nep-cmp New Economics Papers
on Computational Economics
Issue of 2017‒05‒28
seven papers chosen by

  1. Evaluation of the Effects of Integration of Eurasian Economic Union By Sedalishchev, Vladimir; Chokaev, Bekhan; Knobel, Alexander
  2. Innovation and The Precautionary Principle By Caroline Orset
  3. Using Macroeconomic Forecasts to Improve Mean Reverting Trading Strategies By Yash Sharma
  4. CDS Rate Construction Methods by Machine Learning Techniques By Brummelhuis, Raymond; Luo, Zhongmin
  5. A small scale forecasting and simulation model for Azerbaijan (FORSAZ) By Salman Huseynov; Fuad Mammadov
  6. Uncertainty, Learning, and Local Opposition to Hydraulic Fracturing By Hess, Joshua; Manning, Dale; Iverson, Terry; Cutler, Harvey
  7. Conduct Risk - distribution models with very thin Tails By Peter Mitic

  1. By: Sedalishchev, Vladimir (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Chokaev, Bekhan (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Knobel, Alexander (Russian Presidential Academy of National Economy and Public Administration (RANEPA))
    Abstract: The work is devoted to assessment of the economic consequences of the integration policy on removing non-tariff barriers declared in the agreement on the EAEU using the methodology of numerical quantification using the computable general equilibrium (CGE). The general equilibrium model and the complex database for its estimation are constructed. A quantitative assessment of the impact of various scenarios of the EAEU integration on the economies of Russia, Belarus, Kazakhstan and Armenia is given.
    Keywords: ÅÀÝÑ, íåòàðèôíûå áàðüåðû, ýôôåêòû èíòåãðàöèè, ìîäåëü îáùåãî ðàâíîâåñèÿ
    Date: 2017–05
  2. By: Caroline Orset (ECO-PUB - Economie Publique - INRA - Institut National de la Recherche Agronomique - AgroParisTech)
    Abstract: Recent environmental policies favour the polluter pays principle. This principle points out the pollutant financial liability for the eventual incidents induced by his activities. In this context, we analyse the decision of an agent to invest in new industrial activities, the consequences of which on human health and the environment are initially unknown. It is not possible for him to delay investing, but the agent has the opportunity to acquire information and to reduce the cost of an accident. This allows the agent to reduce uncertainty regarding dangers associated with the project and to limit potential damages that it might cause. However, the agent's chosen level of these actions may be considered as insufficient and not acceptable by Society as response in the face of a possible danger. Precautionary state regulation may then be introduced. We get that this regulation may slow down innovation and may favour innovation in countries with less safety requirements. We find that agent may get around the goal of the regulation by ignoring the information on the dangerousness of its project. We then propose some policy tools which stimulate innovation and impose a certain level of risk considered as acceptable for Society to the agent. Finally, we use a numerical analysis based on the Monsanto Company for studying the agent's behaviour with different regulatory frameworks.
    Keywords: uncertainty,the precautionary principle,environment, information acquisition,irreversible investment
    Date: 2017–04–03
  3. By: Yash Sharma
    Abstract: A large class of trading strategies focus on opportunities offered by the yield curve. In particular, a set of yield curve trading strategies are based on the view that the yield curve mean-reverts. Based on these strategies' positive performance, a multiple pairs trading strategy on major currency pairs was implemented. To improve the algorithm's performance, machine learning forecasts of a series of pertinent macroeconomic variables were factored in, by optimizing the weights of the trading signals. This resulted in a clear improvement in the APR over the evaluation period, demonstrating that macroeconomic indicators, not only technical indicators, should be considered in trading strategies.
    Date: 2017–05
  4. By: Brummelhuis, Raymond; Luo, Zhongmin
    Abstract: Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.
    Keywords: Machine Learning; Counterparty Credit Risk; CDS Proxy Construction; Classification.
    JEL: B23 C1 C38 C4 C45 C58 C6
    Date: 2017–05–12
  5. By: Salman Huseynov (Central Bank of Azerbaijan Republic); Fuad Mammadov (Central Bank of Azerbaijan Republic)
    Abstract: In our study, we model both steady state and short-run dynamics of the important aspects of the national economy using quarterly data for the period 1999Q1-2016Q2. We explicitly model government, money market and external sector, but omit household sector, labor market, wage dynamics and volume of the physical capital specifications due to serious data quality issues. Using Fully Modified OLS (FMOLS) co-integration methodology we explore co-integration relations among the variables. Coefficient estimates of short-run dynamics are in compliance with our ex-ante expectations. Stability tests indicate that the system seems to exhibit stability around its steady state values and model variables converges to their steady state values approximately within 140 periods (2016Q3-2050Q4). Impulse-response analysis also show stable convergence of the model and predict economically consistent results. The results of in-sample and out-of-sample simulation exercises for the inflation, the government consumption and the imports are satisfactory. However, it seems that the model cannot adequately capture ex-post dynamics of NFA and reserve money. In general the results indicate that model can be used for the specific policy analysis and forecasting of main macroeconomic variables of Azerbaijan.
    Keywords: general equilibrium; co-integration analysis; forecast evaluation
    JEL: C32 C51 C52 E17
    Date: 2016–11–30
  6. By: Hess, Joshua; Manning, Dale; Iverson, Terry; Cutler, Harvey
    Abstract: The development of oil and gas extraction technologies, including hydraulic fracturing (fracking), has increased fossil fuel reserves in the US. Despite benefits, uncertainty over environmental damages has led to fracking bans, both permanent and temporary, in many jurisdictions. We develop a stochastic dynamic learning model parameterized with a computable general equilibrium model to explore if uncertainty about damages, combined with the ability to learn about risks, can explain fracking bans in practice. Applying the model to a representative Colorado municipality, we quantify the quasi-option value (QOV), which creates an additional incentive to ban fracking temporarily in order to learn, though it only influences policy in a narrow range of oil and gas prices. To our knowledge, this is the first attempt to quantify an economy-wide QOV associated with a local environmental policy decision.
    Keywords: hydraulic fracturing; quasi-option value; stochastic dynamic program; computable general equilibrium model
    JEL: C61 C68 Q34 Q38 Q58
    Date: 2016–12–30
  7. By: Peter Mitic
    Abstract: Regulatory requirements dictate that financial institutions must calculate risk capital (funds that must be retained to cover future losses) at least annually. Procedures for doing this have been well-established for many years, but recent developments in the treatment of conduct risk (the risk of loss due to the relationship between a financial institution and its customers) have cast doubt on 'standard' procedures. Regulations require that operational risk losses should be aggregated by originating event. The effect is that a large number of small and medium-sized losses are aggregated into a small number of very large losses, such that a risk capital calculation produces a hugely inflated result. To solve this problem, a novel distribution based on a one-parameter probability density with an exponential of a fourth power is proposed, where the parameter is to be estimated. Symbolic computation is used to derive the necessary analytical expressions with which to formulate the problem, and is followed by numeric calculations in R. Goodness-of-fit and parameter estimation are both determined by using a novel method developed specifically for use with probability distribution functions. The results compare favourably with an existing model that used a LogGamma Mixture density, for which it was necessary to limit the frequency and severity of the losses. No such limits were needed using the proposed exponential density.
    Date: 2017–05

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.