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on Computational Economics |
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Issue of 2016‒10‒09
fourteen papers chosen by |
| By: | Seyed Amir Hejazi; Kenneth R. Jackson |
| Abstract: | As part of the new regulatory framework of Solvency II, introduced by the European Union, insurance companies are required to monitor their solvency by computing a key risk metric called the Solvency Capital Requirement (SCR). The official description of the SCR is not rigorous and has lead researchers to develop their own mathematical frameworks for calculation of the SCR. These frameworks are complex and are difficult to implement. Recently, Bauer et al. suggested a nested Monte Carlo (MC) simulation framework to calculate the SCR. But the proposed MC framework is computationally expensive even for a simple insurance product. In this paper, we propose incorporating a neural network approach into the nested simulation framework to significantly reduce the computational complexity in the calculation. We study the performance of our neural network approach in estimating the SCR for a large portfolio of an important class of insurance products called Variable Annuities (VAs). Our experiments show that the proposed neural network approach is both efficient and accurate. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1610.01946&r=cmp |
| By: | Ragchaasuren Galindev; Munkh-Ireedui Bayarjargal; Nasantogtokh Nyamdorj; Telmen Tur; Tsolmon Baatarzorig; Tuvshintugs Batdelger |
| Abstract: | The Mongolian economy has experienced unprecedented growth rates driven by the booming mining sector. At the same time, it has become increasingly dependent on the mining sector to the extent that movements in the international price of mining commodities could have disturbing effects on the economy. In this research, we examine the short-run effects of the developments occurring in the mining sector on the economy by calibrating a PEP standard static CGE model to a 2010 Mongolian social accounting matrix. In particular, we consider two scenarios: an increase in the stock of capital and land possessed by the coal sector and a drop in the world price of metal ores. In the former scenario, we find that the shock leads to increased value added, production, employment and exports in the coal sector, resulting in higher real GDP, exports and investment. Moreover, we do not find the associated Dutch disease effects on the other sectors. In the second scenario, we find that the effects on the productions, value added, employment, real GDP and investment are all negative while real exports and government expenditure increase slightly. |
| Keywords: | : Mining boom, CGE model, Dutch disease, Mongolia |
| JEL: | D58 Q33 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:mpiacr:2016-03&r=cmp |
| By: | Joydeep Ghosh; kele Shiferaw; Amarendra Sahoo; Sika Gbegbelegbe |
| Abstract: | A recursive dynamic computable general equilibrium (CGE) model is used to conduct an exante analysis of the economy-wide impacts of new agricultural technologies in India. Differential impacts of changes in productivity of new promising cultivars for irrigated and rainfed maize and wheat are incorporated in the model. Technological change in these crops results in higher future economic growth as well as food security, both in food consumption and availability. While there is considerable scope for increasing the production of both crops through the introduction of new technologies, maize (both irrigated and rainfed) with promising cultivars for higher yield gain generates significant growth in output. The projected gains for wheat are primarily in the rainfed wheat output as this is where the yield gaps are highest from the promising technologies. Lower prices, particularly for maize and wheat, stimulate higher consumption of these cereals and other food commodities. Rural households benefit more than their urban counterparts in food consumption. Although maize’s contribution to the national economy is less than wheat, given the relatively higher estimated yield gains from promising maize technologies, the positive impacts of maize technologies on food security and national income are higher than the impacts of wheat. In view of the land and water constraints in Indian agriculture, maize which is predominantly rainfed and widely adapted could be a viable alternative for the future. However, a joint improvement of maize and wheat productivity would further enhance economic conditions and food security in India. |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:mpiacr:2016-16&r=cmp |
| By: | André Lemelin |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:mpiacr:2015-09&r=cmp |
| By: | Hanfeld, Marc; Schlüter, Stephan |
| Abstract: | We investigate, if it pays off for a company to invest into complex swing option algorithms. We first introduce least squares Monte Carlo as a complex valuation algorithm and explain in detail how it works. Using a simulation study and two backtest scenarios we compare the output of this method with a simple myopic approach, and evaluate the results also from a business point of view. We find that myopic operation performs fairly well, but given a certain contract size and a certain contract flexibility, LSMC clearly prevails. |
| Keywords: | Swing Option,Spot Optimization,Least Squares Monte Carlo |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwqwdp:102016&r=cmp |
| By: | Amarendra Sahoo; Lulit Mitik Beyene; Bekele Shiferaw; Sika Gbegbelegbe |
