Bibliografie

(eng): We introduce wavelet-based methodology for estimation of realized variance allowing its mea- surement in the time-frequency domain. Using smooth wavelets and Maximum Overlap Dis- crete Wavelet Transform, we allow for the decomposition of the realized variance into several investment horizons and jumps. Basing our estimator in the two-scale realized variance frame- work, we are able to utilize all available data and get feasible estimator in the presence of microstructure noise as well. The estimator is tested in a large numerical study of the finite sample performance and is compared to other popular realized variation estimators. We use different simulation settings with changing noise as well as jump level in different price pro- cesses including long memory fractional stochastic volatility model. The results reveal that our wavelet-based estimator is able to estimate and forecast the realized measures with the greatest precision.