Use Of Monte-Carlo Simulation Research Articles

Investment Portfolio with Convex Optimization and Risk Adjustment Using Multi-Factor Model and Multi-Armed Bandit Algorithm

This paper examines the creation of investment portfolios through convex optimization, multifactor models, and the multi-armed bandit (MAB) algorithms, focusing on the KL-UCB strategy to optimize decisions in uncertain settings. It explores the impact of systematic risk factors using the Fama-French three-factor model, estimating the influence of market, size, and value premiums via linear regression. The use of Monte Carlo simulation is detailed for generating potential asset allocations and calculating their expected returns, volatility, and Sharpe ratios. The optimize minimize function from the SciPy library is employed to construct an efficient frontier and determine optimal asset allocation, aiming to maximize returns or minimize volatility across various risk levels. The findings suggest that the strategy of dynamic weight adjustments combined with the KL-UCB algorithm enhances portfolio returns, particularly during market volatility. The research also reveals a portfolio inclination towards large-cap growth stocks due to the negative impacts of size and value premiums. It concludes that dynamic weight adjustment strategies offer significant potential in optimizing portfolio performance in complex market conditions, though leveraging increases risk and should be carefully managed according to investor risk tolerance.

Research on the Validity of Bootstrap LM-Error Test in Spatial Random Effect Models

Under the condition of non-classical distributed errors, the test for spatial dependence in spatial panel data models is still a problem waiting to be solved. In this paper, we apply the FDB (Fast Double Bootstrap) method to spatial panel data models to test spatial dependence. In order to research the validity of the Bootstrap LM-Error test in spatial random effect models under the condition that the error term obeys a normal distribution, heteroscedasticity, or time-series correlation, we construct Bootstrap LM-Error statistics and make use of Monte Carlo simulation from size distortion and power aspects to carry out our research. The Monte Carlo simulation results show that the asymptotic LM-Error test in the spatial random effects model has a large size of distortion when the error term disobeys classical distribution. However, the FDB LM-Error test can effectively correct the size distortion of the asymptotic test with the precondition that there is nearly no loss of power in the FDB test. Obviously, compared to the asymptotic LM-Error test, the FDB LM-Error test is a more valid method to test spatial dependence in a spatial random effects model.

A novel approach for disentangling packed peaks in the energy spectrum of γ detector arrays through γ-γ coincidence analysis

A novel approach has been proposed to effectively separate cascaded and closely packed full energy peaks in the energy spectrum of γ detector arrays. By conducting a sequence of γ-γ coincidence analyses on the energy spectrum, it is feasible to obtain a spectrum that represents the product of these packed peaks. We have designated the newly obtained spectrum as the “product spectrum”. The energy spectrum and the newly obtained product spectrum can be simultaneously fitted, enabling a more precise fitting and separation of the closely packed peaks. This method has been mathematically proven, and validated through the use of Monte Carlo simulation. The systematic errors of this method are also taken into consideration.

Nonparametric adaptive cumulative sum charting scheme for monitoring process location

AbstractFor effective monitoring of the range of sustainable changes in the process median, a nonparametric adaptive cumulative sum signed‐rank (NPACUSUM‐SR) control chart has been suggested in this study. Through the use of Monte Carlo simulation, the numerical analysis of the suggested NPACUSUM‐SR scheme has been calculated for zero and steady states. Run length (RL) profiles have been used to explore and compare the proposed NPACUSUM‐SR chart's in‐control resilience under various symmetrical heavy‐tailed, light‐tailed, and contaminated normal distributions. The suggested NPACUSUM‐SR chart has undergone a thorough investigation of the competition using Wilcoxon signed‐rank statistics and sign statistics under steady‐state and zero‐state conditions. The comparison shows that the suggested NPACUSUM‐SR design is superior to its competitors, especially when considering steady‐state performance over a wide range of shifts. This study examines the viability of the suggested design through a manufacturing‐related application.

