Identification of Mixed Causal-Noncausal Models in Finite Samples

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Gouriéroux and Zakoïan (2013) propose to use noncausal models to parsimoniously capture nonlinear features often observed in financial time series and in particular bubble phenomena. In order to distinguish causal autoregressive processes from purely noncausal or mixed causal-noncausal ones, one has to depart from the Gaussianity assumption on the error distribution. Financial (and to a large extent macroeconomic) data are characterized by large and sudden changes that cannot be captured
by the Normal distribution, which explains why leptokurtic error distributions are often considered in empirical finance. By means of Monte Carlo simulations, this paper investigates the identication of mixed causal-noncausal models in finite samples for different values of the excess kurtosis of the error process. We compare the performance of the MLE, assuming a t-distribution, with that of the
LAD estimator that we propose in this paper. Similar to Davis, Knight and Liu (1992) we find that for infinite variance autoregressive processes both the MLE and LAD estimator converge faster. We further specify the general asymptotic normality results obtained in Andrews, Breidt and Davis (2006)
for the case of t-distributed and Laplacian distributed error terms. We first illustrate our analysis by estimating mixed causal-noncausal autoregressions to model the demand for solar panels in Belgium over the last decade. Then we look at the presence of potential noncausal components in daily realized
volatility measures for 21 equity indexes. The presence of a noncausal component is confirmed in both empirical illustrations.
Original languageEnglish
Article number11
Pages (from-to)307-331
JournalAnnals of Economics and Statistics
Volume123/124
DOIs
Publication statusPublished - 2016

JEL classifications

  • c22 - "Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models"
  • e37 - Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
  • e44 - Financial Markets and the Macroeconomy

Keywords

  • Noncausal models
  • Non-Gaussian distributions
  • Realized volatilities
  • bubbles

Research Output

  • 1 Working paper

Detecting Co-Movements in Noncausal Time Series

Cubadda, G., Hecq, A. & Telg, S., 2 Mar 2017, MPRA Paper, (Munich Personal RePEc Archive; No. 77254).

Research output: Working paperProfessional

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