| Abstract: | This paper assesses the potential impacts from the introduction of high yielding and drought tolerant varieties of major food staples (wheat and maize) in Ethiopia. We develop a dynamic Computable General Equilibrium model with a micro-simulation module to examine the growth, poverty and distributional impacts of agricultural innovations. The analysis shows that introduction of improved varieties of these food staples is likely to boost the cereal sector in the country. Other agricultural sub-sectors grow due to increased labour supply. Given that these staple cereals represent an important share of food consumption for Ethiopian households, the poverty impact of the interventions is positive. Although rural households benefit from higher gains in real consumption, poverty declines more in urban areas compared to the rural. This is mainly because the rural poor are generally far from the poverty line with a higher initial poverty gap compared to urban households and the urban poor benefit from price effects. As productivity-enhancing technologies are introduced, there is a need for policy interventions in rural areas targeting non-agricultural sectors to enhance growth linkages, increase employment and stimulate inclusive growth. |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:lvl:mpiacr:2016-17&r=cmp |
| By: | Justin Van de Ven |
| Abstract: | This paper describes how the parameters of the Lifetime Income Distributional Analysis (LINDA) microsimulation model were defined to reflect survey data for the UK. LINDA is a dynamic programming model of savings and labour supply decisions that has been developed for use by UK policy makers. The model is adapted to project the circumstances of the evolving population cross-section forward through time. This feature of the model, which distinguishes it from much of the related literature, adapts the model for identifying all of the assumed preference parameters on data for a single population cross-section. |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:niesrd:464&r=cmp |
| By: | Laura Roa Castro (IRT SystemX); Julie Stal-Le Cardinal (LGI - Laboratoire Génie Industriel - EA 2606 - CentraleSupélec) |
| Abstract: | During the last decades modelling and simulation technics has grown in importance in the product development context. For example, from an industrial point of view, simulation models seem to be an excellent alternative on vehicle construction and more specifically, in the decision making process. Nevertheless, the simulation activity becomes more difficult with the complexity of the product, highlighting more and more often a collaborative problem on the organization of the product development. But, how can this problem be defined? Several collaborative approaches have been proposed in this field. However, the majority of those approaches concern only one dimension of the problem. This paper introduces the Collaborative Modelling & Simulation System (CM&SS) from a systemic perspective in vehicle industry context. The systemic approach enables the definition of different dimensions of the system aiming at a successful performance of a collaborative simulation. |
| Keywords: | Collaborative design,Organization of product development,Process modelling Collaborative simulation,Systemic approach |
| Date: | 2015–07–27 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-01347363&r=cmp |
| By: | Jang, Tae-Seok |
| Abstract: | In this paper, we develop the waves of optimists and pessimists in an open-economy New Keynesian model á la Gali and Monacelli (2005). We extend the model to include the dynamics of inflation and output generated by the heterogeneous bounded rational agents according to De Grauwe (2011). The effects of social interaction are merged into open DSGE model. In particular, the interaction between heterogeneous agents provides the basis for bounded rational behavior in a two-country model. As a result, the model is able to describe the herding behavior of investors in open economy. The simulation results suggest that the business cycle goes through periods of high volatility when the large number of optimists or pessimists in one country strongly affects a foreign country. |
| Keywords: | animal spirits, bounded rationality, new keynesian, two-country model |
| JEL: | C63 E31 F41 |
| Date: | 2015–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpm:dynare:046&r=cmp |
| By: | André Scholz (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg); Daniel Schubert (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg); Gerhard Wäscher (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg) |
| Abstract: | In manual picker-to-parts order picking systems of the kind considered in this article, human operators (order pickers) walk or ride through the warehouse, retrieving items from their storage location in order to satisfy a given demand specified by customer orders. Each customer order is characterized by a certain due date until which all requested items included in the order are to be retrieved and brought to the depot. For the actual picking process, customer orders may be grouped (batched) into more substantial picking orders (batches). The items of a picking order are then collected on a picker tour through the warehouse. Thus, the picking process of each customer order in the batch is only completed when the picker returns to the depot after the last item of the batch has been picked. Whether and to which extend due dates are violated (tardiness) depends on how the customer orders are batched, how the batches are assigned to order pickers, how the assigned batches are sequenced and how the pickers are routed through the warehouse. Existing literature has only treated special aspects of this problem (i.e. the batching problem or the routing problem) so far. In this paper, for the first time, an approach is proposed which considers all aspects simultaneously. A mathematical model of the problem is introduced that allows for solving small problem instances in reasonable computing times. For larger instances, a variable neighborhood descent (VND) algorithm is presented which includes various neighborhood structures regarding the batching and sequencing problem. Furthermore, two sophisticated routing algorithms are integrated into the VND algorithm. By means of numerical experiments, it is shown that this algorithm provides solutions of excellent quality. |
