Air and water pollution over time and industries with stochastic dominance

We employ a stochastic dominance (SD) approach to analyze the components that contribute to environmental degradation over time. The variables include countries\u2019 greenhouse gas (GHG) emissions and water pollution. Our approach is based on pair-wise SD tests. First, we study the dynamic progress of each separate variable over time, from 1990 to 2005, within 5-year horizons. Then, pair-wise SD tests are used to study the major industry contributors to the overall GHG emissions and water pollution at any given time, to uncover the industry which contributes the most to total emissions and water pollution. While CO2 emissions increased in the first order SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO2 emissions, while the food industry gradually became the major water polluting industry over time. SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO2 emissions, while the food industry gradually

Finite state approximation algorithms for average cost denumerable state Markov decision processes

In this paper we study three finite state, value and policy iteration algorithms for denumerable space Markov decision processes with respect to the average cost criterion. The convergence of these algorithms is guaranteed under a scrambling-type recurrency condition and various "tail" conditions on the transition probabilities. With the value iteration schemes we construct nearly optimal policies by concentrating on a finite set of "important" states and controlling them as well as we can. The policy space algorithm consists of a value determination scheme associated with a policy and a policy improvement step where a "better" policy is determined. Thus a sequence of improved policies is constructed which is shown to converge to the optimal average cost policy. © 1985 Springer-Verlag

Modelling volatility and testing for efficiency in emerging capital markets: the case of the Athens stock exchange

This study employs GARCH type models and tests for their validity over an Emerging Capital Market, the Athens Stock Exchange Market (ASE). Correct specification, of the different models, implies that the Weak Efficient Market Hypothesis does not hold for ASE. There is strong empirical evidence that ASE follows a pattern where last period's daily returns are correlated with today's returns and current volatility is positively related to past realizations. Negative shocks have an asymmetric impact on the daily stock returns series and political instabilities increase volatility over time. The mean of the series does not change during high volatile periods.