nep-cmp New Economics Papers
on Computational Economics
Issue of 2006‒09‒16
six papers chosen by
Stan Miles
York University

  1. Portfolio management implications of volatility shifts: Evidence from simulated data By Viviana Fernandez; Brian M Lucey
  2. Generalized response surface methodology : a new metaheuristic By Kleijnen,Jack P.C.
  3. The adaptive markets hypothesis: evidence from the foreign exchange market By Christopher J. Neely; Paul A. Weller; Joshua M. Ulrich
  4. The Multi-Location Transshipment Problem with Positive Replenishment Lead Times By Gong, Y.; Yucesan, E.
  5. Optimization of simulated inventory systems : optquest and alternatives By Kleijnen,Jack P.C.; Wan,Jie
  6. Using Probabilistic Analysis to Value Power Generation Investments Under Uncertainty By Fabien A. Roques; William J. Nuttall; Newbery, D.M.

  1. By: Viviana Fernandez; Brian M Lucey
    Abstract: Based on weekly data of the Dow Jones Country Titans, the CBT-municipal bond, spot and futures prices of commodities for the period 1992-2005, we analyze the implications for portfolio management of accounting for conditional heteroskedasticity and structural breaks in long-term volatility. In doing so, we first proceed to utilize the ICSS algorithm to detect volatility shifts, and incorporate that information into PGARCH models fitted to the returns series. At the next stage, we simulate returns series and compute a wavelet-based value at risk, which takes into consideration the investor’s time horizon. We repeat the same procedure for artificial data generated from distribution functions fitted to the returns by a semi-parametric procedure, which accounts for fat tails. Our estimation results show that neglecting GARCH effects and volatility shifts may lead us to overestimate financial risk at different time horizons. In addition, we conclude that investors benefit from holding commodities as their low or even negative correlation with stock indices contribute to portfolio diversification.
    Date: 2006
  2. By: Kleijnen,Jack P.C. (Tilburg University, Center for Economic Research)
    Abstract: Generalized Response Surface Methodology (GRSM) is a novel general-purpose metaheuristic based on Box and Wilson.s Response Surface Methodology (RSM). Both GRSM and RSM estimate local gradients to search for the optimal solution. These gradients use local first-order polynomials. GRSM, however, uses these gradients to estimate a better search direction than the steepest ascent direction used by RSM. Moreover, GRSM allows multiple responses, selecting one response as goal and the other responses as constrained variables. Finally, these estimated gradients may be used to test whether the estimated solution is indeed optimal. The focus of this paper is optimization of simulated systems.
    Keywords: experimental design;multivariate regression analysis;least squares; Karush-Kuhn-Tucker conditions;bootstrap
    JEL: C0 C1 C9
    Date: 2006
  3. By: Christopher J. Neely; Paul A. Weller; Joshua M. Ulrich
    Abstract: We analyze the intertemporal stability of returns to technical trading rules in the foreign exchange market by conducting true, out-of-sample tests on previously published rules. The excess returns of the 1970s and 1980s were genuine and not just the result of data mining. But these profit opportunities had disappeared by the mid-1990s for filter and moving average (MA) rules. Returns to less-studied rules, such as channel, ARIMA, genetic programming and Markov rules, also have declined, but have probably not completely disappeared. The volatility of returns makes it difficult to estimate mean returns precisely. The most likely time for a structural break in the MA and filter rule returns is the early 1990s. These regularities are consistent with the Adaptive Markets Hypothesis (Lo, 2004), but not with the Efficient Markets Hypothesis.
    Keywords: Foreign exchange market ; Foreign exchange
    Date: 2006
  4. By: Gong, Y.; Yucesan, E. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. With a single exception, research on transshipments overlooks replenishment lead times. The only approach for two-location inventory systems with non-negligible lead times could not be generalized to a multi-location setting, and the proposed heuristic method cannot guarantee to provide optimal solutions. This paper uses simulation optimization by combining an LP/network flow formulation with infinitesimal perturbation analysis to examine the multi-location transshipment problem with positive replenishment lead times, and demonstrates the computation of the optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys an elegant duality-based gradient computation method to improve computational efficiency. In test problems, our algorithm was also able to achieve better objective values than an existing algorithm.
    Keywords: Transshipment;Simulation Optimization;Infinitesimal Perturbation Analysis (IPA);
    Date: 2006–09–07
  5. By: Kleijnen,Jack P.C.; Wan,Jie (Tilburg University, Center for Economic Research)
    Abstract: This article illustrates simulation optimization through an (s, S) inventory management system. In this system, the goal function to be minimized is the expected value of specific inventory costs. Moreover, specific constraints must be satisfied for some random simulation responses, namely the service or fill rate, and for some deterministic simulation inputs, namely the constraint s < S. Results are reported for three optimization methods, including the popular OptQuest method. The optimality of the resulting solutions is tested through the so-called Karesh-Kuhn-Tucker (KKT) conditions.
    Keywords: OptQuest software;Response Surface Methodology;Karesh-Kuhn-Tucker conditions; service constrained inventory system
    JEL: C0 C1 C9 C15 C44
    Date: 2006
  6. By: Fabien A. Roques; William J. Nuttall; Newbery, D.M.
    Abstract: This paper reviews the limits of the traditional ‘levelised cost’ approach to properly take into account risks and uncertainties when valuing different power generation technologies. We introduce a probabilistic valuation model of investment in three base-load technologies (combined cycle gas turbine, coal plant, and nuclear power plant), and demonstrate using three case studies how such a probabilistic approach provides investors with a much richer analytical framework to assess power investments in liberalised markets. We successively analyse the combined impact of multiple uncertainties on the value of alternative technologies, the value of the operating flexibility of power plant managers to mothball and de-mothball plants, and the value of mixed portfolios of different production technologies that present complementary risk-return profiles.
    Keywords: investment, uncertainty, Monte-Carlo simulation, operating flexibility
    JEL: C15 D81 L94
    Date: 2006–07

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