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Monte Carlo risk assessment as a service in the cloud

Monte Carlo risk assessment as a service in the cloud
Monte Carlo risk assessment as a service in the cloud
Limitations imposed by the traditional practice in financial institutions of running price and risk analysis on the desktop drive analysts to use simplified models in order to obtain acceptable response times. Typically these models make assumptions about the distribution of market events like defaults. One popular model is Gaussian Copula which assumes events are independent and form a “normal” (Gaussian) distribution. This model provides good risk estimates in many situations but unfortunately it systematically underestimates risk for unusual market conditions, the very time when analysts most need good estimates of risk. They run away from using Monte Carlo simulations since they can take days. We propose a Monte Carlo Simulation as a Service (MCSaaS) which takes the benefits from two sides: The accuracy and reliability of typical Monte Carlo simulations and the fast performance of running and completing the service in the Cloud.
In the use of MCSaaS, we propose to remove outliers to enhance the improvement in accuracy. In the process of doing so, we propose three hypotheses. We describe our rationale and steps involved to validate them. We set up three major experiments. We confirm that firstly, MCSaaS with outlier removal can reduce percentage of errors to 0.1%. Secondly, MCSaaS with outlier removal is expected to have slower performance than the one without removal but is kept within 1 second difference. Thirdly, MCSaaS in the Cloud has a significant performance improvement over the Gaussian Copula on Desktop. We describe the architecture of deployment, together with examples and results from a proof of concept implementation which shows our approach is able to match response rates of desktop systems without making simplifying assumptions and the associated potential threat to the accuracy of the results.
monte carlo methods, monte carlo simulation as a service (MCSaaS), least square methods (LSM), gauusian copula
1741-8763
Chang, Victor
a7c75287-b649-4a63-a26c-6af6f26525a4
Walters, Robert John
7b8732fb-3083-4f4d-844e-85a29daaa2c1
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Chang, Victor
a7c75287-b649-4a63-a26c-6af6f26525a4
Walters, Robert John
7b8732fb-3083-4f4d-844e-85a29daaa2c1
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0

Chang, Victor, Walters, Robert John and Wills, Gary (2015) Monte Carlo risk assessment as a service in the cloud. International Journal of Business Integration and Management, 7 (3).

Record type: Article

Abstract

Limitations imposed by the traditional practice in financial institutions of running price and risk analysis on the desktop drive analysts to use simplified models in order to obtain acceptable response times. Typically these models make assumptions about the distribution of market events like defaults. One popular model is Gaussian Copula which assumes events are independent and form a “normal” (Gaussian) distribution. This model provides good risk estimates in many situations but unfortunately it systematically underestimates risk for unusual market conditions, the very time when analysts most need good estimates of risk. They run away from using Monte Carlo simulations since they can take days. We propose a Monte Carlo Simulation as a Service (MCSaaS) which takes the benefits from two sides: The accuracy and reliability of typical Monte Carlo simulations and the fast performance of running and completing the service in the Cloud.
In the use of MCSaaS, we propose to remove outliers to enhance the improvement in accuracy. In the process of doing so, we propose three hypotheses. We describe our rationale and steps involved to validate them. We set up three major experiments. We confirm that firstly, MCSaaS with outlier removal can reduce percentage of errors to 0.1%. Secondly, MCSaaS with outlier removal is expected to have slower performance than the one without removal but is kept within 1 second difference. Thirdly, MCSaaS in the Cloud has a significant performance improvement over the Gaussian Copula on Desktop. We describe the architecture of deployment, together with examples and results from a proof of concept implementation which shows our approach is able to match response rates of desktop systems without making simplifying assumptions and the associated potential threat to the accuracy of the results.

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More information

Published date: 6 August 2015
Keywords: monte carlo methods, monte carlo simulation as a service (MCSaaS), least square methods (LSM), gauusian copula
Organisations: Electronics & Computer Science, Electronic & Software Systems

Identifiers

Local EPrints ID: 363678
URI: https://eprints.soton.ac.uk/id/eprint/363678
ISSN: 1741-8763
PURE UUID: fae727e1-708f-461d-8e8f-0bbf80e92580
ORCID for Gary Wills: ORCID iD orcid.org/0000-0001-5771-4088

Catalogue record

Date deposited: 29 Mar 2014 22:26
Last modified: 06 Jun 2018 13:03

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