Even though most work on change-point estimation focuses on changes in the mean, changes in the variance or in the tail distribution can lead to more extreme events. In this paper, we develop a new method of detecting and estimating the change-points in the tail of multiple time series data. In addition, we adapt existing tail change-point detection methods to our specific problem and conduct a...

Decision makers are often described as seeking higher expected payo¤s and avoiding higher variance in payo¤s. We provide some necessary and some su¢ cient conditions for learning rules, that assume the agent has little prior and feedback information about the environment, to re‡ect such preferences. We adopt the framework of Börgers, Morales and Sarin (2004, Econometrica) who provide similar re...

A convergence criterion for the Monte Carlo estimates will be proposed which can be used as a stopping rule for the Monte Carlo experiments. The proposed criterion searches a convergence band of a given width and length such that the probability of the Monte Carlo sample variance to fall outside of this band is practically null. After the convergence to the process variance realized according t...

A number of variants of the classical Markowitz mean-variance optimization model for portfolio selection have been investigated to render it more realistic. Recently, it has been studied the imposition of a cardinality constraint, setting an upper bound on the number of active positions taken in the portfolio, in an attempt to improve its performance and reduce transactions costs. However, one ...

where t runs from 0 onwards, the supremum is taken over stopping times τ of X , and c > 0 is a given and fixed constant. Using direct martingale arguments we first show that when μ ≤ 0 it is optimal to stop at once and when μ ≥ σ/2 it is optimal not to stop at all. By employing the method of Lagrange multipliers we then show that the nonlinear problem for 0 < μ < σ/2 can be reduced to a family ...

We consider the Generalised Normal Variance-Mean (GNVM) model in which the mixing random variable is Gamma distributed for financial return data. This model generalises the popular Variance-Gamma (VG) distribution. This GNVM model can be interpreted as the addition of noise to a (skew) VG base. In this presentation, we will not only discuss the parameter estimation of the general model, but als...