# Tag Info

6

Yes, it is correct. Underestimation: you under-estimate the risk, so you have more VaR violations than what your model predicts. Ex: With 100 observations, and a 99% VaR, you expect 1 violation but you observe 5 violations. Overestimation: you over-estimate the risk, i.e the risk is less important that you expect. You observe less VaR violations that you ...

5

The VaR of level $\alpha$ a loss random variable (the bigger the worse) is the quantity $q$ such that the loss is bigger with probability $1-\alpha$. Thus we need a $q$ such that $$P[L>q] = 1-\alpha,$$ where we can imagine $\alpha=99\%$ and thus we need the starting point of the $1\%$ tail. Because we have a probability of a loss of size $0$ of $75\%... 4 If the loss distribution is normal with mean$\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$... 4 First, I am quite sure that this is a typo and it should be $$0 < VaR_1 < VaR_0$$ then $$-VaR_0 < -VaR_1$$ and the plot is correct. Second, the put strategy does not change only the expected profit but the whole distribution of the P&L. If you buy a put with strike$K_1 = -VaR_1$then you get compensated for losses below$K_1$. But you ... 3 As a short summary and adaption of the question: You better redefine$\hat{r}_i= \frac{S_{i-1}}{S_1}-1$and$\hat{S}_i = (1+\hat{r}_i)S_0$. The above definition of$\hat{S}_i$yields a sample of potential values for$S$for the future day. This approach is usually applied in historical simulation. The aim here is to use information of the past about the ... 3 These are identical definitions of ES. It's just a matter of expressing losses as negatives or positives. First definition Notice the integral bounds are$a$and$1$: losses are positive; this is so-called Loss(+)/Profit(-). Here alpha might be 95%, as in 95% confidence VaR or ES. Second definition Losses are negative, and the corresponding quantile is ... 3 The standard approach is to multiply by the square root of the number of trading days in a year. If you assume there are 250 trading days in the year, you multiply by$\sqrt{250}$. Investopedia is one source explaining this approach. 3 You don't really have a multivariate case: we can only define VaR (in its usual sense) for a one-dimensional output. Recall that $$\operatorname{VaR}_\alpha(X) = \inf\{v:F_X(v)\geq \alpha\}$$ and since in your case$X = X_1+X_2$you just need to compute$F_X$in terms of$X_1$and$X_2$. For the notation of partial derivatives, I denote the generic ... 3 It depends on the method by which you calculate VaR. Some models (t-distributuion, normal) lead to a form of VaR such that it is just scaled volatility: $$VaR = c \sigma$$ with some proper$c$(e.g.$q_{\alpha}$in the case of normal, bit more complicated for the t-distribution). Then as$\sigma$scales with square-root-of-time so does VaR. If VaR is ... 3 Value at risk is quoted by absolute value. This is the amount of money you can lose, so everyone knows the sign by default. For the second question, the last line explains it. Probability of at least one of the assets losing money is ~9.6%. Probability of both losing money is pretty small and is ignored. So, since 9.6% > 5%, it means that you lose on one of ... 3 If$z_\alpha$is the so-called standard normal$z$-score of the significance level$\alpha$such that $$\frac 1 {\sqrt{2\pi}}\int_{-\infty}^{z_\alpha} e^{-\xi^2/2}d\xi=\alpha$$ and we assume normality, (ignoring skewness and kurtosis,) then we can estimate the$\alpha$quantile of a distribution with cdf$\Phi$as $$\Phi^{-1}(\alpha)=\mu + \sigma z_\alpha.... 3 Skewness decays with time, but the rate of that skewness decay will vary based on the instruments and how they are traded, so a simple estimator such as the square root of time rule is not appropriate. I typically recommend that to scale VaR or ES it makes more sense to lower your confidence level (raise the alpha parameter) to one that makes sense for your ... 3 the risk neutral drift is needed for pricing of derivatives. For a 100\% equity portfolio you can take the real world drift - sometimes a good guess is a drift of zero. For fixed-income you could do the same and might need more sophistication for the variance term. If you have short-dated bonds then you will need a special model for the pull-to-par. For ... 3 VaR is not a good measure of risk taking, in my opinion. It suffers from inherent faulty assumptions (check out VaR Wiki to start) and it omits many other important aspects of risk measurement. When I evaluate an asset's risk and return I like to start looking at the following: Historical risk and returns of an asset. This leads to the Sharpe Ratio, ... 3 You got some things wrong: You don't have to devide sd by \sqrt{n}, the division is already part of the definition of sd. The t distribution has a parameter \nu, the degrees of freedom. The variance of a standard t distributed random variable T is$$ VAR(T) = \nu/(\nu-2). $$Thus you have to define \sigma = sd * \sqrt{(\nu-2)/\nu} and a ... 3 1) You are computing the "actual" VaR, in the sense that you are not forecasting it to see if your VaR model is able to estimate it, but you are just computing the VaR that "has taken place". To obtain a volatility forecast (either in-sample or out-of-sample) you can use the "ugarchforecast" function. 2) I think you are estimating the VaR on the wrong side ... 3 Let's assume T=1 and let S be a geometric gaussian process with zero drift, i.e. \ln(S_1/S_0) is normally distributed with mean -1/2\times\mathrm{VEV}^2 and volatility VEV. Then$$\ln(\mathrm{VaR}/S_0) = -1/2\mathrm{VEV}^2 - \mathrm{VEV} \times 1.96$$with the VAR at 0.975 quantile. This is a quadratic equation in VEV, with solutions$$\mathrm{VEV}... 2 The time scaling of higher moments for ordinary (discrete) returns as per the Wingender paper is illustrated in Excel and VBA in the following spreadsheet demonstration files: Terminal-Wealth-Time-Horizon-Calcs-Normal-and-Modified-VBA and; Liqudity-VaR-With-Correct-Time-Scaling-of-Higher-Moments Available here For more on the weaknesses of the Cornish ... 2 There is a formula for calculating ES from a normal distribution. There is also a formula for ES of arbitrary distributions using a Cornish-Fisher expansions (easy for univariate processes but frustrating for multivariate). However, the most common approach is a scenario representation of the distribution. This could include using the historical distribution ... 2 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\...