Optimized planning for weather-sensitive offshore construction operations - An application to float-over installation of super-heavy offshore topsides

Float-over method offers an efficient, reliable, and practical installation scenario for large-scale offshore topsides. It is, however, highly sensitive to marine environmental conditions and necessitates comprehensive pre-planning. Conventional deterministic approaches methodologies exhibit shortcomings in achieving a cost-effective and time-efficient float-over design, thereby necessitating a comprehensive reconsideration. This study aims to develop a reliability-based methodology for float-over analysis of super-heavy offshore topsides. Incorporating diverse geometric and mechanical limit state functions, the permissible weather window is determined by use of Monte-Carlo Simulation. This methodology leverages the data set generated based on nonlinear coupled multi-body hydrodynamic simulation of the float-over operations. The presented idea provides a decision-making platform that integrates safety and efficiency considerations. The float-over design is optimized utilizing the developed approach and some recommendations are made to enhance applicable codes and guidelines. To reduce the computational expenses, the application of the importance sampling technique is subsequently proposed. Long-term planning of float-over installation is derived finally, optimizing operational time and cost while keeping risks as low as reasonably practical. In conclusion, it is crucial to establish a reliability-based framework for scheduling weather-sensitive offshore construction projects.

Innovation by integration of Drum-Buffer-Rope (DBR) method with Scrum-Kanban and use of Monte Carlo simulation for maximizing throughput in agile project management

Highly volatile, uncertain, complex and ambiguous environments (VUCA) complicate and condition project management. With the emergence of agile project management, it is proposed to co-construct it with the client's active participation. Two used agile methodologies are Scrum and Kanban. Scrum is based on executing fast, interactive cycles (Sprints) for the incremental construction of products. Kanban promotes the balance of the continuous workflow through synchronizing tasks and seeking perfection. The combined use of Scrum-Kanban facilitates the integration of the best of both approaches. The Theory of Constraints (TOC) proposes a method for managing constraints in a system (Constraint Management). The Drum-Buffer-Rope (DBR) method and Buffer Management are practical applications of this theory. This study seeks to maximize the continuous flow of value (Throughput) in agile project management by synergistically integrating the DBR method with Scrum-Kanban. The five-step process is implemented for the planning, executing, and controlling the Kanban board in a Scrum Sprint cycle. Four scenarios are evaluated: (1) Balanced Line; (2) Unbalanced Line; (3) Unbalanced Line Modification 1 - Stable, Robust and Fast; and (4) Unbalanced Line Modification 2 - Focusing and Elevation. Measurement of completed work (Kanban cards in the "Done" column) and final inventory for the Sprint cycle reveals that Simulation 4 is the optimal scenario, achieving the highest average "output" ("Done" cards) with reduced inventory ("Doing" cards). The integration of DBR with Scrum-Kanban maximizes the completed work (Throughput) and minimizes the final inventory of the Sprint cycle, which is corroborated by the principle of Little's Law.

Evaluation of Single Event Upset on a Relay Protection Device

Traditionally, studies have primarily focused on single event effects in aerospace electronics. However, current research has confirmed that atmospheric neutrons can also induce single event effects in China’s advanced technology relay protection devices. Spallation neutron irradiation tests on a Loongson 2K1000 system-on-chip based relay protection device have revealed soft errors, including abnormal sampling, refusal of operation and interlock in the relay protection device. Given the absence of standardized evaluation methods for single event effects on relay protection devices, the following research emphasizes the use of Monte Carlo simulation and software fault injection. Various types of single event upsets, such as single bit upsets, dual bit upsets, and even eight bit upsets, were observed in Monte Carlo simulations where atmospheric neutrons hit the chip from different directions (top and bottom). The simulation results indicated that the single event effect sensitivity of the relay protection device was similar whether the neutron hit from the top or the bottom. Through software fault injection, the study also identified soft errors caused by neutron induced single event upsets on the Loongson 2K1000 system, including failure to execute, system halt, time out, and error result. And the soft error number of system halts and error results exceeded that of time outs and failures to execute in all three tested programs. This research represents a preliminary assessment of single event effects on relay protection devices and is expected to provide valuable insights for evaluating the reliability of advanced technology relay protection devices.

A benchmark for Monte Carlo simulations in gamma-ray spectrometry Part II: True coincidence summing correction factors

The goal of this study is to provide a benchmark for the use of Monte Carlo simulation when applied to coincidence summing corrections. The examples are based on simple geometries: two types of germanium detectors and four kinds of sources, to mimic eight typical measurement conditions. The coincidence corrective factors are computed for four radionuclides. The exercise input files and calculation results with practical recommendations are made available for new users on a dedicated webpage.

