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DTSTART;TZID=America/Toronto:20190425T160000
SEQUENCE:0
TRANSP:TRANSPARENT
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/david-spro
 tt-distinguished-lecture-damir-filipovic-epfl-and
LOCATION:STC - Science Teaching Complex 200 University Ave West Room: 0020 
 Waterloo ON N2L 3G1 Canada
SUMMARY:David Sprott Distinguished Lecture by Damir Filipovic\, EPFL and Sw
 iss\nFinance Institute Senior Chair
CLASS:PUBLIC
DESCRIPTION:A MACHINE LEARNING APPROACH TO PORTFOLIO RISK MANAGEMENT\n\n---
 ----------------------\n\nRisk measurement\, valuation and hedging form an
  integral task in\nportfolio risk management for insurance companies and o
 ther financial\ninstitutions. Portfolio risk arises because the values of
  constituent\nassets and liabilities change over time in response to chan
 ges in the\nunderlying risk factors. The quantification of this risk requ
 ires\nmodeling the dynamic portfolio value process. This boils down to\nco
 mpute conditional expectations of future cash flows over long time\nhoriz
 ons\, e.g.\, up to 40 years and beyond\, which is computationally\nchalle
 nging. \n\nThis lecture presents a framework for dynamic portfolio risk\n
 management in discrete time building on machine learning theory. We\nlear
 n the replicating martingale of the portfolio from a finite\nsample of it
 s terminal cumulative cash flow. The learned replicating\nmartingale is i
 n closed form thanks to a suitable choice of the\nreproducing kernel Hilb
 ert space. We develop an asymptotic theory and\nprove\nconvergence and a c
 entral limit theorem. We also derive finite sample\nerror bounds and conc
 entration inequalities. As application we\ncompute the value at risk and 
 expected shortfall of the one-year loss\nof some stylized portfolios.
DTSTAMP:20260410T080917Z
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