Conditional coverage independence test for value-at-risk (VaR) backtesting
generates the conditional coverage independence (CCI) for value-at-risk (VaR)
backtesting.TestResults
= cci(vbt
)
adds an optional name-value pair argument for
TestResults
= cci(vbt
,Name,Value
)TestLevel
.
To define the likelihood ratio (test statistic) of the cc
test,
first define the following quantities:
'N00'
— Number of periods with no failures
followed by a period with no failures
'N10'
— Number of periods with failures
followed by a period with no failures
'N01'
— Number of periods with no failures
followed by a period with failures
'N11'
— Number of periods with failures
followed by a period with failures
Then define the following conditional probability estimates:
p01
= Probability of having a
failure on period t, given that there was no failure
on period t – 1
p11
= Probability of having a
failure on period t, given that there was a failure
on period t – 1
Define also the unconditional probability estimate of observing a failure:
pUC = Probability of having a failure on period t
The likelihood ratio of the CCI test is then given by
which is asymptotically distributed as a chi-square distribution with 1 degree of freedom.
The p-value of the CCI test is the probability that a
chi-square distribution with 1 degree of freedom exceeds the likelihood ratio
LRatioCCI
,
where F is the cumulative distribution of a chi-square variable with 1 degree of freedom.
The result of the test is to accept if
and reject otherwise, where F is the cumulative distribution of a chi-square variable with 1 degree of freedom.
If one or more of the quantities N00
, N10
,
N01
, or N11
are zero, the likelihood ratio
is handled differently. The likelihood ratio as defined above is composed of three
likelihood functions of the form
For example, in the numerator of the likelihood ratio, there is a likelihood
function of the form L with p =
pUC, n1 = N00
+
N10
, and n2 = N01
+
N11
. There are two such likelihood functions in the
denominator of the likelihood ratio.
It can be shown that whenever n1 = 0
or
n2 = 0
, the likelihood function
L is replaced by the constant value 1
.
Therefore, whenever N00
, N10
,
N01
, or N11
is zero, replace the
corresponding likelihood functions by 1
in the likelihood ratio,
and the likelihood ratio is well-defined.
[1] Christoffersen, P. "Evaluating Interval Forecasts." International Economic Review. Vol. 39, 1998, pp. 841–862.