dividend in he
More informationThe Relationship between Trading Volume, Returns and Volatility: Evidence from the Greek Futures Markets CHRISTOS FLOROS. Abstract
The elaionship beween Trading Volume, eurns and Volailiy: Evidence from he Greek Fuures Markes CHISTOS FLOOS Deparmen of Economics, Universiy of Porsmouh, Locksway oad, Porsmouh, PO4 8JF, UK. E-Mail: Chrisos.Floros@por.ac.uk,
More informationSURVEYING THE RELATIONSHIP BETWEEN STOCK MARKET MAKER AND LIQUIDITY IN TEHRAN STOCK EXCHANGE COMPANIES
Inernaional Journal of Accouning Research Vol., No. 7, 4 SURVEYING THE RELATIONSHIP BETWEEN STOCK MARKET MAKER AND LIQUIDITY IN TEHRAN STOCK EXCHANGE COMPANIES Mohammad Ebrahimi Erdi, Dr. Azim Aslani,
More informationForecasting the dynamics of financial markets. Empirical evidence in the long term
Leonardo Franci (Ialy), Andi Duqi (Ialy), Giuseppe Torluccio (Ialy) Forecasing he dynamics of financial markes. Empirical evidence in he long erm Absrac This sudy aims o verify wheher here are any macroeconomic
More information