Better Confidence Intervals For Importance Sampling
It is well known that for highly skewed distributions the standard method of using the t statistic for the confidence interval of the mean does not give robust results. This is an important problem for importance sampling (IS) as its final distribution is often skewed due to a heavy tailed weight distribution. In this paper, we first explain Hall's transformation and its variants to correct the confidence interval of the mean and then evaluate the performance of these methods for two numerical examples from finance which have closed-form solutions. Finally, we assess the performance of these methods for credit risk examples. Our numerical results suggest that Hall's transformation or one of its variants can be safely used in correcting the two-sided confidence intervals of financial simulations.
Volume (Year): 13 (2010)
Issue (Month): 08 ()
|Contact details of provider:|| Web page: http://www.worldscinet.com/ijtaf/ijtaf.shtml|
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:wsi:ijtafx:v:13:y:2010:i:08:p:1279-1291. 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: (Tai Tone Lim)
If references are entirely missing, you can add them using this form.