Portfolio Risk Measurement via Stochastic Mesh with Average Weight

Ben Feng; Guangwu Liu; Kun Zhang

doi:10.1109/WSC57314.2022.10015494

Portfolio Risk Measurement via Stochastic Mesh with Average Weight

Ben Feng, Guangwu Liu, Kun Zhang

College of Business

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Nested simulation has been widely used in the risk measurement of derivative portfolio. The convergence rate of the mean squared error (MSE) of the standard nested simulation is k^-2/3, where k is the simulation budget. To speed the convergence, we propose a stochastic mesh approach with average weight to portfolio risk measurement under the nested setting. We establish the asymptotic properties of the stochastic mesh estimator for portfolio risk, including the bias, variance and then the MSE. In particular, we show that the MSE converges to zero at a rate of k^-1, which is the same as that under the non-nested setting. The proposed method also allows for path dependence of financial instruments in the portfolio. Numerical experiments show that the proposed method performs well. © 2022 IEEE.

Original language	English
Title of host publication	Proceedings of the 2022 Winter Simulation Conference (WSC)
Publisher	IEEE
Pages	903-914
ISBN (Electronic)	978-1-6654-7661-4
DOIs	https://doi.org/10.1109/WSC57314.2022.10015494
Publication status	Published - Dec 2022
Event	2022 Winter Simulation Conference, WSC 2022 - Guilin, China Duration: 11 Dec 2022 → 14 Dec 2022

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2022-December
ISSN (Print)	0891-7736

Conference

Conference	2022 Winter Simulation Conference, WSC 2022
Place	China
City	Guilin
Period	11/12/22 → 14/12/22

Funding

This research is partially supported by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-03755), the Research Grants Council of Hong Kong under grants GRF 11508620, NSFC/RGC Joint Research Grant N CityU105/21, National Natural Science Foundation of China (NNSFC) grants 72101260 and Public Computing Cloud, Renmin University of China.

RGC Funding Information

RGC-funded

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10.1109/WSC57314.2022.10015494

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Cite this

@inproceedings{2a2eb92ed8894c2f9446d84bd6626093,

title = "Portfolio Risk Measurement via Stochastic Mesh with Average Weight",

abstract = "Nested simulation has been widely used in the risk measurement of derivative portfolio. The convergence rate of the mean squared error (MSE) of the standard nested simulation is k-2/3, where k is the simulation budget. To speed the convergence, we propose a stochastic mesh approach with average weight to portfolio risk measurement under the nested setting. We establish the asymptotic properties of the stochastic mesh estimator for portfolio risk, including the bias, variance and then the MSE. In particular, we show that the MSE converges to zero at a rate of k-1, which is the same as that under the non-nested setting. The proposed method also allows for path dependence of financial instruments in the portfolio. Numerical experiments show that the proposed method performs well. {\textcopyright} 2022 IEEE.",

author = "Ben Feng and Guangwu Liu and Kun Zhang",

year = "2022",

month = dec,

doi = "10.1109/WSC57314.2022.10015494",

language = "English",

series = "Proceedings - Winter Simulation Conference",

publisher = "IEEE",

pages = "903--914",

booktitle = "Proceedings of the 2022 Winter Simulation Conference (WSC)",

address = "United States",

note = "2022 Winter Simulation Conference, WSC 2022 ; Conference date: 11-12-2022 Through 14-12-2022",

}

Portfolio Risk Measurement via Stochastic Mesh with Average Weight. / Feng, Ben; Liu, Guangwu; Zhang, Kun.
Proceedings of the 2022 Winter Simulation Conference (WSC). IEEE, 2022. p. 903-914 (Proceedings - Winter Simulation Conference; Vol. 2022-December).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Portfolio Risk Measurement via Stochastic Mesh with Average Weight

AU - Feng, Ben

AU - Liu, Guangwu

AU - Zhang, Kun

PY - 2022/12

Y1 - 2022/12

N2 - Nested simulation has been widely used in the risk measurement of derivative portfolio. The convergence rate of the mean squared error (MSE) of the standard nested simulation is k-2/3, where k is the simulation budget. To speed the convergence, we propose a stochastic mesh approach with average weight to portfolio risk measurement under the nested setting. We establish the asymptotic properties of the stochastic mesh estimator for portfolio risk, including the bias, variance and then the MSE. In particular, we show that the MSE converges to zero at a rate of k-1, which is the same as that under the non-nested setting. The proposed method also allows for path dependence of financial instruments in the portfolio. Numerical experiments show that the proposed method performs well. © 2022 IEEE.

AB - Nested simulation has been widely used in the risk measurement of derivative portfolio. The convergence rate of the mean squared error (MSE) of the standard nested simulation is k-2/3, where k is the simulation budget. To speed the convergence, we propose a stochastic mesh approach with average weight to portfolio risk measurement under the nested setting. We establish the asymptotic properties of the stochastic mesh estimator for portfolio risk, including the bias, variance and then the MSE. In particular, we show that the MSE converges to zero at a rate of k-1, which is the same as that under the non-nested setting. The proposed method also allows for path dependence of financial instruments in the portfolio. Numerical experiments show that the proposed method performs well. © 2022 IEEE.

UR - http://www.scopus.com/inward/record.url?scp=85147428533&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85147428533&origin=recordpage

U2 - 10.1109/WSC57314.2022.10015494

DO - 10.1109/WSC57314.2022.10015494

M3 - RGC 32 - Refereed conference paper (with host publication)

T3 - Proceedings - Winter Simulation Conference

SP - 903

EP - 914

BT - Proceedings of the 2022 Winter Simulation Conference (WSC)

PB - IEEE

T2 - 2022 Winter Simulation Conference, WSC 2022

Y2 - 11 December 2022 through 14 December 2022

ER -

Portfolio Risk Measurement via Stochastic Mesh with Average Weight

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Ger/HKJRS: Microassembly of Nano-robotic Cantilevers with Superlens of Various Morphologies for Scanning Superlens Applications

GRF: Machine Learning Methods for Portfolio Risk Measurement

Cite this

Portfolio Risk Measurement via Stochastic Mesh with Average Weight

Abstract

Publication series

Conference

Funding

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

Ger/HKJRS: Microassembly of Nano-robotic Cantilevers with Superlens of Various Morphologies for Scanning Superlens Applications

GRF: Machine Learning Methods for Portfolio Risk Measurement

Cite this