We introduce the Realized Exponential GARCH model that can utilize multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between...

We introduce the Realized Exponential GARCH model that can utilize multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between...

We proposed a method to estimate extreme conditional quantiles by combining quantile GARCH model of Xiao and Koenker (2009) and extreme value theory (EVT) approach. We first estimate the latent volatility process using the information of intermediate quantiles. We then apply EVT to the tail...

We use heterogeneous autoregressive (HAR) model with high-frequency data of Hu-Shen 300 index to investigate the volatility-volume relationship via the volatility decomposition approach. Although we find that the continuous component of daily volatility is positively correlated with trading...

Realized measures of volatility based on high frequency data contain valuable information about the unobserved conditional volatility. In this paper, we use the Realized GARCH model developed by Hansen, Huang and Shek (2012) to estimate and forecast price volatility for four agricultural...

Realized kernels use high-frequency data to estimate daily volatility of individual stock prices. They can be applied to either trade or quote data. Here we provide the details of how we suggest implementing them in practice. We compare the estimates based on trade and quote data for the same...

We compare 330 ARCH-type models in terms of their ability to describe the conditional variance. The models are compared out-of-sample using DM-$ exchange rate data and IBM return data, where the latter is based on a new data set of realized variance. We find no evidence that a GARCH(1,1) is...