Yahoo Answers: Answers and Comments for I want to know about how to calculate Value at Risk (VaR) if somebody know about it pls guide me with examples [Small Business]
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Yahoo Answers: Answers and Comments for I want to know about how to calculate Value at Risk (VaR) if somebody know about it pls guide me with examples [Small Business]
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From Anonymous: In the following, return means percentage chan...
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https://answers.yahoo.com/question/index?qid=20070718035741AALO0TN
Tue, 24 Jul 2007 16:54:58 +0000
In the following, return means percentage change in value.
A variety of models exist for estimating VaR. Each model has its own set of assumptions, but the most common assumption is that historical market data is our best estimator for future changes. Common models include:
(a) variancecovariance (VCV), assuming that risk factor returns are always (jointly) normally distributed and that the change in portfolio value is linearly dependent on all risk factor returns,
(b) the historical simulation, assuming that asset returns in the future will have the same distribution as they had in the past (historical market data),
(c) Monte Carlo simulation, where future asset returns are more or less randomly simulated
The variancecovariance, or deltanormal, model was popularized by J.P Morgan (now J.P. Morgan Chase) in the early 1990s when they published the RiskMetrics Technical Document. In the following, we will take the simple case, where the only risk factor for the portfolio is the value of the assets themselves. The following two assumptions enable to translate the VaR estimation problem into a linear algebraic problem:
(1) The portfolio is composed of assets whose deltas are linear, more exactly: the change in the value of the portfolio is linearly dependent on (i.e. is a linear combination of) all the changes in the values of the assets, so that also the portfolio return is linearly dependent on all the asset returns.
(2) The asset returns are jointly normally distributed.
The implication of (1) and (2) is that the portfolio return is normally distributed because it always holds that a linear combination of jointly normally distributed variables is itself normally distributed.
We will use the following notation:
means “of the return on asset i“ (for σ and μ) and "of asset i" (otherwise)
means “of the return on the portfolio” (for σ and μ) and "of the portfolio" (otherwise)
all returns are returns over the holding period
there are N assets
μ= expected value, i. e. mean
σ = standard deviation
V = initial value (in currency units)
= vector of all ωi (T means transposed)
= covariance matrix = matrix of covariances between all N asset returns, i. e. an NxN matrix
The calculation goes as follows.
(i)
(ii)
The normality assumption allows us to zscale the calculated portfolio standard deviation to the appropriate confidence level. So for the 95% confidence level VaR we get:
(iii)
The benefits of the variancecovariance model are the use of a more compact and maintainable data set which can often be bought from third parties, and the speed of calculation using optimized linear algebra libraries. Drawbacks include the assumption that the portfolio is composed of assets whose delta is linear, and the assumption of a normal distribution of asset returns (i. e. market price returns).
Historical simulation is the simplest and most transparent method of calculation. This involves running the current portfolio across a set of historical price changes to yield a distribution of changes in portfolio value, and computing a percentile (the VaR). The benefits of this method are its simplicity to implement, and the fact that it does not assume a normal distribution of asset returns. Drawbacks are the requirement for a large market database, and the computationally intensive calculation.
Monte Carlo simulation is conceptually simple, but is generally computationally more intensive than the methods described above. The generic MC VaR calculation goes as follows:
Decide on N, the number of iterations to perform.
For each iteration:
Generate a random scenario of market moves using some market model.
Revalue the portfolio under the simulated market scenario.
Compute the portfolio profit or loss (PnL) under the simulated scenario. (i.e. subtract the current market value of the portfolio from the market value of the portfolio computed in the previous step).
Sort the resulting PnLs to give us the simulated PnL distribution for the portfolio.
VaR at a particular confidence level is calculated using the percentile function. For example, if we computed 5000 simulations, our estimate of the 95% percentile would correspond to the 250th largest loss, i.e. (1  0.95) * 5000.
Note that we can compute an error term associated with our estimate of VaR and this error will decrease as the number of iterations increases.
Monte Carlo simulation is generally used to compute VaR for portfolios containing securities with nonlinear returns (e.g. options) since the computational effort required is nontrivial. Note that for portfolios without these complicated securities, such as a portfolio of stocks, the variancecovariance method is perfectly suitable and should probably be used instead. Also note that MC VaR is subject to model risk if the market model is not correct.