Rights statement: Copyright © 2019, INFORMS
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Online Risk Monitoring Using Offline Simulation
AU - Jiang, Guangxin
AU - Hong, L. Jeff
AU - Nelson, Barry
N1 - Copyright © 2019, INFORMS
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Estimating portfolio risk measures and classifying portfolio risk levels in real time are important yet challenging tasks. In this paper, we propose to build a logistic regression model using data generated in past simulation experiments and to use the model to predict portfolio risk measures and classify risk levels at any time. We further explore regularization techniques, simulation model structure, and additional simulation budget to enhance the estimators of the logistic regression model to make its predictions more precise. Our numerical results show that the proposed methods work well. Our workmay be viewed as an example of the recently proposed idea of simulation analytics, which treats a simulation model as a data generator and proposes to apply data analytics tools to the simulation outputs to uncover conditional statements. Our work shows that the simulation analytics idea is viable and promising in the field of financial risk management.
AB - Estimating portfolio risk measures and classifying portfolio risk levels in real time are important yet challenging tasks. In this paper, we propose to build a logistic regression model using data generated in past simulation experiments and to use the model to predict portfolio risk measures and classify risk levels at any time. We further explore regularization techniques, simulation model structure, and additional simulation budget to enhance the estimators of the logistic regression model to make its predictions more precise. Our numerical results show that the proposed methods work well. Our workmay be viewed as an example of the recently proposed idea of simulation analytics, which treats a simulation model as a data generator and proposes to apply data analytics tools to the simulation outputs to uncover conditional statements. Our work shows that the simulation analytics idea is viable and promising in the field of financial risk management.
U2 - 10.1287/ijoc.2019.0892
DO - 10.1287/ijoc.2019.0892
M3 - Journal article
VL - 32
SP - 356
EP - 375
JO - INFORMS Journal on Computing
JF - INFORMS Journal on Computing
SN - 1091-9856
IS - 2
ER -