nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒03‒17
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Volatility Discovery By Gustavo Fruet Dias; Cristina M. Scherrer; Fotis Papailias
  2. Quantile Cross-Spectral Measures of Dependence between Economic Variables By Jozef Barun\'ik; Tobias Kley
  3. Volatility transmission between US and Latin American Stock Markets: testing the decoupling hypothesis By Diego A. Agudelo; Marcela Gutiérrez; Laura Cardona
  4. Alternative Formulation of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors By Deschamps, P.
  5. The [phi]-Martingale By Vrins, F.; Jeanblanc, M.
  6. A time-varying long run HEAVY model By BRAIONE, M.
  7. Note on a new Seasonal Fractionally Integrated Separable Spatial Autoregressive Model By Papa Ousmane Cissé; Abdou Kâ Diongue; Dominique Guegan
  8. Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach By Heni Boubaker; Nadia Sghaier

  1. By: Gustavo Fruet Dias (Aarhus University and CREATES); Cristina M. Scherrer (Aarhus University and CREATES); Fotis Papailias (Queen's University Management School)
    Abstract: There is a large literature that investigates how homogenous securities traded on different markets incorporate new information (price discovery analysis). We extend this concept to the stochastic volatility process and investigate how markets contribute to the efficient stochastic volatility which is attached to the common efficient price (volatility discovery analysis). We use daily measures of realized variance as estimates of the latent market integrated variance and adopt the fractionally cointegrated vector autoregressive (FCVAR) framework. We extract the common fractionally stochastic trend associated with the efficient stochastic volatility, which is common to all markets. We evaluate volatility discovery by the adjustment coefficients of the FCVAR. We work with 30 of the most actively traded stocks in the U.S., which span from January 2007 to December 2013. We document that the volatility discovery does not necessarily take place at the same venue as the price discovery. These results hint that market quality and efficiency should be analysed by broader measures which take into consideration the stochastic volatility process.
    Keywords: volatility persistency, realized variance, fractionally cointegrated vector autoregressive (FCVAR), price discovery, high-frequency data
    JEL: G15 G12 G32 C32
    Date: 2016–02–24
  2. By: Jozef Barun\'ik; Tobias Kley
    Abstract: In this paper we introduce quantile cross-spectral analysis of multiple time series which is designed to detect general dependence structures emerging in quantiles of the joint distribution in the frequency domain. We argue that this type of dependence is natural for economic time series but remains invisible when the traditional analysis is employed. To illustrate how such dependence structures can arise between variables in different parts of the joint distribution and across frequencies, we consider quantile vector autoregression processes. We define new estimators which capture the general dependence structure, provide a detailed analysis of their asymptotic properties and discuss how to conduct inference for a general class of possibly nonlinear processes. In an empirical illustration we examine one of the most prominent time series in economics and shed new light on the dependence of bivariate stock market returns.
    Date: 2015–10
  3. By: Diego A. Agudelo; Marcela Gutiérrez; Laura Cardona
    Abstract: Abstract: We test for volatility transmission between US and the six largest Latin American stock markets (Argentina, Brazil, Chile, Colombia, Mexico and Peru) using MGARCH-BEKK models in daily frequency from March 1993 to March 2013. As expected, we find strong evidence of volatility transmission from US to the Latin American markets but not so in the opposite direction. Testing the hypothesis of decoupling between US and Brazil and Mexico the evidence goes against it: the conditional correlations between US and the two emerging markets have steadily increased over the sample period and the volatility transmission have become more significant from 2003 onwards. We also find some evidence on the leadership of Brazil in the region, being the only Latin American stock market consistently transmitting volatility to US.
