nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒07‒27
two papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution By Pesaran, B.; Pesaran, M.H.
  2. Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features By Carlos Enrique Carrasco Gutiérrez; Reinaldo Castro Souza; Osmani Teixeira de Carvalho Guillén

  1. By: Pesaran, B.; Pesaran, M.H.
    Abstract: This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and equity index futures. The results strongly reject the normal-DCC model in favour of a t-DCC specification. The t-DCC model also passes a number of VaR diagnostic tests over an evaluation sample. The estimation results suggest a general trend towards a lower level of return volatility, accompanied by a rising trend in conditional cross correlations in most markets; possibly reflecting the advent of euro in 1999 and increased interdependence of financial markets.
    Keywords: Volatilities and Correlations, Futures Market, Multivariate t, Financial Interdependence, VaR diagnostics.
    JEL: C51 C52 G11
    Date: 2007–06
    URL: http://d.repec.org/n?u=RePEc:cam:camdae:0734&r=ets
  2. By: Carlos Enrique Carrasco Gutiérrez; Reinaldo Castro Souza; Osmani Teixeira de Carvalho Guillén
    Abstract: An important aspect of empirical research based on the vector autoregressive (VAR) model is the choice of the lag order, since all inference in the VAR model depends on the correct model specification. Literature has shown important studies of how to select the lag order of a nonstationary VAR model subject to cointegration restrictions. In this work, we consider an additional weak form (WF) restriction of common cyclical features in the model in order to analyze the appropriate way to select the correct lag order. Two methodologies have been used: the traditional information criteria (AIC, HQ and SC) and an alternative criterion (IC(p,s)) which select simultaneously the lag order p and the rank structure s due to the WF restriction. A Monte-Carlo simulation is used in the analysis. The results indicate that the cost of ignoring additional WF restrictions in vector autoregressive modeling can be high, especially when SC criterion is used.
    Date: 2007–06
    URL: http://d.repec.org/n?u=RePEc:bcb:wpaper:139&r=ets

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