In particular, the capital requirement for general market risk is based on the output of banks' internal value-at-risk models, calibrated to a common supervisory standard.
Empirical work to date presents somewhat conflicting evidence of the accuracy of the value-at-risk models that underlie the market risk capital requirements.
Unlike value-at-risk models
, which are run on a daily basis to assess the market risk in banks' trading activities, credit risk models are run less frequently.
This means that the profit and loss figures used in the backtest could reflect influences not incorporated into the value-at-risk model, potentially introducing bias into the backtest results.
Thus, the supervisory backtest is calibrated to a one-day standard to strike a balance between the need to have a sufficient amount of data to give the backtest statistical power and the desire to determine the accuracy of the value-at-risk model used in the capital calculations.
Value-at-risk models aggregate the several components of price risk into a single quantitative measure of the potential for losses over a specified time horizon.
securities firms, has also advocated the use of value-at-risk models as an important way to measure market risk.
and Basel capital charges Evidence from Emerging and Frontier stock markets", Journal of Financial Stability 8; 303-319.
Although a substantial literature has examined the statistical and economic meaning of Value-at-Risk models
, this article is the first to provide a detailed analysis of the performance of models actually in use.
At one end of the spectrum, the banking and securities industry has a now fairly long history of measuring market risk through value-at-risk models
. The fact that value-at-risk models
were among the first statistical risk models developed reflects the high-frequency and largely continuous nature of market risk and its management,(26) the mark-to-market environment in which most trading activities occur, and the resultant ease of modeling (normality has often been assumed) and availability of comparatively long historical data series around which to calibrate the models.
She also cautioned that value-at-risk models
work very poorly for arbitrage-related businesses, for real estate, and for private equity.
These advances are the outgrowth of both academic research efforts and financial institutions' day-to-day experience with value-at-risk models
. The papers presented in the session on value-at-risk modeling exemplify how academic research can suggest new approaches to addressing real-world problems in risk measurement.