A sparse learning approach to relative-volatility-managed portfolio selection
This paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, and investigates its application to finance to provide an innovative construction of a relative-volatility-managed portfolio. The proposed iter...
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| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/155740 |
| _version_ | 1811687485115203584 |
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| author | Pun, Chi Seng |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Pun, Chi Seng |
| author_sort | Pun, Chi Seng |
| collection | NTU |
| description | This paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, and investigates its application to finance to provide an innovative construction of a relative-volatility-managed portfolio. The proposed iterative algorithm, called DECODE, jointly estimates a performance measure of the market and the effective parameter vector in the optimal portfolio solution, where the relative-volatility timing is introduced into the risk exposure of an efficient portfolio via the control of its sparsity. The portfolio’s risk exposure level, which is linked to its sparsity in the proposed framework, is automatically tuned with the latest market condition without using cross validation. The algorithm is efficient as it costs only a few computations of quadratic programming. We prove that the iterative algorithm converges and show the oracle inequalities of the DECODE, which provide sufficient conditions for a consistent estimate of an optimal portfolio. The algorithm can also handle the curse of dimensionality in that the number of training samples is less than the number of assets. Our empirical studies of over-12-year backtest illustrate the relative-volatility timing feature of the DECODE and the superior out-of-sample performance of the DECODE portfolio, which beats the equally weighted portfolio and improves over the shrinkage portfolio. |
| first_indexed | 2024-10-01T05:17:03Z |
| format | Journal Article |
| id | ntu-10356/155740 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:17:03Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1557402023-02-28T19:59:38Z A sparse learning approach to relative-volatility-managed portfolio selection Pun, Chi Seng School of Physical and Mathematical Sciences Science::Mathematics Direct Estimation Iterative Algorithm This paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, and investigates its application to finance to provide an innovative construction of a relative-volatility-managed portfolio. The proposed iterative algorithm, called DECODE, jointly estimates a performance measure of the market and the effective parameter vector in the optimal portfolio solution, where the relative-volatility timing is introduced into the risk exposure of an efficient portfolio via the control of its sparsity. The portfolio’s risk exposure level, which is linked to its sparsity in the proposed framework, is automatically tuned with the latest market condition without using cross validation. The algorithm is efficient as it costs only a few computations of quadratic programming. We prove that the iterative algorithm converges and show the oracle inequalities of the DECODE, which provide sufficient conditions for a consistent estimate of an optimal portfolio. The algorithm can also handle the curse of dimensionality in that the number of training samples is less than the number of assets. Our empirical studies of over-12-year backtest illustrate the relative-volatility timing feature of the DECODE and the superior out-of-sample performance of the DECODE portfolio, which beats the equally weighted portfolio and improves over the shrinkage portfolio. Ministry of Education (MOE) Nanyang Technological University Submitted/Accepted version This work was funded by the Data Science and Artificial Intelligence Research Centre at NanyangTechnological University, grant M4082115, and the Ministry of Education (Singapore), AcRF Tier 2 grant MOE2017-T2-1-044. 2022-03-16T01:22:03Z 2022-03-16T01:22:03Z 2021 Journal Article Pun, C. S. (2021). A sparse learning approach to relative-volatility-managed portfolio selection. SIAM Journal On Financial Mathematics, 12(1), 410-445. https://dx.doi.org/10.1137/19M1291674 1945-497X https://hdl.handle.net/10356/155740 10.1137/19M1291674 2-s2.0-85104468660 1 12 410 445 en M4082115 MOE2017-T2-1-044 SIAM Journal on Financial Mathematics © 2021 Society for Industrial and Applied Mathematics. All rights reserved. This paper was published in SIAM Journal on Financial Mathematics and is made available with permission of Society for Industrial and Applied Mathematics. application/pdf |
| spellingShingle | Science::Mathematics Direct Estimation Iterative Algorithm Pun, Chi Seng A sparse learning approach to relative-volatility-managed portfolio selection |
| title | A sparse learning approach to relative-volatility-managed portfolio selection |
| title_full | A sparse learning approach to relative-volatility-managed portfolio selection |
| title_fullStr | A sparse learning approach to relative-volatility-managed portfolio selection |
| title_full_unstemmed | A sparse learning approach to relative-volatility-managed portfolio selection |
| title_short | A sparse learning approach to relative-volatility-managed portfolio selection |
| title_sort | sparse learning approach to relative volatility managed portfolio selection |
| topic | Science::Mathematics Direct Estimation Iterative Algorithm |
| url | https://hdl.handle.net/10356/155740 |
| work_keys_str_mv | AT punchiseng asparselearningapproachtorelativevolatilitymanagedportfolioselection AT punchiseng sparselearningapproachtorelativevolatilitymanagedportfolioselection |