
on Computational Economics 
By:  A. S. Hurn (QUT); K. A. Lindsay (Glasgow and QUT); A. J. Mcclelland (Sydney Numerix) 
Abstract:  This paper investigates several competing procedures for computing the price of European and digital options in which the underlying model has a characteristic function that is known in at least semiclosed form. The algorithms for pricing the options investigated here are the halfrange Fourier cosine series, the halfrange Fourier sine series and the fullrange Fourier series. The performance of the algorithms is assessed in simulation experiments which price options in a BlackScholes world where an analytical solution is available and for a simple affine model of stochastic volatility in which there is no closedform solution. The results suggest that the halfrange sine series approximation is the least effective of the three proposed algorithms. It is rather more difficult to distinguish between the performance of the halfrange cosine series and the fullrange Fourier series. There are however two clear differences. First, when the interval over which the density is approximated is relatively large, the fullrange Fourier series is at least as good as the halfrange Fourier cosine series, and outperforms the latter in pricing outofthemoney call options, in particular with maturities of three months or less. Second, the computational time required by the halfrange Fourier cosine series is uniformly longer than that required by the fullrange Fourier series for an interval of fixed length. Taken together, these two conclusions make a strong case for the merit of pricing options using a fullrange range Fourier series as opposed to a halfrange Fourier cosine series. 
Keywords:  Fourier transform, Fourier series, characteristic function, option price 
Date:  2013–01–22 
URL:  http://d.repec.org/n?u=RePEc:qut:auncer:2013_02&r=cmp 
By:  Antoine Bouet (Larefi  Laboratoire d'analyse et de recherche en économie et finance internationales  Université Montesquieu  Bordeaux IV : EA2954); Carmen Estrades (IFPRI  International Food Policy Research Institute  aaa); David Laborde (IFPRI  International Food Policy Research Institute  aaa) 
Abstract:  The objective of this paper is to develop a version of the MIRAGE model with household heterogeneity and a public agent, to better analyze the impact of trade liberalization and other trade reforms on real income and welfare at the household level In a first step, the model disaggregates the representative household into up to 1339 households in five developing countries (Brazil, Pakistan, Tanzania, Uruguay and Vietnam). The sources of income and consumption structure reflect disaggregated statistical information coming from households' surveys. The new model better captures the behavior of the public agent in terms of revenues collected and in terms of expenditures. Since domestic remittances may constitute an important determinant of income redistribution, the new version also endogenizes private interhouseholds transfers. This new version of MIRAGE takes into account the reaction of households to these shocks in an integrated and consistent framework. We study the impact of full trade liberalization on these households. This study concludes that: (i) while the impact of full trade liberalization may be small at the macroeconomic level, the effect on households' real income may be quite substantial at the household level with a great heterogeneity in terms of results; (ii) the major channel of heterogenity of the impact of trade liberalization on households' real income is productive factors' remuneration while the channel of consumption prices of commodities has limited impact; (iii) various domestic policies simultaneously implemented to trade liberalization like modification of public transfers to households or changes in income taxation may significantly change the picture and offer compensation for negative effects of this shock or amplify direct impact of full trade liberalization; (iv) the impact of trade reform on poverty and inequality is significant and diverse from one country to the other. 
Keywords:  CGE modeling, poverty, trade liberalization, households survey 
Date:  2013–01–09 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal00780103&r=cmp 
By:  M. BALLINGS; D. VAN DEN POEL 
Abstract:  We propose an ensemble method for kernel machines. The training data is randomly split into a number of mutually exclusive partitions defined by a row and column parameter. Each partition forms an input space and is transformed by a kernel function into a kernel matrix K. Subsequently, each K is used as training data for a base binary classifier (Random Forest). This results in a number of predictions equal to the number of partitions. A weighted average combines the predictions into one final prediction. To optimize the weights, a genetic algorithm is used. This approach has the advantage of simultaneously promoting (1) diversity, (2) accuracy, and (3) computational speed. (1) Diversity is fostered because the individual K’s are based on a subset of features and observations, (2) accuracy is sought by optimizing the weights with the genetic algorithm, and (3) computational speed is obtained because the computation of each K can be parallelized. Using five times twofold cross validation we benchmark the classification performance of Kernel Factory against Random Forest and KernelInduced Random Forest (KIRF). We find that Kernel Factory has significantly better performance than KernelInduced Random Forest. When the right kernel is specified Kernel Factory is also significantly better than Random Forest. In addition, an opensource Rsoftware package of the algorithm (kernelFactory) is available from CRAN. 