    Keywords: Volatility transmission, MGARCH, decoupling hypothesis, emerging markets, conditional correlation
    JEL: G15 F36 C32
    Date: 2015–10–01
  4. By: Deschamps, P. (Université catholique de Louvain, CORE, Belgium)
    Abstract: This paper investigates three formulations of the leverage effect in a stochastic volatility model with a skewed and heavy-tailed observation distribution. The first formulation is the conventional one, where the observation and evolution errors are correlated. The second is a hierarchical one, where log-volatility depends on the past log-return multiplied by a time-varying latent coefficient. In the third formulation, this coefficient is replaced by a constant. The three models are compared with each other and with a GARCH formulation, using Bayes fac- tors. MCMC estimation relies on a parametric proposal density estimated from the output of a particle smoother. The results, obtained with recent S&P500 and Swiss Market Index data, suggest that the last two leverage formulations strongly dominate the conventional one. The performance of the MCMC method is consistent across models and sample sizes, and its implementation only requires a very modest (and constant) number of filter and smoother particles.
    Keywords: Stochastic volatility models; Markov chain Monte Carlo; Particle methods; Generalized hyperbolic distribution; Bayesian analysis
    JEL: C11 C15 C22 C58
    Date: 2015–05–01
  5. By: Vrins, F. (Université catholique de Louvain, CORE, Belgium); Jeanblanc, M. (Université d’Evry Val d’Essonne)
    Abstract: In this paper we focus on continuous martingales evolving in the unit interval [0,1]. We first review some results about the martingale property of solution to one-dimensional driftless stochastic differential equations. We then provide a simple way to construct and handle such processes. One of these martingales proves to be analytically tractable, and received the specific name of [phi]-martingale. It is shown that up to shifting and rescaling constants, it is the only martingale (with the trivial constant, Brownian motion and Geometric Brownian motion) having a separable coefficient (t, x) = g(t)h(x) that can be obtained via a time-homogeneous mapping of Gaussian processes. The approach is applied to the modeling of stochastic survival probabilities.
    Keywords: continuous stochastic processes, Gaussian processes, bounded martingales, local martingales, Azema supermartingale, credit risk modeling
    JEL: G13 C63
    Date: 2015–04–01
  6. By: BRAIONE, M. (Université catholique de Louvain, CORE, Belgium)
    Abstract: We propose a scalar variation of the multivariate HEAVY model of Noureldin et al. which allows for a time-varying long run component in the specification of the daily conditional covariance matrix. Differently from the original model featuring a BEKK-type parameterization, ours extends it to allow for a separate modeling of the conditional volatilities and the conditional correlation matrix, in a DCC fashion. Estimation is performed in one step by QML and multi-step ahead forecasting is feasible applying the direct approach to the HEAVY-P equation. In an empirical application aiming at modeling and forecasting the conditional covariance matrix of a stock (BAC) and an index (S&P 500), we find that the new model statistically outperforms the original HEAVY model both in-sample and out-of-sample.
    Keywords: HEAVY model, Long term models, Mixed Data Sampling, Direct forecasting
    Date: 2016–02–01
  7. By: Papa Ousmane Cissé (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, LERSTAD - laboratoire d'Etudes et de recherches en Statistiques et Développement - Université Gaston Bergé Sénégal); Abdou Kâ Diongue (LERSTAD - laboratoire d'Etudes et de recherches en Statistiques et Développement - Université Gaston Bergé Sénégal); Dominique Guegan (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In this paper, we introduce a new model called Fractionally Integrated Separable Spatial Autoregressive processes with Seasonality and denoted Seasonal FISSAR for two-dimensional spatial data. We focus on the class of separable spatial models whose correlation structure can be expressed as a product of correlations. This new modelling allows taking into account the seasonality patterns observed in spatial data. We investigate the properties of this new model providing stationary conditions, some explicit expressions form of the autocovariance function and the spectral density function. We establish the asymptotic behaviour of the spectral density function near the seasonal frequencies and perform some simulations to illustrate the behaviour of the model.
    Keywords: spatial autocovariance,spatial stationary process,seasonality,spatial short memory,seasonal long memory,two-dimensional data,separable process
    Date: 2016–02
  8. By: Heni Boubaker; Nadia Sghaier
    Date: 2016–02–18

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