# 14.5 Backtesting With Independence Tests

Independence tests are a form of backtest that assess some form of independence in a value-at-risk measure’s performance from one period to the next. Independence of exceedances * ^{t}I* and independence of loss quantiles

*are separate forms of independence that might be tested for. We have already seen that coverage tests assume the former and most distribution tests assume the latter. If a VaR measure fails an independence test, that can cast doubt on coverage or distribution backtest results obtained for that VaR measure.*

^{t}UThere is no way to directly test for independence, so null hypotheses address specific properties of independence—say exceedances not clustering or loss quantiles not being autocorrelated. Accordingly, backtests for independence can be judged, among other things, based on how broad their null hypotheses are.

###### 14.5.1 Christoffersen’s 1998 Exceedence Independence Test

Christoffersen’s (1998) independence test is a likelihood ratio test that looks for unusually frequent consecutive exceedances—i.e. instances when both ^{t}^{–1}*i* = 1 and * ^{t}i* = 1 for some

*t*. The test is well known, since it was first proposed in an often-cited endorsement of testing for independence of exceedances.

Extending our earlier notation *q*^{*} for the coverage of a VaR measure, we define

[14.12]

[14.13]

These are the VaR measure’s conditional coverages—its actual probabilities of not experiencing an exceedance given that it did not (in the case of ) or did (in the case of ) experience an exceedance in the previous period. Our null hypothesis is that = = *q*^{*}.

If a VaR measure is observed for α + 1 periods, there will be α pairs of consecutive observations (^{t}^{–1}*i*, * ^{t}i*). Disaggregate these as

[14.14]

where α_{00} is the number of pairs (^{t}^{–1}*i*, * ^{t}i*) of the form (0, 0); α

_{01}is the number of the form (0, 1); etc. We want to test if

[14.15]

which would support our null hypothesis. We apply a likelihood ratio test as follows. Assuming doesn’t hold, we estimate and with

[14.16]

[14.17]

Assuming does hold, we estimate *q*^{*} with

[14.18]

Our likelihood ratio is

[14.19]

[14.20]

and –2*log*(Λ) is approximately centrally chi-squared with one degree of freedom—that is –2*log*(Λ) ~ χ^{2}(1,0)—assuming . The 0.95 quantile of the χ^{2}(1,0) distribution is 3.841, so we reject at the .05 significance level if –2*log*(Λ) ≥ 3.841. Similarly, we reject it at the .01 significance level if –2*log*(Λ) ≥ 6.635.

The test largely depends on the frequency with which consecutive exceedances are experienced. As these are inherently rare events, the test has limited power. Also, the test isn’t defined when there are no consecutive exceedances at all, which is common. Christoffersen doesn’t address this situation. In some cases it may be reasonable to simply accept the null hypothesis when there are no consecutive exceedances, but not always. For example, if you backtest a one-day 90% VaR measure with 1,000 days of data, there should be about 10 instances of consecutive exceedances. If there are none, it might be inappropriate to accept the null hypothesis.

###### 14.5.2 A Recommended Standard Loss-Quantile Independence Test

For a recommended standard test, we assess the independence of the values * ^{t}N* obtained by applying the inverse standard normal CDF to the loss quantiles

*:*

^{t}U[14.21]

Note that this is the same transformation we made with [14.10]. As before, given loss quantile data ^{–m}*u*, ^{–m+1}*u*, … , ^{–1}*u*, we apply [14.21] to obtain values ^{–m}*n*, ^{–m+1}*n*, … , ^{–1}*n*.

We adopt the null hypothesis that the autocorrelations

[14.22]

are all 0 for lags *k* = 1, 2, 3, 4 and 5. We test this hypothesis by calculating the sample autocorrelations of our data ^{–m}*n*, ^{–m+1}*n*, … , ^{–1}*n* for those same five lags. We take the maximum of the absolute values of the five sample autocorrelations. That is our test statistic. We reject the null hypothesis at the .05 significance level if the test statistic exceeds the non-rejection value indicated for sample size α + 1 in Exhibit 14.7.

Non-rejection values were calculated for each sample size α + 1 with a Monte Carlo analysis that found the 0.95 (for the .05 significance level) or 0.99 (for the .01 significance level) quantile for the test statistic assuming the null hypothesis.

###### Exercises

In Christoffersen’s 1998 independence test, α_{01} routinely equals α_{10}. Why is this, and what would cause them to differ?

Solution

A VaR measure is to be backtested using Christoffersen’s 1998 independence test. Based on 250 days of exceedence data, α_{00} = 237,* α*_{01} =* α*_{10} = 5, and* α*_{11} = 2. Do we reject the VaR measure at the .10 significance level?

Solution

A VaR measure is to be backtested using our recommended standard independence test and 500 days of data. Values * ^{t}n* are calculated, and their sample autocorrelations are determined to be 0.034, –0.078, –0.124, 0.107 and 0.029 for lags 1 through 5, respectively. Do we reject the VaR measure at the .05 significance level?

Solution