dynamics adapts to the non-normal nature of financial data, which helps to robustify the volatility estimates. The new model … volatility forecasting of stock returns and exchange rates. …

volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding …% and at least 78%). The results on the GMV portfolios show that realized covariance models exhibit lower ex-post volatility …

the variance matrix. Monte Carlo evidence for parameter estimation based on different small sample sizes is provided. We …

In the analysis of multivariate stochastic volatility models, many estimation procedures begin by transforming the data …

orhistorical and Monte Carlo simulation methods. Although these approaches to overall VaR estimation have receivedsubstantial … the underlying statistical distributions, a variety of analyticalmethods and simulation-based methods are available. Aside … and incremental VaR in either a non-normal analytical setting or a MonteCarlo / historical simulation context.This paper …

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the … microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach we can analyze high …

model for the autoregressive coefficient matrices and a multivariate dynamic volatility model for the variance matrix of the … alternative to Bayesian methods which are regularly employed in the empirical literature. A simulation study shows the reliability … and robustness of the method against potential misspecifications of the volatility in the disturbance vector. We further …