because of its extensive applications in financial analysis, stock market volatility modeling is a significantly important issue for stock market practitioners and academicians. using garch models to formulate the conditional variance heteroskedasticity and the taking advantages of panel data technique such as higher degrees of freedom, more flexibility in the control of the omitted or unobserv...

In real investment, there is a relationship between external financing and abnormal stock returns. This study predicts a negative correlation between external financing and stock returns. The dependent variable of the research is stock returns and the independent variables are net financing and equity ratio. Also, control variables of the research includes assets growth, company’s size and comp...

A key problem in financial econometrics is the modeling, estimation and forecasting of conditional return volatility and correlation. Having accurate forecasting models for conditional volatility and correlation is important for accurate derivatives pricing, risk management and asset allocation decisions. It is well known that conditional volatility and correlation are highly predictable. An in...

When forecasting conditional correlations that evolve according to a Dynamic Conditional Correlation (DCC) model, only point forecasts can be obtained at each moment of time. In this paper, we analyze the finite sample properties of a bootstrap procedure to approximate the density of these forecast that also allows obtaining conditional densities for future returns and volatilities. The procedu...

Based on a vector autoregressive model and dynamic conditional correlation generalized heteroskedasticity model, this study explores the relation between international crude oil market Chinese energy stock market. The findings suggest positive one-way spillover effect of returns China’s returns. Furthermore, two markets is time varying.