Estimating Volatilities and Correlations| Financial Risk Manager Exam Questions | FRM Training Video Lecture | Risk Management: An Essential Training - Management

simply learn your pace your place
financial risk manager frm par one of
the frm exam covers the fundamental
tools and techniques used in risk
management and the theories that
underlie their use estimating
volatilities and correlations welcome to
the twenty second lecture in your
preparation for the frm part 1 exam by
now you must have realized that
volatility is an important parameter of
a portfolio in terms of inherent risk
we've used volatility in our
calculations of risk in the previous
session when we calculated value at risk
volatility or standard deviation is used
to quantify the risk of the portfolio we
have learned in our previous sessions
how to calculate volatility or standard
deviation in this session we will learn
some more methods which are more
accurate in terms of estimating the
value of volatility agenda our main
agenda of discussion will be estimating
the volatility or standard deviation
using different methods that apply
variable weights to pass data on returns
we will first begin with the concept of
variable weights in calculation of
variance then we will learn the
different weighing schemes used in
methods such as ewm a exponentially
weighted moving average and G a rch
generalized autoregressive conditional
heteroscedasticity garsh
method and about estimating the weights
parameters through a method called the
maximum likelihood method in the end we
will discuss estimating and covariance
and correlations which we have already
introduced in previous sessions
volatility estimation with variable
weights
let us begin our discussion by
introducing the weighing schemes used in
volatility calculation earlier we have
learned the expression for calculating
variance from past M data the expression
is given in the slide that averages the
squared deviations from the mean return
we take the simple average because we
weigh all the past M data equally that
is they make equal contribution to the
volatility in the present day now
consider volatility as a disturbance if
the disturbance is very high today it is
most likely to be high tomorrow also for
example when there was an attack on the
World Trade Center the markets became
very volatile and it continued for a few
days hence in estimating volatility we
need to give a higher weight to the most
recent data that is we cannot weigh all
past day