# Tag Info

15

You have the correct approach. (1) The simulation generates sampled portfolio values, $P_1,P_2, \dots, P_n$ at time $t=T$. VaR is specified as a left-tail percentile. Order the sample as $$P_{(1)} \leq P_{(2)} \leq \dots \leq P_{(n)}.$$ If you are considering $VaR_\alpha$ at the $100(1-\alpha) \%$ confidence level , then choose the smallest integer $k$ ...

9

Values of VaR are just the inverses of the cumulative distributions. CVaR is not a very commonly used term, its more frequently used synonym is Expected Shortfall. See http://www.maths.manchester.ac.uk/~saralees/chap17.pdf for the list of Expected Shortfall values for more than 20 distributions.

7

Regression analysis, as a minimization of the sum of squared errors, does not require normality of the error term. The requirements are that errors are homoscedastic and uncorrelated. And these are the fundamental assumptions (together with exogeneity). Then estimators are unbiased, optimal (exhibit the minimum variance within the class of unbiased ...

7

The answer to your question is no. Value at Risk is not additive in the sense that $\text{VaR}(X+Y) \neq \text{VaR}(X) + \text{VaR}(Y)$. But I guess your question is more to aimed at finding a formula for your investments than to look at the property itself. I think the only way to get a nice formula for this is to assume that both assets are: Normally ...

7

This issue is incredibly important and I agree there is little practical information about it. To me, the key idea is to find the right matrix completion algorithm that best suits your needs. I work mostly with equity time series and there are substantial missing values issues due to, e.g., as you cite, IPOs with limited history. Recently I have had good ...

7

Suppose $X\sim N(\mu_X,\sigma_X^2)$ and $Y\sim N(\mu_Y,\sigma_Y^2)$ are correlated jointly normal random variables. Then, $$X+Y\sim N(\mu_X+\mu_Y,\sigma^2_X+\sigma_Y^2+2\rho\sigma_X\sigma_Y).$$ Suppose $X$ and $Y$ denote the profit of your portfolio returns (so negative values for $X,Y$ mean losses). Then, the 5% value at risk is the 0.05 quantile of the ...

6

As discussed, banks do use VaR for risk management. They will have something modified for the specific use (i.e. probably not your VaR from a fitted normal distribution), it's likely more sophisticated but the underlying idea is the same. VaR is used for reporting/ceremonial business decisions as much as (or perhaps even more than) it is for trading ...

6

I know this was asked almost two years ago, but I thought I'd answer the question. It appears that the H that you want to estimate is identical to the values you received from the Johansen test, with the exception of rows 1:4 and columns 2:4. You only need to set those values to zeroes and ones, which is fairly easy considering that the diagonal is (very ...

6

Simple example where sub-additivity fails Let there be four possible outcomes $i=1,2,3,4$ that occur with equal probability $\frac{1}{4}$. Payoffs for $X$, $Y$, and $X + Y$ are given by: $$X = \begin{bmatrix}-1\\0\\1\\2 \end{bmatrix} \quad Y = \begin{bmatrix}0\\-1\\1\\2 \end{bmatrix} \quad X + Y = \begin{bmatrix}-1\\-1\\2\\4 \end{bmatrix}$$ What's the ...

5

The given matrix can not represent a covariance matrix since it would imply that asset 1 is negatively correlated to asset 2 and asset 3. But asset 2 is negatively correlated to asset 3 which contradicts the first statement. In general a covariance matrix has to be positive semi-definite and symmetric, and conversely every positive semi-definite symmetric ...

5

The most important difference is that the calculations are based on a "stressed" historical period in the markets as opposed to the most recent X number of years.

5

Depending of $\lambda$, pasts observations will be weighted differently, if you compute the volatility at time $t$ , the $t-1$ observation will be weighted by $(1-\lambda)*\lambda^{0}$, the $t-2$ observation by $(1-\lambda)*\lambda^{1}$ and so on so forth. For $\lambda= 0.94$ : The first observation is weighted by = $(1-0.94) * 0.94^0 =0.06%$ The second ...

5

There are a few different ways to calculate VaR. Historical Method For this method, you calculate the return of your portfolio each day, and get a list of daily returns over your calibration period. Once you have this, then you find the 5th percentile to give the 95% VaR. The advantage of this method is that it is the most straightforward to compute, and ...

5

VaR is not sub-additive in general. Relying on Mark Joshi comment, there are particular cases where it can be. Such cases occur for portfolios containing elliptically distributed risk factors. Of course the normal distribution is among the elliptical distributions family. The latter can be helpful for analytical VaR modelling as an elliptical model is ...

4

Do $N$ MC simulations of $M$ samples, calculating your estimate of VaR for each one $\{\widehat{VaR}_i\}_{i=1}^N$ and you now have an IID sample! Take the sample (or unbiased) standard deviation for your estimate of VaR (this is probably what you mean by error) $SD(\widehat{VaR})=\sqrt{\frac{1}{N-1} \sum_{i=1}^N (\widehat{VaR}_i - \overline{VaR})^2}$ and of ...

