nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒07‒15
25 papers chosen by
Yong Yin
SUNY at Buffalo
  1. Forcasting in large cointegrated processes By Hiroaki Chigira; Taku Yamamoto
  2. Beveridge-Nelson Decomposition with Markov Switching By Chin Nam Low; Heather Anderson; Ralph Snyder
  3. Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models By Francois-Éric Racicot; Raymond Théoret; Alain Coen
  4. The Equilibrium Real Exchange Rate in Peru: BEER Models and Confidence Band Building By Jesús Ferreyra; Jorge Salas
  5. Artificial Neural Network Enhanced Parametric Option Pricing By Panayiotis C. Andreou
  6. Exchange Rates and Fundamentals: Is there a Role for Nonlinearities in Real Time? By Yunus Aksoy; ; Kurmas Akdogan
  7. Learning about Stock Volatility: The Local Scale Model with Homoskedastic Innovations By J. Huston McCulloch; ; Ohio State University
  8. The Fractional OU Process: Term Structure Theory and Application By Esben Hoeg
  9. The Time Varying Volatility of Macroeconomic Fluctuations By Alejandro Justiniano
  10. Semiparametric estimation in perturbed long memory series By Josu Arteche; University of the Basque Country
  11. Bootstrapping Neural tests for conditional heteroskedasticity By Carole Siani
  12. Graphical Methods for Investigating the Finite-sample Properties of Confidence Regions: an application to long memory By Christian de Peretti
  13. Assessing the structural VAR approach to exchange rate pass-through By Ida Wolden Bache
  14. Breaking trend panel unit root tests By Pui Sun Tam
  15. Forecasting Inflation: the Relevance of Higher Moments By Jane M. Binner
  16. Exploring the International Linkages of the Euro Area: a Global VAR Analysis By Stephane Dees; European Central Bank
  17. Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis By Andrea Cipollini
  18. Forecasting stock prices using Genetic Programming and Chance Discovery By Alma Lilia Garcia-Almanza; Edward P.K. Tsang
  19. A Broad-Spectrum Computational Approach for Market Efficiency By Olivier Brandouy
  20. Testing for Structural Breaks and other forms of Non-stationarity: a Misspecification Perspective By Maria Heracleous
  21. Secular Trends in U.S Saving and Consumption By Kaiji Chen
  22. Inference in GARCH when some coefficients are equal to zero By Christian Francq
  23. Euro-Dollar Real Exchange Rate Dynamics in an Estimated Two-Country Model By Pau Rabanal
  24. The Relation of Different Concepts of Causality in Econometrics By Michael Lechner
  25. Forecasting the Term Structure of Variance Swaps By Kai Detlefsen; Wolfgang Härdle
  1. By: Hiroaki Chigira; Taku Yamamoto
    Abstract: It is widely recognized that taking cointegration relationships into consideration is useful in forecasting cointegrated processes. However, there are a few practical problems when forecasting large cointegrated processes using the well-known vector error correction model. First, it is hard to identify the cointegration rank in large models. Second, since the number of parameters to be estimated tends to be large relative to the sample size in large models, estimators will have large standard errors, and so will forecasts. The purpose of the present paper is to propose a new procedure for forecasting large cointegrated processes, which is free from the above problems. In our Monte Carlo experiment, we find that our forecast gains accuracy when we work with a larger model as long as the ratio of the cointegration rank to the number of variables in the process is high.
    Keywords: Forcasting, Cointegration, Large Models
    JEL: C12 C32
    Date: 2006–06
    URL: http://d.repec.org/n?u=RePEc:hst:hstdps:d06-169&r=ets
  2. By: Chin Nam Low (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Heather Anderson (Australia National University); Ralph Snyder (Monash University)
    Abstract: In this paper, we consider the introduction of Markov-switching (MS) processes to both the permanent and transitory components of the Beveridge-Nelson (BN) decomposition. This new class of MS models within the context of BN decomposition provides an alternative framework in the study of business cycle asymmetry. Our approach incorporates Markov switching into a BN decomposition formulated in a single source of error state-space form, allowing regime switches in the long-run multiplier as well as in the short-run parameters.
