completely new sequential importance sampling estimator of the desired tail probability. Numerical experiments suggest that the … sequential importance sampling estimator can be significantly more efficient than its competitor. …

are based on importance sampling techniques. It is shown that such Monte Carlo techniques can be employed successfully for …

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC … rather than a dynamic conditional correlation model; (ii) provides the motivation, which is presently missing, for … standardization of the conditional covariance model to obtain the conditional correlation model; and (iii) shows that the appropriate …

of determining the optimal sampling frequency, which strikes a balance between variance and bias in covariance matrix … estimates due to market microstructure effects such as non-synchronous trading and bid-ask bounce. The optimal sampling …

approach is explored through Monte Carlo simulations. It is shown that sparse sampling for mitigating the impact of … frequency price observations that are contaminated with microstructure noise without the need for sparse sampling, say at … that fewer jumps are detected when sampling intervals increase. …