nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒08‒05
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Two EGARCH models and one fat tail By M. Caivano; A. Harvey
  2. Data cloning estimation of GARCH and COGARCH models By J. Miguel Marín; M. T. Rodríguez Bernal; Eva Romero
  3. Testing for Cointegration in a Double-LSTR Framework By Grote, Claudia; Sibbertsen, Philipp
  4. Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series By Eric Ghysels; J. Isaac Miller

  1. By: M. Caivano; A. Harvey
    Abstract: We compare two EGARCH models which belong to a new class of models in which the dynamics are driven by the score of the conditional distribution of the observations. Models of this kind are called dynamic conditional score (DCS) models and their form facilitates the development of a comprehensive and relatively straightforward theory for the asymptotic distribution of the maximum likelihood estimator. The EGB2 distribution is light-tailed, but with higher kurtosis than the normal. Hence it is complementary to the fat-tailed t. The EGB2-EGARCH model gives a good fit to many exchange rate return series, prompting an investigation into the misleading conclusions liable to be drawn from tail index estimates.
    Keywords: Exchange rates; heavy tails; Hill's estimator, score; robustness; Student's t; tail index
    JEL: C22 G17
    Date: 2013–07–29
  2. By: J. Miguel Marín; M. T. Rodríguez Bernal; Eva Romero
    Abstract: GARCH models include most of the stylized facts of financial time series and they have been largely used to analyze discrete financial time series. In the last years, continuous time models based on discrete GARCH models have been also proposed to deal with non-equally spaced observations, as COGARCH model based on Lévy processes. In this paper, we propose to use the data cloning methodology in order to obtain estimators of GARCH and COGARCH model parameters. Data cloning methodology uses a Bayesian approach to obtain approximate maximum likelihood estimators avoiding numerically maximization of the pseudo-likelihood function. After a simulation study for both GARCH and COGARCH models using data cloning, we apply this technique to model the behavior of some NASDAQ time series
    Keywords: GARCH, Continuous-time GARCH process, Lévy process, COGARCH, Data cloning, Bayesian inference, MCMC algorithm
    Date: 2013–07
  3. By: Grote, Claudia; Sibbertsen, Philipp
    Abstract: This paper investigates the finite-sample properties of the smooth transition-based cointegration test proposed by Kapetanios et al. (2006) when the data generating process under the alternative hypothesis is a globally stationary second order LSTR model. The provided procedure describes an application to long-run equilibrium relations involving real exchange rates with symmetric behaviour. We utilise the properties of the double LSTR transition function that features unit root behaviour within the inner regime and symmetric behaviour in the outer regimes. Hence, under the null hypothesis we imply no cointegration and globally stationary D-LSTR cointegration under the alternative. As a result of the identification problem the limiting distribution derived under the null hypothesis is non-standard. The Double LSTR is capable of producing three-regime TAR nonlinearity when the transition parameter tends to infinity as well as generating exponential-type nonlinearity that closely approximates ESTR nonlinearity. Therefore, we find that the Double LSTR error correction model has power against both of these alternatives.
    Keywords: Cointegration tests, LSTR, Monte carlo simulation, Nonlinear error correction
    JEL: C12 C32
    Date: 2013–07
  4. By: Eric Ghysels; J. Isaac Miller (Department of Economics, University of Missouri-Columbia)
    Abstract: We examine the effects of mixed sampling frequencies and temporal aggregation on standard tests for cointegration. While it is well known that aggregation and sampling frequency do not affect the long-run properties of time series, we find that the effects of aggregation on the size of the tests may be severe. Matching sampling schemes of all series generally reduces size, and the nominal size is obtained when all series are skip-sampled in the same way -- e.g., end-of-period sampling. When matching all schemes is not feasible, the size of the likelihood-based trace test may be improved by using a mixed-frequency model rather than an aggregated model. However, a mixed-frequency strategy may not improve the size distortion of residual-based cointegration tests compared to aggregated series. We test stock prices and dividends for cointegration as an empirical demonstration of the size distortion.
    Keywords: temporal aggregation, mixed sampling frequencies, cointegration, trace test, residual-based cointegration test
    JEL: C12 C32
    Date: 2013–06–28

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