nep-cmp New Economics Papers
on Computational Economics
Issue of 2018‒12‒10
seven papers chosen by
Stan Miles
Thompson Rivers University

  1. Lagged correlation-based deep learning for directional trend change prediction in financial time series By Ben Moews; J. Michael Herrmann; Gbenga Ibikunle
  2. Increase-Decrease Game under Imperfect Competition in Two-stage Zonal Power Markets –​ Part II: Solution Algorithm By Sarfati, Mahir; Hesamzadeh, Mohammad Reza; Holmberg, Pär
  3. Hedging and Pricing European-type, Early-Exercise and Discrete Barrier Options using Algorithm for the Convolution of Legendre Series By Tat Lung Chan
  4. Heterogeneity, distribution and financial fragility of non-financial firms: an agent-based stock-flow consistent (AB-SFC) model By Italo Pedrosa; Dany Lang
  5. Model Averaging and its Use in Economics By Steel, Mark F. J.
  6. Machine learning in algorithmic trading strategy optimization - implementation and efficiency By Przemysław Ryś; Robert Ślepaczuk
  7. Assessing Kuwaiti Energy Pricing Reforms By Manal R. SHEHABI

  1. By: Ben Moews; J. Michael Herrmann; Gbenga Ibikunle
    Abstract: Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems. We approach predictions of directional trend changes via complex lagged correlations between them, excluding any information about the target series from the respective inputs to achieve predictions purely based on such correlations with other series. We propose the use of deep neural networks that employ step-wise linear regressions with exponential smoothing in the preparatory feature engineering for this task, with regression slopes as trend strength indicators for a given time interval. We apply this method to historical stock market data from 2011 to 2016 as a use case example of lagged correlations between large numbers of time series that are heavily influenced by externally arising new information as a random factor. The results demonstrate the viability of the proposed approach, with state-of-the-art accuracies and accounting for the statistical significance of the results for additional validation, as well as important implications for modern financial economics.
    Date: 2018–11
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1811.11287&r=cmp
  2. By: Sarfati, Mahir (Research Institute of Industrial Economics (IFN)); Hesamzadeh, Mohammad Reza (Royal Institute of Technology (KTH)); Holmberg, Pär (Research Institute of Industrial Economics (IFN))
    Abstract: In part I of this paper, we proposed a Mixed-Integer Linear Program (MILP) to analyze imperfect competition of oligopoly producers in two-stage zonal power markets. In part II of this paper, we propose a solution algorithm which decomposes the proposed MILP model into several subproblems and solve them in parallel and iteratively. Our solution algorithm reduces the solution time of the MILP model and it allows us to analyze largescale examples. To tackle the multiple Subgame Perfect Nash Equilibria (SPNE) situation, we propose a SPNE-band approach. The SPNE band is split into several subintervals and the proposed solution algorithm finds a representative SPNE in each subinterval. Each subinterval is independent from each other, so this structure enables us to use parallel computing. We also design a pre-feasibility test to identify the subintervals without SPNE. Our proposed solution algorithm and our SPNE-band approach are demonstrated on the 6-node and the modified IEEE 30-node example systems. The computational tractability of our solution algorithm is illustrated for the IEEE 118-node and 300-node systems.
    Keywords: Modied Benders decomposition; Multiple Subgame Perfect Nash equilibria; Parallel computing; Wholesale electricity market; Zonal pricing
    JEL: C61 C63 C72 D43 L13 L94
    Date: 2018–11–27
    URL: http://d.repec.org/n?u=RePEc:hhs:iuiwop:1254&r=cmp
  3. By: Tat Lung Chan
    Abstract: This paper applies the $\mathcal{O}(N^2)$ algorithm for the convolution of compactly supported Legendre series (the CONLeg method), proposed by \citet{Hal_Tow:2014}, to pricing/hedging European-type, early-exercise and discrete-monitored barrier options under the L\'evy process. The current paper takes advantage of the Chebfun Matlab toolbox \citep[cf.][]{Tre:2014} in computational finance and extends the previous literature \citep[e.g.][]{Ga:2018, Pac:2018} by applying the Chebyshev series in financial modelling. The main purpose of using the CONLeg method is to formulate option pricing and option Greek curves rather than individual prices/Greek values. Moreover, the CONLeg method can retain the global spectral convergence rate in option pricing and hedging when the risk-free smooth probability density function (PDF) is smooth. When the PDF is non-smooth, we also provide a solution to allow the method to gain the accurate algebraic rate. Finally, we show that our method requires a small number of terms to yield fast error convergence and is able to accurately price/hedge any options deep in/out of the money and with very long/short maturities. Compared with existing techniques, this new method performs either favourably or comparably in numerical experiments.
