nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒03‒16
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modeling the Dependence of Conditional Correlations on Volatility By L. Bauwens; Edoardo Otranto
  2. Bootstrap Co-integration Rank Testing: The Effect of Bias-Correcting Parameter Estimates By Cavaliere, Giuseppe; Taylor, A. M. Robert; Trenkler, Carsten
  3. Bootstrapping Realized Multivariate Volatility Measures. By Donovon, Prosper; Goncalves, Silvia; Meddahi, Nour
  4. Inference in non stationary asymmetric garch models By Francq, Christian; Zakoian, Jean-Michel
  5. Macroeconomic Forecasting Using Low-Frequency Filters By João Valle e Azevedo; Ana Pereira
  6. “Determining the Number of Regimes in Markov-Switching VAR and VMA Models” By Maddalena Cavicchioli

  1. By: L. Bauwens; Edoardo Otranto
    Abstract: Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns, but few studies have investigated the determinants of the correlation dynamics. A common opinion is that the market volatility is a major determinant of the correlations. We extend some models to capture explicitly the dependence of the correlations on the volatility of the market of interest. The models differ in the way by which the volatility influences the correlations, which can be transmitted through linear or nonlinear, and direct or indirect effects. They are applied to different data sets to verify the presence and possible regularity of the volatility impact on correlations.
    Keywords: volatility effects; conditional correlation; DCC; Markov switching
    JEL: C32
    Date: 2013
  2. By: Cavaliere, Giuseppe; Taylor, A. M. Robert; Trenkler, Carsten
    Abstract: In this paper we investigate bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of the co-integration rank tests of Johansen (1996). In order to do so we adapt the framework of Kilian (1998) which estimates the bias in the original parameter estimates using the average bias in the corresponding parameter esti- mates taken across a large number of auxiliary bootstrap replications. A number of possible implementations of this procedure are discussed and concrete recommendations made on the basis of finite sample performance evaluated by Monte Carlo simulation methods. Our results show that bootstrap-based bias-correction methods can significantly improve upon the small sample performance of the bootstrap co-integration rank tests. A brief application of the techniques developed in this paper to international dynamic consumption risk sharing within Europe is also considered.
    Keywords: Co-integration , trace test , bias-correction , bootstrap
    JEL: C30 C32
    Date: 2013
  3. By: Donovon, Prosper; Goncalves, Silvia; Meddahi, Nour
    Date: 2013–01
  4. By: Francq, Christian; Zakoian, Jean-Michel
    Abstract: This paper considers the statistical inference of the class of asymmetric power-transformed GARCH(1,1) models in presence of possible explosiveness. We study the explosive behavior of volatility when the strict stationarity condition is not met. This allows us to establish the asymptotic normality of the quasi-maximum likelihood estimator (QMLE) of the parameter, including the power but without the intercept, when strict stationarity does not hold. Two important issues can be tested in this framework: asymmetry and stationarity. The tests exploit the existence of a universal estimator of the asymptotic covariance matrix of the QMLE. By establishing the local asymptotic normality (LAN) property in this nonstationary framework, we can also study optimality issues.
    Keywords: GARCH models; Inconsistency of estimators; Local power of tests; Nonstationarity; Quasi Maximum Likelihood Estimation
    JEL: C01 C12 C13 C22
    Date: 2013–03–01
  5. By: João Valle e Azevedo; Ana Pereira
    Abstract: We explore the use of univariate low-frequency filters in macroeconomic forecasting. This amounts to targeting only specific fluctuations of the time series of interest. We show through simulations that such approach is warranted and, using US data, we confirm empirically that consistent gains in forecast accuracy can be obtained in comparison with a variety of other methods. There is an inherent arbitrariness in the choice of the cut-off defining low and high frequencies, but we show that some patterns characterize the implied optimal (for forecasting) degree of smoothing of the key macroeconomic indicators we analyze. For most variables the optimal choice amounts to disregarding fluctuations well below the standard business cycle cut-off of 32 quarters while generally increasing with the forecast horizon; for inflation and variables related to housing this cut-off lies around 32 quarters for all horizons, which is below the optimal level for federal spending.<br />
    JEL: C14 C32 C51 C53
    Date: 2013
  6. By: Maddalena Cavicchioli (Advanced School of Economics, Department of Economics, University Of Venice Cà Foscari)
    Abstract: We give stable finite order VARMA(p*; q*) representations for M-state Markov switching second-order stationary time series whose autocovariances satisfy a certain matrix relation. The upper bounds for p* and q* are elementary functions of the dimension K of the process, the number M of regimes, the autoregressive and moving average orders of the initial model. If there is no cancellation, the bounds become equalities, and this solves the identification problem. Our class of time series include every M-state Markov switching multivariate moving average models and autoregressive models in which the regime variable is uncorrelated with the observable. Our results include, as particular cases, those obtained by Krolzig (1997), and improve the bounds given by Zhang and Stine (2001) and Francq and Zakoian (2001) for our classes of dynamic models. Data simulations and an application on foreign exchange rates complete the paper.
    Keywords: Second-order stationary time series, VMA models, VAR models, State-Space models, Markov chains, changes in regime, regime number.
    JEL: C01 C32 C50 C52
    Date: 2013

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