nep-cmp New Economics Papers
on Computational Economics
Issue of 2017‒06‒11
nine papers chosen by
Stan Miles
Thompson Rivers University

  1. Deep Learning Bank Distress from News and Numerical Financial Data By Paola Cerchiello; Giancarlo Nicola; Samuel Rönnqvist; Peter Sarlin
  2. Bidirectional Labeling in Column-Generation Algorithms for Pickup-and-Delivery Problems By Timo Gschwind; Stefan Irnich; Ann-Kathrin Rothenbaecher; Christian Tilk
  3. Stabilizing an Unstable Complex Economy By Isabelle Salle; Pascal Seppecher
  4. Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling By Yaxiong Zeng; Diego Klabjan
  5. Should Central Banks Worry About Nonlinearities of their Large-Scale Macroeconomic Models? By Vadym Lepetyuk, Lilia Maliar, Serguei Maliar
  6. Fast calibration of the Libor Market Model with Stochastic Volatility and Displaced Diffusion By Laurent Devineau; Pierre-Edouard Arrouy; Paul Bonnefoy; Alexandre Boumezoued
  7. Impacts of Donald Trump’s Tariff Increase against China on Global Economy: Global Trade Analysis Project (GTAP) Model By Alim Rosyadi, Saiful; Widodo, Tri
  8. Factors of the income inequality in the Baltics: income, policy, demography By Navicke, Jekaterina
  9. Modelling a Dutch Pension Fund’s Capital Requirement for Longevity Risk By Polman, Fabian M.; Krijgsman, Cees; Dajani, Karma; Hemminga, Marcus A.

