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Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze (complex) instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes.
Monte Carlo simulated stock price time series and random number generator (allows for choice of distribution), Steven Whitney; Discussion papers and documents. Monte Carlo Simulation, Prof. Don M. Chance, Louisiana State University; Pricing complex options using a simple Monte Carlo Simulation, Peter Fink (reprint at quantnotes.com)
Monte Carlo method: Pouring out a box of coins on a table, and then computing the ratio of coins that land heads versus tails is a Monte Carlo method of determining the behavior of repeated coin tosses, but it is not a simulation. Monte Carlo simulation: Drawing a large number of pseudo-random uniform variables from the interval [0,1] at one ...
The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path — that is given a path {, …,} to also take {, …,}.The advantage of this technique is twofold: it reduces the number of normal samples to be taken to generate N paths, and it reduces the variance of the sample paths, improving the precision.
Continuous simulation with stocks and flows, some discrete delay and discrete event functionality. Flexible array syntax with mapping among dimensions. Extensive support for time series data import and export, with calibration optimization and Markov chain Monte Carlo for estimation. Monte Carlo and other sensitivity simulation methods.
Monte Carlo simulations will generally have a polynomial time complexity, and will be faster for large numbers of simulation steps. Monte Carlo simulations are also less susceptible to sampling errors, since binomial techniques use discrete time units. This becomes more true the smaller the discrete units become.
Quantitative techniques that use Monte-Carlo simulation with the Gaussian copula and well-specified marginal distributions are effective. [19] Allowing the modeling process to allow for empirical characteristics in stock returns such as autoregression, asymmetric volatility, skewness, and kurtosis is important. Not accounting for these ...
Historical simulation in finance's value at risk (VaR) analysis is a procedure for predicting the value at risk by 'simulating' or constructing the cumulative distribution function (CDF) of assets returns over time assuming that future returns will be directly sampled from past returns.
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