nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒06‒18
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Long Memory and Fractional Integration in High Frequency Financial Time Series By Guglielmo Maria Caporale; Luis A. Gil-Alana
  2. Forecast Combinations By Carlos Capistrán; Allan Timmermann; Marco Aiolfi
  3. Spurious Long-Horizon Regression in Econometrics By Carolina Rodríguez Zamora; Jean Lim
  4. The power log-GARCH model By Genaro Sucarrat; Álvaro Escribano Sáez
  5. An out-of-sample test for nonlinearity in financial time series: An empirical application By Theodore Panagiotidis
  6. Prediction accuracy and sloppiness of log-periodic functions By David Br\'ee; Damien Challet; Pier Paolo Peirano

  1. By: Guglielmo Maria Caporale; Luis A. Gil-Alana
    Abstract: This paper analyses the long-memory properties of high frequency financial time series. It focuses on temporal aggregation and the influence that this might have on the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for the error term. In brief, we find evidence that a lower degree of integration is associated with lower data frequencies. In particular, when the data are collected every 10 minutes there are several cases with values of d strictly smaller than 1, implying mean-reverting behaviour. This holds for all four series examined, namely Open, High, Low and Last observations for the British pound/US dollar spot exchange rate.
    Keywords: High frequency data; long memory; volatility persistence; structural breaks
    JEL: C22
    Date: 2010
  2. By: Carlos Capistrán; Allan Timmermann; Marco Aiolfi
    Abstract: We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based forecasts. We also provide an analysis of the importance of model instability for explaining gains from forecast combination. Analytical and simulation results uncover break scenarios where forecast combinations outperform the best individual forecasting model.
    Keywords: Factor Based Forecasts, Non-linear Forecasts, Structural Breaks, Survey Forecasts, Univariate Forecasts.
    JEL: C53 E
    Date: 2010–06
  3. By: Carolina Rodríguez Zamora; Jean Lim
    Abstract: This paper extends recent research on the behaviour of the t-statistic in a long-horizon regression (LHR). We assume that the explanatory and dependent variables are generated according to the following models: a linear trend stationary process, a broken trend stationary process, a unit root process, and a process with a double unit root. We show that, both asymptotically and in finite samples, the presence of spurious LHR depends on the assumed model for the variables. We propose an asymptotically correct inferential procedure for testing the null hypothesis of no relationship in a LHR, which works whether the variables have a long-run relationship or not. Our theoretical results are applied to an international data set on money and output in order to test for long-run monetary neutrality. Under our new approach and using bootstrap methods, we find that neutrality holds for all countries.
    Keywords: Long-horizon regression, asymptotic theory, deterministic and stochastic trends, unit roots, structural breaks, long-run monetary neutrality.
    JEL: H21 J22 D13
    Date: 2010–06
  4. By: Genaro Sucarrat; Álvaro Escribano Sáez
    Abstract: Exponential models of autoregressive conditional heteroscedasticity (ARCH) are attractive in empirical analysis because they guarantee the non-negativity of volatility, and because they enable richer autoregressive dynamics. However, the currently available models exhibit stability only for a limited number of conditional densities, and the available estimation and inference methods in the case where the conditional density is unknown hold only under very specific and restrictive assumptions. Here, we provide results and simple methods that readily enables consistent estimation and inference of univariate and multivariate power log-GARCH models under very general and non-restrictive assumptions when the power is fixed, via vector ARMA representations. Additionally, stability conditions are obtained under weak assumptions, and the power log-GARCH model can be viewed as nesting certain classes of stochastic volatility models, including the common ASV(1) specification. Finally, our simulations and empirical applications suggest the model class is very useful in practice.
    Keywords: Power ARCH, Exponential GARCH, Log-GARCH, Multivariate GARCH, Stochastic volatility
    JEL: C22 C32 C51 C52
    Date: 2010–06
  5. By: Theodore Panagiotidis (Department of Economics, University of Macedonia)
    Abstract: This paper employs a local information, nearest neighbour forecasting methodology to test for evidence of nonlinearity in financial time series. Evidence from well-known data generating process are provided and compared with returns from the Athens stock exchange given the in-sample evidence of nonlinear dynamics that has appeared in the literature. Nearest neighbour forecasts fail to produce more accurate forecasts from a simple AR model. This does not substantiate the presence of in-sample nonlinearity in the series.
    Keywords: nearest neighbour, nonlinearity
    JEL: C22 C53 G10
    Date: 2010–06
  6. By: David Br\'ee; Damien Challet; Pier Paolo Peirano
    Abstract: We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give an estimate of the day of the crash belongs to the latter category. We discuss in detail why and how the fitting procedure must take into account the sloppy nature of this kind of model. We then test the reliability of LPPLs on synthetic AR(1) data replicating the Hang Seng 1987 crash and show that even this case is borderline regarding predictability of divergence time. We finally argue that current methods used to estimate a probabilistic time window for the divergence time are likely to be over-optimistic.
    Date: 2010–06

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