The Normal-Tail Limit of Expected Shortfall to Value-at-Risk
Summary
This note derives the limiting ratio of expected shortfall (also called CVaR) to value-at-risk for a normally distributed loss as the confidence level approaches one. It first replaces the confidence level with the standard normal quantile, turning the tail limit into a limit as that quantile grows without bound. L'Hôpital's rule then simplifies the expression using the derivative of the normal density and yields a ratio of one.
The derivation assumes a normal loss distribution and the stated VaR and expected shortfall formulas. It illustrates how a change of variables can remove an inverse distribution function from a limit. The result concerns the extreme-confidence limit; it does not establish that the ratio is one at finite confidence levels or for other loss distributions.
Key ideas
- Changing variables from confidence level to the normal quantile simplifies the tail limit.
- For normal losses, expected shortfall divided by VaR tends to one as confidence approaches one.
- L'Hôpital's rule applies after expressing the ratio in terms of the quantile and normal tail probability.
- The conclusion depends on the normal distribution and does not automatically extend to other loss models.
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# CVaR/VaR Ratio as alpha goes to 1
# CVaR/VaR Ratio as alpha goes to 1
I am having trouble taking the following limit of CVaR/VaR for a normal distribution as alpha approaches 1:
$\lim_{\alpha \to 1} \frac{\mu + \sigma \frac{\phi^{-1}(\alpha)}{1-\alpha}}{\mu + \sigma \phi^{-1}(\alpha)}$
First I tried pulling the $(1-\alpha)$ out of the CVaR denominator to get:
$\lim_{\alpha \to 1} \frac{\mu(1-\alpha) + \sigma {\phi^{-1}(\alpha)}}{(1-\alpha)(\mu + \sigma \phi^{-1}(\alpha))}$
Then I thought maybe I need to use L'Hopital's rule, but I have no idea how to do that with an inverse normal imbedded in my function. I feel that I'm probably missing something simple (and my days of calculus are too far behind me). Any hints for how to compute this limit?
Many thanks.
## Answer by QuantIbex (score 6)
https://quant.stackexchange.com/a/10923
If the loss distribution is normal with mean $\mu$ and variance $\sigma^2$, then the Value-at-Risk and Expexted Shortfall (or CVaR) at level $\alpha \in (0, 1)$ are \begin{align*} \mbox{VaR}_\alpha & = \mu + \sigma \Phi^{-1}(\alpha) , \\ \mbox{ES}_\alpha & = \mu + \sigma \frac{\phi\{\Phi^{-1}(\alpha)\}}{1 - \alpha} , \end{align*} where $\phi$ denotes the density function of the standard normal distribution, and $\Phi$ its distribution function.
Recall that the derivative of the density is $\phi'(z) = -z\phi(z)$. Then, setting $x = \Phi^{-1}(\alpha)$ and by l'Hopital's rule, the limit of the ratio is $$ \lim_{\alpha \to 1} \frac{\mbox{ES}_\alpha}{\mbox{VaR}_\alpha} = \lim_{x \to \infty} \frac{\mu \{1 - \Phi(x)\} + \sigma \phi(x)}{(\mu + \sigma x) \{1 - \Phi(x)\} } = \lim_{x \to \infty} \frac{1}{1 - \sigma \frac{1 - \Phi(x)}{(\mu + \sigma x)\phi(x)}}, $$ and by l'Hopital's rule $$ \lim_{x \to \infty} \frac{1 - \Phi(x)}{(\mu + \sigma x)\phi(x)} = \lim_{x \to \infty} \frac{1}{(\mu + \sigma x)x - \sigma} = 0. $$ Thus, $$ \lim_{\alpha \to 1} \frac{\mbox{ES}_\alpha}{\mbox{VaR}_\alpha} = 1 . $$
## Answer by Good Guy Mike (score 2)
https://quant.stackexchange.com/a/10916
I don't know what you did when you tried pulling out $1-\alpha$, the correct expression would be
$\lim_{\alpha \to 1} \frac{\mu(1-\alpha) + \sigma {\phi^{-1}(\alpha)}}{(1-\alpha)(\mu + \sigma \phi^{-1}(\alpha))}$.
Anyhow, you can try using the substitution $\Phi^{-1}(\alpha) = x$, $x \to \infty$ and $\alpha = \Phi(x)$. Then the expression becomes
$\lim\limits_{x \to \infty} \frac{\mu + \sigma x/(1-\Phi(x))}{\mu + \sigma x}$
Then perhaps you can you L'Hospitals from hereon. It becomes a bit messy though, but with some effort you might be able to do it.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.