The Multiplicative Ergodic Theorem provides a novel general methodology to analyze rational expectations models with stochastically varying coefficients. The approach is applied for the first time to economics and analyzes the canonical New Keynesian model with a Taylor rule which switches...

In this paper we outline the Lagrangian constrained optimization method to solve complex problems subject to constraints. Firstly we summarize the Lagrangian constrained optimization routine. Secondly we outline a detailed implementation strategy. Thirdly and finally we provide example and solve...

This paper introduces a new Empirical Mode Decomposition (EMD) Python package called AdvEMDpy that is demonstrably more flexible and which generalises in numerous important ways the existing EMD packages available in Python, R, and MATLAB. The extensions introduced by this AdvEMDpy package both...

Protecting portfolio against extreme losses is a fundamentally difficult task since past experience provides a poor guidance for the future. This paper focuses on a robust approach to the portfolio insurance, which does not require historical calibration, and therefore avoids the hazards of data...

Data envelopment analysis (DEA) is widely used to compare the empirical performance of public institutions such as law enforcement agencies, judicial authorities or national health care systems. Many DEA analysts, however, ignore the fact that DEA efficiency values are non-metric. They...

We consider the optimal dividend control problem to find an optimal strategy under the constraint that dividend rates are restricted such that the expected total discounted dividends is maximized for an insurance company. The evolution of the reserve is modeled by a diffusion process with drift...

We study the dividend optimization problem for a company where surplus in the absence of dividend payments follows a Cramér-Lundberg process compounded by constant force of interest. The company controls the times and amounts of dividend payments subject to reserve constraints that dividends...

The multi-drop container loading problem (MDCLP) requires loading a truck so that boxes can be unloaded at each drop-off point without rearranging other boxes to deliver later. However, modeling such unloading constraints as hard constraints, as done in the literature, considerably limits the...