nep-cmp New Economics Papers
on Computational Economics
Issue of 2014‒11‒07
six papers chosen by
Stan Miles
Thompson Rivers University

  1. The Macro and Sectoral Significance of an FTAAP By KAWASAKI Kenichi
  2. Q-Learning-based financial trading systems with applications By Marco Corazza; Francesco Bertoluzzo
  3. Portfolio Selection with Multiple Spectral Risk Constraints By Carlos Abad; Garud Iyengar
  4. Model Simulations of Resource Use Scenarios for Europe By Kurt Kratena; Mark Sommer
  5. The Role of Product and Process Innovation in CGE Models of Environmental Policy By Claudio Baccianti; Andreas Löschel
  6. Robust Dynamic Optimal Taxation and Environmental Externalities By Li, Xin; Narajabad, Borghan N.; Temzelides, Theodosios

  1. By: KAWASAKI Kenichi
    Abstract: This paper discusses the relative significance of a Free Trade Area of the Asia-Pacific (FTAAP) at the macro and sectoral levels. The impacts of trade liberalization and facilitation measures in an FTAAP are studied using a Computable General Equilibrium (CGE) model of global trade. The dynamic aspects of capital formation and productivity improvements are incorporated into a standard static model based on the most updated version of a global trade database. Real GDP of the APEC economies will be boosted on average by 1.9 percent by trade liberalization measures and 0.4 percent by trade facilitation measures, respectively. However, because of differences in the trade structure of the economies, the relative macroeconomic benefits of the economies from several regional trade agreements are shown to differ largely. Moreover, the relative significance of negative impacts in sensitive sectors such as agriculture may also vary according to several scenarios of trade liberalization.
  2. By: Marco Corazza (Department of Economics, Ca’ Foscari University of Venice; Advanced School of Economics in Venice.); Francesco Bertoluzzo (Department of Economics, Ca’ Foscari University of Venice.)
    Abstract: The design of financial trading systems (FTSs) is a subject of high interest both for the academic environment and for the professional one due to the promises by machine learning methodologies. In this paper we consider the Reinforcement Learning-based policy evaluation approach known as Q-Learning algorithm (QLa). QLa is an algorithm which real-time optimizes its behavior in relation to the responses it gets from the environment in which it operates. In particular: first we introduce the essential aspects of QLa which are of interest for our purposes; second we present some original FTSs based on differently configured QLas; then we apply such FTSs to an artificial time series of daily stock prices and to six real ones from the Italian stock market belonging to the FTSE MIB basket. The results we achieve are generally satisfactory.
    Keywords: Financial trading system, Reinforcement Learning, Q-Learning algorithm, daily stock price time series, FTSE MIB basket.
    JEL: C61 C63 G11
    Date: 2014
  3. By: Carlos Abad; Garud Iyengar
    Abstract: We propose an iterative gradient-based algorithm to efficiently solve the portfolio selection problem with multiple spectral risk constraints. Since the conditional value at risk (CVaR) is a special case of the spectral risk measure, our algorithm solves portfolio selection problems with multiple CVaR constraints. In each step, the algorithm solves very simple separable convex quadratic programs; hence, we show that the spectral risk constrained portfolio selection problem can be solved using the technology developed for solving mean-variance problems. The algorithm extends to the case where the objective is a weighted sum of the mean return and either a weighted combination or the maximum of a set of spectral risk measures. We report numerical results that show that our proposed algorithm is very efficient; it is at least two orders of magnitude faster than the state-of-the-art general purpose solver for all practical instances. One can leverage this efficiency to be robust against model risk by including constraints with respect to several different risk models.
    Date: 2014–10
  4. By: Kurt Kratena; Mark Sommer
    Abstract: This paper describes the introduction of biophysical constraints into a disaggregated dynamic New Keynesian (DYNK) model using the example of different resource use scenarios for Europe, derived from global UNEP scenarios. The DYNK model covers 59 industries and five income groups of households and has similar features to a DSGE model (e.g. QUEST). The model solution converges towards a long-run full employment equilibrium, but exhibits short-run institutional rigidities (imperfect credit and capital markets, wage bargaining). The DYNK model links physical energy and material flow data to production and consumption activities. Different sources of technical change are modelled at the disaggregate level: TFP, factor-bias and material efficiency in production and energy efficiency in private consumption. These components of technical change drive – together with relative prices – economic growth and resource use and therefore decoupling. A scenario of modest resource use reduction (per capita) is implemented by shifting the bias of technological change from labour/capital saving to energy/resource saving. As one example for a scenario of radical reduction of resource use per capita, the radical reduction of energy demand and GHG emissions is analysed. The results show the various interlinkages between different categories of material flows, which lead to co-benefits of policies. Further policy options are discussed (re-use and recycling of material in key industries, structural change in agriculture) and shall be analysed in a follow-up of this paper.
    Keywords: Decoupling of resource use, technological and structural change, policy simulation
    JEL: Q32 Q55 C54
    Date: 2014–09
  5. By: Claudio Baccianti; Andreas Löschel
    Abstract: In the last two decades, large scale CGE models used for environmental policy assessment underwent an important upgrade to integrate endogenous technological progress. Nevertheless, several complexities of innovation are still neglected even if they are of primary interest for policymakers. This paper provides a review of the current state of the art in the CGE modelling literature through a special lens. We discuss how existing models deal with different types of innovation (i.e. product and process innovation) and how differences in innovation activities influence modelling results. We also emphasise the implications of product innovation in a multisector framework, which has received little attention in the literature.
    Keywords: CGE models, ecological innovation, economic growth path, green jobs, innovation, innovation policy, social innovation, socio-ecological transition, sustainable growth
    JEL: O41 O40 O47
    Date: 2014–10
  6. By: Li, Xin (International Monetary Fund); Narajabad, Borghan N. (Board of Governors of the Federal Reserve System (U.S.)); Temzelides, Theodosios (Rice University)
    Abstract: We study a dynamic stochastic general equilibrium model in which agents are concerned about model uncertainty regarding climate change. An externality from greenhouse gas emissions damages the economy's capital stock. We assume that the mapping from climate change to damages is subject to uncertainty, and we use robust control theory techniques to study efficiency and optimal policy. We obtain a sharp analytical solution for the implied environmental externality and characterize dynamic optimal taxation. A small increase in the concern about model uncertainty can cause a significant drop in optimal fossil fuel use. The optimal tax that restores the socially optimal allocation is Pigouvian. Under more general assumptions, we develop a recursive method and solve the model computationally. We find that the introduction of uncertainty matters qualitatively and quantitatively. We study optimal output growth in the presence and in the absence of concerns about uncertainty and find that these concerns can lead to substantially different conclusions.
    Keywords: Climate change; optimal dynamic taxation; uncertainty; robust
    Date: 2014–05–29

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