Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given dataset, a statistical test is required. In this paper, we develop such a test based on the linear state space representation. We provide a...

We consider two multivariate long-memory ARCH models, which extend the univariate long-memory ARCH models, we first consider a long-memory extension of the restricted constant conditional correlations (CCC) model introduced by Bollerslev (1990), and we propose a new unrestricted conditional...

We analyze daily changes of two log foreign exchange (FX) rates involving the Deutsche Mark (DEM) for the period 1975 - 1998, namely FX-rates measured against the US dollar (USD) and the Japanese yen (JPY). Ta account for volatility e1ustering we fit a GARCH(l,l)-model with leptokurtic...

Multivariate Volatility Models belong to the class of nonlinear models for financial data. Here we want to focus on multivariate GARCH models. These models assume that the variance of the innovation distribution follows a time dependent process conditional on information which is generated by...

Let a process SI , ... ,ST obey the conditionally heteroskedastic equation St = Vt Et whcrc Et is a random noise and Vt is the volatility coefficient which in turn obeys an autoregression type equation log v t = w + a S t- l + nt with an additional noise nt. We consider the situation which the...

"The paper documents a new empirical result that a high level of aggregate U.S. idiosyncratic stock return volatility is usually associated with a future appreciation in U.S. dollars. The relation is highly significant for most foreign currencies. For example, idiosyncratic volatility accounts...