As a main step in the numerical solution of control problems in continuous time, the controlled process is approximated by sequences of controlled Markov chains, thus discretizing time and space. A new feature in this context is to allow for delay in the dynamics. The existence of an optimal...

This paper develops a consumption-based asset pricing model in which attitudes towards risk are contingent upon the state of the world. For low (high) level of consumption relative to a subjective metric, counter-cyclical (pro-cyclical) risk aversion implies that consumption shocks generate...

The present paper proposes certain statistical tests, both conceptually simple and computationally easy, for analysing state-specific prima facie probabilistic causality and error correction mechanism in the context of a Markov chain of time series data arranged in a contingency table of present...

The emerging economy is playing an important role in the global market. Market potential of emerging markets is also a determinant in a wide range of market decisions of the global managers. For the reason that the market potential consists of a number of different factors across different...

We consider a continuous-time Markov chain with a finite or countable state space. For a site y and subset H of the state space, the hitting time of y under taboo H is defined to be infinite if the process trajectory hits H before y, and the first hitting time of y otherwise. We investigate the...

Recovery rates are negatively related to default probabilities (Altman et al., 2005). This paper proposes and estimates a model in which this dependence is the result of an unobserved credit cycle: When times are bad, the default probability is high and recovery rates are low; when times are...

In this work we apply the methodology of integral priors to handle Bayesian model selection in binomial regression models with a general link function. These models are very often used to investigate associations and risks in epidemiological studies where one goal is to exhibit whether or not an...

We describe a quasi-Monte Carlo method for the simulation of discrete time Markov chains with continuous multi-dimensional state space. The method simulates copies of the chain in parallel. At each step the copies are reordered according to their successive coordinates. We prove the convergence...

In this paper, we consider Monte Carlo (MC) algorithms based on the use of the resolvent matrix for solving linear algebraic problems. Estimates for the speedup and efficiency of the algorithms are presented. Some numerical examples performed on cluster of workstations using MPI are given.

We propose an intuitive way of how to measure segregation in social and spatial networks. Using random walks, we define the segregation index as the probability that an individual meets an individual from the same social group. The segregation index is a generalization of the isolation index and...