This paper examines the impact of the four unconventional monetary policy announcements followed by the European Central Bank on the stock price of European banks, as well as on the STOXX Europe 600 Banks index, from January 2010 to December 2016. The results show that there is a positive relation between the announced programs and the stock returns of the European banks at the same day of the announcement, while the impact is stronger for the long-term sovereign bond purchases (Securities Markets Programme). We also find that the banks of the countries which benefit most from the unconventional policies are these from Southern Europe. On the contrary, the announcement of these programs displays a limited effect on the banks of the European core countries and those with solid banking system. Finally, the announcements seem to reduce the volatility of stock prices and especially for the Covered Bond Purchase Programme (CBPP3).

This paper examines the effects of the unconventional monetary policy (UMP) launched by the European Central Bank on the cross-market correlations between bond, stock and currency forward markets. Using a dynamic conditional correlation analysis and several robustness tests, we investigate possible differences on the correlation dynamics across four UMP periods and across a range of developed countries and emerging market economies. The empirical results indicate a spillover effect on both developed and emerging markets, although this impact is not identical across assets and countries. We also find that the new UMP phase started in 2014 has a more prominent impact, highlighting differences on the impact between the earlier and the new wave of UMPs and across cross-market correlations.

This study examines the conditional correlation and the resulting optimal hedge ratios between the Credit Default Swap (CDS) spreads of the U.S. metal and mining industries, and the prices of copper, platinum, silver and gold using the daily date from December 14, 2007 to August 18, 2018. It compares volatility and conditional correlation of the CDSs and the metal prices by employing multivariate GARCH family models which capture distinct characteristics of financial time series. It utilizes rolling window estimation techniques and constructs the one-step-ahead out-of-sample forecasts for the dynamic conditional correlations and thereafter the optimal hedge ratios. In general, our results show that copper provides the best possible hedge for dealing with the U.S. metals and mining industries’ credit risks. Our results are robust under alternate model specifications, choice of model refits and distributional assumptions.