News and correlations: an impulse response analysis
Le Pen, Yannick; Sévi, Benoît (2009), News and correlations: an impulse response analysis, 58e Congrès AFSE, 2009-09, Paris (Nanterre), France
TypeCommunication / Conférence
Conference title58e Congrès AFSE
Conference cityParis (Nanterre)
MetadataShow full item record
Abstract (EN)We proceed to an impulse response analysis on the conditional correlations between three stock indices returns: the Nikkei, the FTSE 100 and the S&P 500. As a ﬁrst step, we estimate an extension of the general asymmetric dynamic conditional correlation (GADCC) model proposed by Cappiello, Engle and Sheppard (2006) to model the possible interactions between conditional correlations. In a second step, we apply the deﬁnition of the impulse response function in nonlinear models of Koop, Pesaran and Potter (1996) to the conditional correlations matrix. The estimates of the GADCC model are used to estimate the impact of an innovation on the conditional correlations for different forecast horizons through boostrapped simulations. For each forecast horizon, we estimate the density of the impulse response function with non parametric kernel estimator. These densities show that the impacts of shocks on conditional correlation are most often asymmetric and depend on history. They disappear as the forecast horizon increases. In a ﬁrst step, we estimate the unconditional correlation impulse response functions with random shocks and histories. In a second step, we estimate these impulse response function with the observed history for several recent dates. We end by computing the impulse response function for an observed shock and history.
Subjects / KeywordsInternational Stock Correlation; Generalized Impulse response function; Dynamic conditional correlation
JELE17 - Forecasting and Simulation: Models and Applications
G15 - International Financial Markets
C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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