autoregressive conditional heteroskedasticity model and the dynamic conditional correlation model where distributional assumptions …

forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures … correlation MSV model, the conditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the … outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the …

This note discusses some aspects of the paper by Hu and Tsay (2014), "Principal Volatility Component Analysis". The key … issues are considered, and are also related to existing conditional covariance and correlation models. Some caveats are given … about multivariate models of time-varying conditional covariance and correlation models. …

The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the … definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from … the co-jumps of two assets have a significant impact on future co-volatility, but that the impact is negligible for …

autoregressive conditional heteroskedasticity model and the dynamic conditional correlation model where distributional assumptions …

good on the present set of measures as the stochastic volatility models, with or without dynamic correlation. …The focus of this article is using dynamic correlation models for the calculation of minimum variance hedge ratios …. Modelling the correlation explicitly is shown to produce the best hedges when applied to the simulated data. For financial time …