research-article

Accounting for secondary uncertainty: efficient computation of portfolio risk measures on multi and many core architectures

Authors:

Blesson Varghese,

Andrew Rau-ChaplinAuthors Info & Claims

WHPCF '13: Proceedings of the 6th Workshop on High Performance Computational Finance

Article No.: 3, Pages 1 - 10

https://doi.org/10.1145/2535557.2535562

Published: 18 November 2013 Publication History

Abstract

Aggregate Risk Analysis is a computationally intensive and a data intensive problem, thereby making the application of high-performance computing techniques interesting. In this paper, the design and implementation of a parallel Aggregate Risk Analysis algorithm on multi-core CPU and many-core GPU platforms are explored. The efficient computation of key risk measures, including Probable Maximum Loss (PML) and the Tail Value-at-Risk (TVaR) in the presence of both primary and secondary uncertainty for a portfolio of property catastrophe insurance treaties is considered. Primary Uncertainty is the the uncertainty associated with whether a catastrophe event occurs or not in a simulated year, while Secondary Uncertainty is the uncertainty in the amount of loss when the event occurs.

A number of statistical algorithms are investigated for computing secondary uncertainty. Numerous challenges such as loading large data onto hardware with limited memory and organising it are addressed. The results obtained from experimental studies are encouraging. Consider for example, an aggregate risk analysis involving 800,000 trials, with 1,000 catastrophic events per trial, a million locations, and a complex contract structure taking into account secondary uncertainty. The analysis can be performed in just 41 seconds on a GPU, that is 24x faster than the sequential counterpart on a fast multi-core CPU. The results indicate that GPUs can be used to efficiently accelerate aggregate risk analysis even in the presence of secondary uncertainty.

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Cited By

Hillier JMitchell‐Wallace KJones MFoote MFoote MHillier JMitchell‐Wallace KJones M(2017)Summary and the FutureNatural catastrophe risk management and modelling10.1002/9781118906057.ch6(455-466)Online publication date: 26-Apr-2017
https://doi.org/10.1002/9781118906057.ch6
Burke NRau-Chaplin AVarghese B(2016)Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architecturesConcurrency and Computation: Practice & Experience10.1002/cpe.369528:3(836-847)Online publication date: 10-Mar-2016
https://dl.acm.org/doi/10.1002/cpe.3695
Rau-Chaplin AYao ZZeh N(2015)Industrial-Scale Ad Hoc Risk Analytics Using MapReduceBig Data Analysis: New Algorithms for a New Society10.1007/978-3-319-26989-4_8(177-206)Online publication date: 17-Dec-2015
https://doi.org/10.1007/978-3-319-26989-4_8

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cover image ACM Conferences

WHPCF '13: Proceedings of the 6th Workshop on High Performance Computational Finance

November 2013

65 pages

ISBN:9781450325073

DOI:10.1145/2535557

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 November 2013

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SC13: International Conference for High Performance Computing, Networking, Storage and Analysis

November 18, 2013

Colorado, Denver

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Cited By

Hillier JMitchell‐Wallace KJones MFoote MFoote MHillier JMitchell‐Wallace KJones M(2017)Summary and the FutureNatural catastrophe risk management and modelling10.1002/9781118906057.ch6(455-466)Online publication date: 26-Apr-2017
https://doi.org/10.1002/9781118906057.ch6
Burke NRau-Chaplin AVarghese B(2016)Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architecturesConcurrency and Computation: Practice & Experience10.1002/cpe.369528:3(836-847)Online publication date: 10-Mar-2016
https://dl.acm.org/doi/10.1002/cpe.3695
Rau-Chaplin AYao ZZeh N(2015)Industrial-Scale Ad Hoc Risk Analytics Using MapReduceBig Data Analysis: New Algorithms for a New Society10.1007/978-3-319-26989-4_8(177-206)Online publication date: 17-Dec-2015
https://doi.org/10.1007/978-3-319-26989-4_8

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