nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒04‒24
two papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modelling High Frequency Financial Count Data By Quoreshi, Shahiduzzaman
  2. Using the correlation dimension to detect non-linear dynamics: Evidence from the Athens Stock Exchange By David Chappell; Theodore Panagiotidis

  1. By: Quoreshi, Shahiduzzaman (Department of Economics, Umeå University)
    Abstract: This thesis comprises two papers concerning modelling of financial count data. The papers advance the integer-valued moving average model (INMA), a special case of integer-valued autoregressive moving average (INARMA) model class, and apply the models to the number of stock transactions in intra-day data. <p> Paper [1] advances the INMA model to model the number of transactions in stocks in intra-day data. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study a feasible least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. There is evidence of asymmetric effects of news about prices on the number of transactions. <p> Paper [2] introduces a bivariate integer-valued moving average model (BINMA) and applies the BINMA model to the number of stock transactions in intra-day data. The BINMA model allows for both positive and negative correlations between the count data series. The study shows that the correlation between series in the BINMA model is always smaller than 1 in an absolute sense. The conditional mean, variance and covariance are given. Model extensions to include explanatory variables are suggested. Using the BINMA model for AstraZeneca and Ericsson B it is found that there is positive correlation between the stock transactions series. Empirically, we find support for the use of long-lag bivariate moving average models for the two series.
    Keywords: Count data; Intra-day; High frequency; Time series; Estimation; Long memory; Finance
    JEL: C13 C22 C25 C51 G12 G14
    Date: 2005–04–20
  2. By: David Chappell (Sheffield University); Theodore Panagiotidis (Loughborough University)
    Abstract: The standardised residuals from GARCH models fitted to three stock indices of the Athens Stock Exchange are examined for evidence of chaotic behaviour. In each case the correlation dimension is calculated for a range of embedding dimensions. The results do not support the hypothesis of chaotic behaviour; it appears that each set of residuals is iid.
    Keywords: Non-linear Dynamics, Stock Indices, Chaos, Correlation Dimension
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2005–04–15

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