nep-cmp New Economics Papers
on Computational Economics
Issue of 2017‒02‒12
ten papers chosen by

  1. An Asynchronous Distributed Algorithm for solving Stochastic Unit Commitment By ARAVENA, Ignacio; PAPAVASILIOU, Anthony
  2. Computational Aspects of Assigning Agents to a Line By AZIZ, Haris; HOUGAARD, Jens Leth; MORENO-TERNERO, Juan D.; OSTERDAL, Lars Peter
  3. Computing stable numerical solutions for multidimensional American option pricing problems: a semi-discretization approach By Rafael Company; Vera Egorova; Lucas J\'odar; Fazlollah Soleymani
  4. A Primer on Portfolio Choice with Small Transaction Costs By Johannes Muhle-Karbe; Max Reppen; Halil Mete Soner
  5. “Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting" By Oscar Claveria; Enric Monte; Salvador Torra
  6. Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network By Vinci Chow
  7. Inequality, Redistributive Policies and Multiplier Dynamics in an Agent-Based Model with Credit Rationing By Elisa Palagi; Mauro Napoletano; Andrea Roventini; Jean-Luc Gaffard
  8. Valuing American Options Using Fast Recursive Projections By Antonio Cosma; Stefano Galluccio; Paola Pederzoli; O. Scaillet
  9. Computing the aggregate loss distribution based on numerical inversion of the compound empirical characteristic function of frequency and severity By Viktor Witkovsky; Gejza Wimmer; Tomas Duby
  10. Data Analytics for Non-Life Insurance Pricing By Mario V. Wuthrich; Christoph Buser

