We develop a misspecification test for the multiplicative two-component GARCHMIDAS model suggested in Engle et al. (2013). In the GARCH-MIDAS model a short-term unit variance GARCH component fluctuates around a smoothly timevarying long-term component which is driven by the dynamics of a macroeconomic explanatory variable. We suggest a Lagrange Multiplier statistic for testing the null hypothes...

It is shown empirically that mixed autoregressive moving average regression models with generalized autoregressive conditional heteroskedasticity (Reg-ARMA-GARCH models) can have multimodality in the likelihood that is caused by a dummy variable in the conditional mean. Maximum likelihood estimates at the local and global modes are investigated and turn out to be qualitatively different, leadin...

risk prediction plays an increasing role in financial risk management. this study aims to investigate existence of asymmetry and long memory volatility in tehran stock exchange index daily data over period of 1998-2006. 1467 daily index returns are used for volatility modeling via garch (long & short memory) processes for both normal and t-student innovations. the specification and forecasting ...

How persistent is volatility? In other words, how quickly do financial markets forget large volatility shocks? Figure 1.1, Shephard (attached) shows that daily squared returns on exchange rates and stock indices can have autocorrelations which are significant for many lags. In any stationary ARCH or GARCH model, memory decays exponentially fast. For example, if {εt } are ARCH (1), the {εt} have...

The key problem for option pricing in Garch models is that the risk neutral distribution of the underlying is known in explicit form only one day ahead and not at maturity. This problem was solved in the HestonNandi model (1997), where it is possible to compute the characteristic function of the underlying by a recursive procedure and options can be priced by Inverse Fourier Transform, see Hest...

This research aims to analyze and to compare the ability of different mathematical models, such as artificial neural networks (ANN) and ARCH and GARCH models, to forecast the daily exchange rates Euro/U.S. dollar (USD), identifying which, among all the models applied, produces more accurate forecasts. By empirically comparing the different mathematical models developed in this research, the tra...

This paper studies volatility forecasting in the financial stock market. In general, stock market volatility is time-varying and exhibits clustering properties. Thus, this paper presents the results of using a fuzzy system method to analyze clustering in generalized autoregressive conditional heteroskedasticity (GARCH) models. It also uses the adaptive method of recursive least-squares (RLS) to...

We conduct empirical analyses to model the volatility of stock market in Malaysia. The GARCH type models (symmetric and asymmetric GARCH) are used to model the volatility of stock market in Malaysia. Their performances are compared based on three statistical error measures tools, i.e. mean squared error, root means squared error and mean absolute percentage error for in sample and out sample an...

This paper proposes a new kind of asymmetric GARCH where the conditional variance obeys two diierent regimes with a smooth transition function. In one formulation, the conditional variance reacts diierently to negative and positive shocks while in a second formulation, small and big shocks have separate eeects. The introduction of a threshold allows for a mixed eeect. A Bayesian strategy, based...

In this paper we present two robust estimates for GARCH(p,q) models. The first is defined by the minimization of a conveniently modified likelihood and the second is similarly defined, but includes an additional mechanism for restricting the propagation of the effect of one outlier on the next estimated conditional variances. We study the asymptotic properties of our estimates proving consisten...