Date:  2012–12 
URL:  http://d.repec.org/n?u=RePEc:rug:rugwps:12/825&r=cmp 
By:  Stefano Baccarin (Department of Economics and Statistics (Dipartimento di Scienze EconomicoSociali e MatematicoStatistiche), University of Torino, Italy); Daniele Marazzina (Department of Mathematics, Polytechnic University of Milano, Italy) 
Abstract:  We investigate a portfolio optimization problem for an agent who invests in two assets, a riskfree and a risky asset modeled by a geometric Brownian motion. The investor faces both fixed and proportional transaction costs and liquidity constraints. His objective is to maximize the expected utility from the portfolio liquidation at a terminal finite horizon. The model is formulated as a parabolic impulse control problem and we characterize the value function as the unique constrained viscosity solution of the associated quasivariational inequality. We compute numerically the optimal policy by a an iterative finite element discretization technique, presenting extended numerical results in the case of a constant relative risk aversion utility function. Our results show that, even with small transaction costs and distant horizons, the optimal strategy is essentially a buyandhold trading strategy where the agent recalibrates his portfolio very few times. This contrasts sharply with the continuous interventions of the Merton's model without transaction costs. 
Keywords:  Portfolio Optimization, Quasivariational Inequalities, Transaction Costs, Viscosity Solutions 
JEL:  G11 D92 C61 
Date:  2013–01 
URL:  http://d.repec.org/n?u=RePEc:tur:wpapnw:017&r=cmp 
By:  Michael S. Delgado (Department of Agricultural Economics, Purdue University); Christopher F. Parmeter (Department of Economics, University of Miami) 
Keywords:  Parallel processing, reproducibility, computational efficiency, bootstrap, nonlinear optimization, Monte Carlo 
Date:  2013–01–17 
URL:  http://d.repec.org/n?u=RePEc:mia:wpaper:201306&r=cmp 
By:  Petri, Peter A. (Asian Development Bank Institute); Zhai, Fan (Asian Development Bank Institute) 
Abstract:  Most projections envision continued rapid growth in the members of the Association of Southeast Asian Nations (ASEAN), the People’s Republic of China (PRC), and India (collectively, ACI) over the next two decades. By 2030, they could quadruple their output, virtually eliminate extreme poverty, and dramatically transform the lives of their more than 3 billion citizens. The impact will be felt across the world. This study—a background paper to an Asian Development Bank report—used a Computable General Equilibrium model to examine the likely effects of the region's growth on trade, resources and the environment, as well as the implications of the many risks the region's growth path faces from its internal and external environment. 
Keywords:  asean; prc; india; world economy; aci; great transformation; growth engines 
JEL:  F02 F13 F33 F53 
Date:  2013–01–23 
URL:  http://d.repec.org/n?u=RePEc:ris:adbiwp:0404&r=cmp 
By:  JeanPhilippe Chancelier (CERMICS  Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique  Ecole des Ponts ParisTech); Jérôme Lelong (LJK  Laboratoire Jean Kuntzmann  CNRS : UMR5224  Université Joseph Fourier  Grenoble I  Université Pierre MendèsFrance  Grenoble II  Institut Polytechnique de Grenoble  Grenoble Institute of Technology); Bernard Lapeyre (CERMICS  Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique  Ecole des Ponts ParisTech) 
Abstract:  Financial institutions have massive computations to carry out overnight which are very demanding in terms of the consumed CPU. The challenge is to price many different products on a clusterlike architecture. We have used the Premia software to valuate the financial derivatives. In this work, we explain how Premia can be embedded into Nsp, a scientific software like Matlab, to provide a powerful tool to valuate a whole portfolio. Finally, we have integrated an MPI toolbox into Nsp to enable to use Premia to solve a bunch of pricing problems on a cluster. This unified framework can then be used to test different parallel architectures. 
Keywords:  Premia; Mpi; Nsp 
Date:  2013 
URL:  http://d.repec.org/n?u=RePEc:hal:journl:hal00447845&r=cmp 
By:  D. THORLEUCHTER; D. VAN DEN POEL 
Abstract:  In recent years, governmental and industrial espionage becomes an increased problem for governments and corporations. Especially information about current technology development and research activities are interesting targets for espionage. Thus, we introduce a new and automated methodology that investigates the information leakage risk of projects in research and technology (R&T) processed by an organization concerning governmental or industrial espionage. Latent semantic indexing is applied together with machine based learning and prediction modeling. This identifies semantic textual patterns representing technologies and their corresponding application fields that are of high relevance for the organization’s strategy. These patterns are used to estimate organization’s costs of an information leakage for each project. Further, a web mining approach is processed to identify worldwide knowledge distribution within the relevant technologies and corresponding application fields. This information is used to estimate the probability that an information leakage occur. A risk assessment methodology calculates the information leakage risk for each project. In a case study, the information leakage risk of defense based R&T projects is investigated. This is because defense based R&T is of particularly interest by espionage agents. Overall, it can be shown that the proposed methodology is successful in calculation the espionage information leakage risk of projects. This supports an organization by processing espionage risk management. 
Keywords:  Latent semantic indexing, SVD, Espionage, Risk assessment 
Date:  2012–12 
URL:  http://d.repec.org/n?u=RePEc:rug:rugwps:12/824&r=cmp 