Estimating time-varying conditional correlations between stock and foreign exchange markets
AbstractThis study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 360 (2006)
Issue (Month): 2 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Stock prices; Exchange rates; Multivariate GARCH; News impact surface; Time-varying conditional correlations;
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