scholarly journals Estimating Brasilia Rain Attenuation at THz Frequencies from Historical Data Based in Monte Carlo Simulation and Unscented Transform

2021 ◽  
Author(s):  
Lucas Vasconcelos de Morais ◽  
Leonardo Menezes ◽  
Pedro Moraes
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Historical Data ◽  
Rain Attenuation ◽  
Unscented Transform

scholarly journals Analisis Risiko Finansial Dengan Metode Simulasi Monte Carlo (Studi Kasus: Pt. Phase Delta Control)

2016 ◽  
Vol 8 (1) ◽  
pp. 62
Author(s):  
Atikah Aghdhi Pratiwi ◽  
Rosa Rilantiana
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Historical Data ◽  
Uncertain Variable ◽  
Random Numbers ◽  
Selling Price ◽  
Sales Volume ◽  
Simulation Research ◽  
Cost Of Production ◽  
A Company

AbstractBasically, the purpose of a company is make a profit and enrich the owners of the company. This is manifested by development and achievement of good performance, both in financial and operational perspective. But in reality, not all of companies can achieve good performance. One of them is because exposure of risk. This could threaten achievement of the objectives and existence of the company. Therefore, companies need to have an idea related to possible condition and financial projection in future periods that are affected by risk. One of the possible method is Monte Carlo Simulation. Research will be conducted at PT. Phase Delta Control with historical data related to production/sales volume, cost of production and selling price. Historical data will be used as Monte Carlo Simulation with random numbers that describe probability of each risk variables describing reality. The main result is estimated profitability of PT. Phase Delta Control in given period. Profit estimation will be uncertain variable due to some uncertainty

scholarly journals Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 325
Author(s):  
Emad Mohamed ◽  
Parinaz Jafari ◽  
Simaan AbouRizk
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Wind Farm ◽  
Historical Data ◽  
Expert Knowledge ◽  
Bivariate Distribution ◽  
Expert Elicitation ◽  
Illustrative Case ◽  
Input Modeling

Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).

scholarly journals SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

2017 ◽  
Vol 7 (4) ◽  
pp. 20-23
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Supply Chain ◽  
Cost Estimation ◽  
Historical Data ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Chain Management

In this paper, the conceptual model of risk-based cost estimation for completing tasks within supply chain is presented. This model is a hybrid. Its main unit is based on Monte Carlo Simulation (MCS). Due to the fact that the important and difficult to evaluate input information is vector of risk-occur probabilities the use of artificial intelligence method was proposed. The model assumes the use of fuzzy logic or artificial neural networks – depending on the availability of historical data. The presented model could provide support to managers in making valuation decisions regarding various tasks in supply chain management.

scholarly journals Monte Carlo Simulation Approach to Determine the Optimal Solution of Probabilistic Supply Cost

2020 ◽  
Vol 2 (1) ◽  
pp. 1-6
Author(s):  
Helmi Ramadan ◽  
Prana Ugiana Gio ◽  
Elly Rosmaini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Historical Data ◽  
Optimal Solution ◽  
Economic Order Quantity ◽  
Simulation Approach ◽  
Order Quantity ◽  
Probabilistic Simulation ◽  
The Future

Monte Carlo simulation is a probabilistic simulation where the solution of problem is given based on random process. The random process involves a probabilitydistribution from data variable collected based on historical data. The used model is probabilistic Economic Order Quantity Model (EOQ). This model then assumed use Monte Carlo simulation, so that obtained the total of optimal supply cost in the future. Based on data processing, the result of probabilistic EOQ is $486128,19. After simulation using Monte Carlo simulation where the demand data follows normal distribution and it is obtained the total of supply cost is $46116,05 in 23 months later. Whereas the demand data uses Weibull distribution is obtained the total of supply stock is $482301,76. So that, Monte Carlo simulation can calculate the total of optimal supply in the future based on historical demand data.

scholarly journals PERHITUNGAN VaR PORTOFOLIO SAHAM MENGGUNAKAN DATA HISTORIS DAN DATA SIMULASI MONTE CARLO

2012 ◽  
Author(s):  
WAYAN ARTHINI ◽  
KOMANG DHARMAWAN ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Stock Returns ◽  
Value At Risk ◽  
Time Horizon ◽  
Historical Data ◽  
Portfolio Risk ◽  
Simulation Data ◽  
Risk And Return

Value at Risk (VaR) is the maximum potential loss on a portfolio based on the probability at a certain time.  In this research, portfolio VaR values calculated from historical data and Monte Carlo simulation data. Historical data is processed so as to obtain stock returns, variance, correlation coefficient, and variance-covariance matrix, then the method of Markowitz sought proportion of each stock fund, and portfolio risk and return portfolio. The data was then simulated by Monte Carlo simulation, Exact Monte Carlo Simulation and Expected Monte Carlo Simulation. Exact Monte Carlo simulation have same returns and standard deviation  with historical data, while the Expected Monte Carlo Simulation satistic calculation similar to historical data. The results of this research is the portfolio VaR  with time horizon T=1, T=10, T=22 and the confidence level of 95 %, values obtained VaR between historical data and Monte Carlo simulation data with the method exact and expected. Value of VaR from both Monte Carlo simulation is greater than VaR historical data.

