nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒08‒27
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. Dynamic correlations at different time-scales with Empirical Mode Decomposition By Noemi Nava; T. Di Matteo; Tomaso Aste
  2. Empirical Performance of GARCH Models with Heavy-tailed Innovations By Guo, Zi-Yi
  3. Identification of SVAR Models by Combining Sign Restrictions With External Instruments By Robin Braun; Ralf Brüggemann

  1. By: Noemi Nava; T. Di Matteo; Tomaso Aste
    Abstract: The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales, at different lags and over time. We uncover a rich heterogeneity of interactions which depends on the time-scale and has important led-lag relations which can have practical use for portfolio management, risk estimation and investments.
    Date: 2017–08
  2. By: Guo, Zi-Yi
    Abstract: We introduce a new type of heavy-tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten-Jagannathan-Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy-tailed distribution, the Student’s t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR-terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR-GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum-likelihood estimation of GARCH and GJR-GARCH models with the three types of heavy-tailed distribution is investigated.
    Keywords: Heavy-tailed distribution,GARCH model,Model comparison,Numerical solution
    Date: 2017
  3. By: Robin Braun (Department of Economics, University of Konstanz, Germany); Ralf Brüggemann (Department of Economics, University of Konstanz, Germany)
    Abstract: We identify structural vector autoregressive (SVAR) models by combining sign restrictions with information in external instruments and proxy variables. We incorporate the proxy variables by augmenting the SVAR with equations that relate them to the structural shocks. Our modeling framework allows to simultaneously identify different shocks using either sign restrictions or an external instrument approach, always ensuring that all shocks are orthogonal. The combination of restrictions can also be used to identify a single shock. This entails discarding models that imply structural shocks that have no close relation to the external proxy time series, which narrows down the set of admissible models. Our approach nests the pure sign restriction case and the pure external instrument variable case. We discuss full Bayesian inference, which accounts for both, model and estimation uncertainty. We illustrate the usefulness of our method in SVARs analyzing oil market and monetary policy shocks. Our results suggest that combining sign restrictions with proxy variable information is a promising way to sharpen results from SVAR models.
    Keywords: Structural vector autoregressive model, sign restrictions, external instruments
    JEL: C32 C11 E32 E52
    Date: 2017–08–11

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