Fitting vast dimensional time-varying covariance models.
Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hu...
| Main Authors: | , , |
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| Format: | Working paper |
| Language: | English |
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Oxford-Man Institute of Quantitative Finance
2008
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| _version_ | 1797079576471404544 |
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| author | Engle, R Shephard, N Sheppard, K |
| author_facet | Engle, R Shephard, N Sheppard, K |
| author_sort | Engle, R |
| collection | OXFORD |
| description | Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the cross-sectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared. |
| first_indexed | 2024-03-07T00:47:56Z |
| format | Working paper |
| id | oxford-uuid:85565326-c877-42ae-9b20-a2a3b35b03b1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:47:56Z |
| publishDate | 2008 |
| publisher | Oxford-Man Institute of Quantitative Finance |
| record_format | dspace |
| spelling | oxford-uuid:85565326-c877-42ae-9b20-a2a3b35b03b12022-03-26T21:56:53ZFitting vast dimensional time-varying covariance models.Working paperhttp://purl.org/coar/resource_type/c_8042uuid:85565326-c877-42ae-9b20-a2a3b35b03b1EnglishDepartment of Economics - ePrintsOxford-Man Institute of Quantitative Finance2008Engle, RShephard, NSheppard, KBuilding models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the cross-sectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared. |
| spellingShingle | Engle, R Shephard, N Sheppard, K Fitting vast dimensional time-varying covariance models. |
| title | Fitting vast dimensional time-varying covariance models. |
| title_full | Fitting vast dimensional time-varying covariance models. |
| title_fullStr | Fitting vast dimensional time-varying covariance models. |
| title_full_unstemmed | Fitting vast dimensional time-varying covariance models. |
| title_short | Fitting vast dimensional time-varying covariance models. |
| title_sort | fitting vast dimensional time varying covariance models |
| work_keys_str_mv | AT engler fittingvastdimensionaltimevaryingcovariancemodels AT shephardn fittingvastdimensionaltimevaryingcovariancemodels AT sheppardk fittingvastdimensionaltimevaryingcovariancemodels |