Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are … divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical … applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy and in ation-gap persistence …

This paper proposes a new class of multivariate volatility model that utilising high-frequency data. We call this model …

Availability of high frequency data has improved the capability of computing volatility in an efficient way …. Nevertheless, measuring volatility/covariance from the observation of the asset price is challenging for two main reasons: observed … multivariate volatility, with particular focus on using high frequency data. Exploiting the fact that the method allows to compute …

using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock … independent shocks on volatility through time, while avoiding typical orthogonalization and ordering problems. Volatility impulse … response functions (VIRF) provide information about the impact of independent shocks on volatility. HHś VIRF extends a …

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 of individual stock returns and exchange rate returns. …