intra-day basis, has spurred numerous theoretical advances in the areas of volatility/risk estimation and modeling. In this …

estimation for time-varying volatilities stems from an asymptotic equivalence of the underlying statistical model to a white … noise model with correlation and volatility processes being constant over small intervals. The asymptotic equivalence of the …. -- asymptotic equivalence ; covariation ; integrated covolatility ; microstructure noise ; spectral adaptive estimation …

impact of past values of realized correlation on future values is at least 10% higher when stock returns are negative rather … than positive. This finding supports the conjecture that correlation between stock returns tends to be higher when stock …

This paper proposes a three-step estimation strategy for dynamic conditional correlation models. In the first step … usual normalization. In the third step, the two-step conditional covariance and correlation matrices are regularized by … model. This yields the final, third step smoothed estimate of the conditional covariance and correlation matrices. Due to …

, our method simultaneously provides a consistent estimation of these two components in a one-step procedure. Moreover, in …

for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH … strands of literature in order to deliver improved estimation of large dynamic covariance matrices …

regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multi …-scale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard … century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along …