Yes,

Thanks a lot.

Vince

 -----Original Message-----
From: 	"John D. Martin" <J_Martin@baylor.edu>@ENRON [mailto:IMCEANOTES-+22John+20D+2E+20Martin+22+20+3CJ+5FMartin+40baylor+2Eedu+3E+40ENRON@ENRON.com] 
Sent:	Monday, June 18, 2001 9:32 AM
To:	Kaminski, Vince J
Subject:	Have you seen this?

"Conditional Value-at-Risk for General Loss Distributions"

      BY:  TYRRELL R. ROCKAFELLAR
              University of Washington
              Department of Applied Mathematics
           STANISLAV P. URYASEV
              University of Florida
              Department of Industrial and Systems Engineering

Document:  Available from the SSRN Electronic Paper Collection:
           http://papers.ssrn.com/paper.taf?abstract_id=267256

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           http://www.ise.ufl.edu/uryasev/CVaR2.pdf
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Paper ID:  U of Florida, ISE Dept. Working Paper No. 2001-5
    Date:  April 4, 2001

 Contact:  STANISLAV P. URYASEV
   Email:  Mailto:uryasev@ise.ufl.edu
  Postal:  University of Florida
           Department of Industrial and Systems Engineering
           PO Box 116595
           303 Weil Hall
           Gainesville, FL 32611-6595  USA
   Phone:  352-392-3091
     Fax:  352-392-3537
 Co-Auth:  TYRRELL R. ROCKAFELLAR
   Email:  Mailto:rtr@math.washington.edu
  Postal:  University of Washington
           Department of Applied Mathematics
           408 L Guggenheim Hall
           Box 352420
           Seattle, WA 98195-2420  USA

ABSTRACT:
 Fundamental properties of conditional value-at-risk, as a
 measure of risk with significant advantages over value-at-risk,
 are derived for loss distributions in finance that can involve
 discreetness. Such distributions are of particular importance in
 applications because of the prevalence of models based on
 scenarios and finite sampling. Conditional value-at-risk is able
 to quantify dangers beyond value-at-risk, and moreover it is
 coherent. It provides optimization shortcuts which, through
 linear programming techniques, make practical many large-scale
 calculations that could otherwise be out of reach. The numerical
 efficiency and stability of such calculations, shown in several
 case studies, are illustrated further with an example of index
 tracking.

 Keywords: Value-at-risk, conditional value-at-risk, mean
 shortfall, coherent risk measures, risk sampling, scenarios,
 hedging, index tracking, portfolio optimization, risk management


JEL Classification: G0
______________________________

John D. Martin
Carr P. Collins Chair in Finance
Finance Department
Baylor University
PO Box 98004
Waco, TX 76798
254-710-4473 (Office)
254-710-1092 (Fax)
J_Martin@Baylor.edu
web:    http://hsb.baylor.edu/html/martinj/home.html