nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒08‒08
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Long Memory and Tail dependence in Trading Volume and Volatility By Eduardo Rossi; Paolo Santucci de Magistris
  2. A Nonlinear Panel Unit Root Test under Cross Section Dependence By Mario Cerrato; Christian de Peretti; Rolf Larsson; Nick Sarantis
  3. Optimal Prediction Pools. By John Geweke; Gianni Amisano
  4. A New Simple Test Against Spurious Long Memory Using Temporal Aggregation By Kuswanto, Heri

  1. By: Eduardo Rossi (Dipartimento di economia politica e metodi quantitativi, University of Pavia, Italy.); Paolo Santucci de Magistris (Dipartimento di economia politica e metodi quantitativi, University of Pavia, Italy)
    Abstract: This paper investigates long-run dependencies of volatility and volume, supposing that are driven by the same informative process. Log-realized volatility and log-volume are characterized by upper and lower tail dependence, where the positive tail dependence is mainly due to the jump component. The possibility that volume and volatility are driven by a common fractionally integrated stochastic trend, as the Mixture Distribution Hypothesis prescribes, is rejected. We model the two series with a bivariate Fractionally Integrated VAR specification. The joint density is parameterized by means of with different copula functions, which provide flexibility in modeling the dependence in the extremes and are computationally convenient. Finally, we present a simulation exercise to validate the model.
    Keywords: Realized Volatility, Trading Volume, Fractional Cointegration, Tail dependence, Copula Modeling
    JEL: C32 G12
    Date: 2009–07–13
  2. By: Mario Cerrato; Christian de Peretti; Rolf Larsson; Nick Sarantis
    Abstract: We propose a nonlinear heterogeneous panel unit root test for testing the null hypothesis of unit-roots processes against the alternative that allows a proportion of units to be generated by globally stationary ESTAR processes and a remaining non-zero proportion to be generated by unit root processes. The proposed test is simple to implement and accommodates cross sectional dependence. We show that the distribution of the test statistic is free of nuisance parameters as (N, T) −∞. Monte Carlo simulation shows that our test holds correct size and under the hypothesis that data are generated by globally stationary ESTAR processes has a better power than the recent test proposed in Pesaran [2007]. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided.
    Keywords: Nonlinear panel unit root tests, cross sectional dependence.
    JEL: C12 C15 C22 C23 F31
    Date: 2009–07
  3. By: John Geweke (Departments of Statistics and Economics, University of Iowa, Iowa City, IA, USA.); Gianni Amisano (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave function of the weights and, in general, an optimal linear combination will include several models with positive weights despite the fact that exactly one model has limiting posterior probability one. The paper derives several interesting formal results: for example, a prediction model with positive weight in a pool may have zero weight if some other models are deleted from that pool. The results are illustrated using S&P 500 returns with prediction models from the ARCH, stochastic volatility and Markov mixture families. In this example models that are clearly inferior by the usual scoring criteria have positive weights in optimal linear pools, and these pools substantially outperform their best components. JEL Classification: C11, C53.
    Keywords: forecasting, GARCH, log scoring, Markov mixture, model combination, S&P 500 returns, stochastic volatility.
    Date: 2009–03
  4. By: Kuswanto, Heri
    Abstract: We have developed a new test against spurious long memory based on the invariance of long memory parameter to aggregation. By using the local Whittle estimator, the statistic takes the supremum among combinations of paired aggregated series. Simulations show that the test performs good in finite sample sizes, and is able to distinguish long memory from spurious processes with excellent power. Moreover, the empirical application gives further evidence that the observed long memory in German stock returns is spurious.
    Keywords: Local-Whittle method, Spurious long memory, Change point, Aggregation
    JEL: C12 C22
    Date: 2009–08

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