volatility predictor, the results of an application to tactical asset allocation are presented. -- Multivariate GARCH ; forecast …

models. We show that HF-based predictions yield a significantly lower portfolio volatility than methods employing daily …

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

This paper provides a short update of volatility research surveyed in Poon and Granger (2003). While the last few years … saw major advancement in the theoretical and empirical studies of realised volatility and the dynamic relationship between … stock price, volatility and jumps, the biggest innovation is the range of volatility derivatives created by the industry …

volatility for estimating conditional variances and covariances; (2) alternative currencies; and (3) alternative maturities of … Chang et al. [17], we estimate four multivariate volatility models (namely CCC, VARMA-AGARCH, DCC and BEKK), and calculate …

models. We show that HF-based predictions yield a significantly lower portfolio volatility than methods employing daily …

evolve, many investors continue to forecast volatility using traditional approaches that are ill-suited to the time …-changing nature of volatility. In this paper, I analyze the performance of seven different multivariate-volatility models using a new … poorly when trying to forecast short-term volatility, and that a more dynamic model often provides superior out …

This paper aims at providing new insights on the pricing of aggregate volatility risk by incorporating investor … sentiment in the relation between sensitivity to innovations in implied market volatility and expected stock returns. Using both … cross-sectional and time series analysis, we investigate the effect of the exposure to aggregate volatility risk on stock …