4

EVT has pluses and minuses, but (under certain conditions) provides the best estimate of extreme quantile returns in a portfolio given the data available. Probably the simplest and easiest way to do this is to use the peak over threshold method and fit the Generalized Pareto Distribution (GPD). The GPD is very convenient for calculating VaR and ES. A good ...

4

By definition, your loss cannot be positive, so you'd set the VaR to zero. But it really depends, on how you calculate your VaR. If you calculate your returns, sort them and look at the 5% quantile (which, as you say, may be positive), then you'd simply set your VaR to zero. But if you treat your returns as realizations of some (unknown) random variable, ...

4

To answer you question "is it because X is a mixture of a continous and discrete Random Variable": the answer is no. The mean reasons are (1) the sample size (which is limited / countable) (2) the fact that you're trying to get the tail value and (3) the shape of the KDE (distribution).The theoretical value of 3.088 will be emprirically calculated if and ...

4

Let $u=t^{-1}_v(\alpha)$ and recall that $g_v(u)=c_v(v+u^2)^{-\frac{v+1}2}$ for some constant $c_v$. By the formulas you provided, $$\begin{eqnarray*}\lim_{\alpha\to 1^-}\frac{\mathrm{ES}_\alpha(X)}{\mathrm{VaR}_\alpha(X)}&=&\lim_{\alpha\to 1^-} \frac{g_v(t^{-1}_v(\alpha))}{(1-\alpha)(v-1)\left(\frac{t^{-1}(\alpha)}{v+(t^{-1}(\alpha))^2}\right)}\\ =\... 4 What are the underlying assumptions for doing this Assumption: Historical returns are lognormally distributed with no autocorrelation. can those assumptions be tested statistically Testing: \sqrt{xy} = \sqrt{x} \sqrt{y} Substitute time t and variance \sigma^2 for x and y respectively \sqrt{t\sigma^2} = \sqrt{t} \sqrt{\sigma^2} = \sigma\... 4 If you have a covariance matrix, Q the VaR is a measure of the standard deviation of the portfolio, ie.$$VaR, V \propto \sqrt{S^T Q S}$$and,$$ \frac{\partial V}{\partial S} = \frac{QS}{V} $$Suppose you had 3 assets, with large positions in the first two assets, and small position in the third, AND that the first two were perfectly negatively correlated,... 4 Good question! I think there's some semantics to be thought about first: The word Hedging commonly implies that you want to hedge the changes in the present value of your total position (\Pi=PV(A) +wPV(B)), with w the hedging weight. This statement can be understood locally: I want to hedge ('immunize') my portfolio to local changes in the **underlying** ... 3 More often than not, I prefer to work with a scenario representation. That is, I will simulate from the distribution and calculate the VaR and CVaR as appropriate. This is especially the case for forward-looking analysis of portfolios' CVaR, rather than in evaluating the historical returns of some portfolio. If for some reason I can't do the scenario ... 3 In general I would answer your question in the following way: Alternatives to VaR which share most of its helpful properties but not its shortcomings are the so called coherent risk measures. They have the following properties: monotonicity sub-additivity homogeneity and translational invariance One example would be the conditional value-at-risk. You ... 3 Given that by delta means that if the price goes up by 0.01% i.e. one basis point, you gain 15 and vice versa if the price goes down by one basis point. You know that the daily standard deviation is 2.2%, than again you know that  220*15 = 3300 is the standard deviation of your portfolio. So, since we are using a normal distribution you can look at a table ... 3 You need to isolate the risk factors that impact your forward contract, which is your spot fx rate, and the two rates of each currency that underlies the forward contract. You therefore need to estimate the VaRs of each of those risk factors. You also need the correlations between the underlying risk factors. For example, a forward to buy USD in exchange ... 3 You could simulate many (100000) 3 day price paths for the stock using the geometric brownian motion. Then for each simulated path, calculate the option value and store them. Then calculate the return difference for each of the calls and order them from smallest to largest. The 5% cutoff is your 3 day 95% VaR. 3 Yes, it exists and it is called ccgarch package. You can install that by simply running in R install.packages("ccgarch") and learn more about that on the CRAN relative paper. Moreover, I suggest you to read this lecture hold by the author during an R conference. Hope this help. 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) = \frac{\nu}{(\nu-2)}. $$Thus you have to define$$\sigma = sd * \sqrt{\frac{(\nu-2)}{\...

3

VaR gives us an idea of possible losses given our current portfolio and the markets as they are today. The idea behind stressed VaR is to get an idea of possible losses given more worse market conditions. To do this we will "stress" the inputs such as volatilities, interest rates FX rates etc. Thus making them much more unfavorable than they really are.

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