Frequency distribution - Revision history

imported>Doug Williamson: Classify page.

2020-11-27T00:31:00Z

Classify page.

← Older revision		Revision as of 00:31, 27 November 2020
Line 1:		Line 1:
	''Statistics''		''Statistics''.

	A description of the relative number of times that given outcomes have occurred, or are expected to occur, relative to the whole population.		A description of the relative number of times that given outcomes have occurred, or are expected to occur, relative to the whole population.
Line 24:		Line 24:
	* [[Cumulative frequency distributions]]		* [[Cumulative frequency distributions]]
	* [[Decile]]		* [[Decile]]
			* [[Distribution]]
	* [[Frequency curve]]		* [[Frequency curve]]
	* [[Frequency polygon]]		* [[Frequency polygon]]
Line 35:		Line 36:
	* [[Probability]]		* [[Probability]]
	* [[Value at risk]]		* [[Value at risk]]

			[[Category:The_business_context]]
			[[Category:Identify_and_assess_risks]]
			[[Category:Manage_risks]]
			[[Category:Risk_frameworks]]
			[[Category:Risk_reporting]]

imported>Doug Williamson: Removed link

2016-11-16T14:00:03Z

Removed link

← Older revision		Revision as of 14:00, 16 November 2016
Line 1:		Line 1:
	''Statistics.''		''Statistics''

	A description of the relative number of times that given outcomes have occurred, or are expected to occur, relative to the whole population.		A description of the relative number of times that given outcomes have occurred, or are expected to occur, relative to the whole population.
Line 22:		Line 22:
	== See also ==		== See also ==
	* [[Binomial distribution]]		* [[Binomial distribution]]
	* [[CertFMM]]
	* [[Cumulative frequency distributions]]		* [[Cumulative frequency distributions]]
	* [[Decile]]		* [[Decile]]

imported>Doug Williamson: Link with Qualifications page.

2015-03-30T07:47:19Z

Link with Qualifications page.

← Older revision		Revision as of 07:47, 30 March 2015
Line 22:		Line 22:
	== See also ==		== See also ==
	* [[Binomial distribution]]		* [[Binomial distribution]]
			* [[CertFMM]]
	* [[Cumulative frequency distributions]]		* [[Cumulative frequency distributions]]
	* [[Decile]]		* [[Decile]]

imported>Doug Williamson: Spacing.

2015-03-30T07:46:28Z

Spacing.

← Older revision		Revision as of 07:46, 30 March 2015
Line 6:		Line 6:

	All three of these types of distribution are used in practice as approximations to model the distributions of financial variables.		All three of these types of distribution are used in practice as approximations to model the distributions of financial variables.


	1. ''Normal distributions'' are usually the simplest approximations to work with, and are assumed by - for example - many Value at Risk analysis models and measures. A theoretical shortcoming of using normal distributions as a model is that they assume an infinitely large downside potential including negative prices; whereas many financial variables - such as asset prices - cannot in practice fall so far as to become negative.		1. ''Normal distributions'' are usually the simplest approximations to work with, and are assumed by - for example - many Value at Risk analysis models and measures. A theoretical shortcoming of using normal distributions as a model is that they assume an infinitely large downside potential including negative prices; whereas many financial variables - such as asset prices - cannot in practice fall so far as to become negative.


	2. ''Lognormal distributions'' usually describe better the theoretical range of financial variables such as traded equity prices, which theoretically have no upside limit but which cannot fall below zero.		2. ''Lognormal distributions'' usually describe better the theoretical range of financial variables such as traded equity prices, which theoretically have no upside limit but which cannot fall below zero.


	3. In practice, observed financial returns are usually more closely approximated by ''leptokurtic distributions'', with a greater frequency both of very high and of very low returns, than predicted by the comparable normal or lognormal distribution. So in risk analysis, if a population distribution is assumed to be normal or lognormal, but is in reality leptokurtic, downside risk will be understated.		3. In practice, observed financial returns are usually more closely approximated by ''leptokurtic distributions'', with a greater frequency both of very high and of very low returns, than predicted by the comparable normal or lognormal distribution. So in risk analysis, if a population distribution is assumed to be normal or lognormal, but is in reality leptokurtic, downside risk will be understated.

imported>Doug Williamson: Reference lognormal distributions in the context of leptokurtic distributions. Note that Binomial and Poisson distributions are theoretical.

2014-05-31T11:35:11Z

Reference lognormal distributions in the context of leptokurtic distributions. Note that Binomial and Poisson distributions are theoretical.

← Older revision		Revision as of 11:35, 31 May 2014
Line 11:		Line 11:
	2. ''Lognormal distributions'' usually describe better the theoretical range of financial variables such as traded equity prices, which theoretically have no upside limit but which cannot fall below zero.		2. ''Lognormal distributions'' usually describe better the theoretical range of financial variables such as traded equity prices, which theoretically have no upside limit but which cannot fall below zero.

