Asymmetry and Leverage in Conditional Volatility Models

Size: px
Start display at page:

Download "Asymmetry and Leverage in Conditional Volatility Models"

Transcription

1 Economerics 04,, 45-50; doi:0.3390/economerics03045 OPEN ACCESS economerics ISSN Aricle Asymmery and Leverage in Condiional Volailiy Models Micael McAleer,,3,4 Deparmen of Quaniaive Finance, Naional Tsing Hua Universiy, Hsincu 0, Taiwan; Tel.: ; Fax: Economeric Insiue, Erasmus Scool of Economics, Erasmus Universiy Roerdam, Burgemeeser Oudlaan 50, 306 PA Roerdam, Te Neerlands 3 Tinbergen Insiue, Roerdam 3000 DR, Te Neerlands 4 Deparmen of Quaniaive Economics, Compluense Universiy of Madrid, Madrid, 8040, Spain Received: 8 Sepember 04; in revised form: 9 Sepember 04 / Acceped: 9 Sepember 04 / Publised: 4 Sepember 04 Absrac: Te ree mos popular univariae condiional volailiy models are e generalized auoregressive condiional eeroskedasiciy (GARCH) model of Engle (98) and Bollerslev (986), e GJR (or resold GARCH) model of Glosen, Jagannaan and Runkle (99), and e exponenial GARCH (or EGARCH) model of Nelson (990, 99). Te underlying socasic specificaion o obain GARCH was demonsraed by Tsay (987), and a of EGARCH was sown recenly in McAleer and Hafner (04). Tese models are imporan in esimaing and forecasing volailiy, as well as in capuring asymmery, wic is e differen effecs on condiional volailiy of posiive and negaive effecs of equal magniude, and purporedly in capuring leverage, wic is e negaive correlaion beween reurns socks and subsequen socks o volailiy. As ere seems o be some confusion in e lieraure beween asymmery and leverage, as well as wic asymmeric models are purpored o be able o capure leverage, e purpose of e paper is ree-fold, namely, () o derive e GJR model from a random coefficien auoregressive process, wi appropriae regulariy condiions; () o sow a leverage is no possible in e GJR and EGARCH models; and (3) o presen e inerpreaion of e parameers of e ree popular univariae condiional volailiy models in a unified manner. Keywords: condiional volailiy models; random coefficien auoregressive processes; random coefficien complex nonlinear moving average process; asymmery; leverage JEL classificaions: C; C5; C58; G3

2 Economerics 04, 46. Inroducion Te ree mos popular univariae condiional volailiy models are e generalized auoregressive condiional eeroskedasiciy (GARCH) model of Engle (98) [] and Bollerslev (986) [], e GJR (or resold GARCH) model of Glosen, Jagannaan and Runkle (99) [3], and e exponenial GARCH (or EGARCH) model of Nelson (990, 99) [4,5]. Te underlying socasic specificaion o obain GARCH was demonsraed by Tsay (987) [6], and a of EGARCH was sown recenly in McAleer and Hafner (04) [7]. Tese models are imporan in esimaing and forecasing volailiy and in capuring asymmery, wic is e differen effecs on condiional volailiy of posiive and negaive effecs of equal magniude; furermore, ey are purporedly imporan in capuring leverage, wic is e negaive correlaion beween reurns socks and subsequen socks o volailiy. Te purpose of e paper is ree-fold, namely, () o derive e GJR model from a random coefficien auoregressive process, wi appropriae regulariy condiions; () o sow a leverage is no possible in e GJR and EGARCH models; and (3) o presen e inerpreaion of e parameers of e ree popular univariae condiional volailiy models in a unified manner. Te derivaion of ree well known condiional volailiy models, namely GARCH, GJR and EGARCH, from eir respecive underlying socasic processes raises wo imporan issues: () e regulariy condiions for eac condiional volailiy model can be derived in a sraigforward manner; and () e GJR and EGARCH models can be sown o capure asymmery, bu ey can also be sown o be unable o capure leverage. Te paper is organized as follows. In Secion, e GARCH, GJR and EGARCH models are derived from differen socasic processes, e firs wo from random coefficien auoregressive processes and e ird from a random coefficien complex nonlinear moving average process. I is sown a asymmery is possible for GJR and EGARCH, bu a leverage is no possible. Issues relaed o esimaion are also discussed, wi a view o guaraneeing posiive esimaes of condiional volailiy. Some concluding commens are given in Secion 3.. Socasic Processes for Condiional Volailiy Models.. Random Coefficien Auoregressive Process and GARCH Consider e condiional mean of financial reurns as in e following: were e reurns, y = se a ime, and y E ) ( y I () P ), log P represens e log-difference in sock prices ( I is e informaion is condiionally eeroskedasic. In order o derive condiional volailiy specificaions, i is necessary o specify e socasic processes underlying e reurns socks,.

3 Economerics 04, 47 Consider e following random coefficien auoregressive process of order one: () were ~ iid ( 0, ). Tsay (987) [6] sowed a e ARCH() model of Engle (98) [] could be derived from Equaion () as: were is condiional volailiy, and E( I ) (3) I is e informaion se a ime. Te use of an infinie lag leng for e random coefficien auoregressive process in Equaion (), wi appropriae resricions on e random coefficiens, can be sown o lead o e GARCH model of Bollerslev (986) []. As e ARCH and GARCH models are symmeric, in a posiive and negaive socks of equal magniude ave idenical effecs on condiional volailiy, ere is no asymmery, and ence also no leverage, wereby negaive socks increase condiional volailiy and posiive socks decrease condiional volailiy (see Black (976) [8]). I is wor noing a a leas one of or mus be posiive for condiional volailiy o be posiive, wi > 0 and > 0 regarded as sufficien condiions for posiiviy of condiional volailiy. From e specificaion of Equaion (), i is clear a bo and sould be posiive as ey are e variances of wo differen socasic processes. From a pracical perspecive, a failure o impose e posiiviy resricions on e parameers can increase e probabiliy of obaining negaive esimaes of condiional volailiy. For example, in e curren version R04a of MATLAB, no posiiviy is imposed in esimaing e parameers of GARCH. Similar commens apply o oer sandard economeric, financial economeric and saisical sofware packages, and o e GJR and EGARCH models a are discussed below... Random Coefficien Auoregressive Process and GJR Te GJR model of Glosen, Jagannaan and Runkle (99) [3] can be derived as a simple exension of e random coefficien auoregressive process in Equaion (), wi an indicaor variable I ) a disinguises beween e differen effecs of posiive and negaive reurns socks on condiional volailiy, namely: I( ) ( (4) were I( ) = wen < 0, I( ) = 0 wen 0.

