What is Continuous Probability Distributions | Financial Risk Manager Certification & Training Video Lecture | Risk Management: An Essential Training - Management

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financial risk manager frm part 1 of the
frm exam covers the fundamental tools
and techniques used in risk management
and the theories that underlie their use
continuous probability distributions in
this session we will learn mainly about
the different types of continuous
probability distributions we will start
by learning about the key properties of
various continuous probability
distributions and then we will move on
to discuss the differences between them
continuous probability distribution is
the distribution in which the random
variable of study is continuous in
nature instead of discrete the random
variable takes all the values in a given
range including all the real numbers
agenda we will discuss some of the
continuous probability distributions
such as normal distribution log normal
distribution beta distribution gamma
distribution viable distribution
exponential distribution logistic
distribution t distribution chi-squared
distribution and extreme value
distribution darp requires basic
understanding of these distributions and
their properties and students are
advised not to go into too much detail
in terms of analytical interpretation
and calculations the distributions which
are more important such as normal
distribution student's t-distribution
and log normal distribution have been
discussed in extensive detail elsewhere
in the frm curriculum normal
distribution starting off with random
normal distribution normal distribution
is a type of continuous distribution
that is most extensively used it is
bell-shaped and has a single peak at the
center of the distribution generally
finance-related random variables follow
normal distribution so knowledge about
normal distribution is vital in
understanding portfolio theory the
normal curve falls off smoothly in
either direction from the central value
which is the mean of the distribution so
the normal curve is symmetric is
completely described by its mean new and
its variance Sigma squared the MU mean
of the distribution is referred to as
the location parameter and the Sigma
standard deviation it measures how the
distribution is spread out known as
scale parameter the standard normal
distribution has a mean of zero and a
standard deviation of one