What is Monte Carlo simulation?
Monte Carlo Simulation is a mathematical model for calculating the probability of a specific result by accidental testing or sampling a wide range of scenarios and variables. The simulation, which was used for the first time by mathematician Stanilaw Ulam, who worked on the Manhattan project during World War II, provides analysts for difficult decisions and solving complex problems that have more areas of uncertainty. The Monte Carlo simulation, named after a casino-lost resort in Monaco, uses historical statistical data to generate millions of different financial results by accidentally inserting components into each run that can affect the end result, such as account return, volatility or correlation. Once the scenarios are formulated, the method calculates the likelihood of a specific result. Unlike standard financial planning analyzes that use long -term averages and estimates of future growth or savings, Monte Carlo simulation, available in software and myb applications, mohOU to provide a more realistic means of handling variables and measurement of probability of financial risk or reward.
Monte Carlo methods are often used for personal financial planning, portfolio evaluation, bond valuation and linking options and companies or project financing. Although the calculations of probability are not new, David B. Hertz first promoted them in the finances in 1964 by his article "Risk Analysis in Capital Investments" published in the Harvard Business Review. Phelim Boyle used the method for a derivative award in 1977 and published his article "Options: Monte Carlo's approach" in the Journal of Financial Economics. This technique is harder to use with American possibilities, and there are certain events that cannot be predicted with the results of the basic assumptions.
Simulation offers several different advantages of different forms of financial analýzy. In addition to creating probabilities of possible endpoints of the strategy, the method of wording of graph and graph creation facilitates and promotes better communication to investors and shareholders. The Monte Carlo simulation emphasizes the relative impact of each variable on the lower line. Using this simulation, analysts can also see exactly how certain combinations of inputs affect and interplay each other. Understanding positive and negative interdependence between variables provides more accurate analysis of the risk of any tool.
Risk analysis This method includes the use of probability to describe variables. The known probability distribution is a normal or bell curve, with users determined by the expected value and a standard deviation curve defining the change. Prices of energy and inflation rate can be displayed by bell curves. Lognormal distribution shows positive variables with unlimited potential to increase, such as oil reserves or stock prices. Uniform, triangleLníkové and discreet are examples of other possible probability distribution. Values that are randomly sampled from probability curves are sent in sets called iteration.