4 Economerics 04, 48 Te condiional expecaion of e squared reurns socks in (3), wic is ypically referred o as e GJR (or resold GARCH) model, can be sown o be an exension of Equaion (3), as follows: E( I ) I( ) (5) Te use of an infinie lag leng for e random coefficien auoregressive process in Equaion (4), wi appropriae resricions on e random coefficiens, can be sown o lead o e sandard GJR model wi lagged condiional volailiy. As GARCH is nesed wiin GJR, e inerpreaion of e coefficiens in e wo models is essenially e same, apar from e parameer associaed wi asymmery. I is wor noing a a leas one of (,, ) mus be posiive for condiional volailiy o be posiive, wi > 0, > 0 and > 0 regarded as sufficien condiions for posiiviy of condiional volailiy. From e specificaion of Equaion (4), i is clear a all ree parameers sould be posiive as ey are e variances of ree differen socasic processes. Te GJR model is asymmeric, in a posiive and negaive socks of equal magniude ave differen effecs on condiional volailiy. Terefore, asymmery exiss for GJR if: asymmery for GJR: 0. A special case of asymmery is leverage, wic is e negaive correlaion beween reurns socks and subsequen socks o volailiy (see Black (976) [8]). Te condiions for leverage in e GJR model in Equaion (5) are: leverage for GJR: 0 and 0. I is clear a leverage is no possible for GJR as bo and, wic are e variances of wo socasic processes, mus be posiive. As in e case of GARCH, e posiiviy resricions on e parameers of GJR are ypically no imposed in esimaion using sandard economeric, financial economeric and saisical sofware packages..3. Random Coefficien Complex Nonlinear Moving Average Process and EGARCH Anoer condiional volailiy model a can accommodae asymmery is e EGARCH model of Nelson (990, 99) [4,5]. McAleer and Hafner (04) [7] sowed a EGARCH could be derived from a random coefficien complex nonlinear moving average (RCCNMA) process, as follows: (6) were is a complex-valued funcion of.

5 Economerics 04, 49 Te condiional variance of e squared reurns socks in Equaion (6) is given as: I is wor noing a e ransformaion of approximaion given by: can be used o replace E (7) log ( I ) log( ( in Equaion (7) wi + in Equaion (7) is no logarimic, bu e log )). Te use of an infinie lag for e RCCNMA process in Equaion (6) would yield, afer suiable logarimic approximaion, e sandard EGARCH model wi lagged condiional volailiy. EGARCH differs from GARCH and GJR in a, given e logarimic ransformaion, no sign resricions on (,, ) are necessary for condiional volailiy o be posiive. However, i is clear from e RCCNMA process in Equaion (6) a all ree parameers sould be posiive as ey are e variances of ree differen socasic processes. Terefore, asymmery exiss for EGARCH if: asymmery for EGARCH: 0. Te condiions for leverage in e EGARCH model in Equaion (7) are: leverage for EGARCH: 0 and. As acknowledged in McAleer and Hafner (04) [7], leverage is no possible as bo and, wic are e variances of wo socasic processes, mus be posiive. As EGARCH is non-nesed wi bo GARCH and GJR, e inerpreaion of e coefficiens in EGARCH is no e same as in e oer wo condiional volailiy models, aloug e definiions of asymmery and leverage are idenical. Te derivaions in Secion are inended o keep e number of iid processes o a minimum for ease of presenaion. As in e case of GARCH and GJR, e posiiviy resricions on e parameers of GJR are ypically no imposed in esimaion using sandard economeric, financial economeric and saisical sofware packages. 3. Conclusions Te paper was concerned wi e ree mos widely-used univariae condiional volailiy models, namely e GARCH, GJR (or resold GARCH) and EGARCH models. Tese models are imporan in esimaing and forecasing volailiy, as well as in capuring asymmery, wic is e differen effecs on condiional volailiy of posiive and negaive effecs of equal magniude, and purporedly in capuring leverage, wic is e negaive correlaion beween reurns socks and subsequen socks o volailiy. As discussed in Secion, a failure o impose e posiiviy resricions on e parameers of e condiional volailiy models can increase e probabiliy of obaining negaive esimaes of condiional volailiy. In sandard economeric, financial economeric and saisical sofware packages, i is ypically e case a no posiiviy is imposed in esimaing e parameers of e ree mos popular condiional volailiy models.

6 Economerics 04, 50 As ere seems o be some confusion in e lieraure beween asymmery and leverage, as well as wic asymmeric models are purpored o be able o capure leverage, e purpose of e paper was ree-fold, namely, () o derive e GJR model from a random coefficien auoregressive process, wi appropriae regulariy condiions; () o sow a leverage is no possible in e GJR and EGARCH models; and (3) o presen e inerpreaion of e parameers of e ree popular univariae condiional volailiy models in a unified manner. Acknowledgemens Te auor wises o ank Massimiliano Caporin, Marc Paolella, Pawel Polak, e Edior-in-Cief, Kerry Paerson, and a reviewer for elpful commens and suggesions. For financial suppor, e auor wises o acknowledge e Ausralian Researc Council and e Naional Science Council, Taiwan. Conflics of Ineres Te auor as no conflics of ineres. References. Engle, R.F. Auoregressive condiional eeroscedasiciy wi esimaes of e variance of Unied Kingdom inflaion. Economerica 98, 50, Bollerslev, T. Generalised auoregressive condiional eeroscedasiciy. J. Econom. 986, 3, Glosen, L.R.; Jagannaan, R.; Runkle, D.E. On e relaion beween e expeced value and volailiy of nominal excess reurn on socks. J. Financ.99, 46, Nelson, D.B. ARCH models as diffusion approximaions. J. Econom. 990, 45, Nelson, D.B. Condiional eeroskedasiciy in asse reurns: A new approac. Economerica 99, 59, Tsay, R.S. Condiional eeroscedasic ime series models. J. Am. Sa. Assoc. 987, 8, McAleer, M.; Hafner, C. A one line derivaion of EGARCH. Economerics 04,, Black, F. Sudies of Sock Marke Volailiy Canges. In Proceedings of e American Saisical Associaion, Business and Economic Saisics Secion, Wasingon, DC, USA, 976; pp by e auors; licensee MDPI, Basel, Swizerland. Tis aricle is an open access aricle disribued under e erms and condiions of e Creaive Commons Aribuion license (p://creaivecommons.org/licenses/by/4.0/).

Asymmetry and Leverage in Conditional Volatility Models*

Asymmetry and Leverage in Conditional Volatility Models* Asymmery and Leverage in Condiional Volailiy Models* Micael McAleer Deparmen of Quaniaive Finance Naional Tsing Hua Universiy Taiwan and Economeric Insiue Erasmus Scool of Economics Erasmus Universiy Roerdam

More information

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND Asymmery and Leverage in Condiional Volailiy Models Michael McAleer WORKING PAPER

More information

A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process*

A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process* A One Line Derivaion of DCC: Alicaion of a Vecor Random Coefficien Moving Average Process* Chrisian M. Hafner Insiu de saisique, biosaisique e sciences acuarielles Universié caholique de Louvain Michael

More information

Estimation of Asymmetric Garch Models: The Estimating Functions Approach

Estimation of Asymmetric Garch Models: The Estimating Functions Approach Inernaional Journal of Applied Science and ecnology Vol. 4, No. 5; Ocober 4 simaion of Asymmeric Garc Models: e simaing Funcions Approac Mr. imoy Ndonye Muunga Prof. Ali Salim Islam Dr. Luke Akong o Orawo

More information

Forecasting Volatility in Tehran Stock Market with GARCH Models

Forecasting Volatility in Tehran Stock Market with GARCH Models J. Basic. Appl. Sci. Res., (1)150-155, 01 01, TexRoad Publicaion ISSN 090-4304 Journal of Basic and Applied Scienific Researc www.exroad.com Forecasing Volailiy in Teran Sock Marke wi GARCH Models Medi

More information

Tourism forecasting using conditional volatility models

Tourism forecasting using conditional volatility models Tourism forecasing using condiional volailiy models ABSTRACT Condiional volailiy models are used in ourism demand sudies o model he effecs of shocks on demand volailiy, which arise from changes in poliical,

More information

Volatility. Many economic series, and most financial series, display conditional volatility

Volatility. Many economic series, and most financial series, display conditional volatility Volailiy Many economic series, and mos financial series, display condiional volailiy The condiional variance changes over ime There are periods of high volailiy When large changes frequenly occur And periods

More information

Modelling international tourist arrivals and volatility fortaiwan

Modelling international tourist arrivals and volatility fortaiwan 8 World IMACS / MODSIM Congress Cairns Ausralia 3-7 Jul 009 p://mssanz.org.au/modsim09 Modelling inernaional ouris arrivals and volaili fortaiwan Cang C.-L. M. McAleer and D. Sloje 3 Deparmen of Applied

More information

DYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus University Toruń Piotr Fiszeder Nicolaus Copernicus University in Toruń

DYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus University Toruń Piotr Fiszeder Nicolaus Copernicus University in Toruń DYNAMIC ECONOMETRIC MODELS Vol. 7 Nicolaus Copernicus Universiy Toruń 006 Pior Fiszeder Nicolaus Copernicus Universiy in Toruń Consequences of Congruence for GARCH Modelling. Inroducion In 98 Granger formulaed

More information

Chapter 5. Heterocedastic Models. Introduction to time series (2008) 1

Chapter 5. Heterocedastic Models. Introduction to time series (2008) 1 Chaper 5 Heerocedasic Models Inroducion o ime series (2008) 1 Chaper 5. Conens. 5.1. The ARCH model. 5.2. The GARCH model. 5.3. The exponenial GARCH model. 5.4. The CHARMA model. 5.5. Random coefficien

More information

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND Dynamic Condiional Correlaions for Asymmeric Processes Manabu Asai and Michael McAleer

More information

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models

A Specification Test for Linear Dynamic Stochastic General Equilibrium Models Journal of Saisical and Economeric Mehods, vol.1, no.2, 2012, 65-70 ISSN: 2241-0384 (prin), 2241-0376 (online) Scienpress Ld, 2012 A Specificaion Tes for Linear Dynamic Sochasic General Equilibrium Models

More information

econometrics ISSN

econometrics ISSN Economerics 2013, 1, 115-126; doi:10.3390/economerics1010115 Aricle economerics ISSN 2225-1146 www.mdpi.com/journal/economerics Ten Things You Should Know abou he Dynamic Condiional Correlaion Represenaion

More information

UNIVERSITÀ DEGLI STUDI DI PADOVA. Dipartimento di Scienze Economiche Marco Fanno

UNIVERSITÀ DEGLI STUDI DI PADOVA. Dipartimento di Scienze Economiche Marco Fanno UNIVERSITÀ DEGLI STUDI DI PADOVA Diparimeno di Scienze Economiche Marco Fanno MODELLING INTERNATIONAL TOURIST ARRIVALS AND VOLATILITY: AN APPLICATION TO TAIWAN CHIA-LIN CHANG Naional Chung Hsing Universiy

More information

OPTIMAL PREDICTION UNDER LINLIN LOSS: EMPIRICAL EVIDENCE Yasemin Bardakci St. Cloud State University

OPTIMAL PREDICTION UNDER LINLIN LOSS: EMPIRICAL EVIDENCE Yasemin Bardakci St. Cloud State University OPTIMAL PREDICTION UNDER LINLIN LOSS: EMPIRICAL EVIDENCE Yasemin Bardakci S. Cloud Sae Universiy Absrac: I compare e forecass of reurns from e mean predicor (opimal under MSE), wi e pseudo-opimal and opimal

More information

Modeling the Volatility of Shanghai Composite Index

Modeling the Volatility of Shanghai Composite Index Modeling he Volailiy of Shanghai Composie Index wih GARCH Family Models Auhor: Yuchen Du Supervisor: Changli He Essay in Saisics, Advanced Level Dalarna Universiy Sweden Modeling he volailiy of Shanghai

More information

Stochastic Reliability Analysis of Two Identical Cold Standby Units with Geometric Failure & Repair Rates

Stochastic Reliability Analysis of Two Identical Cold Standby Units with Geometric Failure & Repair Rates DOI: 0.545/mjis.07.500 Socasic Reliabiliy Analysis of Two Idenical Cold Sandby Unis wi Geomeric Failure & Repair Raes NITIN BHARDWAJ AND BHUPENDER PARASHAR Email: niinbardwaj@jssaen.ac.in; parasar_b@jssaen.ac.in

More information

Ten Things You Should Know About the Dynamic Conditional Correlation Representation*

Ten Things You Should Know About the Dynamic Conditional Correlation Representation* Ten Things You Should Know Abou he Dynamic Condiional Correlaion Represenaion* Massimiliano Caporin Deparmen of Economics and Managemen Marco Fanno Universiy of Padova Ialy Michael McAleer Economeric Insiue

More information

Daily Tourist Arrivals, Exchange Rates and Volatility for Korea and Taiwan

Daily Tourist Arrivals, Exchange Rates and Volatility for Korea and Taiwan Daily Touris Arrivals, Exchange Raes and Volailiy for Korea and Taiwan Chia-Lin Chang Deparmen of Applied Economics Naional Chung Hsing Universiy Taichung, Taiwan Michael McAleer Economeric Insiue Erasmus

More information

DAILY TOURIST ARRIVALS, EXCHANGE RATES AND VOLATILITY FOR KOREA AND TAIWAN*

DAILY TOURIST ARRIVALS, EXCHANGE RATES AND VOLATILITY FOR KOREA AND TAIWAN* 241 THE KOREAN ECONOMIC REVIEW Volume 25, Number 2, Winer 2009 DAILY TOURIST ARRIVALS, EXCHANGE RATES AND VOLATILITY FOR KOREA AND TAIWAN* CHIA-LIN CHANG** MICHAEL MCALEER*** Boh domesic and inernaional

More information

Modelling the Volatility in Short and Long Haul Japanese Tourist Arrivals to New Zealand and Taiwan*

Modelling the Volatility in Short and Long Haul Japanese Tourist Arrivals to New Zealand and Taiwan* Modelling he Volailiy in Shor and Long Haul Japanese Touris Arrivals o New Zealand and Taiwan* Chia-Lin Chang** Deparmen of Applied Economics Deparmen of Finance Naional Chung Hsing Universiy Taichung,

More information

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin

ACE 562 Fall Lecture 4: Simple Linear Regression Model: Specification and Estimation. by Professor Scott H. Irwin ACE 56 Fall 005 Lecure 4: Simple Linear Regression Model: Specificaion and Esimaion by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Simple Regression: Economic and Saisical Model

More information

Our main purpose in this section is to undertake an examination of the stock

Our main purpose in this section is to undertake an examination of the stock 3. Caial gains ax and e sock rice volailiy Our main urose in is secion is o underake an examinaion of e sock rice volailiy by considering ow e raional seculaor s olding canges afer e ax rae on caial gains

More information

How to Deal with Structural Breaks in Practical Cointegration Analysis

How to Deal with Structural Breaks in Practical Cointegration Analysis How o Deal wih Srucural Breaks in Pracical Coinegraion Analysis Roselyne Joyeux * School of Economic and Financial Sudies Macquarie Universiy December 00 ABSTRACT In his noe we consider he reamen of srucural

More information

A Note on the Equivalence of Fractional Relaxation Equations to Differential Equations with Varying Coefficients

A Note on the Equivalence of Fractional Relaxation Equations to Differential Equations with Varying Coefficients mahemaics Aricle A Noe on he Equivalence of Fracional Relaxaion Equaions o Differenial Equaions wih Varying Coefficiens Francesco Mainardi Deparmen of Physics and Asronomy, Universiy of Bologna, and he

More information

The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models

The Effect of Nonzero Autocorrelation Coefficients on the Distributions of Durbin-Watson Test Estimator: Three Autoregressive Models EJ Exper Journal of Economi c s ( 4 ), 85-9 9 4 Th e Au h or. Publi sh ed by Sp rin In v esify. ISS N 3 5 9-7 7 4 Econ omics.e xp erjou rn a ls.com The Effec of Nonzero Auocorrelaion Coefficiens on he

More information

Granger Causality Among Pre-Crisis East Asian Exchange Rates. (Running Title: Granger Causality Among Pre-Crisis East Asian Exchange Rates)

Granger Causality Among Pre-Crisis East Asian Exchange Rates. (Running Title: Granger Causality Among Pre-Crisis East Asian Exchange Rates) Granger Causaliy Among PreCrisis Eas Asian Exchange Raes (Running Tile: Granger Causaliy Among PreCrisis Eas Asian Exchange Raes) Joseph D. ALBA and Donghyun PARK *, School of Humaniies and Social Sciences

More information

DEPARTMENT OF STATISTICS

DEPARTMENT OF STATISTICS A Tes for Mulivariae ARCH Effecs R. Sco Hacker and Abdulnasser Haemi-J 004: DEPARTMENT OF STATISTICS S-0 07 LUND SWEDEN A Tes for Mulivariae ARCH Effecs R. Sco Hacker Jönköping Inernaional Business School

More information

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH NEW ZEALAND Thresholds News Impac Surfaces and Dynamic Asymmeric Mulivariae GARCH Massimiliano

More information

Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH *

Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH * Thresholds, News Impac Surfaces and Dynamic Asymmeric Mulivariae GARCH * Massimiliano Caporin Deparmen of Economic Sciences Universiy of Padova Michael McAleer Deparmen of Quaniaive Economics Compluense

More information

Recursive Modelling of Symmetric and Asymmetric Volatility in the Presence of Extreme Observations *

Recursive Modelling of Symmetric and Asymmetric Volatility in the Presence of Extreme Observations * Recursive Modelling of Symmeric and Asymmeric in he Presence of Exreme Observaions * Hock Guan Ng Deparmen of Accouning and Finance Universiy of Wesern Ausralia Michael McAleer Deparmen of Economics Universiy

More information

Testing for a Single Factor Model in the Multivariate State Space Framework

Testing for a Single Factor Model in the Multivariate State Space Framework esing for a Single Facor Model in he Mulivariae Sae Space Framework Chen C.-Y. M. Chiba and M. Kobayashi Inernaional Graduae School of Social Sciences Yokohama Naional Universiy Japan Faculy of Economics

More information

Return threshold model analysis of two stock markets: Evidence study of. Italy and Germany s stock returns

Return threshold model analysis of two stock markets: Evidence study of. Italy and Germany s stock returns Jan., Volume 9, No. (Serial No.79) Cinese Business Reiew, ISSN 537-56, USA Reurn resold model analysis of wo sock markes: Eidence sudy of Ialy and Germany s sock reurns Wann-Jyi Horng, Yu-Ceng Cen, Weir-Sen

More information

Some Ratio and Product Estimators Using Known Value of Population Parameters

Some Ratio and Product Estimators Using Known Value of Population Parameters Rajes Sing Deparmen of Maemaics, SRM Universi Deli NCR, Sonepa- 9, India Sacin Malik Deparmen of Saisics, Banaras Hindu Universi Varanasi-, India Florenin Smarandace Deparmen of Maemaics, Universi of New

More information

Exponential Ratio-Product Type Estimators Under Second Order Approximation In Stratified Random Sampling

Exponential Ratio-Product Type Estimators Under Second Order Approximation In Stratified Random Sampling Rajes Sing Prayas Sarma Deparmen of Saisics Banaras Hindu Universiy aranasi-005 India Florenin Smarandace Universiy of New Mexico Gallup USA Exponenial Raio-Produc Type Esimaors Under Second Order Approximaion

More information

Multivariate Volatility Impulse Response Analysis of GFC News Events

Multivariate Volatility Impulse Response Analysis of GFC News Events Insiuo Compluense de Análisis Económico Mulivariae Volailiy Impulse Response Analysis of GFC News Evens David E. Allen School of Mahemaics and Saisics Universiy of Sydney and School of Business Universiy

More information

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1

Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1 Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies

More information

THE CATCH PROCESS (continued)

THE CATCH PROCESS (continued) THE CATCH PROCESS (coninued) In our previous derivaion of e relaionsip beween CPUE and fis abundance we assumed a all e fising unis and all e fis were spaially omogeneous. Now we explore wa appens wen

More information

Analysing Trends and Volatility in Atmospheric Carbon Dioxide Concentration Levels

Analysing Trends and Volatility in Atmospheric Carbon Dioxide Concentration Levels Inernaional Congress on Environmenal Modelling and Sofware Brigham Young Universiy BYU ScholarsArchive 2nd Inernaional Congress on Environmenal Modelling and Sofware - Osnabrück, Germany - June 2004 Jul

More information

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND

DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND DEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH NEW ZEALAND Thresholds News Impac Surfaces and Dynamic Asymmeric Mulivariae GARCH* Massimiliano

More information

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé

Bias in Conditional and Unconditional Fixed Effects Logit Estimation: a Correction * Tom Coupé Bias in Condiional and Uncondiional Fixed Effecs Logi Esimaion: a Correcion * Tom Coupé Economics Educaion and Research Consorium, Naional Universiy of Kyiv Mohyla Academy Address: Vul Voloska 10, 04070

More information

Trends and Volatilities in Heterogeneous Patent Quality in Taiwan

Trends and Volatilities in Heterogeneous Patent Quality in Taiwan Received April, 009 / Acceped May 9, 009 J. Tecnol. Manag. Innov. 009, Volume 4, Issue Trends and Volailiies in Heerogeneous Paen Qualiy in Taiwan Wen-Ceng Lu *, Jong-Rong Cen, I-Hsuan Tung 3 Absrac Tis

More information

THE PREDICTIVE DENSITY OF A GARCH(1,1) PROCESS. Contents

THE PREDICTIVE DENSITY OF A GARCH(1,1) PROCESS. Contents THE PREDICTIVE DENSITY OF A GARCH(,) PROCESS K. ABADIR, A. LUATI, P. PARUOLO Absrac. Tis paper derives e predicive probabiliy densiy funcion of a GARCH(,) process, under Gaussian or Suden innovaions. Te

More information

Comparison between the Discrete and Continuous Time Models

Comparison between the Discrete and Continuous Time Models Comparison beween e Discree and Coninuous Time Models D. Sulsky June 21, 2012 1 Discree o Coninuous Recall e discree ime model Î = AIS Ŝ = S Î. Tese equaions ell us ow e populaion canges from one day o

More information

Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles

Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance

More information

Stochastic Model for Cancer Cell Growth through Single Forward Mutation

Stochastic Model for Cancer Cell Growth through Single Forward Mutation Journal of Modern Applied Saisical Mehods Volume 16 Issue 1 Aricle 31 5-1-2017 Sochasic Model for Cancer Cell Growh hrough Single Forward Muaion Jayabharahiraj Jayabalan Pondicherry Universiy, jayabharahi8@gmail.com

More information

02. MOTION. Questions and Answers

02. MOTION. Questions and Answers CLASS-09 02. MOTION Quesions and Answers PHYSICAL SCIENCE 1. Se moves a a consan speed in a consan direcion.. Reprase e same senence in fewer words using conceps relaed o moion. Se moves wi uniform velociy.

More information

A corporate-crime perspective on fisheries: liability rules and non-compliance

A corporate-crime perspective on fisheries: liability rules and non-compliance A corporae-crime perspecive on fiseries: liabiliy rules and non-compliance FRANK JENSEN, Corresponding auor Universiy of Copenagen, Deparmen of Food and Resource Economics, Roligedsvej 3, 1958 Frederiksberg

More information

Multivariate Volatility Impulse Response Analysis of GFC News Events

Multivariate Volatility Impulse Response Analysis of GFC News Events TI 2015-089/III Tinbergen Insiue Discussion Paper Mulivariae Volailiy Impulse Response Analysis of GFC News Evens David E. Allen 1 Michael McAleer 2 Rober Powell 3 AbhayK. Singh 3 1 Universiy of Sydney,

More information

ACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H.

ACE 562 Fall Lecture 5: The Simple Linear Regression Model: Sampling Properties of the Least Squares Estimators. by Professor Scott H. ACE 56 Fall 005 Lecure 5: he Simple Linear Regression Model: Sampling Properies of he Leas Squares Esimaors by Professor Sco H. Irwin Required Reading: Griffihs, Hill and Judge. "Inference in he Simple

More information

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H.

ACE 564 Spring Lecture 7. Extensions of The Multiple Regression Model: Dummy Independent Variables. by Professor Scott H. ACE 564 Spring 2006 Lecure 7 Exensions of The Muliple Regression Model: Dumm Independen Variables b Professor Sco H. Irwin Readings: Griffihs, Hill and Judge. "Dumm Variables and Varing Coefficien Models

More information

A Hybrid Model for Improving. Malaysian Gold Forecast Accuracy

A Hybrid Model for Improving. Malaysian Gold Forecast Accuracy In. Journal of Mah. Analysis, Vol. 8, 2014, no. 28, 1377-1387 HIKARI Ld, www.m-hikari.com hp://dx.doi.org/10.12988/ijma.2014.45139 A Hybrid Model for Improving Malaysian Gold Forecas Accuracy Maizah Hura

More information

Smooth Transition Autoregressive-GARCH Model in Forecasting Non-linear Economic Time Series Data

Smooth Transition Autoregressive-GARCH Model in Forecasting Non-linear Economic Time Series Data Journal of Saisical and conomeric Mehods, vol., no., 03, -9 ISSN: 05-5057 (prin version), 05-5065(online) Scienpress d, 03 Smooh Transiion Auoregressive-GARCH Model in Forecasing Non-linear conomic Time

More information

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.

Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate. Inroducion Gordon Model (1962): D P = r g r = consan discoun rae, g = consan dividend growh rae. If raional expecaions of fuure discoun raes and dividend growh vary over ime, so should he D/P raio. Since

More information

The relationship between stock returns and volatility in the seventeen largest international stock markets: A semi-parametric approach

The relationship between stock returns and volatility in the seventeen largest international stock markets: A semi-parametric approach MPRA Munich Personal RePEc Archive The relaionship beween sock reurns and volailiy in he seveneen larges inernaional sock markes: A semi-parameric approach Dimirios Dimiriou and Theodore Simos Deparmen

More information

THE PREDICTIVE DENSITY OF A GARCH(1,1) PROCESS. Contents

THE PREDICTIVE DENSITY OF A GARCH(1,1) PROCESS. Contents THE PREDICTIVE DENSITY OF A GARCH(,) PROCESS K. ABADIR, A. LUATI, P. PARUOLO Absrac. Tis paper derives e predicive probabiliy densiy funcion of a GARCH(,) process, under Gaussian or Suden innovaions. Te

More information

Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models Forecasing Sock Exchange Movemens Using Arificial Neural Nework Models and Hybrid Models Erkam GÜREEN and Gülgün KAYAKUTLU Isanbul Technical Universiy, Deparmen of Indusrial Engineering, Maçka, 34367 Isanbul,

More information

Forecasting optimally

Forecasting optimally I) ile: Forecas Evaluaion II) Conens: Evaluaing forecass, properies of opimal forecass, esing properies of opimal forecass, saisical comparison of forecas accuracy III) Documenaion: - Diebold, Francis

More information

2. Random Search with Complete BST

2. Random Search with Complete BST Tecnical Repor: TR-BUAA-ACT-06-0 Complexiy Analysis of Random Searc wi AVL Tree Gongwei Fu, Hailong Sun Scool of Compuer Science, Beiang Universiy {FuGW, SunHL@ac.buaa.edu.cn April 7, 006 TR-BUAA-ACT-06-0.

More information

An International Comparison of Foreign Patents Registered in the USA

An International Comparison of Foreign Patents Registered in the USA An Inernaional Comparison of Foreign Paens Regisered in he USA Michael McAleer a, Felix Chan a and Dora Marinova b a Deparmen of Economics, Universiy of Wesern Ausralia b Insiue for Susainabiliy and Technology

More information

Vehicle Arrival Models : Headway

Vehicle Arrival Models : Headway Chaper 12 Vehicle Arrival Models : Headway 12.1 Inroducion Modelling arrival of vehicle a secion of road is an imporan sep in raffic flow modelling. I has imporan applicaion in raffic flow simulaion where

More information

Approximating the Powers with Large Exponents and Bases Close to Unit, and the Associated Sequence of Nested Limits

Approximating the Powers with Large Exponents and Bases Close to Unit, and the Associated Sequence of Nested Limits In. J. Conemp. Ma. Sciences Vol. 6 211 no. 43 2135-2145 Approximaing e Powers wi Large Exponens and Bases Close o Uni and e Associaed Sequence of Nesed Limis Vio Lampre Universiy of Ljubljana Slovenia

More information

Higher Order Difference Schemes for Heat Equation

Higher Order Difference Schemes for Heat Equation Available a p://pvau.edu/aa Appl. Appl. Ma. ISSN: 9-966 Vol., Issue (Deceber 009), pp. 6 7 (Previously, Vol., No. ) Applicaions and Applied Maeaics: An Inernaional Journal (AAM) Higer Order Difference

More information

Spillover Effects in Forecasting Volatility and VaR *

Spillover Effects in Forecasting Volatility and VaR * Spillover Effecs in Forecasing Volailiy and VaR * Michael McAleer and Bernardo da Veiga School of Economics and Commerce Universiy of Wesern Ausralia January 2005 * The auhors wish o hank Dave Allen, Felix

More information

Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model:

Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model: Dynamic Economeric Models: A. Auoregressive Model: Y = + 0 X 1 Y -1 + 2 Y -2 + k Y -k + e (Wih lagged dependen variable(s) on he RHS) B. Disribued-lag Model: Y = + 0 X + 1 X -1 + 2 X -2 + + k X -k + e

More information

OBJECTIVES OF TIME SERIES ANALYSIS

OBJECTIVES OF TIME SERIES ANALYSIS OBJECTIVES OF TIME SERIES ANALYSIS Undersanding he dynamic or imedependen srucure of he observaions of a single series (univariae analysis) Forecasing of fuure observaions Asceraining he leading, lagging

More information

Forecasting Volatility of Returns for Corn using GARCH Models

Forecasting Volatility of Returns for Corn using GARCH Models he exas Journal of Agriculure and Naural Resources 6:4-55 (03) 4 Agriculural Consorium of exas Forecasing Volailiy of Reurns for Corn using GARCH Models Naveen Musunuru * Mark Yu Arley Larson Deparmen

More information

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method

Improved Approximate Solutions for Nonlinear Evolutions Equations in Mathematical Physics Using the Reduced Differential Transform Method Journal of Applied Mahemaics & Bioinformaics, vol., no., 01, 1-14 ISSN: 179-660 (prin), 179-699 (online) Scienpress Ld, 01 Improved Approimae Soluions for Nonlinear Evoluions Equaions in Mahemaical Physics

More information

An Empirical Analysis of the Exchange Rate Volatility: Application of Brazilian and Australian Exchange Markets

An Empirical Analysis of the Exchange Rate Volatility: Application of Brazilian and Australian Exchange Markets An Empirical Analysis of he Exchange Rae Volailiy: Applicaion of Brazilian and Ausralian Exchange Markes Wann-Jyi Horng Deparmen of Hospial and Healh Care Adminisraion, Chia Nan Universiy of Pharmacy &

More information

The General Linear Test in the Ridge Regression

The General Linear Test in the Ridge Regression ommunicaions for Saisical Applicaions Mehods 2014, Vol. 21, No. 4, 297 307 DOI: hp://dx.doi.org/10.5351/sam.2014.21.4.297 Prin ISSN 2287-7843 / Online ISSN 2383-4757 The General Linear Tes in he Ridge

More information

Scholars Journal of Economics, Business and Management e-issn

Scholars Journal of Economics, Business and Management e-issn Scholars Journal of Economics, Business and Managemen e-issn 2348-5302 Pınar Torun e al.; Sch J Econ Bus Manag, 204; (7):29-297 p-issn 2348-8875 SAS Publishers (Scholars Academic and Scienific Publishers)

More information

The Impact of News on Measures of Undiversifiable. Risk: Evidence from the UK Stock Market *

The Impact of News on Measures of Undiversifiable. Risk: Evidence from the UK Stock Market * The Impac of News on easures of Undiversifiable Risk: Evidence from he UK ock arke Chris Brooks IA Cenre Deparmen of Economics Universiy of Reading Ólan T. Henry Deparmen of Economics Universiy of elbourne

More information

Method For Solving Fuzzy Integro-Differential Equation By Using Fuzzy Laplace Transformation

Method For Solving Fuzzy Integro-Differential Equation By Using Fuzzy Laplace Transformation INERNAIONAL JOURNAL OF SCIENIFIC & ECHNOLOGY RESEARCH VOLUME 3 ISSUE 5 May 4 ISSN 77-866 Meod For Solving Fuzzy Inegro-Differenial Equaion By Using Fuzzy Laplace ransformaion Manmoan Das Danji alukdar

More information

Computer Simulates the Effect of Internal Restriction on Residuals in Linear Regression Model with First-order Autoregressive Procedures

Computer Simulates the Effect of Internal Restriction on Residuals in Linear Regression Model with First-order Autoregressive Procedures MPRA Munich Personal RePEc Archive Compuer Simulaes he Effec of Inernal Resricion on Residuals in Linear Regression Model wih Firs-order Auoregressive Procedures Mei-Yu Lee Deparmen of Applied Finance,

More information

School and Workshop on Market Microstructure: Design, Efficiency and Statistical Regularities March 2011

School and Workshop on Market Microstructure: Design, Efficiency and Statistical Regularities March 2011 2229-12 School and Workshop on Marke Microsrucure: Design, Efficiency and Saisical Regulariies 21-25 March 2011 Some mahemaical properies of order book models Frederic ABERGEL Ecole Cenrale Paris Grande

More information

A New Unit Root Test against Asymmetric ESTAR Nonlinearity with Smooth Breaks

A New Unit Root Test against Asymmetric ESTAR Nonlinearity with Smooth Breaks Iran. Econ. Rev. Vol., No., 08. pp. 5-6 A New Uni Roo es agains Asymmeric ESAR Nonlineariy wih Smooh Breaks Omid Ranjbar*, sangyao Chang, Zahra (Mila) Elmi 3, Chien-Chiang Lee 4 Received: December 7, 06

More information

Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

Modelling Long Memory Volatility in Agricultural Commodity Futures Returns CIRJE-F-680 Modelling Long Memory Volailiy in Agriculural Commodiy Fuures Reurns Roengchai Tansucha Maejo Universiy Chia-Lin Chang Naional Chung Hsing Universiy Michael McAleer Erasmus Universiy Roerdam

More information

Solutions to Odd Number Exercises in Chapter 6

Solutions to Odd Number Exercises in Chapter 6 1 Soluions o Odd Number Exercises in 6.1 R y eˆ 1.7151 y 6.3 From eˆ ( T K) ˆ R 1 1 SST SST SST (1 R ) 55.36(1.7911) we have, ˆ 6.414 T K ( ) 6.5 y ye ye y e 1 1 Consider he erms e and xe b b x e y e b

More information

Volatility Asymmetry and Correlation of the MILA Stock Indexes

Volatility Asymmetry and Correlation of the MILA Stock Indexes Volailiy Asymmery and Correlaion of he MILA Sock Indexes Absrac The inegraion of he sock exchanges ino MILA implies he need o analyze he marke reacions in erms of volailiy and correlaions o macroeconomic

More information

Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A

Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A Licenciaura de ADE y Licenciaura conjuna Derecho y ADE Hoja de ejercicios PARTE A 1. Consider he following models Δy = 0.8 + ε (1 + 0.8L) Δ 1 y = ε where ε and ε are independen whie noise processes. In

More information

Forecasting Value-At-Risk with a Parsimonious Portfolio Spillover GARCH (PS-GARCH) Model *

Forecasting Value-At-Risk with a Parsimonious Portfolio Spillover GARCH (PS-GARCH) Model * Forecasing Value-A-Risk wih a Parsimonious Porfolio Spillover GARCH (PS-GARCH) Model * Michael McAleer and Bernardo da Veiga School of Economics and Commerce Universiy of Wesern Ausralia Updaed: March

More information

Box-Jenkins Modelling of Nigerian Stock Prices Data

Box-Jenkins Modelling of Nigerian Stock Prices Data Greener Journal of Science Engineering and Technological Research ISSN: 76-7835 Vol. (), pp. 03-038, Sepember 0. Research Aricle Box-Jenkins Modelling of Nigerian Sock Prices Daa Ee Harrison Euk*, Barholomew

More information

Vol. 5 (1), S/No11, February, 2016: ISSN: (Print) ISSN (Online) DOI:

Vol. 5 (1), S/No11, February, 2016: ISSN: (Print) ISSN (Online) DOI: STECH VOL 5 () FEBRUARY, 06 7 Vol. 5 (), S/No, Februar, 06: 7-39 ISSN: 5-8590 (Prin) ISSN 7-545 (Online) DOI: p://dx.doi.org/0.434/sec.v5i.3 Inerval Forecas for Smoo Transiion Auoregressive Model Ekosuei,

More information

Modelling Long Memory Volatility in Agricultural Commodity Futures Returns*

Modelling Long Memory Volatility in Agricultural Commodity Futures Returns* Modelling Long Memory Volailiy in Agriculural Commodiy Fuures Reurns* Chia-Lin Chang Deparmen of Applied Economics Deparmen of Finance Naional Chung Hsing Universiy Taichung, Taiwan Michael McAleer Economeric

More information

Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS

Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS Name SOLUTIONS Financial Economerics Jeffrey R. Russell Miderm Winer 009 SOLUTIONS You have 80 minues o complee he exam. Use can use a calculaor and noes. Try o fi all your work in he space provided. If

More information

Delay and Its Time-Derivative Dependent Stable Criterion for Differential-Algebraic Systems

