Hierarchical forecasting based on AR-GARCH model in a coherent structure
This paper compares the accuracy of the aggregate forecasting with the bottom-up forecasting based on AR-GARCH model for the return rate of simulated Dow Jones Industrial Average. Most of the existing stock price index studies did not consider the hierarchical structure and often missed the coherent...
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| Veröffentlicht in: | European journal of operational research 2007-01, Vol.176 (2), p.1033-1040 |
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| container_title | European journal of operational research |
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| creator | Sohn, So Young Lim, Michael |
| description | This paper compares the accuracy of the aggregate forecasting with the bottom-up forecasting based on AR-GARCH model for the return rate of simulated Dow Jones Industrial Average. Most of the existing stock price index studies did not consider the hierarchical structure and often missed the coherent relationships between individual components. In this experiment, we simulated 30 coherent components based on AR(2)-GARCH(1,
1) model. Then we evaluated the performance of both forecasting methods ignoring the coherent structure. The results of our experiment indicated that the accuracy of forecasting method varied depending on the correlation degree of 30 coherent components, however the data noise did not significantly influenced the performance of hierarchical forecasting method. |
| doi_str_mv | 10.1016/j.ejor.2005.08.019 |
| format | Article |
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1) model. Then we evaluated the performance of both forecasting methods ignoring the coherent structure. The results of our experiment indicated that the accuracy of forecasting method varied depending on the correlation degree of 30 coherent components, however the data noise did not significantly influenced the performance of hierarchical forecasting method.</description><subject>Applied sciences</subject><subject>AR-GARCH model</subject><subject>Business forecasts</subject><subject>Coherent structure</subject><subject>Dow Jones averages</subject><subject>Dow Jones Industrial Average</subject><subject>Exact sciences and technology</subject><subject>Financial performance</subject><subject>Forecasting</subject><subject>Hierarchical forecasting</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Operations research</subject><subject>Portfolio theory</subject><subject>Rates of return</subject><subject>Simulation</subject><subject>Stochastic models</subject><subject>Studies</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9kcFq3DAQhkVoIdu0L5CTKPRodyTZkgy9LEubLSQUQnsWijzKyuzaW8kbyNtnzIb2FsFoBPr_meEbxq4F1AKE_jrUOEy5lgBtDbYG0V2wlbBGVtpqeMdWoIyppBTmkn0oZQAA0Yp2xbbbhNnnsEvB73mcMgZf5jQ-8gdfsOfTyNf31c36frPlh6nHPU8j9zxMO8w4zrzM-RTmU8aP7H30-4KfXvMV-_Pj--_Ntrr9dfNzs76tQiPNXOlgoAla2V7Z2KC3nfK-NY3ulNA99NHHB2tRWYgyBhWi1Eqo3nbWRulbpa7Y53PdY57-nrDMbphOeaSWTkIjGkkMSCTPopCnUjJGd8zp4POzE-AWYG5wCzC3AHNgHQEj093ZlPGI4Z8D6ZAUi3tyyguj6X6mIKuhlJYnxXH5BKWofANuNx-o3pfXSX0huDH7MaTyfxKrOiPalnTfzjokbE-0DldCwjFgn2gbs-un9NbYL9L2mUA</recordid><startdate>20070116</startdate><enddate>20070116</enddate><creator>Sohn, So Young</creator><creator>Lim, Michael</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20070116</creationdate><title>Hierarchical forecasting based on AR-GARCH model in a coherent structure</title><author>Sohn, So Young ; Lim, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c427t-6c704c638d38f4ea893aa57469316d0dfafb88e380f2fc3cf26313d8988f2a533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Applied sciences</topic><topic>AR-GARCH model</topic><topic>Business forecasts</topic><topic>Coherent structure</topic><topic>Dow Jones averages</topic><topic>Dow Jones Industrial Average</topic><topic>Exact sciences and technology</topic><topic>Financial performance</topic><topic>Forecasting</topic><topic>Hierarchical forecasting</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Operations research</topic><topic>Portfolio theory</topic><topic>Rates of return</topic><topic>Simulation</topic><topic>Stochastic models</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sohn, So Young</creatorcontrib><creatorcontrib>Lim, Michael</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sohn, So Young</au><au>Lim, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hierarchical forecasting based on AR-GARCH model in a coherent structure</atitle><jtitle>European journal of operational research</jtitle><date>2007-01-16</date><risdate>2007</risdate><volume>176</volume><issue>2</issue><spage>1033</spage><epage>1040</epage><pages>1033-1040</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><abstract>This paper compares the accuracy of the aggregate forecasting with the bottom-up forecasting based on AR-GARCH model for the return rate of simulated Dow Jones Industrial Average. Most of the existing stock price index studies did not consider the hierarchical structure and often missed the coherent relationships between individual components. In this experiment, we simulated 30 coherent components based on AR(2)-GARCH(1,
1) model. Then we evaluated the performance of both forecasting methods ignoring the coherent structure. The results of our experiment indicated that the accuracy of forecasting method varied depending on the correlation degree of 30 coherent components, however the data noise did not significantly influenced the performance of hierarchical forecasting method.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ejor.2005.08.019</doi><tpages>8</tpages></addata></record> |
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| identifier | ISSN: 0377-2217 |
| ispartof | European journal of operational research, 2007-01, Vol.176 (2), p.1033-1040 |
| issn | 0377-2217 1872-6860 |
| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals |
| subjects | Applied sciences AR-GARCH model Business forecasts Coherent structure Dow Jones averages Dow Jones Industrial Average Exact sciences and technology Financial performance Forecasting Hierarchical forecasting Operational research and scientific management Operational research. Management science Operations research Portfolio theory Rates of return Simulation Stochastic models Studies |
| title | Hierarchical forecasting based on AR-GARCH model in a coherent structure |
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