We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns....

A set of multivariate GARCH models is estimated and its empirical validity is compared from the calculation of the Value at Risk. Data used are the daily returns of the nominal exchange rate of the Colombian peso vis-a-vis the American dollar, euro, sterling and Japanese yen for the period...

This paper explores the asymmetric predictability of realized semivariances and the variation of signed jumps in China's stock market with high frequency data from 2006 to 2013. Our empirical results show that, (1) future volatilities are more (less) related to the historical realized...

Testing for constant expected returns and forecasting future returns necessitate the information beyond a single predictor. We consider the predictive regression model with multiple predictors which are potentially strongly persistent and cointegrated. Instrumental variables based tests for...

Examination over multiple horizons has been a routine in testing asset return predictability in finance and macroeconomics. In a simple predictive regression model, we find that the popular scaled test for multiple-horizon predictability has zero null rejection rate if the forecast horizon...

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized...

Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, I estimate an asymmetric Autoregressive Conditional Heteroscedasticity...

This paper compares different GARCH models in terms of their out-of-sample predictive ability of leveraged loan market volatility. The study investigates whether the asymmetric effects of good and bad news on volatility is present and how distributional assumptions affect the selection of GARCH...

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...