This paper shows that, even if volatility is accurately predicted by correctly specified GARCH models, however such predictions are not very useful for traders when the conditional volatility does not vary "enough" over time, being therefore quite close to the unconditional one. It is shown that a low R in the Mincer-Zarnowitz regression implies flat (although correctly predicted) volatility, a...

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter...

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 develops a parametric family of models of generalized autoregressive heteroscedasticity (garch). The family nests the most popular symmetric and asymmetric garch models, thereby highlighting the relation between the models and their treatment of asymmetry. Furthermore, the structure permits nested tests of different types of asymmetry and functional forms. U.S. stock return data reje...

agricultural activities are risky activities. in these activities, various natural, social and economic risks have created fragile and vulnerable situation for producers. price risk in agricultural products has caused financial problems for many producers and farmers. to deal with these price risks and price fluctuations, there are varieties of tools. this paper focused on futures markets instr...

We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for geometric ergodicity and existence of moments. Because of path dependence, maximum likelihood estimation is no...

This paper provides a new empirical guidance for modeling a skewed and fat-tailed error distribution underlying the traditional GARCH models for equity returns based on empirical findings on Realized Volatility (RV), constructed from the summation of higher-frequency squared (demeaned) returns. Based on an 80-year sample of U.S. daily stock market returns, I find that the distribution of monthl...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to the clustering structure, and the second focusing on correlation parameters. Differently from the trad...

There are many studies on the business cycle indicators in the past decades, but mostly focusing on the asymmetric and non-linear features of business cycles incorporated into the conditional mean equation rather than the conditional variance formulation. Recently, the hypothesis of volatility asymmetry in business cycle indicators has been re-examined by, for instance, Ho and Tsui (2003 and 20...

A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio Value at Risk and Conditional Value at Risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using tradi...