Can we Improve Understanding of the Financial Market Dependencies in the Crisis by their Decomposition?

Study of the financial market dependencies have become one of the most active and successful areas of research in the time series econometrics and economic forecasting during the recent decades. Current financial crisis have shown that understanding of the dependencies in the markets is crucial and it has even boosted the interest of researchers. This work brings new theoretical framework for the realized covariation estimation generalizing the current knowledge and bringing the estimation to the time-frequency domain for the first time. Usage of wavelets allows us to decompose the correlation measures into several investment horizons. Our estimator is moreover able to separate individual jumps, co-jumps and true covariation from the high frequency data, thus brings better understanding of the dependence. The results have crucial impact on the portfolio diversification especially in the crisis years as they point to the strong dynamic relationships at various investment horizons. Results suggest that understanding jumps and co-jumps is important for forecasting the covariance and the correlation as they have large impact on these measures. Our results have significant economic value as wrong assumption about the dependence process will have direct impact on the forecasting and portfolio valuation.