When using an Euler discretisation to simulate a mean-reverting square root process, one runs into the problem that while the process itself is guaranteed to be nonnegative, the discretisation is not. Although an exact and efficient simulation algorithm exists for this process, at present this...

The characteristic functions of many affine jump-diffusion models, such as Heston’s stochastic volatility model and all of its extensions, involve multivalued functions such as the complex logarithm. If we restrict the logarithm to its principal branch, as is done in most software packages,...

At the time of writing this article, Fourier inversion is the computational method of choice for a fast and accurate calculation of plain vanilla option prices in models with an analytically available characteristic function. Shifting the contour of integration along the complex plane allows for...

We model and measure simultaneous large losses of the market value of insurers to understand the impact of shocks on the insurance sector. The downside risk of insurers is explicitly modelled by common and idiosyncratic risk factors. Since reinsurance is important for the capacity of insurers,...

A sequence of real numbers (<I>x_n</I>) is Benford if the significands, i.e. the fraction

This paper points out the importance of Stochastic Dominance (SD) efficient sets being convex. We review

We apply the stochastic evolutionary approach of equilibrium selection to macroeconomic models in which a complementarity at the macro level is present. These models often exhibit multiple Pareto-ranked Nash equilibria, and the best response-correspondence of an individual increases with a...

In a Bayesian analysis, different models can be compared on the basis of the expected or marginal likelihood they attain. Many methods have been devised to compute the marginal likelihood, but simplicity is not the strongest point of most methods. At the same time, the precision of methods is...

In linear-quadratic control (LQC) problems with singular control cost matrix and/or singular transition matrix, we derive a reduction of the dimension of the Riccati matrix, simplifying iteration and solution. Employing a novel transformation, we show that, under a certain rank condition, the...

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiently from such a distribution, a location-scale transformation and a transformation to polar coordinates are used....