  1. By: Paola Cerchiello (Department of Economics and Management, University of Pavia); Giancarlo Nicola (Department of Economics and Management, University of Pavia); Samuel Rönnqvist (Turku Centre for Computer Science - TUCS, Åbo Akademi University); Peter Sarlin (Hanken School of Economics, RiskLab Finland)
    Abstract: In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and symbolic text input onto a reduced latent semantic space. Afterwards, a second supervised neural network is trained combining news data with standard financial figures to classify banks whether in distressed or tranquil states, based on a small set of known distress events. Then the final aim is not only the improvement of the predictive performance of the classifier but also to assess the importance of news data in the classification process. Does news data really bring more useful information not contained in standard financial variables? Our results seem to confirm such hypothesis.
    Keywords: behavioural finance, financial news, deep learning, bank distress, Word2vec.
    JEL: C83 C12 E58 E61 G02 G14
    Date: 2017–05
  2. By: Timo Gschwind (Johannes Gutenberg-University Mainz, Germany); Stefan Irnich (Johannes Gutenberg-University Mainz, Germany); Ann-Kathrin Rothenbaecher (Johannes Gutenberg-University Mainz, Germany); Christian Tilk (Johannes Gutenberg-University Mainz, Germany)
    Abstract: For the exact solution of many types of vehicle-routing problems, column-generation based algorithms have become predominant. The column-generation subproblems are then variants of the shortest-path problem with resource constraints which can be solved well with dynamic-programming labeling algorithms. For vehicle-routing problems with a pickup-and-delivery structure, the strongest known dominance between two labels requires the delivery triangle inequality (DTI) for reduced costs to hold. When the direction of labeling is altered from forward labeling to backward labeling, the DTI requirement becomes the pickup triangle inequality (PTI). DTI and PTI cannot be guaranteed at the same time. The consequence seemed to be that bidirectional labeling, one of the most successful acceleration techniques developed over the last years, is not applicable with a strong dominance in both directions for problems with a pickup-and-delivery structure. Surely, relying on a weak dominance in one direction is feasible but makes the bidirectional approach less powerful. In this paper, we show that bidirectional labeling with the strongest dominance rules in forward as well as backward direction is possible and computationally bene?cial. A full-?edged branch-cut-and-price algorithm is tested on the pickup-and-delivery problem with time windows (PDPTW).
    Keywords: vehicle routing, pickup-and-delivery, shortest-path problem with resource constraints, bidirectional labeling, column generation
    Date: 2017–05–24
  3. By: Isabelle Salle (Utrecht School of Economics - Utrecht University [Utrecht]); Pascal Seppecher (CEPN - Centre d'Economie de l'Université Paris Nord - Université Paris 13 - USPC - Université Sorbonne Paris Cité - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This paper analyzes a range of alternative specifications of the interest rate policy rule within a macroeconomic, stock-flow consistent, agent-based model. In this model, firms' leverage strategies evolve under the selection pressure of market competition. The resulting process of collective adaptation generates endogenous booms and busts along credit cycles. As feedback loops on aggregate demand affect the goods and the labor markets, the real and the financial sides of the economy are closely interconnected. The baseline scenario is able to qualitatively reproduce a wide range of stylized facts, and to match quantitative orders of magnitude of the main economic indicators. We find that, despite the implementation of credit and balance sheet related prudential policies, the emerging dynamics feature strong instability. Targeting movements in the net worth of firms help dampen the credit cycles, and simultaneously reduce financial and macroeconomic volatility, but does not eliminate the occurrence of financial crises along with high costs in terms of unemployment.
    Keywords: Agent-based modeling, Credit cycles, Monetary and Macroprudential policies, Leaning against the wind
    Date: 2017–05–25
  4. By: Yaxiong Zeng; Diego Klabjan
    Abstract: In this work, we design a machine learning based method, online adaptive primal support vector regression (SVR), to model the implied volatility surface. The algorithm proposed is the first derivation and implementation of an online primal kernel SVR. It features enhancements that allow online adaptive learning by embedding the idea of local fitness and budget maintenance. To accelerate our algorithm, we implement its most computationally intensive parts in a Field Programmable Gate Arrays hardware. Using intraday tick data from the E-mini S&P 500 options market, we show that our algorithm outperforms two competing methods and the Gaussian kernel is a better choice than the linear kernel. Sensitivity analysis is also presented to demonstrate how hyper parameters affect the error rates and the number of support vectors in our models.
    Date: 2017–06
  5. By: Vadym Lepetyuk, Lilia Maliar, Serguei Maliar
    Abstract: How wrong could policymakers be when using linearized solutions to their macroeconomic models instead of nonlinear global solutions? This question became of much practical interest during the Great Recession and the recent zero lower bound crisis. We assess the importance of nonlinearities in a scaled-down version of the Terms of Trade Economic Model (ToTEM), the main projection and policy analysis model of the Bank of Canada. In a meticulously calibrated “baby” ToTEM model with 21 state variables, we find that local and global solutions have similar qualitative implications in the context of the recent episode of the effective lower bound on nominal interest rates in Canada. We conclude that the Bank of Canada’s analysis would not improve significantly by using global nonlinear methods instead of a simple linearization method augmented to include occasionally binding constraints. However, we also find that even minor modifications in the model's assumptions, such as a variation in the closing condition, can make nonlinearities quantitatively important.
    Keywords: Business fluctuations and cycles, Econometric and statistical methods, Economic models
    JEL: C61 C63 C68 E31 E52
    Date: 2017
  6. By: Laurent Devineau; Pierre-Edouard Arrouy; Paul Bonnefoy; Alexandre Boumezoued
    Abstract: This paper demonstrates the efficiency of using Edgeworth and Gram-Charlier expansions in the calibration of the Libor Market Model with Stochastic Volatility and Displaced Diffusion (DD-SV-LMM). Our approach brings together two research areas; first, the results regarding the SV-LMM since the work of Wu and Zhang (2006), especially on the moment generating function, and second the approximation of density distributions based on Edgeworth or Gram-Charlier expansions. By exploring the analytical tractability of moments up to fourth order, we are able to perform an adjustment of the reference Bachelier model with normal volatilities for skewness and kurtosis, and as a by-product to derive a smile formula relating the volatility to the moneyness with interpretable parameters. As a main conclusion, our numerical results show a 98% reduction in computational time for the DD-SV-LMM calibration process compared to the classical numerical integration method developed by Heston (1993).
    Date: 2017–06
  7. By: Alim Rosyadi, Saiful; Widodo, Tri
    Abstract: This paper aims to analyze the possible impacts of the US import tariff against China on global economy. The GTAP model is implemented. The simulation scenarios depicted short-run effects of full-protection and manufacturing protection with appropriate retaliation response from China. On global level, the policy was projected to lead to decline in GDP, terms-of-trade, and welfare; and increase in trade balance for United States and China. Trade diversion phenomena would occur, predicting steep decline in bilateral trade between the two countries and increasing export towards their third trading partners.
    Keywords: Donald Trump, Tariff Increase, GTAP Model
    JEL: F13 F17
    Date: 2017–05–29
  8. By: Navicke, Jekaterina
    Abstract: This paper aims to disentangle the driving factors behind the changes in income inequality in the Baltics since the EU accession, distinguishing between primary income effect, discrete changes in tax-benefit policies and demographic effect. Evaluation of the three effects was based on counterfactual scenarios, which were constructed using taxbenefit microsimulation and re-weighting techniques. Decomposition of the total change in inequality showed that income and policy effects are dominant in the Baltics. Policy effects were inequality reducing before the crisis and for the period after the EU accession as a whole. Income effects were inequality increasing before the crisis and as a whole. Despite rapid demographic changes in the Baltics, the demographic effects on income inequality were marginal and in general inequality-increasing.
    Date: 2017–05–31
  9. By: Polman, Fabian M.; Krijgsman, Cees; Dajani, Karma; Hemminga, Marcus A.
    Abstract: Longevity risk is the risk arising from uncertainty in the prediction of future mortality. This risk must be faced by pension funds. The legislation for Dutch pension funds prescribes that the pension funds need to keep in reserve a certain level of capital for this risk. De Nederlandsche Bank (DNB), the regulator of the legislation, suggests a method for calculating this capital requirement. In this paper an alternative method is developed, that provides a better insight in the current risk. Moreover, it turns out that the resulting capital requirement from our method is less than half of the capital requirement calculated using the method suggested by DNB.
    Keywords: Longevity risk, capital requirement for longevity risk, Dutch pension fund, stochastic mortality, Monte Carlo simulations
    JEL: C15 C51 C53 G23 H55 J11
    Date: 2017–05–04

This nep-cmp issue is ©2017 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.