If I were a crazy Russian hedge fund strategist, I would suggest looking more closely at the theory relating to Brownian motions and thus the application and theory of random walks (which they are about to undertake)!

You people that make assumptions as these, to bet farms, need to think in terms of a drunk, who has consumed high quantities of vodka, as you proceed on your “random walk”>

The daytrader, as a drunk, strikes out upon a walk Monday, away from the lamp post/computer terminal, which contains capital efficiency, i.e, your utility for future value, future consumption; the farm.

From this PC daytrade station, you will lurch forward by making a step to call a broker via your terminal screen, and thus take random bets every 1000/second (OR WHATEVER); after two SUCH steps or lurches, you may wobble about and return to your original position from which you started.

Your random pattern of daytrading, however, is subject to a statistical pattern, where the losses or gains you make in relation to your starting point, ARE proportional to the square root of the number of chips you have placed upon the DAYTRADE CASINO table.

Thus, your current position is somewhat related to your level of cognition and ability to discern how much vodka you should drink and thus how wild your next bets will be, because the distance to your next gain will be related to your loss and you will have to return to your starting point with either empty pockets and a shirt covered in FOUL SMELLING mucus, or stay where you are and wait until sober, and thus be able to make a more educated guess as to the reality associated with yield returns!

I propose a drink, a toast to all our daytrader friends that will be lost in the great blizzard of 2008. Za vashe zdorovye!!!!

I love you crazy guys, I embrace you AND HOPE TO SEE YOU AT THE NEXT LAMP POST!!!!!

Ok, Hello (out there), the key factor in this model, as with many models is time, time in this case, refers to 1 day, e.g, the possible loss to the model in one days time.

Thus, if you make the wrong bet and the time-based result impacts of performance related to 1 day, your model must assume that your confidence remains the same in regard to VAR going forward.

Thus your model can gain in negative momentum in the form of trying to anticipate the probability of either more good days or bad days, but still related to the next day VAR.

This is how a drunk gambler at a casino justifies making bigger bets after tossing “good money” after “bad money” by using theoretical statistical deviations from reality to justify the next bet, which more than likely will be the wrong bet.

The solution to this problem is more vodka!

Future value is all about confidence levels!!!!!!!!!!

The confidence level is the interval estimate in which the VaR would not be expected to exceed the maximum loss. Commonly used confidence levels are 99% and 95%. Confidence levels are not indications of probabilities.

Consider a trading portfolio. Its market value in US dollars today is known, but its market value tomorrow is not known. The investment bank holding that portfolio might report that its portfolio has a 1-day VaR of $4 million at the 95% confidence level. This implies that under normal trading conditions the bank can be 95% confident that a change in the value of its portfolio would not result in a decrease of more than $4 million during 1 day. This is equivalent to saying that there is a 5% confidence level that the value of its portfolio will decrease by $4 million or more during 1 day. A 95% confidence interval does not imply a 95% chance of the event happening, the actual probability of the event cannot be determined.
The key point to note is that the target confidence level (95% in the above example) is the given parameter here; the output from the calculation ($4 million in the above example) is the maximum loss (the value at risk) at that confidence level.

▪ 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 non-linear returns (e.g., options) because the computational effort required is non-trivial. Note that for portfolios without these complicated securities, such as a portfolio of stocks, the variance-covariance 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.

According to “Quantitative Risk Management”, McNeil, Frey, Embrechts, 2005 pg. 38.
Given some confidence level the VaR of the portfolio at the confidence level α is given by the smallest number l such that the probability that the loss L exceeds l is not larger than (1 − α)

In probabilistic terms VaR is a quantile of the loss distribution.

http://en.wikipedia.org/wiki/Value_at_Risk