A Monte Carlo simulation study of medical cyclotron accelerator in order to design a neutron shield reducing staff absorbed dose

Cyclotron is one of the accelerators that is widely used in the field of diagnostic and therapeutic radiopharmaceutical production. During nuclear reactions, occur in cyclotrons, in addition to the desired nuclide, neutron and gamma radiation are also produced. The resulting radioactivity depends on the type and energy of the emitted particles, as well as on the materials irradiated by the primary beam and the secondary radiation field. Secondary neutrons and components activated with secondary neutrons may affect the staff absorbed dose. Activated cyclotron components are a major source of potential radiation to staff who maintain and repair the device.The correct determination of neutron and gamma radiation is a basic condition for the design of protection and, as a result, the protection of employees. It is impossible to perform such a study in humans, and the only method to estimate the dose is the use of Monte Carlo simulation. In this research, by using GEANT4 Monte Carlo codes, the intensity and spectrum of neutrons and the dose of different organs inside, outside of the cyclotron room and also the in the operator's room were calculated. Three different materials of steel with special alloy, polyethylene, and cadmium boron were used for designing a shield. The neutron spectrum was calculated in different shield designs and compared with the design without shield.The optimum barrier for radiation protection against neutron depends on the energy of beam. In the case of cyclotrons that used for FDG (Fluorodeoxyglucose) production, the maximum neutron energy is 2 MeV. According to our simulation results, polyethylene (PE)is the best material for shielding.

Optimal weather window for transportation of large-scale offshore structures

Offshore transportation is recognized as one of the most dominant sources of human and financial loss, among all varieties of maritime and offshore operations. The conventional deterministic approach to address the corresponding risks, therefore, needs to be reconsidered. This study aims at developing a reliability-based methodology for marine transportation analysis of large-scale offshore modules. Taking vessel stability and strength as the limiting criteria, the allowable weather window is extracted by use of Monte Carlo simulation. Comparing the results of conventional deterministic and reliability-based approaches, the efficiency of the proposed methodology is well demonstrated. The importance sampling technique is also proposed subsequently to reduce the computational time and effort. The developed framework is ultimately employed for optimal planning of offshore transportation, aiming to minimize weather-related downtime. The application of the proposed probabilistic approach for planning the installation phase of offshore projects leads to a significant reduction in costs while keeping the risk level as low as reasonably practicable.

Critical and Compensation Behaviors of a Ferrimagnetic Multilayer System

By the use of Monte Carlo simulation, we have investigated the phase diagrams and magnetic properties of a ferrimagnetic Ising multilayer system with mixed spins (1, [Formula: see text]). The ground-state phase diagrams are first constructed for [Formula: see text]. To examine the influences of certain physical parameters of the system, namely the exchange interactions, the number of layers and the crystal field, we have studied the thermal behavior of the system magnetizations and the corresponding phase diagrams. The results obtained in this work show the existence of second- and first-order phase transitions and compensation temperatures. Moreover, we have investigated the hysteresis behavior, where the multi-loop phenomena are observed.

Optimal stochastic self-scheduling of a water-energy virtual power plant considering data clustering and multiple storage systems

As the world moves towards the integration of different water and energy resources, as well as storage systems, it is necessary to develop the conventional structure of virtual power plants (VPPs). In this work, a mixed-integer nonlinear programming (MINLP) model is established for stochastic self-scheduling problem of a new VPP structure, namely water-energy VPP. The proposed VPP, which participates in the electricity market to maximize its daily profit, aggregates different energy and water resources including wind turbines, compressed air energy storage (CAES), gas boiler, electrical boiler, absorption chiller, ice storage, water storage, and water well to supply water demand, as well as different types of energy demands such as cooling, power, and heat demands. To cope with the uncertain behavior of wind power, a scenario-based approach is employed. First, with the use of Monte Carlo simulation in MATLAB, a number of scenarios are obtained, and afterwards, K-means technique, which is a fast and efficient data clustering method, is implemented for the scenario reduction. Additionally, the impact of the consideration of CAES system on the performance of the proposed water-energy is discussed. The numerical results indicate that with the deployment of CAES system into the structure of the water-energy VPP, the expected daily profit is increased up to 4.26 %.