	3. In practice, observed financial returns are usually more closely approximated by ''leptokurtic distributions'', with a greater frequency both of very high and of very low returns, than predicted by the comparable normal distribution. So in risk analysis, if a population distribution is assumed to be normal, but is in reality leptokurtic, downside risk will be understated.		3. In practice, observed financial returns are usually more closely approximated by ''leptokurtic distributions'', with a greater frequency both of very high and of very low returns, than predicted by the comparable normal or lognormal distribution. So in risk analysis, if a population distribution is assumed to be normal or lognormal, but is in reality leptokurtic, downside risk will be understated.


	Other ~~common~~ types of frequency distribution include Binomial distributions and Poisson distributions.		Other commonly used types of theoretical frequency distribution include Binomial distributions and Poisson distributions.

imported>Doug Williamson: Spacing 27/8/13

2013-08-27T13:25:04Z

Spacing 27/8/13

← Older revision		Revision as of 13:25, 27 August 2013
Line 1:		Line 1:
	''Statistics.''		''Statistics.''

	A description of the relative number of times that given outcomes have occurred, or are expected to occur, relative to the whole population.		A description of the relative number of times that given outcomes have occurred, or are expected to occur, relative to the whole population.

	Three important frequency distributions are the Normal distribution, Lognormal distribution, and Leptokurtic distribution, described below~~. All three of these types of distribution are used in practice as approximations to model the distributions of financial variables~~.		Three important frequency distributions are the Normal distribution, Lognormal distribution, and Leptokurtic distribution, described below.

	~~''Normal distributions''~~ are ~~usually the simplest~~ approximations to ~~work with, and are assumed by - for example - many Value at Risk analysis models and measures. A theoretical shortcoming~~ of ~~using normal distributions as a model is that they assume an infinitely large downside potential including negative prices; whereas many~~ financial variables ~~- such as asset prices - cannot in practice fall so far as to become negative~~.		All three of these types of distribution are used in practice as approximations to model the distributions of financial variables.

	''Lognormal distributions'' usually describe better the theoretical range of financial variables such as traded equity prices, which theoretically have no upside limit but which cannot fall below zero.		1. ''Normal distributions'' are usually the simplest approximations to work with, and are assumed by - for example - many Value at Risk analysis models and measures. A theoretical shortcoming of using normal distributions as a model is that they assume an infinitely large downside potential including negative prices; whereas many financial variables - such as asset prices - cannot in practice fall so far as to become negative.

			2. ''Lognormal distributions'' usually describe better the theoretical range of financial variables such as traded equity prices, which theoretically have no upside limit but which cannot fall below zero.

			3. In practice, observed financial returns are usually more closely approximated by ''leptokurtic distributions'', with a greater frequency both of very high and of very low returns, than predicted by the comparable normal distribution. So in risk analysis, if a population distribution is assumed to be normal, but is in reality leptokurtic, downside risk will be understated.

	In practice, observed financial returns are usually more closely approximated by ''leptokurtic distributions'', with a greater frequency both of very high and of very low returns, than predicted by the comparable normal distribution. So in risk analysis, if a population distribution is assumed to be normal, but is in reality leptokurtic, downside risk will be understated.

	Other common types of frequency distribution include Binomial distributions and Poisson distributions.		Other common types of frequency distribution include Binomial distributions and Poisson distributions.


	== See also ==		== See also ==
Line 27:		Line 32:
	* [[Probability]]		* [[Probability]]
	* [[Value at risk]]		* [[Value at risk]]

imported>Administrator: CSV import

2012-10-23T14:19:41Z

CSV import

New page

''Statistics.''
A description of the relative number of times that given outcomes have occurred, or are expected to occur, relative to the whole population.

Three important frequency distributions are the Normal distribution, Lognormal distribution, and Leptokurtic distribution, described below. All three of these types of distribution are used in practice as approximations to model the distributions of financial variables.

''Normal distributions'' are usually the simplest approximations to work with, and are assumed by - for example - many Value at Risk analysis models and measures. A theoretical shortcoming of using normal distributions as a model is that they assume an infinitely large downside potential including negative prices; whereas many financial variables - such as asset prices - cannot in practice fall so far as to become negative.

''Lognormal distributions'' usually describe better the theoretical range of financial variables such as traded equity prices, which theoretically have no upside limit but which cannot fall below zero.

In practice, observed financial returns are usually more closely approximated by ''leptokurtic distributions'', with a greater frequency both of very high and of very low returns, than predicted by the comparable normal distribution. So in risk analysis, if a population distribution is assumed to be normal, but is in reality leptokurtic, downside risk will be understated.

Other common types of frequency distribution include Binomial distributions and Poisson distributions.

== See also ==
* [[Binomial distribution]]
* [[Cumulative frequency distributions]]
* [[Decile]]
* [[Frequency curve]]
* [[Frequency polygon]]
* [[Grouped frequency distribution]]
* [[Histogram]]
* [[Leptokurtic frequency distribution]]
* [[Lognormal frequency distribution]]
* [[Normal frequency distribution]]
* [[Percentile]]
* [[Poisson distribution]]
* [[Probability]]
* [[Value at risk]]