New classes of nonlinearly self-adjoint evolution equations of third- and fifth-order

In a recent communication Nail Ibragimov introduced the concept of nonlinearly self-adjoint differential equation [N. H. Ibragimov, Nonlinear self-adjointness and conservation laws, J. Phys. A: Math. Theor., vol. 44, 432002, 8 pp., (2011)]. In the present communication a nonlinear self-adjoint classification of a general class of fifth-order evolution equation with time dependent coefficients is presented. As a result five subclasses of nonlinearly self-adjoint equations of fifth-order and four subclasses of nonlinearly self-adjoint equations of third-order are obtained. From the Ibragimov's theorem on conservation laws [N. H. Ibragimov, A new conservation theorem, J. Math. Anal. Appl., vol. 333, 311--328, (2007)] conservation laws for some of these equations are established

Measuring Inequality in CIS Countries: Theory and Empirics

Distributions of many variables of interest in developed economic and financial markets, including income and wealth, exhibit heavy tails as in the case of Pareto or power laws. Many commonly used income and wealth inequality measures are very sensitive to extremes and outliers generated by these distributions due to their heavy-tailedness properties. This paper focuses on robust analysis of distributions and heavy-tailedness characteristics for data on income and wealth for the World, Russia and post-Soviet Central Asian economies. Among other results, it provides robust estimates of heavy-tailedness parameters for income and wealth in the markets considered and their comparisons with the benchmark values that are well-established for distributions of these variables in developed economies. The paper further provides applications of the obtained empirical results to inference on inequality measures and discusses their implications for market demand and economic equilibrium.Income inequality, wealth inequality, CIS countries, Russian economy, post-Soviet economies, heavy-tailedness, power laws, Pareto distribution, income inequality, market demand, economic equilibrium

Portfolio Diversification and Value at Risk Under Thick-Tailedness

We present a unified approach to value at risk analysis under heavy-tailedness using new majorization theory for linear combinations of thick-tailed random variables that we develop. Among other results, we show that the stylized fact that portfolio diversification is always preferable is reversed for extremely heavy-tailed risks or returns. The stylized facts on diversification are nevertheless robust to thick-tailedness of risks or returns as long as their distributions are not extremely long-tailed. We further demonstrate that the value at risk is a coherent measure of risk if distributions of risks are not extremely heavy-tailed. However, coherency of the value at risk is always violated under extreme thick-tailedness. Extensions of the results to the case of dependence, including convolutions of a-symmetric distributions and models with common stochs are provided.

Copula-Based Dependence Characterizations and Modeling for Time Series

This paper develops a new unified approach to copula-based modeling and characterizations for time series and stochastic processes. We obtain complete characterizations of many time series dependence structures in terms of copulas corresponding to their finite-dimensional distributions. In particular, we focus on copula- based representations for Markov chains of arbitrary order, m-dependent and r-independent time series as well as martingales and conditionally symmetric processes. Our results provide new methods for modeling time series that have prescribed dependence structures such as, for instance, higher order Markov processes as well as non-Markovian processes that nevertheless satisfy Chapman-Kolmogorov stochastic equations. We also focus on the construction and analysis of new classes of copulas that have flexibility to combine many different dependence properties for time series. Among other results, we present a study of new classes of cop- ulas based on expansions by linear functions (Eyraud-Farlie-Gumbel-Mongenstern copulas), power functions (power copulas) and Fourier polynomials (Fourier copulas) and introduce methods for modeling time series using these classes of dependence functions. We also focus on the study of weak convergence of empirical copula processes in the time series context and obtain new results on asymptotic gaussianity of such processes for a wide class of beta mixing sequences.

Shifting paradigms: on the robustness of economic models to heavy-tailedness assumptions

The structure of many models in economics and finance depends on majorization properties of convolutions of distributions. In this paper, we analyze robustness of these properties and the models based on them to heavy-tailedness assumptions. We show, in particular, that majorization properties of linear combinations of log-concavely distributed signals are reversed for very long-tailed distributions. As applications of the results, we study robustness of monotone consistency of the sample mean, value at risk analysis and the model of demand-driven innovation and spatial competition as well as that of optimal bundling strategies for a multiproduct monopolist in the case of an arbitrary degree of complementarity or substitutability among the goods. The implications of the models remain valid for not too heavy-tailed distributions. However, their main properties are reversed in the very thick-tailed settingRobustness, heavy-tailed distributions, innovation and spatial competition, firm growth, Gibrat's law, optimal bundling strategies, multiproduct monopolist, Vickrey auction, value at risk, coherent measures of risk, monotone consistency