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Risk-Averse Action Selection Using Extreme Value Theory Estimates of the CVaR
[article]
2020
arXiv
pre-print
In a wide variety of sequential decision making problems, it can be important to estimate the impact of rare events in order to minimize risk exposure. A popular risk measure is the conditional value-at-risk (CVaR), which is commonly estimated by averaging observations that occur beyond a quantile at a given confidence level. When this confidence level is very high, this estimation method can exhibit high variance due to the limited number of samples above the corresponding quantile. To
arXiv:1912.01718v2
fatcat:fvrr2gtqgfalnfxjrwzxcoqo3u