Conditional Monte Carlo: A Simulation Technique for Stochastic Network Analysis

1971 ◽  
Vol 18 (3) ◽  
pp. 207-217 ◽  
Author(s):  
John M. Burt ◽  
Mark B. Garman
Keyword(s):  
Monte Carlo ◽  
Network Analysis ◽  
Simulation Technique ◽  
Stochastic Network ◽  
Conditional Monte Carlo

Extreme Response Prediction for Fixed Offshore Structures by Monte Carlo Time Simulation Technique

2016 ◽  
Author(s):  
M. K. Abu Husain ◽  
N. I. Mohd Zaki ◽  
M. B. Johari ◽  
G. Najafian
Keyword(s):  
Monte Carlo ◽  
Statistical Properties ◽  
Simulation Technique ◽  
Random Wave ◽  
Wave Loading ◽  
Offshore Structure ◽  
Sampling Variability ◽  
Time Simulation ◽  
Wide Range ◽  
Extreme Response

For an offshore structure, wind, wave, current, tide, ice and gravitational forces are all important sources of loading which exhibit a high degree of statistical uncertainty. The capability to predict the probability distribution of the response extreme values during the service life of the structure is essential for safe and economical design of these structures. Many different techniques have been introduced for evaluation of statistical properties of response. In each case, sea-states are characterised by an appropriate water surface elevation spectrum, covering a wide range of frequencies. In reality, the most versatile and reliable technique for predicting the statistical properties of the response of an offshore structure to random wave loading is the time domain simulation technique. To this end, conventional time simulation (CTS) procedure or commonly called Monte Carlo time simulation method is the best known technique for predicting the short-term and long-term statistical properties of the response of an offshore structure to random wave loading due to its capability of accounting for various nonlinearities. However, this technique requires very long simulations in order to reduce the sampling variability to acceptable levels. In this paper, the effect of sampling variability of a Monte Carlo technique is investigated.

Influence of the Back-Jump Correlations on the Fermi-Dirac Statistics

1991 ◽  
Vol 02 (01) ◽  
pp. 227-231
Author(s):  
T. BARSZCZAK ◽  
R. KUTNER
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Technique ◽  
Monte Carlo Simulation Technique

The influence of the essential Bardeen-Herring back-jump correlations on the Fermi-Dirac statistics is studied by the Monte Carlo simulation technique and semi-analytically.

scholarly journals Variance and Dimension Reduction Monte Carlo Method for Pricing European Multi-Asset Options with Stochastic Volatilities

2019 ◽  
Vol 11 (3) ◽  
pp. 815 ◽  
Author(s):  
Yijuan Liang ◽  
Xiuchuan Xu
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Convergence Rates ◽  
Option Price ◽  
Correlation Coefficients ◽  
Control Variate ◽  
Stochastic Volatilities ◽  
Conditional Monte Carlo ◽  
Mc Simulation ◽  
Martingale Representation Theorem

Pricing multi-asset options has always been one of the key problems in financial engineering because of their high dimensionality and the low convergence rates of pricing algorithms. This paper studies a method to accelerate Monte Carlo (MC) simulations for pricing multi-asset options with stochastic volatilities. First, a conditional Monte Carlo (CMC) pricing formula is constructed to reduce the dimension and variance of the MC simulation. Then, an efficient martingale control variate (CV), based on the martingale representation theorem, is designed by selecting volatility parameters in the approximated option price for further variance reduction. Numerical tests illustrated the sensitivity of the CMC method to correlation coefficients and the effectiveness and robustness of our martingale CV method. The idea in this paper is also applicable for the valuation of other derivatives with stochastic volatility.

scholarly journals Forecasting the Project Duration Average and Standard Deviation from Deterministic Schedule Information

2020 ◽  
Vol 10 (2) ◽  
pp. 654 ◽  
Author(s):  
Pablo Ballesteros-Pérez ◽  
Alberto Cerezo-Narváez ◽  
Manuel Otero-Mateo ◽  
Andrés Pastor-Fernández ◽  
Jingxiao Zhang ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Network Analysis ◽  
Construction Projects ◽  
Project Evaluation ◽  
Alternative Methods ◽  
Stochastic Network ◽  
Project Duration ◽  
Deterministic Scheduling ◽  
Mathematical Expressions ◽  
Additional Calculation

Most construction managers use deterministic scheduling techniques to plan construction projects and estimate their duration. However, deterministic techniques are known to underestimate the project duration. Alternative methods, such as Stochastic Network Analysis, have rarely been adopted in practical contexts as they are commonly computer-intensive, require extensive historical information, have limited contextual/local validity and/or require skills most practitioners have not been trained for. In this paper, we propose some mathematical expressions to approximate the average and the standard deviation of a project duration from basic deterministic schedule information. The expressions’ performance is successfully tested in a 4100-network dataset with varied activity durations and activity durations variability. Calculations are quite straightforward and can be implemented manually. Furthermore, unlike the Project Evaluation and Review Technique (PERT), they allow drawing inferences about the probability of project duration in the presence of several critical and subcritical paths with minimal additional calculation.

Sign in / Sign up

Export Citation Format

Share Document