nep-ets New Economics Papers
on Econometric Time Series
Issue of 2011‒01‒16
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Asymmetric GARCH and the financial crisis: a preliminary study By Výrost, Tomáš; Baumöhl, Eduard
  2. On the relationship of persistence and number of breaks in volatility: new evidence for three CEE countries By Výrost, Tomáš; Baumöhl, Eduard; Lyócsa, Štefan
  3. The exact linkage between the Beveridge-Nelson decomposition and other permanent-transitory decompositions. By Giacomo Sbrana
  4. Forecasting damped trend exponential smoothing: an algebraic viewpoint. By Giacomo Sbrana
  5. Combining predictive densities using Bayesian filtering with applications to US economics data By Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk

  1. By: Výrost, Tomáš; Baumöhl, Eduard
    Abstract: The paper deals with estimation of both general GARCH as well as asymmetric EGARCH and TGARCH models, used to model the leverage effect of good news and bad news on market volatility. We estimate the models using daily returns of S&P 500 stock index and describe the news impact curves (NICs) for these models. When estimating the crisis series, we show the possibility of using a news impact surface to describe the results from models of higher orders.
    Keywords: volatility modeling; financial crisis; asymmetric GARCH class models; news impact curve
    JEL: C5 G15 C22
    Date: 2009–11–03
  2. By: Výrost, Tomáš; Baumöhl, Eduard; Lyócsa, Štefan
    Abstract: In this article, we contribute to the discussion of volatility persistence in the presence of sudden changes. We follow previous research, particularly Wang and Moore (2009), who analysed stock market returns in five Central and Eastern European countries using the Iterated Cumulative Sum of Squares (ICSS) algorithm for detecting multiple breaks and the test (IT) proposed by Inclán and Tiao (1994). We complement this analysis by using the κ1 and κ2 statistic introduced by Sansó et al. (2004), which lead us to the hypothesis that the estimated persistence in volatility depends inversely on the number of breakpoints in volatility. We explored this claim through a simulation study, where by randomizing an increasing number of breakpoints over the sample, we estimated kernel density of the persistence measure. The results confirmed the relationship between persistence and the number of breakpoints. It also showed that the use of break detection algorithms leads to lower persistence estimates, even within the class of models with an equal number of breaks. Therefore, the overall decrease in persistence can be attributed both to the number of breaks and their position, as suggested by the chosen break detection tests.
    Keywords: volatility persistence; GARCH model; ICSS procedure; CEE stock markets
    JEL: G15 C22
    Date: 2011–01–06
  3. By: Giacomo Sbrana (BETA/CNRS, Université de Strasbourg, France.)
    Date: 2010
  4. By: Giacomo Sbrana (BETA/CNRS, Université de Strasbourg, France.)
    Date: 2010
  5. By: Monica Billio (University of Venice, GRETA Assoc. and School for Advanced Studies in Venice); Roberto Casarin (University of Breccia and GRETA Assoc); Francesco Ravazzolo (Norges Bank (Central Bank of Norway)); Herman K. van Dijk (Econometrics and Tinbergen Institutes, Erasmus University Rotterdam)
    Abstract: Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures forevaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
    Keywords: Density Forecast Combination, Survey Forecast, Bayesian Filtering, Sequential Monte Carlo
    JEL: C11 C15 C53 E37
    Date: 2010–12–21

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