nep-cmp New Economics Papers
on Computational Economics
Issue of 2007‒04‒21
four papers chosen by
Stan Miles
Thompson Rivers University

  1. Some new test functions for global optimization and performance of repulsive particle swarm method By Mishra, Sudhanshu
  2. Forecasting crude oil and natural gas spot prices by classification methods By Viviana Fernández
  3. Copula-based measures of dependence structure in assets returns By Viviana Fernandez
  4. Oil Prices and the Russian Economy. Some Simulation Studies with NiGEM By Paavo Suni

  1. By: Mishra, Sudhanshu
    Abstract: In this paper we introduce some new test functions to assess the performance of global optimization methods. These functions have been selected partly because several of them are aesthetically appealing and partly because a few of them are really difficult to optimize, while all the functions are multi-modal. Each function has been graphically presented to appreciate its geometrical appearance. To optimize these functions we have used the Repulsive Particle Swarm (RPS) method. We have also appended a computer program of the RPS method. Except two functions, namely the 'crowned cross' and the 'cross-legged table' functions all other new test functions are optimized by the RPS program.The program has also been tested with success on a number of well-established benchmark functions. However, the program fails miserably in optimizing the Bukin and a couple of other functions.
    Keywords: Repulsive particle swarm method; Global optimization; New test functions; Bird function; Pen-holder function; Crowned cross function; Cross-legged table function; Cross function; Cross in tray function; Carrom table function; Holder table function; Test-tube holder function
    JEL: C63 C88 C61
    Date: 2006–08–23
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:2718&r=cmp
  2. By: Viviana Fernández
    Abstract: In this article, we forecast crude oil and natural gas spot prices at a daily frequency based on two classification techniques: artificial neural networks (ANN) and support vector machines (SVM). As a benchmark, we utilize an autoregressive integrated moving average (ARIMA) specification. We evaluate out-of-sample forecast based on encompassing tests and mean-squared prediction error (MSPE). We find that at short-time horizons (e.g., 2-4 days), ARIMA tends to outperform both ANN and SVM. However, at longer-time horizons (e.g., 10-20 days), we find that in general ARIMA is encompassed by these two methods, and linear combinations of ANN and SVM forecasts are more accurate than the corresponding individual forecasts. Based on MSPE calculations, we reach similar conclusions: the two classification methods under consideration outperform ARIMA at longer time horizons.
    Date: 2006
    URL: http://d.repec.org/n?u=RePEc:edj:ceauch:229&r=cmp
  3. By: Viviana Fernandez
    Abstract: Copula modeling has become an increasingly popular tool in finance to model assets returns dependency. In essence, copulas enable us to extract the dependence structure from the joint distribution function of a set of random variables and, at the same time, to separate the dependence structure from the univariate marginal behavior. In this study, based on U.S. stock data, we illustrate how tail-dependency tests may be misleading as a tool to select a copula that closely mimics the dependency structure of the data. This problem becomes more severe when the data is scaled by conditional volatility and/or filtered out for serial correlation. The discussion is complemented, under more general settings, with Monte Carlo simulations.
    Date: 2006
    URL: http://d.repec.org/n?u=RePEc:edj:ceauch:228&r=cmp
  4. By: Paavo Suni
    Abstract: Russia has greatly benefited both from exporting more energy commodities in volume terms and from the improvement of it’s terms of trade due to the rise in oil and other commodity prices in the 2000’s. To study the impacts, the counterfactual simulation for the years 2001-2006 and the “usual” oil price rise simulations for the future were made. According to the counterfactual simulations, the role of oil has been a key driver in the recent Russian economic development in the 2000’s. The average GDP growth in 2001-6 would have been around 4 per cent, around 2.5 percentage points lower than in the actual case. The effect was strongest in the last years of the period bringing the growth even below one per cent in 2006 instead of more than 6 per cent. The strong effect is due to large and rising price difference between the actual and counterfactual oil prices especially in the years 2003-6, which would have meant pronouncedly smaller oil income into the economy than actually took place. In the other simulations, the effects of the permanent 20 USD price rise to the baseline was compared. The economy reacted initially strongly to the shocks with e.g. raising GDP growth and current account strongly. The effect was, however, quickly vanishing after the rise. The temporary end of the current commodity boom would cause serious difficulties in the Russian eco-nomic development as the fuel for the engine would dry. The more robust growth would necessitate drastic changes in the economic structure from resource based economy towards more normal economic structure. Given the short and rather undeveloped Russia time series and from this reason also rather undeveloped models, the results contain large uncertainty. However, simulations provide one useful benchmark on the size of the effects of the energy price rise on the Russian economy.
    Keywords: Russian economy, simulation, oil price
    JEL: Q32 Q43 F47
    Date: 2007–04–18
    URL: http://d.repec.org/n?u=RePEc:rif:dpaper:1088&r=cmp

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