Online Estimation and Optimization of Utility-Based Shortfall Risk. Article Swipe
Utility-Based Shortfall Risk (UBSR) is a risk metric that is increasingly popular in financial applications, owing to certain desirable properties that it enjoys. We consider the problem of estimating UBSR in a recursive setting, where samples from the underlying loss distribution are available one-at-a-time. We cast the UBSR estimation problem as a root finding problem, and propose stochastic approximation-based estimations schemes. We derive non-asymptotic bounds on the estimation error in the number of samples. We also consider the problem of UBSR optimization within a parameterized class of random variables. We propose a stochastic gradient descent based algorithm for UBSR optimization, and derive non-asymptotic bounds on its convergence.
Related Topics
Concepts
Parameterized complexity
Mathematical optimization
Stochastic optimization
Convergence (economics)
Metric (unit)
Optimization problem
Computer science
Stochastic gradient descent
Expected shortfall
Class (philosophy)
Gradient descent
Mathematics
Stochastic programming
Risk management
Algorithm
Finance
Economics
Operations management
Artificial neural network
Artificial intelligence
Economic growth
Machine learning
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/pdf/2111.08805.pdf
- OA Status
- green
- References
- 42
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3211934220
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3211934220Canonical identifier for this work in OpenAlex
- Title
-
Online Estimation and Optimization of Utility-Based Shortfall Risk.Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-16Full publication date if available
- Authors
-
Arvind S. Menon, L. A. Prashanth, Krishna JagannathanList of authors in order
- Landing page
-
https://arxiv.org/pdf/2111.08805.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2111.08805.pdfDirect OA link when available
- Concepts
-
Parameterized complexity, Mathematical optimization, Stochastic optimization, Convergence (economics), Metric (unit), Optimization problem, Computer science, Stochastic gradient descent, Expected shortfall, Class (philosophy), Gradient descent, Mathematics, Stochastic programming, Risk management, Algorithm, Finance, Economics, Operations management, Artificial neural network, Artificial intelligence, Economic growth, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.estimations | 59 |
| abstract_inverted_index.convergence. | 106 |
| abstract_inverted_index.distribution | 40 |
| abstract_inverted_index.increasingly | 10 |
| abstract_inverted_index.optimization | 81 |
| abstract_inverted_index.Utility-Based | 0 |
| abstract_inverted_index.applications, | 14 |
| abstract_inverted_index.optimization, | 99 |
| abstract_inverted_index.parameterized | 84 |
| abstract_inverted_index.non-asymptotic | 63, 102 |
| abstract_inverted_index.one-at-a-time. | 43 |
| abstract_inverted_index.approximation-based | 58 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |