BDS ANFIS ANFIS ARMA
|
|
- Ami Brittany Doyle
- 5 years ago
- Views:
Transcription
1 * MGN G7 C5 C58 C53 JEL
2 3 7 TAR 6 ARCH Chaos Theory Deterministic 3 Exponential Smoothing Autoregressive Integrated Moving Average 5 Autoregressive Conditional Heteroskedasticity 6 Bilinear 7 Threshold Auto Regressive
3 Zhang and Hu Artificial Neural Network 3 Prtugal Unobserved Component Models 5 Church and Curram 6 Dimension Correlation
4 Generalized Auto Regresive Conditional Heteroskedasticity Adaptive Neuro Fuzzy Inference System 3 Tully and lucey Threshold 5 Exponetial 6 Dunis and Nattani 7 Quantitive 8 Nearest Neighbors 9 Correct Directional Change 0 Risk Adjusted Return Parisi and Diaz Rooling Neural Network
5 Fahimifard Yayar 3 Levenberg Marquardt Algorithm Takagi Sugeno
6 3 q d p p p, q p, q q 3 p, q = ~ 0, 008 q p p, d, q 3 98 ARCH ARCH Volatility Clustering Engel 3 Bollerslev
7 ARCH E ARCHM p, q ARCHp I 986 ARCHq = + + q 986 ARCH p q p, q 00 = ARCH in Mean Model Exponential 3 Integrated Johnston and Scott 5 Bouchad and Matacz 6 Soft Computing
8 TAR ARCH =,, i t f = w i b i0 b 0 w ij Exponential Smoothing Input Pattern
9 IID DBS, =,,, 6 m m,t W m,t W Scheinnkman, Dechert & Brock
10 =H 0 =H 0 =H 0 =H =H =H 39 Z %0 6 %5 Z 6
11 World Gold Council wwwgoldorg Training Set 3 Test Set Out of sample
12 Skewness Kurtosis
13 5 p, d, q 986 t MA AR 0 9 AR 0, 9 5 Granger and Newbold Augmented DickeyFuller test
14 AR MA 5
15 5, 6, 6 35
16 5 0 M= M=3 M= M=5 M=
17 Matlab Sugeno fuzzy inference
18 6 99 MAE 5 TIC RMSE MAPE = 7 = 00 8 = 9 = Hykin Root Mean Squared Error RMSE 3 Mean Absolute Error MAE Mean Absolute Percentage Error MAPE 5 Theil inequality coefficient TIC 6 Brooks 7 Gerlow, Irowin and Liu
19 = Z t+s = If y t+s *f t,s >0 Z t+s =0 Otherwise T T t s f t,s t Y t = Z t+s = If y t+s y t f t,s y t >0 Z t+s =0 Otherwise 3 MGN Leitch and Tanner Pesaran and Timmerman 3 Correct Sign Predictions Correct Direction Change Predictions
20 00 e,t e,t D t S t =, +, =,,, =, = 5 x p x p x m MGN = 6 MGN N D t S t sd 00 N t 5 CSP CDCP TIC MAPE MAE RMSE , Diebold and Mariano
21 MGN ARAM 7
22 CDCP CSP TIC MAPE MAE RMSE MGN
23 ANN Bollerslev, T 986 Generalized Autoregressive Conditional Heteroskedasticity Journal of Economics; Brock, W A, W Dechert, J Scheinkman 987 A Test for Independence Based on the Correlation Dimension Working paper, University of Winconsin at Madison, University of Houston, and University of Chicago 7 Brooks, C 008 Introductory Econometrics for Finance Second Edition, Cambridge University Press 8 Church, k b, Curram S P 996 Forecasting consumers' expenditure A comparison between econometric and neural network models International Journal of Forecasting,,, Diebold F, Mariano R 00 Comparing predictive accuracy Journal of Business and Economic Statistics 0, 3 0 Engle F R 98 Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation Econometrica, Fahimifard, S M, Salarpour, M, Sabouhi, M, Shirzady, S 009 Application of to Agricultural Economic Variables Forecasting Case Study Poultry Retail Price, Artificial Intelligence, 657
24 Gerlow, M, Irwin, S, Liu, T 993 Economic Evaluation of Commodity Price Forecasting Models International Journal of Forecasting, Granger, C W J, Newbold, P 986 Forecasting economic time series Orlando Academic Press Gujarati D 008 Basic Econometrics 5 th Edition McGrawHill 5 Haykin, S 99, Neural Networks a Comprehensive Foundation Macmillan, New York 6 httpwwwgoldorg 7 Johnston k, Scott, E 000 Models and The Stochastic Process Underlying Exchange Rate Price Change Journal of Financial and Strategic Decisions, Vol pp 3 8 Leitch, G and Tanner, JE 99 Economic Forecast Evaluation Profits versus the Conventional Error Measures The American Economic Review AER, 83, Parisi, A, Parisi, F, Díaz, D 008 Forecasting gold price changes Rolling and recursive neural network models, Journal of Multinational Financial Management, Elsevier, vol 85, pages 7787, December 0 Pesaran, M H, Timmerman, A 99 A Simple Nonparametric Test of Predictive Performance journal of Business & Economic Statistics 0,, 665 Prtugal, N S 995, Neural networks versus time series Methods A Forecasting Exercises, th International Symposium on Forecasting, Sweden Tully, E, Lucey, B M 007 A power examination of the gold market Research in international business and finance, Yayar, M, Hekim, M, Yelmaz, V, Bakirci, F 0 A comparison of and techniques in the forecasting of electric energy consumption of Tokat province in Turkey Zarranezhad, M, Raoofi, A, kiyani, p 0 Evaluation and comparison of performance of and in forecasting the daily gold prices, The First international conference on econometrics and methods applications 5 Zhang, G, Hu, MY 998 Neural Network Forecasting of the British PoundUS Dollar Exchange Rate, International Journal of Management Science, Zhang, G, Hu, MY 00 A Simulation Study of Artificial Neural Networks for Nonliner Time Series Forecasting Comput Oper Res 8, pp Zhang, G, Hu, MY 00 Forecasting with Artificial Neural Networks The state of the Art, Neurocomputing 56,pp 05 3
9) Time series econometrics
30C00200 Econometrics 9) Time series econometrics Timo Kuosmanen Professor Management Science http://nomepre.net/index.php/timokuosmanen 1 Macroeconomic data: GDP Inflation rate Examples of time series
More informationFinancial Econometrics
Financial Econometrics Nonlinear time series analysis Gerald P. Dwyer Trinity College, Dublin January 2016 Outline 1 Nonlinearity Does nonlinearity matter? Nonlinear models Tests for nonlinearity Forecasting
More informationECONOMICS 7200 MODERN TIME SERIES ANALYSIS Econometric Theory and Applications
ECONOMICS 7200 MODERN TIME SERIES ANALYSIS Econometric Theory and Applications Yongmiao Hong Department of Economics & Department of Statistical Sciences Cornell University Spring 2019 Time and uncertainty
More informationForecasting Major Vegetable Crops Productions in Tunisia
International Journal of Research in Business Studies and Management Volume 2, Issue 6, June 2015, PP 15-19 ISSN 2394-5923 (Print) & ISSN 2394-5931 (Online) Forecasting Major Vegetable Crops Productions
More informationNearest-Neighbor Forecasts Of U.S. Interest Rates
1 Nearest-Neighbor Forecasts Of U.S. Interest Rates John Barkoulas 1 Department of Economics University of Tennessee 534 Stokely Management Center Knoxville, TN 37996 Christopher F. Baum Department of
More informationA Non-Parametric Approach of Heteroskedasticity Robust Estimation of Vector-Autoregressive (VAR) Models
Journal of Finance and Investment Analysis, vol.1, no.1, 2012, 55-67 ISSN: 2241-0988 (print version), 2241-0996 (online) International Scientific Press, 2012 A Non-Parametric Approach of Heteroskedasticity
More informationEcon 423 Lecture Notes: Additional Topics in Time Series 1
Econ 423 Lecture Notes: Additional Topics in Time Series 1 John C. Chao April 25, 2017 1 These notes are based in large part on Chapter 16 of Stock and Watson (2011). They are for instructional purposes
More informationForecasting exchange rate volatility using conditional variance models selected by information criteria
Forecasting exchange rate volatility using conditional variance models selected by information criteria Article Accepted Version Brooks, C. and Burke, S. (1998) Forecasting exchange rate volatility using
More informationFor the full text of this licence, please go to:
This item was submitted to Loughborough s Institutional Repository by the author and is made available under the following Creative Commons Licence conditions. For the full text of this licence, please
More information(SARIMA) SARIMA 1390 **
* SARIMA sharzeie@utacir ** amirhoseinghafarinejad@gmailcom (SARIMA 6 88 9 85 9 Q4 C45 D G JEL * 90 ** ( 85 000 600 55 9 000,0 9 85 (ATM ( 89 00, (ANN Automated Teller Machine Artificial Neural Networks
More informationA Test of the GARCH(1,1) Specification for Daily Stock Returns
A Test of the GARCH(1,1) Specification for Daily Stock Returns Richard A. Ashley Department of Economics Virginia Tech (VPI) ashleyr@vt.edu Douglas M. Patterson Department of Finance Virginia Tech (VPI)
More informationUniversity of Pretoria Department of Economics Working Paper Series
University of Pretoria Department of Economics Working Paper Series Predicting Stock Returns and Volatility Using Consumption-Aggregate Wealth Ratios: A Nonlinear Approach Stelios Bekiros IPAG Business
More informationSample Exam Questions for Econometrics
Sample Exam Questions for Econometrics 1 a) What is meant by marginalisation and conditioning in the process of model reduction within the dynamic modelling tradition? (30%) b) Having derived a model for
More informationThe GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a
2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA 2016) The GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a 1 Longdong University,Qingyang,Gansu province,745000 a
More informationLocation Multiplicative Error Model. Asymptotic Inference and Empirical Analysis
: Asymptotic Inference and Empirical Analysis Qian Li Department of Mathematics and Statistics University of Missouri-Kansas City ql35d@mail.umkc.edu October 29, 2015 Outline of Topics Introduction GARCH
More informationThe Comparative Performance of Alternative Out-ofsample Predictability Tests with Non-linear Models
The Comparative Performance of Alternative Out-ofsample Predictability Tests with Non-linear Models Yu Liu, University of Texas at El Paso Ruxandra Prodan, University of Houston Alex Nikolsko-Rzhevskyy,
More informationArma-Arch Modeling Of The Returns Of First Bank Of Nigeria
Arma-Arch Modeling Of The Returns Of First Bank Of Nigeria Emmanuel Alphonsus Akpan Imoh Udo Moffat Department of Mathematics and Statistics University of Uyo, Nigeria Ntiedo Bassey Ekpo Department of
More informationPrediction of Seasonal Rainfall Data in India using Fuzzy Stochastic Modelling
Global Journal of Pure and Applied Mathematics. ISSN 0973-1768 Volume 13, Number 9 (2017), pp. 6167-6174 Research India Publications http://www.ripublication.com Prediction of Seasonal Rainfall Data in
More informationA radial basis function artificial neural network test for ARCH
Economics Letters 69 (000) 5 3 www.elsevier.com/ locate/ econbase A radial basis function artificial neural network test for ARCH * Andrew P. Blake, George Kapetanios National Institute of Economic and
More informationAPPLIED TIME SERIES ECONOMETRICS
APPLIED TIME SERIES ECONOMETRICS Edited by HELMUT LÜTKEPOHL European University Institute, Florence MARKUS KRÄTZIG Humboldt University, Berlin CAMBRIDGE UNIVERSITY PRESS Contents Preface Notation and Abbreviations
More informationSCIENCE & TECHNOLOGY
Pertanika J. Sci. & Technol. 5 (3): 787-796 (017) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Combination of Forecasts with an Application to Unemployment Rate Muniroh, M. F.
More informationForecasting. Bernt Arne Ødegaard. 16 August 2018
Forecasting Bernt Arne Ødegaard 6 August 208 Contents Forecasting. Choice of forecasting model - theory................2 Choice of forecasting model - common practice......... 2.3 In sample testing of
More informationVolatility. Gerald P. Dwyer. February Clemson University
Volatility Gerald P. Dwyer Clemson University February 2016 Outline 1 Volatility Characteristics of Time Series Heteroskedasticity Simpler Estimation Strategies Exponentially Weighted Moving Average Use
More informationPredict GARCH Based Volatility of Shanghai Composite Index by Recurrent Relevant Vector Machines and Recurrent Least Square Support Vector Machines
Predict GARCH Based Volatility of Shanghai Composite Index by Recurrent Relevant Vector Machines and Recurrent Least Square Support Vector Machines Phichhang Ou (Corresponding author) School of Business,
More informationOil price volatility in the Philippines using generalized autoregressive conditional heteroscedasticity
Oil price volatility in the Philippines using generalized autoregressive conditional heteroscedasticity Carl Ceasar F. Talungon University of Southern Mindanao, Cotabato Province, Philippines Email: carlceasar04@gmail.com
More informationA Bootstrap Test for Causality with Endogenous Lag Length Choice. - theory and application in finance
CESIS Electronic Working Paper Series Paper No. 223 A Bootstrap Test for Causality with Endogenous Lag Length Choice - theory and application in finance R. Scott Hacker and Abdulnasser Hatemi-J April 200
More informationAnalytical derivates of the APARCH model
Analytical derivates of the APARCH model Sébastien Laurent Forthcoming in Computational Economics October 24, 2003 Abstract his paper derives analytical expressions for the score of the APARCH model of
More informationA SEASONAL FUZZY TIME SERIES FORECASTING METHOD BASED ON GUSTAFSON-KESSEL FUZZY CLUSTERING *
No.2, Vol.1, Winter 2012 2012 Published by JSES. A SEASONAL FUZZY TIME SERIES FORECASTING METHOD BASED ON GUSTAFSON-KESSEL * Faruk ALPASLAN a, Ozge CAGCAG b Abstract Fuzzy time series forecasting methods
More informationDiscussion Papers in Economics. Ali Choudhary (University of Surrey and State Bank of Pakistan) & Adnan Haider (State Bank of Pakistan) DP 08/08
Discussion Papers in Economics NEURAL NETWORK MODELS FOR INFLATION FORECASTING: AN APPRAISAL By Ali Choudhary (University of Surrey and State Bank of Pakistan) & Adnan Haider (State Bank of Pakistan) DP
More informationFrequency Forecasting using Time Series ARIMA model
Frequency Forecasting using Time Series ARIMA model Manish Kumar Tikariha DGM(O) NSPCL Bhilai Abstract In view of stringent regulatory stance and recent tariff guidelines, Deviation Settlement mechanism
More informationNonlinear Characterization of Activity Dynamics in Online Collaboration Websites
Nonlinear Characterization of Activity Dynamics in Online Collaboration Websites Tiago Santos 1 Simon Walk 2 Denis Helic 3 1 Know-Center, Graz, Austria 2 Stanford University 3 Graz University of Technology
More informationChaos in world banking sector? Paulo Rogério Faustino Matos Maurício Benegas José Henrique Calenzo Costa
27 Chaos in world banking sector? Paulo Rogério Faustino Matos Maurício Benegas José Henrique Calenzo Costa FORTALEZA JULHO 2018 UNIVERSIDADE FEDERAL DO CEARÁ PROGRAMA DE PÓS-GRADUAÇÃO EM ECONOMIA - CAEN
More informationJOURNAL OF MANAGEMENT SCIENCES IN CHINA. R t. t = 0 + R t - 1, R t - 2, ARCH. j 0, j = 1,2,, p
6 2 2003 4 JOURNAL OF MANAGEMENT SCIENCES IN CHINA Vol 6 No 2 Apr 2003 GARCH ( 300072) : ARCH GARCH GARCH GARCH : ; GARCH ; ; :F830 :A :1007-9807 (2003) 02-0068 - 06 0 2 t = 0 + 2 i t - i = 0 +( L) 2 t
More informationBootstrap tests of multiple inequality restrictions on variance ratios
Economics Letters 91 (2006) 343 348 www.elsevier.com/locate/econbase Bootstrap tests of multiple inequality restrictions on variance ratios Jeff Fleming a, Chris Kirby b, *, Barbara Ostdiek a a Jones Graduate
More informationNonparametric Estimation of Functional-Coefficient Autoregressive Models
Nonparametric Estimation of Functional-Coefficient Autoregressive Models PEDRO A. MORETTIN and CHANG CHIANN Department of Statistics, University of São Paulo Introduction Nonlinear Models: - Exponential
More informationStock index returns density prediction using GARCH models: Frequentist or Bayesian estimation?
MPRA Munich Personal RePEc Archive Stock index returns density prediction using GARCH models: Frequentist or Bayesian estimation? Ardia, David; Lennart, Hoogerheide and Nienke, Corré aeris CAPITAL AG,
More informationTheodore Panagiotidis*^ and Gianluigi Pelloni**
Free University of Bozen - Bolzano School of Economics Bolzano, Italy Working Paper No. 13 IS NON-LINEAR SERIAL DEPENDENCE PRESENT IN THE US UNEMPLOYMENT RATE AND THE GROWTH RATES OF EMPLOYMENT SECTORAL
More information10) Time series econometrics
30C00200 Econometrics 10) Time series econometrics Timo Kuosmanen Professor, Ph.D. 1 Topics today Static vs. dynamic time series model Suprious regression Stationary and nonstationary time series Unit
More informationThe Size and Power of Four Tests for Detecting Autoregressive Conditional Heteroskedasticity in the Presence of Serial Correlation
The Size and Power of Four s for Detecting Conditional Heteroskedasticity in the Presence of Serial Correlation A. Stan Hurn Department of Economics Unversity of Melbourne Australia and A. David McDonald
More informationDo we need Experts for Time Series Forecasting?
Do we need Experts for Time Series Forecasting? Christiane Lemke and Bogdan Gabrys Bournemouth University - School of Design, Engineering and Computing Poole House, Talbot Campus, Poole, BH12 5BB - United
More informationMODELLING TIME SERIES WITH CONDITIONAL HETEROSCEDASTICITY
MODELLING TIME SERIES WITH CONDITIONAL HETEROSCEDASTICITY The simple ARCH Model Eva Rubliková Ekonomická univerzita Bratislava Manuela Magalhães Hill Department of Quantitative Methods, INSTITUTO SUPERIOR
More informationConvergence of Heuristic-based Estimators of the GARCH Model
Convergence of Heuristic-based Estimators of the GARCH Model Alexandru Mandes 1, Cristian Gatu 1, and Peter Winker 2 Abstract he GARCH econometric model is able to describe the volatility of financial
More informationGeneralized Autoregressive Score Models
Generalized Autoregressive Score Models by: Drew Creal, Siem Jan Koopman, André Lucas To capture the dynamic behavior of univariate and multivariate time series processes, we can allow parameters to be
More informationGARCH processes probabilistic properties (Part 1)
GARCH processes probabilistic properties (Part 1) Alexander Lindner Centre of Mathematical Sciences Technical University of Munich D 85747 Garching Germany lindner@ma.tum.de http://www-m1.ma.tum.de/m4/pers/lindner/
More informationFinQuiz Notes
Reading 9 A time series is any series of data that varies over time e.g. the quarterly sales for a company during the past five years or daily returns of a security. When assumptions of the regression
More informationDiagnostic Test for GARCH Models Based on Absolute Residual Autocorrelations
Diagnostic Test for GARCH Models Based on Absolute Residual Autocorrelations Farhat Iqbal Department of Statistics, University of Balochistan Quetta-Pakistan farhatiqb@gmail.com Abstract In this paper
More informationA new method for short-term load forecasting based on chaotic time series and neural network
A new method for short-term load forecasting based on chaotic time series and neural network Sajjad Kouhi*, Navid Taghizadegan Electrical Engineering Department, Azarbaijan Shahid Madani University, Tabriz,
More informationDynamicAsymmetricGARCH
DynamicAsymmetricGARCH Massimiliano Caporin Dipartimento di Scienze Economiche Università Ca Foscari di Venezia Michael McAleer School of Economics and Commerce University of Western Australia Revised:
More informationA FUZZY NEURAL NETWORK MODEL FOR FORECASTING STOCK PRICE
A FUZZY NEURAL NETWORK MODEL FOR FORECASTING STOCK PRICE Li Sheng Institute of intelligent information engineering Zheiang University Hangzhou, 3007, P. R. China ABSTRACT In this paper, a neural network-driven
More informationA Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships
Article A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships Shuang Guan 1 and Aiwu Zhao 2, * 1 Rensselaer Polytechnic Institute, Troy, NY 12180, USA; guans@rpi.edu
More informationGARCH Models. Eduardo Rossi University of Pavia. December Rossi GARCH Financial Econometrics / 50
GARCH Models Eduardo Rossi University of Pavia December 013 Rossi GARCH Financial Econometrics - 013 1 / 50 Outline 1 Stylized Facts ARCH model: definition 3 GARCH model 4 EGARCH 5 Asymmetric Models 6
More informationRe-Considering the Necessary Condition for Futures. Market Efficiency: An Application to Dairy Futures by Dwight R. Sanders and Mark R.
Re-Considering the Necessary Condition for Futures Market Efficiency: An Application to Dairy Futures by Dwight R. Sanders and Mark R. Manfredo Suggested citation format: Sanders, D. R., and M. R. Manfredo.
More informationProgram. The. provide the. coefficientss. (b) References. y Watson. probability (1991), "A. Stock. Arouba, Diebold conditions" based on monthly
Macroeconomic Forecasting Topics October 6 th to 10 th, 2014 Banco Central de Venezuela Caracas, Venezuela Program Professor: Pablo Lavado The aim of this course is to provide the basis for short term
More informationSzilárd MADARAS, 1 Lehel GYÖRFY 2 1. Introduction. DOI: /auseb
Acta Univ. Sapientiae, Economics and Business, 4 (2016) 33 41 DOI: 10.1515/auseb-2016-0002 Non-Linearity and Non-Stationarity of Exchange Rate Time Series in Three Central-Eastern European Countries Regarding
More informationOLS Assumptions Violation and Its Treatment: An Empirical Test of Gross Domestic Product Relationship with Exchange Rate, Inflation and Interest Rate
J. Appl. Environ. Biol. Sci., 6(5S)43-54, 2016 2016, TextRoad Publication ISSN: 2090-4274 Journal of Applied Environmental and Biological Sciences www.textroad.com OLS Assumptions Violation and Its Treatment:
More informationKalman Filter and SVR Combinations in Forecasting US Unemployment
Kalman Filter and SVR Combinations in Forecasting US Unemployment Georgios Sermpinis 1, Charalampos Stasinakis 1, and Andreas Karathanasopoulos 2 1 University of Glasgow Business School georgios.sermpinis@glasgow.ac.uk,
More informationTime Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY
Time Series Analysis James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY & Contents PREFACE xiii 1 1.1. 1.2. Difference Equations First-Order Difference Equations 1 /?th-order Difference
More informationApplied Time Series Topics
Applied Time Series Topics Ivan Medovikov Brock University April 16, 2013 Ivan Medovikov, Brock University Applied Time Series Topics 1/34 Overview 1. Non-stationary data and consequences 2. Trends and
More informationProblem set 1 - Solutions
EMPIRICAL FINANCE AND FINANCIAL ECONOMETRICS - MODULE (8448) Problem set 1 - Solutions Exercise 1 -Solutions 1. The correct answer is (a). In fact, the process generating daily prices is usually assumed
More informationFORECASTING AND MODEL SELECTION
FORECASTING AND MODEL SELECTION Anurag Prasad Department of Mathematics and Statistics Indian Institute of Technology Kanpur, India REACH Symposium, March 15-18, 2008 1 Forecasting and Model Selection
More informationDEPARTMENT OF ECONOMICS
ISSN 0819-64 ISBN 0 7340 616 1 THE UNIVERSITY OF MELBOURNE DEPARTMENT OF ECONOMICS RESEARCH PAPER NUMBER 959 FEBRUARY 006 TESTING FOR RATE-DEPENDENCE AND ASYMMETRY IN INFLATION UNCERTAINTY: EVIDENCE FROM
More informationProperties of Estimates of Daily GARCH Parameters. Based on Intra-day Observations. John W. Galbraith and Victoria Zinde-Walsh
3.. Properties of Estimates of Daily GARCH Parameters Based on Intra-day Observations John W. Galbraith and Victoria Zinde-Walsh Department of Economics McGill University 855 Sherbrooke St. West Montreal,
More informationElectricity price forecasting in Turkey with artificial neural network models
Electricity price forecasting in Turkey with artificial neural network models AUTHORS ARTICLE INFO DOI JOURNAL Fazıl Gökgöz Fahrettin Filiz Fazıl Gökgöz and Fahrettin Filiz (2016). Electricity price forecasting
More informationFORECASTING COARSE RICE PRICES IN BANGLADESH
Progress. Agric. 22(1 & 2): 193 201, 2011 ISSN 1017-8139 FORECASTING COARSE RICE PRICES IN BANGLADESH M. F. Hassan*, M. A. Islam 1, M. F. Imam 2 and S. M. Sayem 3 Department of Agricultural Statistics,
More informationComparison of ARCH / GARCH model and Elman Recurrent Neural Network on data return of closing price stock
Journal of Physics: Conference Series PAPER OPEN ACCESS Comparison of ARCH / GARCH model and Elman Recurrent Neural Network on data return of closing price stock To cite this article: Vania Orva Nur Laily
More informationOn Forecast Strength of Some Linear and Non Linear Time Series Models for Stationary Data Structure
American Journal of Mathematics and Statistics 2015, 5(4): 163-177 DOI: 10.5923/j.ajms.20150504.01 On Forecast Strength of Some Linear and Non Linear Imam Akeyede 1,*, Babatunde Lateef Adeleke 2, Waheed
More informationM-estimators for augmented GARCH(1,1) processes
M-estimators for augmented GARCH(1,1) processes Freiburg, DAGStat 2013 Fabian Tinkl 19.03.2013 Chair of Statistics and Econometrics FAU Erlangen-Nuremberg Outline Introduction The augmented GARCH(1,1)
More informationFORECASTING TIME SERIES DATA USING HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL
FORECASTING TIME SERIES DATA USING HYBRID GREY RELATIONAL ARTIFICIAL NEURAL NETWORK AND AUTO REGRESSIVE INTEGRATED MOVING AVERAGE MODEL Roselina Sallehuddin, Siti Mariyam Hj. Shamsuddin, Siti Zaiton Mohd.
More informationPredicting the Electricity Demand Response via Data-driven Inverse Optimization
Predicting the Electricity Demand Response via Data-driven Inverse Optimization Workshop on Demand Response and Energy Storage Modeling Zagreb, Croatia Juan M. Morales 1 1 Department of Applied Mathematics,
More informationPrediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks
Int. J. of Thermal & Environmental Engineering Volume 14, No. 2 (2017) 103-108 Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks M. A. Hamdan a*, E. Abdelhafez b
More informationUniversity of Kent Department of Economics Discussion Papers
University of Kent Department of Economics Discussion Papers Testing for Granger (non-) Causality in a Time Varying Coefficient VAR Model Dimitris K. Christopoulos and Miguel León-Ledesma January 28 KDPE
More informationForecasting Crude Oil Price Using Neural Networks
CMU. Journal (2006) Vol. 5(3) 377 Forecasting Crude Oil Price Using Neural Networks Komsan Suriya * Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand *Corresponding author. E-mail:
More informationEconometric Forecasting
Graham Elliott Econometric Forecasting Course Description We will review the theory of econometric forecasting with a view to understanding current research and methods. By econometric forecasting we mean
More informationControl and Out-of-Sample Validation of Dependent Risks
Control and Out-of-Sample Validation of Dependent Risks Christian, Gourieroux and Wei, Liu May 25, 2007 Abstract The aim of this paper is to propose a methodology for validating the reserve levels proposed
More informationNonlinear Parameter Estimation for State-Space ARCH Models with Missing Observations
Nonlinear Parameter Estimation for State-Space ARCH Models with Missing Observations SEBASTIÁN OSSANDÓN Pontificia Universidad Católica de Valparaíso Instituto de Matemáticas Blanco Viel 596, Cerro Barón,
More informationInternational Monetary Policy Spillovers
International Monetary Policy Spillovers Dennis Nsafoah Department of Economics University of Calgary Canada November 1, 2017 1 Abstract This paper uses monthly data (from January 1997 to April 2017) to
More informationDEPARTMENT 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 Discussion of Principal Volatility Component Analysis by Yu-Pin Hu and Ruey Tsay
More informationThe value of competitive information in forecasting FMCG retail product sales and category effects
The value of competitive information in forecasting FMCG retail product sales and category effects Professor Robert Fildes r.fildes@lancaster.ac.uk Dr Tao Huang t.huang@lancaster.ac.uk Dr Didier Soopramanien
More informationEvaluating USDA Forecasts of Farm Assets: Ted Covey & Ken Erickson
Evaluating USDA Forecasts of Farm Assets: 1986-2002 Ted Covey & Ken Erickson Agricultural Finance Markets in Transition Proceedings of The Annual Meeting of NCT-194 Hosted by the Center for the Study of
More informationDepth Based Procedures For Estimation ARMA and GARCH Models
Depth Based Procedures For Estimation ARMA and GARCH Models Daniel Kosiorowski Cracow University of Economics COMPSTAT 2010 Paris 22 27 August 2010 I. Motivations 1. Highly effective tools of analysis
More informationInternational Symposium on Mathematical Sciences & Computing Research (ismsc) 2015 (ismsc 15)
Model Performance Between Linear Vector Autoregressive and Markov Switching Vector Autoregressive Models on Modelling Structural Change in Time Series Data Phoong Seuk Wai Department of Economics Facultyof
More information13. Estimation and Extensions in the ARCH model. MA6622, Ernesto Mordecki, CityU, HK, References for this Lecture:
13. Estimation and Extensions in the ARCH model MA6622, Ernesto Mordecki, CityU, HK, 2006. References for this Lecture: Robert F. Engle. GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics,
More informationTime Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY
Time Series Analysis James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY PREFACE xiii 1 Difference Equations 1.1. First-Order Difference Equations 1 1.2. pth-order Difference Equations 7
More information1 Phelix spot and futures returns: descriptive statistics
MULTIVARIATE VOLATILITY MODELING OF ELECTRICITY FUTURES: ONLINE APPENDIX Luc Bauwens 1, Christian Hafner 2, and Diane Pierret 3 October 13, 2011 1 Phelix spot and futures returns: descriptive statistics
More informationModeling speech signals in the time frequency domain using GARCH
Signal Processing () 53 59 Fast communication Modeling speech signals in the time frequency domain using GARCH Israel Cohen Department of Electrical Engineering, Technion Israel Institute of Technology,
More informationParameter Estimation for ARCH(1) Models Based on Kalman Filter
Applied Mathematical Sciences, Vol. 8, 2014, no. 56, 2783-2791 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.43164 Parameter Estimation for ARCH(1) Models Based on Kalman Filter Jelloul
More informationSubsampling Cumulative Covariance Estimator
Subsampling Cumulative Covariance Estimator Taro Kanatani Faculty of Economics, Shiga University 1-1-1 Bamba, Hikone, Shiga 5228522, Japan February 2009 Abstract In this paper subsampling Cumulative Covariance
More informationSwitzerland, July 2007
GARCH A Case Study presented at the Meielisalp Workshop on Computational Finance and Financial Engineering www.rmetrics.org itp.phys.ethz.ch SP500 Yohan Chalabi, EPFL Lausanne, Diethelm Würtz, ITP ETH
More informationCOMPARISON OF CLEAR-SKY MODELS FOR EVALUATING SOLAR FORECASTING SKILL
COMPARISON OF CLEAR-SKY MODELS FOR EVALUATING SOLAR FORECASTING SKILL Ricardo Marquez Mechanical Engineering and Applied Mechanics School of Engineering University of California Merced Carlos F. M. Coimbra
More informationPREDICTIONS AGGREGATION BY COUNTRY TO IMPROVE THE ACCURACY OF EUROPEAN UNION GDP RATE FORECASTS? Mihaela Simionescu *
PREDICTIONS AGGREGATION BY COUNTRY TO IMPROVE THE ACCURACY OF EUROPEAN UNION GDP RATE FORECASTS? Mihaela Simionescu * Address for corespondence: Institute for Economic Forecasting of the Romanian Academy
More informationA note on adaptation in garch models Gloria González-Rivera a a
This article was downloaded by: [CDL Journals Account] On: 3 February 2011 Access details: Access Details: [subscription number 922973516] Publisher Taylor & Francis Informa Ltd Registered in England and
More informationExtended IS-LM model - construction and analysis of behavior
Extended IS-LM model - construction and analysis of behavior David Martinčík Department of Economics and Finance, Faculty of Economics, University of West Bohemia, martinci@kef.zcu.cz Blanka Šedivá Department
More informationThis is the author s final accepted version.
Bagdatoglou, G., Kontonikas, A., and Wohar, M. E. (2015) Forecasting US inflation using dynamic general-to-specific model selection. Bulletin of Economic Research, 68(2), pp. 151-167. (doi:10.1111/boer.12041)
More informationAgricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System
Agricultural Economics Research Review Vol. 26 (No.2) July-December 2013 pp 229-239 Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System Girish K. Jha *a
More informationECONOMETRIC REVIEWS, 5(1), (1986) MODELING THE PERSISTENCE OF CONDITIONAL VARIANCES: A COMMENT
ECONOMETRIC REVIEWS, 5(1), 51-56 (1986) MODELING THE PERSISTENCE OF CONDITIONAL VARIANCES: A COMMENT Professors Engle and Bollerslev have delivered an excellent blend of "forest" and "trees"; their important
More informationJuly 31, 2009 / Ben Kedem Symposium
ing The s ing The Department of Statistics North Carolina State University July 31, 2009 / Ben Kedem Symposium Outline ing The s 1 2 s 3 4 5 Ben Kedem ing The s Ben has made many contributions to time
More informationHeterogeneous Expectations, Exchange Rate Dynamics and Predictability
Heterogeneous Expectations, Exchange Rate Dynamics and Predictability Sebastiano Manzan and Frank H. Westerhoff Department of Economics, University of Leicester University Road, Leicester LE1 7RH, United
More informationDepartment of Economics, UCSB UC Santa Barbara
Department of Economics, UCSB UC Santa Barbara Title: Past trend versus future expectation: test of exchange rate volatility Author: Sengupta, Jati K., University of California, Santa Barbara Sfeir, Raymond,
More informationEconometric Forecasting
Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna October 1, 2014 Outline Introduction Model-free extrapolation Univariate time-series models Trend
More informationSHORT TERM LOAD FORECASTING
Indian Institute of Technology Kanpur (IITK) and Indian Energy Exchange (IEX) are delighted to announce Training Program on "Power Procurement Strategy and Power Exchanges" 28-30 July, 2014 SHORT TERM
More information