In this paper we derive conditions for the conditional covariance matrix to be positive definite in a general vector ARCH model. The conditions can be easily extended to the diagonal vector GARCH model. For the general vector GARCH model, analytical expressions for the conditions in terms of the...

Behavioral economics provides several motivations for the common observation that agents appear somewhat unwilling to deviate from recent choices: salience, inertia, the formation of habits, the use of rules of thumb, or the locking in on certain modes of behavior due to learning by doing. This...

In some recent papers, such as Elliott & van der Hoek, Hu & Öksendal, a fractional Black-Scholes model have been proposed as an improvement of the classical Black-Scholes model. Common to these fractional Black-Scholes models, is that the driving Brownian motion is replaced by a fractional...

We propose a seasonal cointegration model [SECM] for quarterly data which includes variables with different numbers of unit roots and thus needs to be transformed in different ways in order to yield stationarity. A Monte Carlo simulation is carried out to investigate the consequences of...

This note examines the accuracy of methods that are commonly used to approximate AR(1)-processes with discrete Markov chains. The quadrature-based method suggested by Tauchen and Hussey (1991) generates excellent approximations with a small number of nodes when the autocorrelation is low or...

The value of preventing a fatality or (saving) a statistical life is an important question in health economics as well as environmental economics. This paper reviews and adds new insights to several of the issues discussed in the literature. For example, how do we define the value of a...

We study the consequences of 'leniency' - reduced legal sanctions for wrongdoers who spontaneously self-report to law enforcers - on corruption, drug dealing, and other forms of sequential, bilateral, illegal trade. We find that when not properly designed, leniency may be highly...