nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒01‒26
two papers chosen by
Yong Yin
SUNY at Buffalo

  1. Nonparametric Estimates for Conditional Quantiles of Time Series By Jürgen Franke; Peter Mwita; Weining Wang;
  2. Weak diffusion limits of dynamic conditional correlation models By Christian M. Hafner; Sebastien Laurent; Francesco Violante

  1. By: Jürgen Franke; Peter Mwita; Weining Wang;
    Abstract: We consider the problem of estimating the conditional quantile of a time series fYtg at time t given covariates Xt, where Xt can ei- ther exogenous variables or lagged variables of Yt . The conditional quantile is estimated by inverting a kernel estimate of the conditional distribution function, and we prove its asymptotic normality and uni- form strong consistency. The performance of the estimate for light and heavy-tailed distributions of the innovations are evaluated by a simulation study. Finally, the technique is applied to estimate VaR of stocks in DAX, and its performance is compared with the existing standard methods using backtesting.
    Keywords: conditional quantile, kernel estimate, quantile autoregression, time series, uniform consistency, value-at-risk
    JEL: C00 C14 C50 C58
    Date: 2014–01
  2. By: Christian M. Hafner (Université catholique de Louvain, ISBA & CORE); Sebastien Laurent (Aix-Marseille University (Aix-Marseille School of Economics)); Francesco Violante (Aarhus University and CREATES)
    Abstract: The properties of dynamic conditional correlation (DCC) models are still not entirely understood. This paper fills one of the gaps by deriving weak diffusion limits of a modified version of the classical DCC model. The limiting system of stochastic differential equations is characterized by a diffusion matrix of reduced rank. The degeneracy is due to perfect collinearity between the innovations of the volatility and correlation dynamics. For the special case of constant conditional correlations, a non-degenerate diffusion limit can be obtained. Alternative sets of conditions are considered for the rate of convergence of the parameters, obtaining time-varying but deterministic variances and/or correlations. A Monte Carlo experiment confirms that the quasi approximate maximum likelihood (QAML) method to estimate the diffusion parameters is inconsistent for any fixed frequency, but that it may provide reasonable approximations for sufficiently large frequencies and sample sizes.
    Keywords: cDCC, Weak diffusion limits, QAML, CCC, GARCH diffusion
    JEL: C13 C22 C51
    Date: 2015–01–14

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