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Monte Carlo methods in finance
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<p><a href="/facts/Monte_Carlo_method/AkHjY7jc">Monte Carlo methods</a> are used in <a href="/facts/Corporate_finance/ugQCsFBQ">corporate finance</a> and <a href="/facts/Mathematical_finance/P9otEltQ">mathematical finance</a> to value and analyze (complex) <a href="/facts/Financial_instrument/2d93t6Ya">instruments</a>, <a href="/facts/Portfolio_(finance)/hTnbPr1C">portfolios</a> and <a href="/facts/Investment/VrMookcv">investments</a> by <a href="/facts/Simulation/Q4QJV1oI">simulating</a> the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes. This is usually done by help of <a href="/facts/Stochastic_asset_model/yEsjknjT">stochastic asset models</a>. The advantage of Monte Carlo methods over other techniques increases as the dimensions (sources of uncertainty) of the problem increase. </p><p>Monte Carlo methods were first introduced to finance in 1964 by <a href="/facts/David_B._Hertz/zQy8TMAV">David B. Hertz</a> through his <i><a href="/facts/Harvard_Business_Review/leWeeqyj">Harvard Business Review</a></i> article, discussing their application in <a href="/facts/Corporate_Finance/ugQCsFBQ">Corporate Finance</a>. In 1977, <a href="/facts/Phelim_Boyle/pTamzmM1">Phelim Boyle</a> pioneered the use of simulation in <a href="/facts/Derivative_(finance)/jx4CbakW">derivative valuation</a> in his seminal <i><a href="/facts/Journal_of_Financial_Economics/LTSkHn7A">Journal of Financial Economics</a></i> paper. </p><p>This article discusses typical financial problems in which Monte Carlo methods are used. It also touches on the use of so-called "quasi-random" methods such as the use of <a href="/facts/Sobol_sequence/ao3XRSKW">Sobol sequences</a>. </p>