Best-Arm Identification Using Extreme Value Theory Estimates of the CVaR
Dylan Troop (),
Frédéric Godin () and
Jia Yuan Yu ()
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Dylan Troop: Concordia Institute of Information System Engineering, Concordia University, Montréal, QC H3G 1M8, Canada
Frédéric Godin: Department of Mathematics and Statistics, Concordia University, Montréal, QC H3G 1M8, Canada
Jia Yuan Yu: Concordia Institute of Information System Engineering, Concordia University, Montréal, QC H3G 1M8, Canada
JRFM, 2022, vol. 15, issue 4, 1-15
We consider a risk-aware multi-armed bandit framework with the goal of avoiding catastrophic risk. Such a framework has multiple applications in financial risk management. We introduce a new conditional value-at-risk (CVaR) estimation procedure combining extreme value theory with automated threshold selection by ordered goodness-of-fit tests, and we apply this procedure to a pure exploration best-arm identification problem under a fixed budget. We empirically compare our results with the commonly used sample average estimator of the CVaR, and we show a significant performance improvement when the underlying arm distributions are heavy-tailed.
Keywords: sequential decision making; multi-armed bandits; conditional value-at-risk; extreme value theory; heavy-tailed distributions; risk-aware reinforcement learning (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:15:y:2022:i:4:p:172-:d:789276
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