2

It is correct! You can also see it this way: $$\text{CVaR}_\alpha(X)=\mathbb{E}(X|X\leq \text{VaR}_\alpha(X)) = \frac{\int_{\mathbb{R}} x\cdot 1_{X\leq \text{VaR}_\alpha(X)}dF(x)}{\int_\mathbb{R}1_{X\leq \text{VaR}_\alpha(X)}dF(x)} = \frac{1}{\alpha} \int_{-\infty}^{\text{VaR}_\alpha(X)}xdF(x)$$ The sign problem still remains (in both versions). If you ...

2

Note that \begin{align*} \mathbb{E}\big(L \mid L\geq q_\alpha(L)\big) &= \frac{\mathbb{E}\big(\pmb{1}_{\{L\geq q_\alpha(L)\}} L\big)}{\mathbb{P}\big(L\geq q_\alpha(L) \big)}. \end{align*} The formula follows immediately.

2

The best solution is to matrix-price these bonds first. For each bond, either find a comparable bond or use your own judgment to determine the appropriate spread to a benchmark curve (e.g., OAS to LIBOR), then use the daily LIBOR curve and the corresponding OAS to obtain the daily prices.

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Better to compute it by yourself either using Historical simmulation, Monte Carlo, or simple parametric method such as variance-covariance. Alternatively subscribe toBloomberg Risk Analytics, populate the ISIN(s) for your ETF(s) and get the relevant metrics.

2

I won't base my answer on your example as i couldn't understand what you mean. Firstly, when you ask a question "what is better?" you should address this question to the model and not the output values. Secondly, model is good only when it as accurately as possible explains the reality (with a degree of confidence). The model is useless if it ...

2

I think you are right. What he calls the approximation is the correct amount, the other is an approximation.

2

As you and @Malick noted, VaR only gives a certain threshold given a certain confidence but says nothing about what happens beyond that point (tail risk). For loss distributions with long tails, this would underestimate the risk. Regarding VaR having a problem with diversification - VaR is technically not a coherent risk measure. In simple terms, we would ...

2

Have you considered Marginal Contribution to Total risk (MCTR)? You can decompose your risk across securities/sub-sectors/sectors, such that sum(weight of security * MCTR of security ) = portfolio risk (standard deviation). A good discussion on the topic can be found in Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and ...

2

If I'm correct Backtesting VaR usually boils down to two conditions: The unconditional coverage hypothesis : the probability of an ex-post violation must be equal to the coverage rate. (ie : if 0.01 confidence level, you should get 1% violation). You can test it with the Kupiec Test . The independence hypothesis, your VaR violations should be independent. ...

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