nep-cmp New Economics Papers
on Computational Economics
Issue of 2016‒07‒09
eight papers chosen by
Stan Miles
Thompson Rivers University

  1. A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities By Seyed Amir Hejazi; Kenneth R. Jackson
  2. JAS-mine: A new platform for microsimulation and agent-based modelling By Matteo Richiardi; Ross E Richardson
  3. Catching up with history: A methodology to validate global CGE models By Michiel van Dijk; George Philippidis; Geert Woltjer
  4. A Genetic Algorithm for the Multi-Compartment Vehicle Routing Problem with Flexible Compartment Sizes By Henriette Koch; Tino Henke; Gerhard Wäscher
  5. Prudential regulation in an artificial banking system By Curto, José Dias; Quinaz, Pedro Miguel Mateus Dias
  6. Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework By Tamal Datta Chaudhuri; Indranil Ghosh
  7. Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks By Hendrik Strobelt; Sebastian Gehrmann; Bernd Huber; Pfister, Hanspeter; Alexander M. Rush
  8. A multilayer approach for price dynamics in financial markets By Alessio Emanuele Biondo; Alessandro Pluchino; Andrea Rapisarda

  1. By: Seyed Amir Hejazi; Kenneth R. Jackson
    Abstract: Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to value their large portfolios of VA products. Because the MC simulations are computationally demanding, especially for intraday valuations, insurance companies need more efficient valuation techniques. Recently, a framework based on traditional spatial interpolation techniques has been proposed that can significantly decrease the computational complexity of MC simulation (Gan and Lin, 2015). However, traditional interpolation techniques require the definition of a distance function that can significantly impact their accuracy. Moreover, none of the traditional spatial interpolation techniques provide all of the key properties of accuracy, efficiency, and granularity (Hejazi et al., 2015). In this paper, we present a neural network approach for the spatial interpolation framework that affords an efficient way to find an effective distance function. The proposed approach is accurate, efficient, and provides an accurate granular view of the input portfolio. Our numerical experiments illustrate the superiority of the performance of the proposed neural network approach compared to the traditional spatial interpolation schemes.
    Date: 2016–06
  2. By: Matteo Richiardi (Institute for New Economic Thinking at the Oxford Martin School Nuffield College, Oxford, UK); Ross E Richardson (Institute for New Economic Thinking at the Oxford Martin School Nuffield College, Oxford, UK)
    Abstract: We introduce JAS-mine, a new Java-based computational platform that features tools to support the development of large-scale, data-driven, discrete-event simulations. JAS-mine is specifically designed for both agent-based and microsimulation modelling, anticipating a convergence between the two approaches. An embedded relational database management system provides tools for sophisticated input-output communications and data storage, allowing the power of relational databases to be used within an object-oriented framework. The JAS-mine philosophy encourages the separation of distinct concepts, objects and functionalities of the simulation model, and advocates and supports transparency, flexibility and modularity in model design. For instance, JAS-mine allows to store the list of regressors and the estimated coefficients externally to code, making it easy to change the specification of the regression models used in the simulation and achieving a complete parallelisation between the tasks of the econometricians and those of the programmers. Moreover, tools for uncertainty analysis and search over the parameter space are also built in.
    Keywords: Simulation platform, Microsimulation, Agent-based, Software, Open-source.
    Date: 2016–06–21
  3. By: Michiel van Dijk; George Philippidis; Geert Woltjer
    Abstract: As a key adjunct to the process of policy formulation, market models are often called upon to quantify possible opportunities and threats. Significant improvements in computational power, database and modelling capacity contributed to a widespread usage of computable general equilibrium (CGE) frameworks in an array of policy fields. Curiously, however, in contrast to modelling efforts in, for example, the biophysical sciences, CGE model findings are seldom subjected to any systematic validation procedure. A cursory review of the literature reveals isolated single country CGE model validation exercises, although with a dearth of available data, there is a paucity of equivalent studies which implement such a procedure in a global CGE context. This paper takes a first step in this direction by proposing a systematic methodological procedure for evaluating global CGE model performance, using a consistent macro and sectoral historical time series dataset and validation statistics taken from the biophysical literature. Focusing on sectoral output trends, the results show that model simulation performs better than extrapolation from past trends. Notwithstanding, simulation error remains high in some sectors, particularly in small economies which have undergone rapid growth. Further econometric tests reveal that simulation error is mainly caused by sector specific factors rather than country specific characteristics. The latter observation is consistent with previous research on productivity specifications in CGE models, which in concert with the validation techniques proposed in this paper, serves as a promising avenue of future research.