    JEL: C22 C51 E32
    Date: 2006–07
    URL: http://d.repec.org/n?u=RePEc:iae:iaewps:wp2006n14&r=ets
  3. By: Francois-Éric Racicot (Département des sciences administratives, Université du Québec (Outaouais) et LRSP); Raymond Théoret (Département de stratégie des affaires, Université du Québec (Montréal)); Alain Coen (Département de stratégie des affaires, Université du Québec (Montréal))
    Abstract: A very promising literature has been recently devoted to the modeling of ultra-high-frequency (UHF) data. Our first aim is to develop an empirical application of Autoregressive Conditional Duration GARCH models and the realized volatility to forecast future volatilities on irregularly spaced data. We also compare the out sample performances of ACD GARCH models with the realized volatility method. We propose a procedure to take into account the time deformation and show how to use these models for computing daily VaR.
    Keywords: Realized volatility, Ultra High Frequency GARCH, time deformation, financial markets, Daily VaR.
    JEL: C22 C53 G14
    Date: 2006–07–06
    URL: http://d.repec.org/n?u=RePEc:pqs:wpaper:152006&r=ets
  4. By: Jesús Ferreyra (Central Bank of Peru); Jorge Salas (Central Bank of Peru)
    Abstract: This paper uses the "Behavioral Equilibrium Exchange Rate" (BEER) approach to estimate the equilibrium real exchange rate (RER) for Peru. A bootstrap technique is then employed to build confidence bands for the equilibrium path, so that it is possible to determine whether exchange rate misalignments are statistically significant. Additionally, structural breaks are modeled in the long-run relationship between the RER and its fundamentals. Using quarterly data for 1980.I-2005.III, the authors find that the long-run behavior of the Peruvian RER is explained by the following fundamentals: net foreign liabilities, terms of trade, and, less conclusively, government expenditure and openness. Moreover, the ratio of tradable to non-tradable sector productivities, both in domestic terms and relative to trading partners, appears as an additional RER fundamental only since the 1990s. Finally, there is evidence of some statistically significant RER misalignment episodes over the analyzed period.
    Keywords: Equilibrium Real Exchange Rate, BEER Models, Cointegration, Structural Break, Bootstrap
    JEL: F31 F41 C15 C22
    Date: 2006–06
    URL: http://d.repec.org/n?u=RePEc:rbp:wpaper:2006-006&r=ets
  5. By: Panayiotis C. Andreou (University of Cyprus)
    Keywords: Option pricing, implied volatilities, implied parameters, artificial neural networks, optimization
    JEL: G13 G14
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:118&r=ets
  6. By: Yunus Aksoy; ; Kurmas Akdogan
    Keywords: monetary model, exchange rates, nonlinear adjustment, real time, unit roots, forecasting
    JEL: F31 F37
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:12&r=ets
  7. By: J. Huston McCulloch; ; Ohio State University
    Keywords: Local Scale Model, Adaptive Learning, IGARCH, State-Space Model, Stock volatility
    JEL: C32 G10
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:173&r=ets
  8. By: Esben Hoeg (Aarhus School of Business)
    Keywords: Fractional bond pricing equation; fractional Brownian motion;fractional Ornstein-Uhlenbeck process;long memory;Kalman Filter
    JEL: C22 C51 E43
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:194&r=ets
  9. By: Alejandro Justiniano (Board of Governors of the Federal Reserve)
    Keywords: Great Moderation, Stochastic Volatility, Investment Specific Technology Shock, Relative Price of Investment, DSGE Models
    JEL: C32 E32
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:219&r=ets
  10. By: Josu Arteche; University of the Basque Country
    Keywords: long memory, stochastic volatility, semiparametric estimation
    JEL: C22 C13
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:22&r=ets
  11. By: Carole Siani (University of Lyon 1)
    Keywords: Bootstrap, Artificial Neural Networks, ARCH models, inference tests
    JEL: C14 C15 C45
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:301&r=ets
  12. By: Christian de Peretti (Department of Economics University of Evry-Val-d'Essonne)
    Keywords: Graphical method, confidence region, long memory, double bootstrap, inverting tests
    JEL: C14 C15 C63
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:304&r=ets
  13. By: Ida Wolden Bache (Research Department Norges Bank (Central Bank of Norway))
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:309&r=ets
  14. By: Pui Sun Tam (University of Macau)
