Cross‐market correlations and transmission of information

Abstract

We investigate characteristics of cross-market correlations using daily data from U.S. stock, bond, money, and currency futures markets using a new multivariate GARCH model that permits direct hypothesis testing on conditional correlations. We find evidence that arrival of information in a market affects subsequent cross-market conditional correlations in the sample period following the stock market crash of 1987, but there is little evidence of such a relationship in the precrash period. In the postcrash period, we also find evidence that the prime rate of interest affects daily correlations between futures returns. Furthermore, we find that conditional correlations between currency futures and other markets decline steeply a few months before the crash and revert to normal dynamics after the crash.

Return, volatility spillovers and dynamic correlation in the BRIC equity markets: An analysis using a bivariate EGARCH framework

“…The off diagonal elements of the covariance matrix ∑ t are defined in a manner similar to that in Darbar and Deb (2002). The key is to define a time varying conditional correlation which when combined with the conditional variances given the Eqs.…”

Characterizing the Volatility Transmission across International Stock Markets

“…Results of test of pattern hypothesis show that for the last 20 years', barring a few cases 19 the spillover of volatility between international stock markets, in general, follow a definite pattern. Out of total 10 stock markets, Egypt and Japan display maximum inclination towards randomness (3 cases each), both as a source and as a sink; while markets like Australia, China, India, Israel and the UK show definite pattern in spillover with respect to all other markets both as a source and as a sink.…”

“…The off-diagonal elements of the covariance matrix Σ t are defined in a manner similar to that in Darbar and Deb [19]. The key is to define a time varying conditional correlation which when combined with the conditional variances given in the Equations (3) and (4) generate the required conditional covariance.…”

Volatility spillovers and dynamic correlation in European bond markets

“…To take into account the time-variability of conditional correlation, we include into the volatility model specification a dynamic conditional correlation structure. In order to ensure that the conditional correlation ρ iEU,t falls into the range [−1,1], an index function ξ iEU,t ∈ (−∞,∞) is used following a methodology similar to the one developed by Darbar and Deb (2002). The correlation index function is assumed to depend on the cross-products of the standardized innovations and the past values of the index function.…”

“…Erb et al, 1994;Longin and Solnik, 1995, amongst others) provide evidence that support the time-variability of correlation. This study builds upon the methodology developed by Darbar and Deb (2002) and models volatility spillovers assuming a time-varying conditional correlation. Finally, extending the sample period beyond the launch of Euro in January 1999, allows us to test how the bond markets interdependence has changed after this major event.…”