Monte Carlo method for pricing complex financial derivatives: An innovative approach to the control of convergence

Bendato, Ilaria; Giribone, Pietro; Mosca, Roberto

doi:10.12988/ams.2015.58552

Cited by 11 publications

(8 citation statements)

References 4 publications

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“…For this reason, the Authors are starting to test the algorithm in the context of simulation models, in particular application of Discrete Event Simulation (DES) and Monte Carlo Simulation [4][5][6].…”

Section: Resultsmentioning

confidence: 99%

Attraction force optimization (AFO): a deterministic nature-inspired heuristic for solving optimization problems in stochastic simulation

Bendato¹,

Lucia²,

Giribone³

et al. 2016

ams

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The paper presents a new optimization heuristic called AFO-Attraction Force Optimization, able to maximize discontinuous, non-differentiable and highly nonlinear functions in discrete simulation problems. The algorithm was developed specifically to overcome the limitations of traditional search algorithms in optimization problems performed on discreteevent simulation models used, for example, to study industrial systems and processes. Such applications are characterized by three particular aspects: the response surfaces of the objective function is not known to the experimenter, a few number of independent variables are involved, very high computational time for each single simulation experiment. In this context it is therefore essential to use an optimization algorithm that on one hand tries to explore as effectively as possible the entire domain of investigation but, in the same time, does not require an excessive number of experiments. The article, after a quick overview of the most known optimization techniques, explains the properties of AFO, its strengths and limitations compared to other search algorithms. The operating principle of the heuristic, inspired by the laws of attraction occurring in nature, is discussed in detail in the case of 1, 2 and Ndimensional functions from a theoretical and applicative point of view. The algorithm was then validated using the most common 2-dimensional and N-dimensio-990 I. Bendato et al. nal benchmark functions. The results are absolutely positive if compared, for the same initial conditions, with the traditional methods up to 10-dimensional vector spaces. A higher number of independent variables is generally not of interest for discrete simulation optimization problems in industrial applications (our research field).

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Section: Resultsmentioning

confidence: 99%

Attraction force optimization (AFO): a deterministic nature-inspired heuristic for solving optimization problems in stochastic simulation

Bendato¹,

Lucia²,

Giribone³

et al. 2016

ams

View full text Add to dashboard Cite

show abstract

“…By implementing the values from the central tests for the factorial project in Business Plan I, the MSPE [15] average and variance curves were determined ( Figure 6). They allowed us to define, based on their adjustment, a number of replications equal to 300 as the right scope of the experimental sample.…”

Section: Case Study: Presentation and Results Discussionmentioning

confidence: 99%

A generalized method to stochastically assess investments in photovoltaic plants

Revetria¹,

Guizzi²,

Giribone³

et al. 2015

Software Engineering and Applications/ 831: Advances in Power and Energy Systems

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In this paper, the Authors present the results of their research conducted to stochastically define a method to assess investments in electric production plants using renewable energy with particular reference to photovoltaic plants. The method is broken down into six sequential steps where particularly suitable instruments are used for stochastic applications such as: the Monte Carlo method, the Design of Experiments and the Response Surface Methodology. It is therefore possible to obtain regression meta-models as output that allow us to highlight the cause and effect relationship between some input variables and the main assessment parameters of an investment. The method's declared objective is to allow possible investors to use a reliable assessment instrument and to provide clearly interpretable quantitative answers.

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“…The production profiles are therefore determined, taking into account the technological features of the RES installed. Using the Monte Carlo method [8,9,10,11,12], it is then possible to obtain the PDF for the total production profile. At this point, as both the total hourly load profile and the RES power production profile are available, the hourly lack of power can be calculated (again as PDF).…”

Section: The Methodologymentioning

confidence: 99%

A model for integrating electric power supply with renewable energy sources in manufacturing companies

Revetria

Guizzi²,

Giribone

et al. 2015

Software Engineering and Applications/ 831: Advances in Power and Energy Systems

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In this paper, the authors, consistent with the philosophy of Sustainable Manufacturing, propose a generalised methodology for replacing/supplementing traditional energy sources with renewable energy sources. The methodology, developed stochastically, makes extensive use of Discrete Event Simulation and the Monte Carlo method to optimize, for each hour of operation, the ratio of production from Renewable Energy Sources to supply from traditional sources. The final output is summarised as a Cumulative Distribution Function of the hourly lack of power produced by Renewable Energy Sources compared to system demands. In this way, the manager can, sufficiently in advance, select the most appropriate procurement strategy.

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Monte Carlo method for pricing complex financial derivatives: An innovative approach to the control of convergence

Cited by 11 publications

References 4 publications

Attraction force optimization (AFO): a deterministic nature-inspired heuristic for solving optimization problems in stochastic simulation

Attraction force optimization (AFO): a deterministic nature-inspired heuristic for solving optimization problems in stochastic simulation

A generalized method to stochastically assess investments in photovoltaic plants

A model for integrating electric power supply with renewable energy sources in manufacturing companies

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