Functional data analysis (FDA) is a part of modern multivariate statistics that analyses data providing information about curves, surfaces or anything else varying over a certain continuum. In economics and empirical finance we often have to deal with time series of functional data, where we cannot easily decide, whether they are to be considered as homogeneous or heterogeneous. At present a discussion on adequate tests of homogenity for functional data is carried. We propose a novel statistic for detetecting a structural change in functional time series based on a local Wilcoxon statistic induced by a local depth function proposed by Paindaveine and Van Bever (2013).

We establish dual representations for systemic risk measures based on acceptance sets in a general setting. We deal with systemic risk measures of both "first allocate, then aggregate" and "first aggregate, then allocate" type. In both cases, we provide a detailed analysis of the corresponding systemic acceptance sets and their support functions. The same approach delivers a simple and self-contained proof of the dual representation of utility-based risk measures for univariate positions.

Distributed securities exchanges may become de facto fragmented if they span geographical regions with asymmetric computer infrastructure. First, we build an economic model of a decentralized exchange with two miner clusters, standing in for compact areas of economic activity (e.g., cities). "Local" miners in the area with relatively higher trading activity only join a decentralized exchange if they enjoy a large speed advantage over "long-distance" competitors. This is due to a transfer of economic value across miners, specifically from high- to low-activity clusters. Second, we estimate the speed advantage of "local" over "long-distance" miners in a series of Monte Carlo experiments over a two-cluster, unstructured peer-to-peer network simulated in C. We find that the speed advantage increases in the level of infrastructure asymmetry between clusters. Cross-region DEX blockchains are feasible as long as the asymmetry levels in trading activity and infrastructure availability across regions are positively correlated.

General Motors or a local business, which one is better to be stimulated in post-crisis recessions, where government stimulation is meant to overcome recessions? Due to the budget constraints, it is quite relevant to ask how one can increase the chance of economic recovery. One of the key elements to answer this question is to understand metastable features of the economic networks. Ising model has been suggested for studying such features in the literature. In the homogenous networks one needs at least a minimum activation, forcing an Ising network to switch its local equilibria, where such minimum is independent of the nodes characteristics. In the scale free networks however, when one aims to push the network to switch its vacuum, she faces the question of which nodes are better to be stimulated to minimize the cost. In the paper it has been shown that stimulation of the high degree nodes costs less in general. Despite regular networks, in the scale free networks, the stimulation cost depends on the networks features such as assortativity. Though we have utilized the Ising model to tackle a problem in economics, our analysis shed lights on many other problems concerning stimulations of socio-economic systems.

The famous St Petersburg Paradox shows that the theory of expected value does not capture the real-world economics of decision-making problem. Over the years, many economic theories were developed to resolve the paradox and explain the subjective utility of the expected outcomes and risk aversion. In this paper, we use the concept of the net utility to resolve the St Petersburg paradox. The reason why the principle of absolute instead of net utility does not work is because it is a first order approximation of some unknown utility function. Because the net utility concept is able to explain both behavioral economics and the St Petersburg paradox it is deemed a universal approach to handling utility. Finally, this paper explored how artificial intelligent (AI) agent will make choices and observed that if AI agent uses the nominal utility approach it will see infinite reward while if it uses the net utility approach it will see the limited reward that human beings see.