Efficient Pricing of Spread Options with Stochastic Rates and Stochastic Volatility

Spread options are notoriously difficult to price without the use of Monte Carlo simulation. Some strides have been made in recent years through the application of Fourier transform methods; however, to date, these methods have only been applied to specific underlying processes including two-factor geometric Brownian motion (gBm) and three-factor stochastic volatility models. In this paper, we derive the characteristic function for the two-asset Heston–Hull–White model with a full correlation matrix and apply the two-dimensional fast Fourier transform (FFT) method to price equity spread options. Our findings suggest that the FFT is up to 50 times faster than Monte Carlo and yields similar accuracy. Furthermore, stochastic interest rates can have a material impact on long-dated out-of-the-money spread options.

Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data

In this work, we presented the type I half logistic Burr-Weibull distribution, which is a unique continuous distribution. It offers several superior benefits in fitting various sorts of data. Estimates of the model parameters based on classical and nonclassical approaches are offered. Also, the Bayesian estimates of the model parameters were examined. The Bayesian estimate method employs the Monte Carlo Markov chain approach for the posterior function since the posterior function came from an uncertain distribution. The use of Monte Carlo simulation is to assess the parameters. We established the superiority of the proposed distribution by utilising real COVID-19 data from varied countries such as Saudi Arabia and Italy to highlight the relevance and flexibility of the provided technique. We proved our superiority using both real data.

The use of Monte Carlo simulation to evaluate the optical properties of polyester fabric treated with titanium dioxide nanopigments

AbstractThe current study utilises Monte Carlo simulation and Mie scattering theory to estimate the reflectance spectra of fabric coated with titanium dioxide nanopigments of various diameters and concentrations. Image processing was carried out and experimental data were gathered to evaluate the performance of Monte Carlo simulation. The distribution and location of the nanopigments on the surface of fabric were determined using the Monte Carlo method. Reflection of the fabric was calculated based on Monte Carlo simulation with the partitive mixing method and Mie theory. According to the experimental and simulation results, the reflectance of coated samples was increased by increasing the concentration and number of titanium dioxide nanoparticles. There was a good match between the results obtained by Monte Carlo simulation and the experimental results. For coated samples (dTiO2: 500 nm), the root mean square error between measured and predicted reflectance by the Monte Carlo and partitive mixing method and by Monte Carlo and Mie theory was 0.022 and 0.0078, respectively. The results indicate that the performance of the Monte Carlo and Mie method was better than that of the Monte Carlo and partitive mixing method. According to t‐test analysis, there was no statistically significant difference between the experimental data and Monte Carlo simulation.

Use of Monte Carlo Simulation in the Viewpoint of Mathematical Problem Solving

목적 본 연구에서는 문제 해결 관점에서 몬테카를로 시뮬레이션을 활용하는 방안에 대해 소고하고자 하였다. 방법 본 연구는 소고의 의미 그대로 단면적인 고찰, 연구자의 생각을 낮춰서 쓴 글의 형태를 취하고 있다. 이를 위해 선행 연구를 분석하여 확률 교육과 시뮬레이션, 몬테카를로 시뮬레이션에 관해 서술하였으며, 실제 사례를 기반으로 수학 문제 해결 단계에서 몬테카를로 시뮬레이션의 활용 방법을 구체적으로 제시하였다. 결과 본고의 첫 번째 장에서는 학교수학에서 문제 해결과 몬테카를로 시뮬레이션의 중요성을 서술하였다. 두 번째 장에서는 확률교육과 시뮬레이션, 그리고 몬테카를로 시뮬레이션에 대해 고찰하였다. 세 번째 장에서는 Polya(1957)의 문제 해결 단계 중 문제의 이해, 계획과 반성 단계에서 몬테카를로 시뮬레이션이 유용하게 활용될 수 있음을 서술하였다. 마지막으로 네 번째 장에서는 창의 융합형 인재 양성을 위한 교육 정책에 맞추어 몬테카를로 시뮬레이션을 활용한 문제 해결에 대한 연구가 필요함을 시사점으로 제시하였다. 결론 본 연구는 수학 기반 융합적 문제 해결을 위해 몬테카를로 시뮬레이션의 활용 방안을 제시하는 데 의의가 있다.

