Extreme-quantile tracking for financial time series
Time series of financial asset values exhibit well known statistical features such as heavy tails and volatility clustering. Strongly present in some series, nonstationarity is a feature that has been somewhat overlooked. This may however be a highly relevant feature when estimating extreme quantiles (VaR) for such series. We propose a nonparametric extension of the classical Peaks-Over-Threshold method to fit the time varying volatility in situations where the stationarity assumption is strongly violated by erratic changes of regime. A back testing study for the UBS share price over the subprime crisis reveals that our approach provides better extreme-quantile (VaR) estimates than methods that ignore nonstationarity.
When requesting a correction, please mention this item's handle: RePEc:chf:rpseri:rp1127. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marilyn Barja)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.