Risk Estimation via Regression
We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate financial risk factors and an inner simulation level is used to price securities and compute portfolio losses given risk factor outcomes. The mean...
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| Veröffentlicht in: | Operations research 2015-09, Vol.63 (5), p.1077-1097 |
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| container_title | Operations research |
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| creator | Broadie, Mark Du, Yiping Moallemi, Ciamac C. |
| description | We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate financial risk factors and an inner simulation level is used to price securities and compute portfolio losses given risk factor outcomes. The mean squared error (MSE) of standard nested simulation converges at the rate
k
−2/3
, where
k
measures computational effort. The proposed regression method combines information from different risk factor realizations to provide a better estimate of the portfolio loss function. The MSE of the regression method converges at the rate
k
−1
until reaching an asymptotic bias level which depends on the magnitude of the regression error. Numerical results consistent with our theoretical analysis are provided and numerical comparisons with other methods are also given. |
| doi_str_mv | 10.1287/opre.2015.1419 |
| format | Article |
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k
−2/3
, where
k
measures computational effort. The proposed regression method combines information from different risk factor realizations to provide a better estimate of the portfolio loss function. The MSE of the regression method converges at the rate
k
−1
until reaching an asymptotic bias level which depends on the magnitude of the regression error. Numerical results consistent with our theoretical analysis are provided and numerical comparisons with other methods are also given.</description><identifier>ISSN: 0030-364X</identifier><identifier>EISSN: 1526-5463</identifier><identifier>DOI: 10.1287/opre.2015.1419</identifier><language>eng</language><publisher>Linthicum: INFORMS</publisher><subject>Analysis ; Asymptotic methods ; CONTEXTUAL AREAS ; Convergence ; decision analysis: risk ; Estimating techniques ; Financial risk ; Mean square errors ; Monte Carlo method ; Monte Carlo simulation ; Operations research ; Regression ; Regression analysis ; Risk analysis ; Risk assessment ; Simulation ; statistics: estimation</subject><ispartof>Operations research, 2015-09, Vol.63 (5), p.1077-1097</ispartof><rights>2015 INFORMS</rights><rights>COPYRIGHT 2015 Institute for Operations Research and the Management Sciences</rights><rights>COPYRIGHT 2015 Institute for Operations Research and the Management Sciences</rights><rights>Copyright Institute for Operations Research and the Management Sciences Sep/Oct 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c670t-9b6a7af1e4c1e88a31d9d31fe8be702a3b28bd5ea8211df072552285082e2fe33</citedby><cites>FETCH-LOGICAL-c670t-9b6a7af1e4c1e88a31d9d31fe8be702a3b28bd5ea8211df072552285082e2fe33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/24540434$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://pubsonline.informs.org/doi/full/10.1287/opre.2015.1419$$EHTML$$P50$$Ginforms$$H</linktohtml><link.rule.ids>314,776,780,799,3679,27901,27902,57992,58225,62589</link.rule.ids></links><search><creatorcontrib>Broadie, Mark</creatorcontrib><creatorcontrib>Du, Yiping</creatorcontrib><creatorcontrib>Moallemi, Ciamac C.</creatorcontrib><title>Risk Estimation via Regression</title><title>Operations research</title><description>We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate financial risk factors and an inner simulation level is used to price securities and compute portfolio losses given risk factor outcomes. The mean squared error (MSE) of standard nested simulation converges at the rate
k
−2/3
, where
k
measures computational effort. The proposed regression method combines information from different risk factor realizations to provide a better estimate of the portfolio loss function. The MSE of the regression method converges at the rate
k
−1