    JEL: C52 D58 C68
    Date: 2016–05
  4. By: Henriette Koch (Department of Management Science, Otto-von-Guericke University Magdeburg); Tino Henke (Department of Management Science, Otto-von-Guericke University Magdeburg); Gerhard Wäscher (Department of Management Science, Otto-von-Guericke University Magdeburg; School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University)
    Abstract: In this paper, a genetic algorithm for the multi-compartment vehicle routing problem with continuously flexible compartment sizes is proposed. In this problem, supplies of several product types have to be collected from customer locations and transported to a depot at minimal cost. In order to avoid mixing of different product types which are transported in the same vehicle, the vehicle’s capacity can be separated into a limited number of compartments. The size of each compartment can be selected arbitrarily within the limits of the vehicle’s capacity, and in each compartment one or several supplies of the same product type can be transported. For solving this problem, a genetic algorithm is presented. The performance of the proposed algorithm is evaluated by means of extensive numerical experiments. Furthermore, the economic benefits of using continuously flexible compartments are investigated.
    Keywords: vehicle routing, multiple compartments, genetic algorithm, heuristics
    Date: 2016–06
  5. By: Curto, José Dias; Quinaz, Pedro Miguel Mateus Dias
    Abstract: This study constitutes an exploratory analysis of the economic role of banks under different prudential frameworks. It considers an agent-based computational model populated by consumers, firms, banks and a central bank whose out-of-equilibrium interactions replicate the conjunct dynamics of a banking system, a financial market and the real economy. A calibrated version of the model is shown to provide an intelligible account of several recurrent economic phenomena and it can be a privileged ground for policy analysis. The authors' investigation provides a relevant methodological contribution to the field of banking research and sheds new light into the role of banks and their prudential regulation. Specifically, the results suggest that banks are key economic agents. Through their financial intermediation activity, credit institutions facilitate investment and promote growth.
    Keywords: agent-based computational model,financial intermediation,prudential policy,bank regulation
    JEL: C63 G28
    Date: 2016
  6. By: Tamal Datta Chaudhuri; Indranil Ghosh
    Abstract: Any discussion on exchange rate movements and forecasting should include explanatory variables from both the current account and the capital account of the balance of payments. In this paper, we include such factors to forecast the value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting political instability and lack of mechanism for enforcement of contracts that can affect both direct foreign investment and also portfolio investment, have been incorporated. The explanatory variables chosen are the 3 month Rupee Dollar futures exchange rate (FX4), NIFTY returns (NIFTYR), Dow Jones Industrial Average returns (DJIAR), Hang Seng returns (HSR), DAX returns (DR), crude oil price (COP), CBOE VIX (CV) and India VIX (IV). To forecast the exchange rate, we have used two different classes of frameworks namely, Artificial Neural Network (ANN) based models and Time Series Econometric models. Multilayer Feed Forward Neural Network (MLFFNN) and Nonlinear Autoregressive models with Exogenous Input (NARX) Neural Network are the approaches that we have used as ANN models. Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) techniques are the ones that we have used as Time Series Econometric methods. Within our framework, our results indicate that, although the two different approaches are quite efficient in forecasting the exchange rate, MLFNN and NARX are the most efficient.
    Date: 2016–07
  7. By: Hendrik Strobelt; Sebastian Gehrmann; Bernd Huber; Pfister, Hanspeter; Alexander M. Rush
    Abstract: Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understanding these models have studied the changes in hidden state representations over time and noticed some interpretable patterns but also significant noise. In this work, we present LSTMVis a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics. The tool allows a user to select a hypothesis input range to focus on local state changes, to match these states changes to similar patterns in a large data set, and to align these results with domain specific structural annotations. We further show several use cases of the tool for analyzing specific hidden state properties on datasets containing nesting, phrase structure, and chord progressions, and demonstrate how the tool can be used to isolate patterns for further statistical analysis.
    Date: 2016–01
  8. By: Alessio Emanuele Biondo; Alessandro Pluchino; Andrea Rapisarda
    Abstract: We introduce a new Self-Organized Criticality (SOC) model for simulating price evolution in an artificial financial market, based on a multilayer network of traders. The model also implements, in a quite realistic way with respect to previous studies, the order book dy- namics, by considering two assets with variable fundamental prices. Fat tails in the probability distributions of normalized returns are observed, together with other features of real financial markets.
    Date: 2016–06

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