A Radial Basis Function Approach to Financial Time Series Analysis
Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of prac...
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| 語言: | en_US |
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2004
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| 在線閱讀: | http://hdl.handle.net/1721.1/6783 |
| _version_ | 1826189622136274944 |
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| author | Hutchinson, James M. |
| author_facet | Hutchinson, James M. |
| author_sort | Hutchinson, James M. |
| collection | MIT |
| description | Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction. |
| first_indexed | 2024-09-23T08:18:32Z |
| id | mit-1721.1/6783 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:18:32Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/67832019-04-09T17:50:30Z A Radial Basis Function Approach to Financial Time Series Analysis Hutchinson, James M. radial basis functions option pricing parametersestimation time series prediction confidence stock market Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction. 2004-10-20T14:45:36Z 2004-10-20T14:45:36Z 1993-12-01 AITR-1457 http://hdl.handle.net/1721.1/6783 en_US AITR-1457 160 p. 681549 bytes 2849290 bytes application/octet-stream application/pdf application/octet-stream application/pdf |
| spellingShingle | radial basis functions option pricing parametersestimation time series prediction confidence stock market Hutchinson, James M. A Radial Basis Function Approach to Financial Time Series Analysis |
| title | A Radial Basis Function Approach to Financial Time Series Analysis |
| title_full | A Radial Basis Function Approach to Financial Time Series Analysis |
| title_fullStr | A Radial Basis Function Approach to Financial Time Series Analysis |
| title_full_unstemmed | A Radial Basis Function Approach to Financial Time Series Analysis |
| title_short | A Radial Basis Function Approach to Financial Time Series Analysis |
| title_sort | radial basis function approach to financial time series analysis |
| topic | radial basis functions option pricing parametersestimation time series prediction confidence stock market |
| url | http://hdl.handle.net/1721.1/6783 |
| work_keys_str_mv | AT hutchinsonjamesm aradialbasisfunctionapproachtofinancialtimeseriesanalysis AT hutchinsonjamesm radialbasisfunctionapproachtofinancialtimeseriesanalysis |