  1. By: ARAVENA, Ignacio (Université catholique de Louvain, CORE, Belgium); PAPAVASILIOU, Anthony (Université catholique de Louvain, CORE, Belgium)
    Abstract: We present an asynchronous algorithm for solving the stochastic unit commitment (SUC) problem using scenario decomposition. The algorithm is motivated by the scale of problem and significant di erences in run times observed among scenario subproblems, which can result in inetic subgradient methods. The algorithm recovers candidate primal solutions from the solutions of scenario subproblems using recombination heuristics. The asynchronous algorithm is implemented in a high performance computing cluster and we conduct numerical experiments for two-stage SUC instances of the Western Electricity Coordinating Council (WECC) system and of the Central Western European (CWE) system. The WECC system that we study consist of 130 thermal generators, 182 nodes and 319 lines with hourly resolution and up to 1000 scenarios, while the CWE system consist of 656 thermal generators, 679 nodes and 1073 lines, with quarterly resolution and up to 120 scenarios. When using 10 nodes of the cluster per instance, the algorithm provides solutions that are within 2% of optimality to all problems within 47 minutes for WECC and 3 hours, 54 minutes for CWE. Moreover, we find that an equivalent synchronous parallel subgradient algorithm would leave processors idle up to 84% of the time, an observation which underscores the need for designing asynchronous optimization schemes in order to fully exploit distributed computing on real world applications.
    Keywords: Asynchronous algorithm, coordinate descent method, high performance computing, stochastic programming, unit commitment
    Date: 2016–11–18
  2. By: AZIZ, Haris (University of New South Wales); HOUGAARD, Jens Leth (University of Copenhagen); MORENO-TERNERO, Juan D. (Université catholique de Louvain, CORE, Belgium); OSTERDAL, Lars Peter (Copenhagen Business School)
    Abstract: We consider the problem of assigning agents to slots on a line, where only one agent can be served at a slot and each agent prefers to be served as close as possible to his target. We introduce a general approach to compute aggregate gap-minimizing assignments, as well as gap-egalitarian assignments. The approach relies on an algorithm which is shown to be faster than general purpose algorithms for the assignment problem. We also extend the approach to probabilistic assignments and explore the computational features of existing, as well as new, methods for this setting.
    Keywords: Random assignment, congested facility, aggregate gap minimization, gap-egalitarian assignments, computational speed
    JEL: C78 D61 D63
    Date: 2016–12–10
  3. By: Rafael Company; Vera Egorova; Lucas J\'odar; Fazlollah Soleymani
    Abstract: The matter of the stability for multi-asset American option pricing problems is a present remaining challenge. In this paper a general transformation of variables allows to remove cross derivative terms reducing the stencil of the proposed numerical scheme and underlying computational cost. Solution of a such problem is constructed by starting with a semi-discretization approach followed by a full discretization using exponential time differencing and matrix quadrature rules. To the best of our knowledge the stability of the numerical solution is treated in this paper for the first time. Analysis of the time variation of the numerical solution with respect to previous time level together with the use of logarithmic norm of matrices are the basis of the stability result. Sufficient stability conditions on step sizes, that also guarantee positivity and boundedness of the solution, are found. Numerical examples for two and three asset problems justify the stability conditions and prove its competitiveness with other relevant methods.
    Date: 2017–01
  4. By: Johannes Muhle-Karbe (University of Michigan); Max Reppen (ETH Zurich); Halil Mete Soner (ETH Zurich and Swiss Finance Institute)
    Abstract: This survey is an introduction to asymptotic methods for portfolio-choice problems with small transaction costs. We outline how to derive the corresponding dynamic programming equations and simplify them in the small-cost limit. This allows to obtain explicit solutions in a wide range of settings, which we illustrate for a model with mean-reverting expected returns and proportional transaction costs. For even more complex models, we present a policy iteration scheme that allows to compute the solution numerically.
    JEL: G11 C61
  5. By: Oscar Claveria (AQR Research Group-IREA. University of Barcelona. Av.Diagonal 696; 08034 Barcelona, Spain.); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).); Salvador Torra (Riskcenter-IREA, University of Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain.)
    Abstract: This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions simultaneously. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for concatenation.
    Keywords: Regional forecasting, tourism demand, multiple-input multiple-output (MIMO), Gaussian process regression, neural networks, machine learning. JEL classification:
    Date: 2017–01
  6. By: Vinci Chow
    Abstract: In Chinese societies where superstition is of paramount importance, vehicle license plates with desirable numbers can fetch for very high prices in auctions. Unlike auctions of other valuable items, however, license plates do not get an estimated price before auction. In this paper, I construct a deep recurrent neural network to predict the prices of vehicle license plates in Hong Kong based on the characters on a plate. Trained with 13-years of historical auction prices, the deep RNN outperforms previous models by significant margin.
    Date: 2017–01
  7. By: Elisa Palagi; Mauro Napoletano; Andrea Roventini; Jean-Luc Gaffard
    Abstract: We build an agent-based model populated by households with heterogenous and time-varying financial conditions in order to study how different inequality shocks affect income dynamics and the effects of different types of fiscal policy responses. We show that inequality shocks generate persistent falls in aggregate income by increasing the fraction of credit-constrained households and by lowering aggregate consumption. Furthermore, we experiment with different types of fiscal policies to counter the effects of inequality-generated recessions, namely deficit-spending direct government consumption and redistributive subsidies financed by different types of taxes. We find that subsidies are in general associated with higher fiscal multipliers than direct government expenditure, as they appear to be better suited to sustain consumption of lower income households after the shock. In addition, we show that the effectiveness of redistributive subsidies increases if they are financed by taxing financial incomes or savings. Downloads
    Keywords: income inequality, scal multipliers, redistributive policies, credit-rationing, agent-based models
    Date: 2017–06–02
  8. By: Antonio Cosma (Université du Luxembourg); Stefano Galluccio (BNP Paribas Fixed Income); Paola Pederzoli (University of Geneva); O. Scaillet (University of Geneva GSEM and GFRI and Swiss Finance Institute)
    Abstract: We introduce a fast and widely applicable numerical pricing method that uses recursive projections. We characterize its convergence speed. We find that the early exercise boundary of an American call option on a discrete dividend paying stock is higher under the Merton and Heston models than under the Black-Scholes model, as opposed to the continuous dividend case. A large database of call options on stocks with quarterly dividends shows that adding stochastic volatility and jumps to the Black-Scholes benchmark reduces the amount foregone by call holders failing to optimally exercise by 25\%. Transaction fees cannot fully explain the suboptimal behavior.
  9. By: Viktor Witkovsky; Gejza Wimmer; Tomas Duby
    Abstract: A non-parametric method for evaluation of the aggregate loss distribution (ALD) by combining and numerically inverting the empirical characteristic functions (CFs) is presented and illustrated. This approach to evaluate ALD is based on purely non-parametric considerations, i.e., based on the empirical CFs of frequency and severity of the claims in the actuarial risk applications. This approach can be, however, naturally generalized to a more complex semi-parametric modeling approach, e.g., by incorporating the generalized Pareto distribution fit of the severity distribution heavy tails, and/or by considering the weighted mixture of the parametric CFs (used to model the expert knowledge) and the empirical CFs (used to incorporate the knowledge based on the historical data - internal and/or external). Here we present a simple and yet efficient method and algorithms for numerical inversion of the CF, suitable for evaluation of the ALDs and the associated measures of interest important for applications, as, e.g., the value at risk (VaR). The presented approach is based on combination of the Gil-Pelaez inversion formulae for deriving the probability distribution (PDF and CDF) from the compound (empirical) CF and the trapezoidal rule used for numerical integration. The applicability of the suggested approach is illustrated by analysis of a well know insurance dataset, the Danish fire loss data.
    Date: 2017–01
  10. By: Mario V. Wuthrich (RiskLab, ETH Zurich and Swiss Finance Institute); Christoph Buser
    Abstract: These notes aim at giving a broad skill set to the actuarial profession in non-life insurance pricing and data science. We start from the classical world of generalized linear models, generalized additive models and credibility theory. These methods form the basis of the deeper statistical understanding. We then present several machine learning techniques such as regression trees, bagging, random forest, boosting and support vector machines. Finally, we provide methodologies for analyzing telematic car driving data.
    Keywords: non-life insurance pricing, car insurance pricing, generalized linear models, generalized additive models, credibility theory, neural networks, regression trees, CART, bootstrap, bagging, random forest, boosting, support vector machines, telematic data, data science, machine learning, data analytics
    JEL: G22 G28

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