nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒12‒04
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. A coupled component GARCH model for intraday and overnight volatility By Linton, O.; Wu, J.
  2. Forecasting stock market returns by summing the frequency-decomposed parts By Faria, Gonçalo; Verona, Fabio
  3. On Wigner-Ville Spectra and the Unicity of Time-Varying Quantile-Based Spectral Densities By Stefan Birr; Holger Dette; Marc Hallin; Tobias Kley; Stanislav Volgushev
  4. Network Quantile Autoregression By Xuening Zhu; Wolfgang K. Härdle; Weining Wang; Hangsheng Wang

  1. By: Linton, O.; Wu, J.
    Abstract: We propose a semi-parametric coupled component GARCH model for intraday and overnight volatility that allows the two periods to have different properties. To capture the very heavy tails of overnight returns, we adopt a dynamic conditional score model with t innovations. We propose a several step estimation procedure that captures the nonparametric slowly moving components by kernel estimation and the dynamic parameters by t maximum likelihood. We establish the consistency and asymptotic normality of our estimation procedures. We extend the modelling to the multivariate case. We apply our model to the study of the component stocks of the Dow Jones industrial average over the period 1991-2016. We show that actually overnight volatility has increased in importance during this period. In addition, our model provides better intraday volatility forecast since it takes account of the full dynamic consequences of the overnight shock and previous ones.
    Date: 2016–12–01
  2. By: Faria, Gonçalo; Verona, Fabio
    Abstract: We forecast stock market returns by applying, within a Ferreira and Santa-Clara (2011) sum-of-the-parts framework, a frequency decomposition of several predictors of stock returns. The method delivers statistically and economically significant improvements over historical mean forecasts, with monthly out-of-sample R2 of 3.27% and annual utility gains of 403 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the predictors with the highest predictive power from the noisy parts, and from the fact that the frequency-decomposed predictors carry complementary information that captures both the long-term trend and the higher frequency movements of stock market returns.
    Keywords: predictability, stock returns, equity premium, asset allocation, frequency domain, wavelets
    JEL: G11 G12 G14 G17
    Date: 2016–11–28
  3. By: Stefan Birr; Holger Dette; Marc Hallin; Tobias Kley; Stanislav Volgushev
    Abstract: The unicity of the time-varying quantile-based spectrum proposed in Birr et al. (2016) is established via an asymptotic representation result involving Wigner-Ville spectra.
    Keywords: copula-based spectrum; laplace spectrum; quantile-based spectrum; time-varying spectrum; wigner-ville spectrum
    Date: 2016–11
  4. By: Xuening Zhu; Wolfgang K. Härdle; Weining Wang; Hangsheng Wang
    Abstract: It is a challenging task to understand the complex dependency structures in an ultra-high dimensional network, especially when one concentrates on the tail dependency. To tackle this problem, we consider a network quantile autoregres- sion model (NQAR) to characterize the dynamic quantile behavior in a complex system. In particular, we relate responses to its connected nodes and node spe- ci c characteristics in a quantile autoregression process. A minimum contrast estimation approach for the NQAR model is introduced, and the asymptotic properties are studied. Finally, we demonstrate the usage of our model by in- vestigating the nancial contagions in the Chinese stock market accounting for shared ownership of companies.
    JEL: C12
    Date: 2016–11

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