Mathematical Modeling for Contingency Determination for Nuclear Power Plant Construction Projects

2014 ◽  
Author(s):  
Wei Zhao ◽  
Shenjun Xu
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Construction Projects ◽  
Nuclear Power ◽  
Historical Data ◽  
Plant Construction ◽  
Lump Sum

This paper uses the China AP1000 project as an example to exhibit the application of quantitative risk management in nuclear power plant construction projects. For those lump sum contracts, one of the most significant purposes of quantitative risk management is to determine the contingency, i.e. the reserved money and time for projects. This paper studies the application of Monte Carlo simulation in determining the contingency, taking into account the distinctive features of nuclear power projects. Most nuclear power projects, especially advanced ones such as Generation III and above, meet one common obstacle in estimating key economic indicators — the absence of historical data due to its avant-garde design. As cost estimators of the coal power plant contractors may collect their data from thousands of previous cases, nuclear power plant contractors, especially in many developing countries, do not have a shared database of financial data. Some first-of-a-kind nuclear power plants have absolutely no historical data to look up. This paper aims to provide a resolution to this problem. First, the feasibility and representativeness of different probability distributions are compared based on their respective skewness and kurtosis to determine the best-suited distribution in nuclear power projects. This paper also analyzes the use of second-order Monte Carlo simulation in reducing the error caused by the biased estimation of inexperienced risk assessment engineers.

scholarly journals Monte Carlo Simulation of the Moments of a Copula-Dependent Risk Process with Weibull Interwaiting Time

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 109
Author(s):  
Sharifah Farah Syed Yusoff Alhabshi ◽  
Zamira Hasanah Zamzuri ◽  
Siti Norafidah Mohd Ramli
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Counting Process ◽  
Historical Data ◽  
Risk Process ◽  
Capital Requirements ◽  
Dependence Structure ◽  
Insurance Risk ◽  
Poisson Count

The widely used Poisson count process in insurance claims modeling is no longer valid if the claims occurrences exhibit dispersion. In this paper, we consider the aggregate discounted claims of an insurance risk portfolio under Weibull counting process to allow for dispersed datasets. A copula is used to define the dependence structure between the interwaiting time and its subsequent claims amount. We use a Monte Carlo simulation to compute the higher-order moments of the risk portfolio, the premiums and the value-at-risk based on the New Zealand catastrophe historical data. The simulation outcomes under the negative dependence parameter θ, shows the highest value of moments when claims experience exhibit overdispersion. Conversely, the underdispersed scenario yields the highest value of moments when θ is positive. These results lead to higher premiums being charged and more capital requirements to be set aside to cope with unfavorable events borne by the insurers.

A Comparative Study of Using Risk Adjusted Discount Rate and Historical-Based Monte Carlo Simulation to Evaluate Risk/Uncertainty in Oil and Gas Investment

2021 ◽  
Author(s):  
D. I. Lidyanto
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Discount Rate ◽  
Net Present Value ◽  
Oil And Gas ◽  
Historical Data ◽  
Capital Expenditure ◽  
Simulation Method ◽  
Development Project ◽  
Risk Uncertainty

This paper presents a comparative analysis of the use of two methods, Risk Adjusted Discount Rate (RADR) and Monte Carlo Simulation, in evaluating the risks and uncertainties in an oil and gas investment proposal. Basically, RADR method is the same as the usual discounted cash flow. But the discount rate already considers any risk/uncertainty that a project will face. Thus, some percentage, based on trusted publisher, will be added to the discount rate. While using monte carlo simulation, an economic model, with base discount rate, will be evaluated by creating hundreds of possible iterations that continually change the major economic assumption based on historical data such as production, capital expenditure, operating expenditure, oil and gas price. The purpose of this paper is to compare the use of two methods, RADR and Historical-Based Monte Carlo Simulation in evaluating risk/uncertainty in oil and gas investment proposal. There are four real oil and gas projects which will be evaluated: Project 1 (Gas Development Project), Project 2 (Shallow Water Development Project), Project 3 (Offshore Development Project), and Project 4 (EOR Development Project). The Net Present Value (NPV) of each project with those two methods will be evaluated and analyzed. The comparison study shows that NPV Calculation with Historical-Based Monte Carlo Simulation tend to have higher NPV. This is important to maintain the level of project attractiveness. Historical Based Monte Carlo Simulation method also shows the real risks and uncertainties because it is based on the historical data. Besides, this method gives real picture of what the project might face in the future instead of allowing static variables to be introduced into potential dynamic model. However, to make Historical-Based Monte Carlo Simulation robust, complete historical database is needed. While, Risk Adjusted Discount Rate method can simply be used by trusted publication.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

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