The recursive prediction and filtering formulas of the Kalman filter are difficult to implement in nonlinear state space models. For Gaussian linear state space models, or for models with qualitative state variables, the recursive formulas of the filter require the updating of a finite number of...

We propose a novel time-changed L évy LIBOR market model for the joint pricing of caps and swaptions. The time changes are split into three components. The first component allows us to match the volatility term structure, the second generates stochastic volatility, and the third one...

We propose an affi ne two-factor model for the pricing of single-tranche collateralized debt obligations by following the general top-down framework introduced in Filipovic et al. [2011]. Apart from being analytically tractable, this model has the feature that it incorporates a catastrophic risk...

We estimate a general microstructure model of the transitory and permanent impact of order flow on stock prices. Jumps are detected in both the transaction price (observation equation) and fundamental value (state equation). The model's parameters and variances are updated in real time. Prices...

We propose to date and analyze the financial cycle using the Maximum Overlap Discrete Wavelet Transform (MODWT). Our presentation points out limitations of the methods derived from the classical business cycle literature, while stressing their connection with wavelet analysis. The fundamental...

We examine the relationship between MIDAS regressions and the estimation of state space models applied to mixed frequency data. While in some cases the binding function is known, in general it is not, and therefore indirect inference is called for. The approach is appealing when we consider...

Conventional tests of present-value models over-reject the null of no predictability. In order to better account for the intrinsic probability of detecting predictive relations by chance alone, we develop a new nonparametric Monte Carlo testing method, which does not rely on distributional...

Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic...

We analyse the behaviour of a non-linear model of coupled stock and bond prices exhibiting periodically collapsing bubbles. By using the formalism of dynamical system theory, we explain what drives the bubbles and how foreshocks or aftershocks are generated. A dynamical phase space...