    Keywords: Panel unit root, structural breaks, response surface, bootstrap
    JEL: C12 C15 C23
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:341&r=ets
  15. By: Jane M. Binner (Aston University)
    Keywords: relative price distribution, higher moments, out-of-sample inflation forecasting
    JEL: C22 C43 E27
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:407&r=ets
  16. By: Stephane Dees; European Central Bank
    Keywords: Global VAR (GVAR), Global interdependencies, global macroeconomic modeling, impulse responses
    JEL: C32 E17 F47
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:47&r=ets
  17. By: Andrea Cipollini (University of Essex)
    Keywords: Financial Contagion, Dynamic Factor Model, Stochastic Simulation
    JEL: C32 C51 F34
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:477&r=ets
  18. By: Alma Lilia Garcia-Almanza (COMPUTER SCIENCE UNIVERSITY OF ESSEX); Edward P.K. Tsang
    Keywords: Forecasting, Chance discovery, Genetic programming, machine learning
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:489&r=ets
  19. By: Olivier Brandouy (LEM)
    Keywords: efficient market hypothesis, large scale simulations, bootstrap
    JEL: G14 C63
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:492&r=ets
  20. By: Maria Heracleous (American University)
    Keywords: Maximum Entropy Bootstrap, Non-Stationarity,
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:493&r=ets
  21. By: Kaiji Chen (Economics University of Oslo)
    Keywords: Consumption, Saving
    JEL: E2
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:494&r=ets
  22. By: Christian Francq
    Keywords: C12, C13, C22
    JEL: C13 C12 C22
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:64&r=ets
  23. By: Pau Rabanal (IMF)
    Keywords: Real Exchange Rates, Bayesian Estimation, Model Comparison.
    JEL: F41 C11
    Date: 2006–07–04
    URL: http://d.repec.org/n?u=RePEc:sce:scecfa:87&r=ets
  24. By: Michael Lechner
    Abstract: Granger and Sims non-causality (GSNC) are compared to non-causality based on concepts popular in the microeconometrics and programme evaluation literature (potential outcome non-causality, PONC). GSNC is defined as a set of restrictions on joint distributions of random variables with observable sample counterparts, whereas PONC combines restrictions on partially unobservable variables (potential outcomes) with different identifying assumptions that relate potential to observable outcomes. Based on a dynamic model of potential outcomes, we find that in general neither of the concepts implies each other without further assumptions. However, identifying assumptions of the sequential selection on observable type provide the link between those concepts, such that GSNC implies PONC, and vice versa.
    Keywords: Granger causality, Sims causality, Rubin causality, potential outcome model, dynamic treatments
    JEL: C21 C22 C23
    Date: 2006–06
    URL: http://d.repec.org/n?u=RePEc:usg:dp2006:2006-15&r=ets
  25. By: Kai Detlefsen; Wolfgang Härdle
    Abstract: Recently, Diebold and Li (2003) obtained good forecasting results for yield curves in a reparametrized Nelson-Siegel framework. We analyze similar modeling approaches for price curves of variance swaps that serve nowadays as hedging instruments for options on realized variance. We consider the popular Heston model, reparametrize its variance swap price formula and model the entire variance swap curves by two exponential factors whose loadings evolve dynamically on a weekly basis. Generalizing this approach we consider a reparametrization of the three-dimensional Nelson-Siegel factor model. We show that these factors can be interpreted as level, slope and curvature and how they can be estimated directly from characteristic points of the curves. Moreover, we analyze a semiparametric factor model. Estimating autoregressive models for the factor loadings we get termstructure forecasts that we compare in addition to the random walk and the static Heston model that is often used in industry. In contrast to the results of Diebold and Li (2003) on yield curves, no model produces better forecasts of variance swap curves than the random walk but forecasting the Heston model improves the popular static Heston model. Moreover, the Heston model is better than the flexible semiparametric approach that outperforms the Nelson-Siegel model.
    Keywords: Term structure, Variance swap curve, Heston model, Nelson-Siegel curve, Semiparametric factor model
    JEL: G1 D4 C5
    Date: 2006–07
    URL: http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2006-052&r=ets
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