until reaching an asymptotic bias level which depends on the magnitude of the regression error. Numerical results consistent with our theoretical analysis are provided and numerical comparisons with other methods are also given.</description><subject>Analysis</subject><subject>Asymptotic methods</subject><subject>CONTEXTUAL AREAS</subject><subject>Convergence</subject><subject>decision analysis: risk</subject><subject>Estimating techniques</subject><subject>Financial risk</subject><subject>Mean square errors</subject><subject>Monte Carlo method</subject><subject>Monte Carlo simulation</subject><subject>Operations research</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Risk analysis</subject><subject>Risk assessment</subject><subject>Simulation</subject><subject>statistics: estimation</subject><issn>0030-364X</issn><issn>1526-5463</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><recordid>eNqFkctLAzEQxoMoWB9Xb0pB8OTWvHd7lFIfIAii4C1kd2fX1Ha3ZrKi_71ZKmqhIDkMGX7fl8x8hBwxOmI8Sy_apYcRp0yNmGTjLTJgiutESS22yYBSQROh5fMu2UOcUUrHSqsBOXlw-DqcYnALG1zbDN-dHT5A7QExXg_ITmXnCIffdZ88XU0fJzfJ3f317eTyLil0SkMyzrVNbcVAFgyyzApWjkvBKshySCm3IudZXiqwGWesrGjKleI8UzTjwCsQYp-crnyXvn3rAIOZtZ1v4pOG6zTVOg4of6nazsG4pmqDt8XCYWEupZCKSapopJINVA0NeDtvG6hcbK_xow18PCUsXLFRcLYmiEyAj1DbDtGsg-d_wLxD1_RrbdDVLwFX_KaPFL5F9FCZpY-x-E_DqOkjNn3Epo_Y9BFHwfFKMMPQ-h-aSyVptP3dRD-UX-B_fl-PQq0t</recordid><startdate>20150901</startdate><enddate>20150901</enddate><creator>Broadie, Mark</creator><creator>Du, Yiping</creator><creator>Moallemi, Ciamac C.</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>XI7</scope><scope>JQ2</scope><scope>K9.</scope></search><sort><creationdate>20150901</creationdate><title>Risk Estimation via Regression</title><author>Broadie, Mark ; Du, Yiping ; Moallemi, Ciamac C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c670t-9b6a7af1e4c1e88a31d9d31fe8be702a3b28bd5ea8211df072552285082e2fe33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Analysis</topic><topic>Asymptotic methods</topic><topic>CONTEXTUAL AREAS</topic><topic>Convergence</topic><topic>decision analysis: risk</topic><topic>Estimating techniques</topic><topic>Financial risk</topic><topic>Mean square errors</topic><topic>Monte Carlo method</topic><topic>Monte Carlo simulation</topic><topic>Operations research</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Risk analysis</topic><topic>Risk assessment</topic><topic>Simulation</topic><topic>statistics: estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Broadie, Mark</creatorcontrib><creatorcontrib>Du, Yiping</creatorcontrib><creatorcontrib>Moallemi, Ciamac C.</creatorcontrib><collection>CrossRef</collection><collection>Gale Business: Insights</collection><collection>Business Insights: Essentials</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Broadie, Mark</au><au>Du, Yiping</au><au>Moallemi, Ciamac C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Risk Estimation via Regression</atitle><jtitle>Operations research</jtitle><date>2015-09-01</date><risdate>2015</risdate><volume>63</volume><issue>5</issue><spage>1077</spage><epage>1097</epage><pages>1077-1097</pages><issn>0030-364X</issn><eissn>1526-5463</eissn><abstract>We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate financial risk factors and an inner simulation level is used to price securities and compute portfolio losses given risk factor outcomes. The mean squared error (MSE) of standard nested simulation converges at the rate
k
−2/3
, where
k
measures computational effort. The proposed regression method combines information from different risk factor realizations to provide a better estimate of the portfolio loss function. The MSE of the regression method converges at the rate
k
−1
until reaching an asymptotic bias level which depends on the magnitude of the regression error. Numerical results consistent with our theoretical analysis are provided and numerical comparisons with other methods are also given.</abstract><cop>Linthicum</cop><pub>INFORMS</pub><doi>10.1287/opre.2015.1419</doi><tpages>21</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | Operations research, 2015-09, Vol.63 (5), p.1077-1097 |
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| source | Jstor Complete Legacy; Informs; EBSCOhost Business Source Complete |
| subjects | Analysis Asymptotic methods CONTEXTUAL AREAS Convergence decision analysis: risk Estimating techniques Financial risk Mean square errors Monte Carlo method Monte Carlo simulation Operations research Regression Regression analysis Risk analysis Risk assessment Simulation statistics: estimation |
| title | Risk Estimation via Regression |
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