    Date: 2018–11
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1811.09257&r=cmp
  4. By: Italo Pedrosa (Federal University of Rio de Janeiro); Dany Lang (Centre d'Economie de l'Université de Paris Nord (CEPN))
    Abstract: In Minsky's Financial Instability Hypothesis (FIH), financial fragility of non-financial firms tends to increase endogenously over the cycle along with the macroeconomic leverage ratio. This analysis has been criticized for two main complementary reasons: firstly, it does not duly consider the aggregate pro-cyclicallity of profits; secondly, due to an overly aggregate analysis, some inferences about the relation between aggregate leverage and systemic fragility are potentially misleading. In this paper, we take these criticisms into account by building an agent-based stock-flow consistent model which integrates the real and financial sides of the economy in a fundamentally dynamic environment. We calibrate and simulate our model and show that the dynamics generated are in line with empirical evidence both at the micro and the macro levels. We create a financial fragility index and examine how systemic financial fragility relates to the aggregate leverage along the cycle. We show that our model yields both Minskian regimes, in which the aggregate leverage increases along with investment, and Steindlian regimes, where investment brings leverage down. Our key findings are that the sensitivity of financial fragility to aggregate leverage is not as big as assumed in the literature; and that the distribution of profits amongst firms does matter for the stability of the system, both statically (immediately for financial fragility) and dynamically (because of the dynamics of leverage).
    Keywords: financial fragility; firms; leverage; cash flow; distribution
    JEL: C63 D39 E32 G01 G32 O31
    Date: 2018–11
    URL: http://d.repec.org/n?u=RePEc:upn:wpaper:2018-11&r=cmp
  5. By: Steel, Mark F. J.
    Abstract: The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas, among which growth economics, production modelling, finance and forecasting macroeconomic quantities.
    Keywords: Bayesian methods; Model uncertainty; Normal linear model; Prior specification; Robustness
    JEL: C11 C15 C20 C52 O47
    Date: 2017–09–19
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:90110&r=cmp
  6. By: Przemysław Ryś (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw)
    Abstract: The main aim of this paper was to formulate and analyze the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of a strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The methods operated on the in sample data, containing 20 years of daily prices between 1998 and 2017. The problem was presented for three sets of two assets portfolios. In the first case, a strategy was trading on the SPX and DAX index futures, in the second on the AAPL and MSFT stocks and finally, in the third case on the HGF and CBF commodities futures. The major hypothesis verified in this thesis is that machine learning methods select strategies with evaluation criterion near to the highest one, but in significantly lower execution time than the Exhaustive Search.
    Keywords: machine learning, algorithm, trading, investment, automatization, strategy, optimization, differential evolutionary method, cross-validation, overfitting
    JEL: C4 C45 C61 C15 G14 G17
    Date: 2018
    URL: http://d.repec.org/n?u=RePEc:war:wpaper:2018-25&r=cmp
  7. By: Manal R. SHEHABI (Business School, The University of Western Australia and Oxford Institute for Energy Studies)
    Abstract: From mid-2014 Kuwait has experienced a substantial drop in its petroleum export price and,consequently, government revenue, causing a severe fiscal deficit and impaired economic performance. Cutting energy subsidies has become a policy priority. In the face of widespread opposition, the government raised gasoline prices in August 2016, proclaiming such reform the key to solving economic problems; yet recent policy discussions have not addressed the mechanism of pricing reforms. The paper offers a quantification and assessment of energy pricing reform in the current low petroleum price environment via a general equilibrium model of the Kuwaiti economy that embodies the structure of its economy and its labor market, its oligopolistic industries, and external flows associated with its sovereign wealth fund. Simulations clarify the required adjustments, including the seldom discussed expatriate labor exit and the decline in oligopoly rents. While necessary, subsidy reform implies trade-offs, notably between fiscal stabilization and cost of living sustainability. The results confirm that successful implementation must be accompanied by carefully designed mitigation measures and associated microeconomic reforms.
    Keywords: petroleum; price volatility; general equilibrium; subsidy, oligopoly; sovereign wealth fund; expatriate labor; Kuwait, CGE
    JEL: C68 D43 D58 E24 E62 F41 H50 L13 L43 O53 Q43
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:uwa:wpaper:17-08&r=cmp

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