Market risk

Portfolio risk measures

The principal portfolio measures of market risk are Value at Risk (VaR – a statistical loss measure, as defined on page 56) and stress loss which are applied to both trading and non-trading portfolios.

Value at Risk (VaR)

VaR is a statistically based estimate of the potential loss on the current portfolio from adverse market movements. It expresses the “maximum amount we might lose, but only to a certain level of confidence (99%) and there is therefore a specified statistical probability (1%) that actual loss could be greater than our VaR estimate. Our VaR model assumes a certain “holding period until positions can be closed (10 days) and it assumes that market moves occurring over this holding period will follow a similar pattern to those that have occurred over 10-day periods in the past. Our assessment of past movements is based on data for the past five years and we apply these historical changes in rates, prices, indices etc. (“risk factors) directly to our current positions, a method known as historical simulation. We also measure and report VaR on a 1-day holding period for information and for the purposes of backtesting, as explained below.

The Chairman’s Office annually approves a 10-day VaR limit for UBS as a whole, covering both trading and non-trading businesses, and allocations to the Business Groups, the largest being to the Investment Bank. Within the Business Groups, limits are allocated to lower organizational levels as necessary.

Our VaR measure captures both “general and “residual market risk. General market risk includes directional movements in interest rates, changes in slope or shape of yield curves, widening or tightening of credit spreads by rating class, and directional movements in equity market indices, exchange rates, and precious metal and energy prices. It also includes changes in option implied volatilities in all risk types. Residual risks are risks that cannot be explained by general market moves – broadly changes in the prices of individual debt and equity securities resulting from factors specific to individual issuers. For equity arbitrage strategies, where we are typically long in the stock of one company and short in that of another, we apply a “deal break methodology that assesses the probability of collapse of a merger or takeover with the stock prices reverting to pre-announcement levels. This is a one-off jump move (“event risk), generating the same potential loss for both 10-day and 1-day VaR. It is a somewhat conservative measure but there have been isolated occasions when the break up of a deal has led to large negative contributions to revenues.

The distribution of potential profits and losses produced by historical simulation provides an indication of potential trading revenue volatility, and a change in the general level of VaR would normally be expected to lead to a corresponding change in the volatility of daily trading revenues. However, the 10-day VaR measure takes no account of the mitigating action that can be, and in practice is taken in the event of adverse market moves. The absolute level of 10-day VaR should not, therefore, be interpreted as the likely range of daily trading revenues. VaR based on a 1-day holding period provides a closer estimate of the likely range of daily mark to market profit and loss we are likely to incur on the current portfolio under normal market conditions, but is still based on past events and is dependent upon the quality of available market data. The quality of the VaR model is therefore continuously monitored by backtesting the VaR results for trading books. In backtesting we compare the 1-day VaR calculated on positions at close of business each business day with the actual revenues arising on the same positions on the next business day. These revenues (“backtesting revenues) exclude non-trading revenues such as commissions and fees, and revenues from intraday trading. If the revenue is negative and exceeds the 1-day VaR, a “backtesting exception is considered to have occurred. When VaR is measured at a 99% confidence level, a backtesting exception is expected, on average, one day in a hundred. It should be recognized, however, that neither 1-day nor 10-day VaR, nor the worst case losses in the VaR distributions, reflect the worst loss that could occur as a result of extreme, unusual or unprecedented market conditions. All backtesting exceptions and any exceptional revenues on the profit side of the VaR distribution are investigated, and all backtesting results are reported to senior business management, the Group CRO and Business Group CROs. Although we apply VaR measures to market risk positions arising in non-trading books (generally those carried at amortized cost), we do not backtest the results because the basis of risk measurement is not consistent with the basis of revenue recognition.

Our base metals and soft commodities businesses are not currently captured in VaR, but the model is being enhanced to incorporate the new business. In the meantime it is subject to volume constraints and close monitoring. We estimate that its current impact on reported VaR for the Investment Bank as a whole would be negligible, although it may have a more material impact on the risk type “other, where it will be reported. While an expansion of the business is planned we do not expect it to give rise to a significant increase in overall market risk, given the relatively low correlation of commodity markets with financial markets and the continuing dominance of credit spread and equity arbitrage positions in our risk profile.

Stress loss

Stress loss measures quantify our exposure to more extreme market movements than are normally reflected in VaR and are an essential complement to VaR. VaR measures market risk on a continuous and consistent basis, but it is based on observed historical movements and correlations. Stress loss measures do not have to be (and should not be) constrained by historical events – they are designed to ensure that a wide range of possible outcomes is explored and that we have a full understanding of our vulnerabilities. We therefore consider a variety of stress scenarios within a governance and control framework that is designed to be comprehensive, transparent and responsive to market conditions and developments in the world economy.

Our “standard scenarios_ are forward-looking, macro-economic scenarios, bringing together various combinations of potential market events to reflect the most common types of stress scenario. They cover the conditions that might be seen in an industrial country market crash with a range of yield curve and credit spread behavior, and in an emerging market crisis, with and without currency pegs breaking. We also have a “general recovery_ scenario. We run the standard scenarios continuously, and it is against these that we track the development of our stress loss exposure and make comparisons from one period to the next. We also set limits on stress loss exposure measured against these scenarios for all Business Groups. The scenarios and their components are reviewed and re-approved annually by the Chairman’s Office.

We also run ad hoc and position-centric scenarios i.e. scenarios reflecting current concerns, such as sharp movements in energy prices or the impact of increased geopolitical instability in specific regions, and scenarios that attempt to capture any particular vulnerabilities or aspects of our exposure that are not fully covered by the standard scenarios. Such scenarios, by definition, must be constantly adapted to changing circumstances and portfolios. We do not apply limits against them but the results are reported to senior management.

While the standard scenarios are broadly based on generic elements of past market crises, there may be major stress events of the past that we consider to be of continuing relevance. Once they have dropped out of the five year historical time series used for VaR, we may therefore continue to apply them directly to our positions. The results can be used to benchmark the severity of our other stress scenarios and to ensure that we retain the memory of past events, although we would not apply limits to such scenarios.

Finally, we analyze the VaR results beyond the 99% confidence level (the “tails_ of the distribution) to better understand the potential risks of the portfolio and to help identify risk concentrations. The results of this analysis are valuable in their own right but can also be used to formulate position-centric stress tests.

Most major financial institutions employ stress tests, but their approaches differ widely and there is no benchmark or industry standard in terms of stress scenarios or the way they are applied to an institution’s positions. Furthermore, the impact of a given stress scenario, even if measured in the same way across institutions, depends entirely on the make up of each institution’s portfolio, and a scenario highly applicable to one institution may have no relevance to another. Comparison of stress results between institutions can therefore be highly misleading, and for this reason we do not publish quantitative stress results.