We propose what be believe to be a novel approach to perform calculations for rational density functions using state space representations of the densities. By standard results from realization theory, a rational probability density function is considered to be the transfer function of a linear...

Three situations in which filtering theory is used in mathematical finance are illustrated at different levels of detail. The three problems originate from the following different works: 1) On estimating the stochastic volatility model from observed bilateral exchange rate news, by R. Mahieu,...

We propose what be believe to be a novel approach to perform calculations for rational density functions using state space representations of the densities. By standard results from realization theory, a rational probability density function is considered to be the transfer function of a linear...

We consider a two-factor Heath–Jarrow–Morton (HJM) model under the risk neutral measure and show that it may be decoupled into a particular dynamic Nelson–Siegel (NS) model plus a somewhat counter-intuitive adjustment (lying outside the NS family) which keeps it arbitrage-free. We assess...

We propose a procedure to take model risk into account in the computation of capital reserves. This addresses the need to make the allocation of capital reserves to positions in given markets dependent on the extent to which reliable models are available. The proposed procedure can be used in...

This article proposes an improved method for the construction of principal components in macroeconomic forecasting. The underlying idea is to maximize the amount of variance of the original predictor variables that is retained by the components in order to reduce the variance involved in...

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the...

Dynamics in iron ore markets are driven by changes in economic activities that affect commodity markets, trade flows, and shipping activity and safety. This article investigates the relation between these variables in Southeast Asia and the Australasian region. Steel production in China, Japan,...

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the...