Monte Carlo method - Revision history

imported>Doug Williamson: Link with Monte-Carlo analysis

2015-05-20T09:14:30Z

Link with Monte-Carlo analysis

← Older revision		Revision as of 09:14, 20 May 2015
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	== See also ==		== See also ==
	* [[Monte-Carlo analysis]]		* [[Monte Carlo analysis]]
	* [[Stochastic]]		* [[Stochastic]]
	* [[Value at risk]]		* [[Value at risk]]

	[[Category:Risk_frameworks]]		[[Category:Risk_frameworks]]

imported>Doug Williamson: Link with Monte-Carlo analysis

2015-05-20T09:12:04Z

Link with Monte-Carlo analysis

← Older revision		Revision as of 09:12, 20 May 2015
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	== See also ==		== See also ==
			* [[Monte-Carlo analysis]]
	* [[Stochastic]]		* [[Stochastic]]
	* [[Value at risk]]		* [[Value at risk]]

	[[Category:Risk_frameworks]]		[[Category:Risk_frameworks]]

imported>Doug Williamson: Simplify.

2015-04-19T10:35:45Z

Simplify.

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 == Monte Carlo methods in VaR analysis ==
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 :A distribution of returns is eventually produced, from which a VaR figure can be measured.
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 == See also ==
 * [[Stochastic]]
 * [[Value at risk]]
 [[Category:Risk_frameworks]]

imported>Doug Williamson: Broaden definition to non-Value at Risk applications.

2014-05-16T08:44:42Z

Broaden definition to non-Value at Risk applications.

← Older revision		Revision as of 08:44, 16 May 2014
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			== Monte Carlo methods in VaR analysis ==


	In Value at Risk analysis, an alternative method for calculating the probability distribution (rather than using the Delta-normal method or the Historical simulation method).		In Value at Risk analysis, an alternative method for calculating the probability distribution (rather than using the Delta-normal method or the Historical simulation method).

	Monte Carlo simulations consist of two steps:		Monte Carlo simulations consist of two steps:

	:First, a stochastic process for financial variables is specified as well as process parameters.		:First, a stochastic (random) process for financial variables is specified as well as process parameters.

	:Both historical data and appropriate judgement can be used for such parameters as risk and correlations.		:Both historical data and appropriate judgement can be used for such parameters as risk and correlations.


	:Second, fictitious price paths are simulated for all variables of interest. At each horizon considered, the portfolio is marked-to-market using full valuation.		:Second, multiple fictitious price paths are simulated for all variables of interest. At each horizon considered, the portfolio is marked-to-market using full valuation.

	:A distribution of returns is eventually produced, from which a VaR figure can be measured.		:A distribution of returns is eventually produced, from which a VaR figure can be measured.
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	Disadvantages of Monte Carlo methods include their relative complexity.		Disadvantages of Monte Carlo methods include their relative complexity.


			== Monte Carlo methods in other applications ==

			More generally, Monte Carlo methods are the simulation of multiple fictitious outcomes, using a combination of historical and judgemental parameters and a randomised process.

			The name originated from the famous Monte Carlo casino.


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	* [[Value at risk]]		* [[Value at risk]]

	[[Category:~~Managing_Risk~~]]		[[Category:Risk_frameworks]]

imported>Doug Williamson: Category added 9/10/13

2013-10-09T14:12:40Z

Category added 9/10/13

← Older revision		Revision as of 14:12, 9 October 2013
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	* [[Stochastic]]		* [[Stochastic]]
	* [[Value at risk]]		* [[Value at risk]]

			[[Category:Managing_Risk]]

imported>Doug Williamson: Spacing 22/8/13

2013-08-22T08:33:25Z

Spacing 22/8/13

← Older revision		Revision as of 08:33, 22 August 2013
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	In Value at Risk analysis, an alternative method for calculating the probability distribution (rather than using the Delta-normal method or the Historical simulation method).		In Value at Risk analysis, an alternative method for calculating the probability distribution (rather than using the Delta-normal method or the Historical simulation method).

	Monte Carlo simulations consist of two steps.		Monte Carlo simulations consist of two steps:

	First, a stochastic process for financial variables is specified as well as process parameters. Both historical data and appropriate judgement can be used for such parameters as risk and correlations.		:First, a stochastic process for financial variables is specified as well as process parameters.

			:Both historical data and appropriate judgement can be used for such parameters as risk and correlations.


			:Second, fictitious price paths are simulated for all variables of interest. At each horizon considered, the portfolio is marked-to-market using full valuation.

			:A distribution of returns is eventually produced, from which a VaR figure can be measured.

	Second, fictitious price paths are simulated for all variables of interest. At each horizon considered, the portfolio is marked-to-market using full valuation. A distribution of returns is eventually produced, from which a VaR figure can be measured.

	Comparing the methods:		Comparing the methods:

	The Delta-normal method is the simplest method to implement. The main drawbacks are the assumption that risk factors have normal distributions, and the assumption that the assets are linear (in other words, that they do not contain options).		:1. The Delta-normal method is the simplest method to implement.

			:The main drawbacks are the assumption that risk factors have normal distributions, and the assumption that the assets are linear (in other words, that they do not contain options).

			:2. The Historical simulation method is also relatively simple to implement.

	~~The Historical simulation method is also relatively simple to implement.~~ The main drawback is that the historical information used may not adequately represent future probability distributions. (This is also a drawback of the delta-normal method.)		:The main drawback is that the historical information used may not adequately represent future probability distributions. (This is also a drawback of the delta-normal method.)


			Monte Carlo techniques are designed to address these shortcomings.

			Disadvantages of Monte Carlo methods include their relative complexity.

	~~Monte Carlo techniques are designed to address this shortcoming. Disadvantages of Monte Carlo methods include their relative complexity.~~

	== See also ==		== See also ==
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	* [[Stochastic]]		* [[Stochastic]]
	* [[Value at risk]]		* [[Value at risk]]

imported>Administrator: CSV import

2012-10-23T14:20:06Z

CSV import

New page

In Value at Risk analysis, an alternative method for calculating the probability distribution (rather than using the Delta-normal method or the Historical simulation method).

Monte Carlo simulations consist of two steps.

First, a stochastic process for financial variables is specified as well as process parameters. Both historical data and appropriate judgement can be used for such parameters as risk and correlations.

Second, fictitious price paths are simulated for all variables of interest. At each horizon considered, the portfolio is marked-to-market using full valuation. A distribution of returns is eventually produced, from which a VaR figure can be measured.

Comparing the methods:

The Delta-normal method is the simplest method to implement. The main drawbacks are the assumption that risk factors have normal distributions, and the assumption that the assets are linear (in other words, that they do not contain options).

The Historical simulation method is also relatively simple to implement. The main drawback is that the historical information used may not adequately represent future probability distributions. (This is also a drawback of the delta-normal method.)

Monte Carlo techniques are designed to address this shortcoming. Disadvantages of Monte Carlo methods include their relative complexity.

== See also ==
* [[Delta-normal method]]
* [[Historical simulation method]]
* [[Stochastic]]
* [[Value at risk]]