Backtesting VaR
Sorry for the inconvenience, but this lecture is restricted to members of Experten.
You are currently not logged in. You need to login or register and become a member of this institution. Checkout our help to find out how you can become a member of an institution.
About the Lecture
The lecture Backtesting VaR by Edu Pristine is from the course Archiv - Market Risks. It contains the following chapters:
- Value at Risk
- VaR Parameters - Holding Period
- VaR Parameters - Confidence Interval
- Backtesting and Exceptions
- Difficulties in Backtesting
- Identify Type1 & Type 2 Errors
- Conditional Coverage in Backtesting Framework
- Basel Committee Rules
Author of lecture Backtesting VaR
Edu Pristine
Customer reviews
5,0 of 5 stars
| 5 Stars |
|
5 |
| 4 Stars |
|
0 |
| 3 Stars |
|
0 |
| 2 Stars |
|
0 |
| 1 Star |
|
0 |
Excerpts from the accompanying material
... to be less restricted, but focus on the tail of that distribution — the worst p percent of outcomes. VaR on a portfolio is the maximum loss we might ...
... markets in which the institution operates. The holding period is, ideally, the length of time it takes to ensure orderly liquidation of positions in that market. However, other factors favor a short holding period: The assumption that the portfolio does ...
... low confidence levels to get a reasonable proportion of excess-loss observations. The choice of confidence level also depends on theoretical ...
... - comparing the historical VaR forecasts with their respective actual portfolio losses. When the model is constructed perfectly, the number of observations accruing outside VaR should be in line with the confidence level. Such observations are ...
... This means the actual return would not be the same as return expected by the VaR calculator. Major difficulty in backtesting but could be taken care of by conducting backtesting on daily returns. Normally, calculating VaR (5%) will represent close to 5 percent loss, say 6 or ...
... an inaccurate model (Type 2 error). Note that the confidence level at which we accept or reject a model ...
... is a predictable number of exceptions and they are equally distributed across time. If there is a bunching of exceptions then the market correlation has changed or our trading positions have altered. Christofferson has proposed extending the unconditional coverage test statistic (LRuc) to allow for ...
... . The penalty for banks with five to nine exceptions is subject to the supervisor's discretion. And is based on the type of model error that caused the exception. ...
... for causes of exceptions and guidance for supervisors: The basic integrity test for model is lacking. Exceptions are because of incorrect data or error ...
... B. Basic integrity of the model is lacking. C. Intraday trading activity. D. Simply bad luck. 2. When an inaccurate model is accepted then it is ...
... 5.99 and he also developed a statistic to determine the serial independence of deviations using the log-likelihood ratio test (LR ind). The statements are most likely 4. There are four base ...
... category is that the model accuracy needs to improve. 2. B. Type II error occurs when an inaccurate model is accepted. 3. A. LR cc > 5.99 for rejecting the model, the second statement is ...