Delay and Its Time-Derivative Dependent Stable Criterion for Differential-Algebraic Systems Applied Maemaics 6 7 4- Publised Online June 6 in SciRes p://wwwscirporg/journal/am p://dxdoiorg/46/am67 Delay and Is ime-derivaive Dependen Sable Crierion for Differenial-Algebraic Sysems Hui Liu Yucai

More information

On two general nonlocal differential equations problems of fractional orders

On two general nonlocal differential equations problems of fractional orders Malaya Journal of Maemaik, Vol. 6, No. 3, 478-482, 28 ps://doi.org/.26637/mjm63/3 On wo general nonlocal differenial equaions problems of fracional orders Abd El-Salam S. A. * and Gaafar F. M.2 Absrac

More information

Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation

Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation WORKING PAPER 01: Robus criical values for uni roo ess for series wih condiional heeroscedasiciy errors: An applicaion of he simple NoVaS ransformaion Panagiois Manalos ECONOMETRICS AND STATISTICS ISSN

More information

ARCH IN SHORT-TERM INTEREST RATES: CASE STUDY USA

ARCH IN SHORT-TERM INTEREST RATES: CASE STUDY USA Arch in Shor-Term Ineres Raes: Case Sudy USA ARCH IN SHORT-TERM INTEREST RATES: CASE STUDY USA Adrian Ausin, Universiy of Wes Georgia Swarna (Bashu) Du, Universiy of Wes Georgia ABSTRACT We invesigae ARCH

More information

BOOTSTRAP PREDICTION INTERVALS FOR TIME SERIES MODELS WITH HETROSCEDASTIC ERRORS. Department of Statistics, Islamia College, Peshawar, KP, Pakistan 2

BOOTSTRAP PREDICTION INTERVALS FOR TIME SERIES MODELS WITH HETROSCEDASTIC ERRORS. Department of Statistics, Islamia College, Peshawar, KP, Pakistan 2 Pak. J. Sais. 017 Vol. 33(1), 1-13 BOOTSTRAP PREDICTIO ITERVAS FOR TIME SERIES MODES WITH HETROSCEDASTIC ERRORS Amjad Ali 1, Sajjad Ahmad Khan, Alamgir 3 Umair Khalil and Dos Muhammad Khan 1 Deparmen of

More information

A DCC-GARCH MODEL TO ESTIMATE

A DCC-GARCH MODEL TO ESTIMATE 8. A DCC-GARCH MODEL TO ESTIMATE THE RISK TO THE CAPITAL MARKET IN ROMANIA Marius ACATRINEI 1 Adrian GORUN 2 Nicu MARCU 3 Absrac In his paper we propose o sudy if he sandard and asymmeric dynamic condiional

More information

Økonomisk Kandidateksamen 2005(II) Econometrics 2. Solution

Økonomisk Kandidateksamen 2005(II) Econometrics 2. Solution Økonomisk Kandidaeksamen 2005(II) Economerics 2 Soluion his is he proposed soluion for he exam in Economerics 2. For compleeness he soluion gives formal answers o mos of he quesions alhough his is no always

More information

Mathematical Theory and Modeling ISSN (Paper) ISSN (Online) Vol 3, No.3, 2013

Mathematical Theory and Modeling ISSN (Paper) ISSN (Online) Vol 3, No.3, 2013 Mahemaical Theory and Modeling ISSN -580 (Paper) ISSN 5-05 (Online) Vol, No., 0 www.iise.org The ffec of Inverse Transformaion on he Uni Mean and Consan Variance Assumpions of a Muliplicaive rror Model

More information

Regression with Time Series Data

Regression with Time Series Data Regression wih Time Series Daa y = β 0 + β 1 x 1 +...+ β k x k + u Serial Correlaion and Heeroskedasiciy Time Series - Serial Correlaion and Heeroskedasiciy 1 Serially Correlaed Errors: Consequences Wih

More information

Applied Econometrics GARCH Models - Extensions. Roman Horvath Lecture 2

Applied Econometrics GARCH Models - Extensions. Roman Horvath Lecture 2 Applied Economerics GARCH Models - Exensions Roman Horva Lecre Conens GARCH EGARCH, GARCH-M Mlivariae GARCH Sylized facs in finance Unpredicabiliy Volailiy Fa ails Efficien markes Time-varying (rblen vs.

More information

Outline. lse-logo. Outline. Outline. 1 Wald Test. 2 The Likelihood Ratio Test. 3 Lagrange Multiplier Tests

Outline. lse-logo. Outline. Outline. 1 Wald Test. 2 The Likelihood Ratio Test. 3 Lagrange Multiplier Tests Ouline Ouline Hypohesis Tes wihin he Maximum Likelihood Framework There are hree main frequenis approaches o inference wihin he Maximum Likelihood framework: he Wald es, he Likelihood Raio es and he Lagrange

More information

Variance Bounds Tests for the Hypothesis of Efficient Stock Market

Variance Bounds Tests for the Hypothesis of Efficient Stock Market 67 Variance Bounds Tess of Efficien Sock Marke Hypohesis Vol III(1) Variance Bounds Tess for he Hypohesis of Efficien Sock Marke Marco Maisenbacher * Inroducion The heory of efficien financial markes was

More information

Robust estimation based on the first- and third-moment restrictions of the power transformation model

Robust estimation based on the first- and third-moment restrictions of the power transformation model h Inernaional Congress on Modelling and Simulaion, Adelaide, Ausralia, 6 December 3 www.mssanz.org.au/modsim3 Robus esimaion based on he firs- and hird-momen resricions of he power ransformaion Nawaa,

More information

Linear Combinations of Volatility Forecasts for the WIG20 and Polish Exchange Rates

Linear Combinations of Volatility Forecasts for the WIG20 and Polish Exchange Rates Eliza Buszkowska Universiy of Poznań, Poland Linear Combinaions of Volailiy Forecass for he WIG0 and Polish Exchange Raes Absrak. As is known forecas combinaions may be beer forecass hen forecass obained

More information

PREDICTION OF HIGH-FREQUENCY DATA: APPLICATION TO EXCHANGE RATES TIME SERIES

PREDICTION OF HIGH-FREQUENCY DATA: APPLICATION TO EXCHANGE RATES TIME SERIES PREDICTION OF HIGH-FREQUENCY DATA: APPLICATION TO EXCHANGE RATES TIME SERIES Milan Marček Medis, spol. s r.o., Pri Dobroke, 659/8, 948 0 Nira, Silesian Universiy, Insiue of compuer Science, Opava, Czech

More information