Invariant Probabilistic Sensitivity Analysis

被引:60
作者
Baucells, Manel [1 ,2 ]
Borgonovo, Emanuele [3 ,4 ]
机构
[1] RAND Corp, Santa Monica, CA 90401 USA
[2] Univ Pompeu Fabra, Dept Econ & Business, Barcelona 08018, Spain
[3] Bocconi Univ, Dept Decis Sci, I-20136 Milan, Italy
[4] Bocconi Univ, ELEUSI, I-20136 Milan, Italy
关键词
probabilistic sensitivity; investment valuation; risk analysis; decision analysis; scale invariance; UNCERTAINTY IMPORTANCE; DECISION-ANALYSIS; RISK; INFORMATION; DISTANCE; STREAMS; INCOME; MODEL;
D O I
10.1287/mnsc.2013.1719
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
In evaluating opportunities, investors wish to identify key sources of uncertainty. We propose a new way to measure how sensitive model outputs are to each probabilistic input (e.g., revenues, growth, idiosyncratic risk parameters). We base our approach on measuring the distance between cumulative distributions (risk profiles) using a metric that is invariant to monotonic transformations. Thus, the sensitivity measure will not vary by alternative specifications of the utility function over the output. To measure separation, we propose using either Kuiper's metric or Kolmogorov-Smirnov's metric. We illustrate the advantages of our proposed sensitivity measure by comparing it with others, most notably, the contribution-to-variance measures. Our measure can be obtained as a by-product of a Monte Carlo simulation. We illustrate our approach in several examples, focusing on investment analysis situations.
引用
收藏
页码:2536 / 2549
页数:14
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