Engaging medical physics students in active and authentic learning through the use of monte-carlo simulation and inverse treatment planning

PurposeEngagement and participation of students with the learning process has been recognised as a growing problem across the higher education sector. The aim of this study was to investigate the value and impact of introducing Problem-based Learning (PBL) activities into a radiotherapy physics unit of a postgraduate medical physics course. MethodsComputer-based problem solving activities on 1) monte-carlo modelling of a linear accelerator and 2) inverse radiotherapy treatment planning were designed and implemented into a one-semester unit on radiotherapy physics. The value and impact of the activities on the student learning were evaluated through student surveys, a focus group, and peer observation of the sessions by members of the learning design team. Student attendance and grade profile data are also reported. ResultsOverall the results indicated that students had a positive experience with the new problem solving activities that were implemented. Survey responses from a number of students indicated a desire for increased theoretical and technical support prior to and during activities. Another underlying theme that emerged from survey and focus group response was the perceived lack of reward in terms of marks for their efforts working on the learning activities. This may have influenced students’ choices around attendance and participation. No significant changes were noted in the overall grades achieved in the unit. ConclusionsStudents appreciated the more hands-on approach to learning in the form of more authentic activities that they could directly relate to clinical radiotherapy. Further work is required to update and integrate assessment into the new learning delivery model more directly.

Research on the trend of Chinese stock market based on Monte Carlo simulation method

The quantitative trading strategy of stock market in developed countries has been very mature, but the quantitative trading strategy of China's stock market has only begun to develop in recent years. In this paper, the closing levels of CSI 300 Index from 2002 to 2022 were selected as the research objects. Monte Carlo simulation method was used to simulate the closing levels of the next 30 trading days, and the simulation results of the last trading day were counted and analyzed. It is found that most of the results of Monte Carlo simulation are in the reasonable range, and the occasional extreme case has similar cases in history. Therefore, the use of Monte Carlo simulation can roughly simulate the future period of income and risk situation, to provide a certain degree of help for investors to make decisions.

Endogeneity Effect on AR (1) Models in Small Samples

This study examines the endogeneity effect on autoregressive linear models of AR (1) in small samples, making use of the Ordinary Least Square (OLS) estimator, Two-Stage Least Squares (2SLS) estimator, and Generalized Method of Moment (GMM) estimator, based on the sensitivity analysis of sample size and specification errors in estimator determination in linear regression model through the use of Monte Carlo simulation and application to real-life data. The simulation indicates that 2SLS and GMM estimators show the smallest biases when the sample size is varied from n = 10, 25, 50 to 100. The estimator that performs best when sample size n = 10 across autocorrelation (ρ) and significant correlation (α) at all levels of replication of 10,000 is GMM. In the real-life data, OLS and 2SLS exhibit higher endogeneity characteristics from the dataset used. The empirical analysis base on MSE criteria GMM is the best estimator for dealing with external shock factors to inflations embedded with endogeneity in the linear model. When endogeneity and autocorrelation are bedeviled in a linear AR (1) model, in small samples, using the GMM estimator will provide the best results in small samples than using 2SLS and OLS.

Flexible Pavement Life Cycle Cost Analysis by Using Monte-Carlo Method and the Suggestions for Developing Countries

Flexible Roadpavement plays an essential role in developing an effective, economic, and safe operation road network of any country. In Vietnam, a developing country, the selection of a suitable flexible pavement structure is always a challenge due to fiscal limitations. The traditional determinant method (TDM) by which pavement structures are selected mainly on the basis of initial construction costs and traffic load has been used for many years in the nation. This paper presents the use of Monte Carlo simulation to analyzethe entire flexible pavement life cycle cost. Data including initial and maintenance costs and road user costs were collected from several different types of existing flexible pavement in Nghe An province, Vietnam. Random variations of several main inputs were explored in order to develop density distribution functions. These functions then were used as the bases for Monte Carlo simulation. One million simulation runs were implemented and the Net Present Values (NPVs) among pavement types were compared under the light of risk analysis. Research results showed that TDM method provided bias and uncertain results compared to that of Monte Carlo one. In terms of long-term pavement performance, a low-cost pavement structure should not always be considered as a wise selection. Some other suggestions for a developing country as Vietnam were also included.