Value-at-Risk calculates by how much the market value of the portfolio may change over a given horizon with a certain confidence level. For example, a 10-day 95% VaR of 1 mln USD means that the drop in market value over a 10-day period will not be more than 1 mln USD in 95% of the cases. Besides that, the KYOS KyVaR model includes positions arising from all contracts and assets (power plants, gas storage, swing contract). It is possible to compare the VaR with the market risk on previous trading days, thereby highlighting potential trends. The model displays the VaR in a flexible format, so the user can identify the main sources of risk: per book, per commodity or per period.
The main drivers for the Value-at-Risk are (i) the positions and (ii) the price volatility of commodity markets. The VaR model shows both the positions and the volatility per month, giving full insight in the risk drivers.
Each day the model compares the calculated VaR with the appropriate VaR limit. You can set the limit on the total portfolio, but also on a sub-level (book, commodity, etc.). In case a VaR limit is breached, a clear signal is given which can be used to reduce market risk.
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Of course we have made sure to fully embed KyVaR in the KYOS Analytical Platform. Automated data feeds ensure that you get up-to-date VaR calculations every day.
The standard VaR model in the KYOS Analytical Platform is based on the variance-covariance matrix. It is referred to as parametric VaR, normal VaR or varcovar VaR. It is actually the most easy to use and interpret results. As an input it requires the volatilities and correlations of all monthly prices. Historical market prices from the basis to automatically calculate these parameters. The user may overwrite the volatility estimates, either to test sensitivities or because he has more accurate estimates from option markets.
As alternative methodologies, KYOS offers the Monte Carlo simulation approach and the historical simulation approach. Especially when the market price returns do not have a very nice Normal distribution, this may be more accurate. For the Monte Carlo simulation approach, we are using the price simulations from KySim. For the historical simulation approach, the model takes the variations in market prices directly from the history, without any distribution assumption; that is therefore the non-parametric VaR approach.
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