| Keywords: | Order Picking, Order Batching, Batch Sequencing, Picker Routing, Traveling Salesman, Variable Neighborhood Descent |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:mag:wpaper:160005&r=cmp |
| By: | Stanislao Gualdi (CentraleSupélec); Antoine Mandel (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics) |
| Abstract: | We propose a simple dynamical model of the formation of production networks among monopolistically competitive firms. The model subsumes the standard general equilibrium approach a la Arrow-Debreu but displays a wide set of potential dynamic behaviors. It robustly reproduces key stylized facts of firms' demographics. Our main result is that competition between intermediate good producers generically leads to the emergence of scale-free production networks. |
| Keywords: | Macroeconomic Modelling,Agent-based Computational Economics |
| Date: | 2016–09–19 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:hal-01370207&r=cmp |
| By: | Markus K. Brunnermeier (Princeton University); Sam Langfield (European Systemic Risk Board); Marco Pagano (Università di Napoli Federico II); Ricardo Reis (Centre for Macroeconomics (CFM); London School of Economics and Political Science (LSE)); Stijn Van Nieuwerburgh (New York University); Dimitri Vayanos (London School of Economics and Political Science (LSE)) |
| Abstract: | The euro crisis was fueled by the diabolic loop between sovereign risk and bank risk, coupled with cross-border flight-to-safety capital flows. European Safe Bonds (ESBies), a union-wide safe asset without joint liability, would help to resolve these problems. We make three contributions. First, numerical simulations show that ESBies would be at least as safe as German bunds and approximately double the supply of euro safe assets when protected by a 30%-thick junior tranche. Second, a model shows how, when and why the two features of ESBies — diversification and seniority — can weaken the diabolic loop and its diffusion across countries. Third, we propose a step-by-step guide on how to create ESBies, starting with limited issuance by public or private-sector entities. |
| Date: | 2016–09 |
| URL: | http://d.repec.org/n?u=RePEc:cfm:wpaper:1627&r=cmp |
| By: | Alsayyed, Nidal; Zhu, Weihang |
| Abstract: | Natural Capital (NC) is the limited form of capital assets or service (tangible or intangible) that satisfies basic and social conditions for human existence and protection The aim of this paper is twofold; First we examine and test empirically the conventional financing models and discuss their performance in regulating the financing structure of NC funds using a parametric estimating approach Generalized Moments Method (GMM). Second we estimate the NC dynamics using a nonparametric approach Artificial Neural Network (ANN). Two neural network models are proposed. The first model uses spreads between interest rates of 10 different maturities as the only explanatory variables of interest rate changes relative to renewable energy funding structure. The second model introduces two factors, spreads and interest rates’ levels. Using historical U.S. Treasury bill rates, Treasury bond yields, and renewable energy financing bond; we compare the ability of each model to predict the most economical structure of NC. Data are daily and cover the period from January 3rd 2005 to December 29th 2015. Results suggest that, neural network models generate different NC yield curves. Neural network models outperform the parametric standard models. The most successful forecast is obtained with the two factors neural network model. |
| Keywords: | Neural Network; Natural Capital; Interest rate. |
| JEL: | C45 P28 Q42 |
| Date: | 2016–08–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:74191&r=cmp |
| By: | Jakub Nowotarski; Rafal Weron |
| Abstract: | Since the inception of competitive power markets two decades ago, electricity price forecasting (EPF) has gradually become a fundamental process for energy companies' decision making mechanisms. Over the years, the bulk of research has concerned point predictions. However, the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply, demand and prices. Academics and practitioners alike have come to understand that probabilistic electricity price (and load) forecasting is now more important for energy systems planning and operations than ever before. With this paper we offer a tutorial review of probabilistic EPF and present much needed guidelines for the rigorous use of methods, measures and tests, in line with the paradigm of 'maximizing sharpness subject to reliability'. The paper can be treated as an update and a further extension of the otherwise comprehensive EPF review of Weron (2014, IJF) or as a standalone treatment of a fascinating and underdeveloped topic, that has a much broader reach than EPF itself. |
| Keywords: | Electricity price forecasting; Probabilistic forecast; Reliability; Sharpness; Day-ahead market; Autoregression; Neural network |
| JEL: | C22 C32 C51 C53 Q47 |
| Date: | 2016–09–25 |
| URL: | http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1607&r=cmp |