this paper investigates the relationship between inflation and growth uncertainty in iran for the period of 1988-2008 by using quarterly data. we employ generalized autoregressive conditional heteroscedasticity in mean (garch-m) model to estimate time-varying conditional residual variance of growth, as a standard measures of growth uncertainty. the empirical evidence shows that growth uncertain...

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...

Since 1970, with the gradual acceleration of economic globalization and rapid development information technology, financial market has become increasingly unstable. Therefore, we must enhance our competitiveness in market, ability to resist risks, master effective measures such as measuring risks. In this paper, GARCH-M model VAR method are used study value at risk make an empirical analysis. F...

the research problem investigated in this paper is modeling volatility and analyzing risk and return’s relationship in tehran stock exchange using garch-family models including garch(1,1), garch(2,2), egarch(1,1), pgarch(1,1), tgarch(1,1), garch(1,1)-m and cgarch(1,1). using the daily returns of tehran stock exchange companies, we focused on two portfolios of all the companies during a 10-year-...

We reveal that in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on that, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; ...

In the empirical finance literature findings on the risk-return tradeoff in excess stock market returns are ambiguous. In this study, we develop a new QR-GARCH-M model combining a probit model for a binary business cycle indicator and a regime switching GARCH-in-mean model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is...

This paper investigates the forecasting ability of five different versions of GARCH models. The five GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-X and GARCH-GJR. Forecast errors based on four emerging stock futures portfolio return (based on forecasted hedge ratio) forecasts are employed to evaluate out-ofsample forecasting ability of the five GARCH models. Daily data...

To capture the missed information in the standardized errors by parametric multivariate generalized autoregressive conditional heteroskedasticity (MV-GARCH) model, we propose a new semiparametric MV-GARCH (SM-GARCH) model. This SM-GARCH model is a twostep model: firstly estimating parametric MV-GARCH model, then using nonparametric skills to model the conditional covariance matrix of the standa...

GARCH models with Markov-switching regimes are often used for volatility analysis of …nancial time series. Such models imply less persistence in the conditional variance than the standard GARCH model, and potentially provide a signi…cant improvement in volatility forecast. Nevertheless, conditions for asymptotic wide-sense stationarity have been derived only for some degenerated models. In this...