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Testing adaptive market efficiency in the presence of non-Gaussian uncertainties
One of the central debates in finance concerns the Efficient Market Hypothesis (EMH)—wherein markets are assumed to be efficient in the absolute sense. However, the possibility of time-varying weak-form market efficiency has received increasing attention in recent years. Under the Adaptive Market Hy...
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| Format: | Thesis |
| Language: | English |
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African Institute of Financial Markets and Risk Management
2020
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| _version_ | 1867613256745484288 |
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| access_status_str | Open Access |
| author | Wakandigara, Vykta |
| author2 | Kulikova, Maria |
| author_browse | Kulikova, Maria Wakandigara, Vykta |
| author_facet | Kulikova, Maria Wakandigara, Vykta |
| author_sort | Wakandigara, Vykta |
| collection | Thesis |
| description | One of the central debates in finance concerns the Efficient Market Hypothesis (EMH)—wherein markets are assumed to be efficient in the absolute sense. However, the possibility of time-varying weak-form market efficiency has received increasing attention in recent years. Under the Adaptive Market Hypothesis (AMH) it is postulated that market efficiency is dynamic, which advocates using models with non-constant coefficients. The concept of evolving efficiency has yielded a Test for Evolving Efficiency (TEE) and following that, a Generalised Test for Evolving Efficiency (GTEE) – both with an associated Kalman filtering (KF) technique. Unfortunately, these methods assume that the inherent stochastic processes are Gaussian despite widespread evidence that many real financial time series are nonGaussian. Unlike the classical KF, modern filters such as the maximum correntropy Kalman filters (MCC-KF) have been shown to be less sensitive to non-Gaussian uncertainties. These filters utilise a similarity measure known as correntropy– which incorporates higher order information than the mean square criterion that is utilised in the classical KF. As a result, they have been shown to improve filter robustness against outliers or impulsive noises. In this paper, the South African and American stock markets are tested for adaptive market efficiency using both the standard KF and the MCC-KF. A simulation study shows that the MCC-KF is a more robust estimator of adaptive efficiency but it less accurately estimates unknown system parameters. The South African stock market is found to be inefficient prior to August 2004 but achieves efficiency thereafter. Testing the S&P500 does not provide evidence of inefficiency in the American stock markets. The GTEE, implemented with the MCC-KF, is selected as the bestperforming test for the S&P500. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31299 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:15.376Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | African Institute of Financial Markets and Risk Management |
| publisherStr | African Institute of Financial Markets and Risk Management |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31299 Testing adaptive market efficiency in the presence of non-Gaussian uncertainties Wakandigara, Vykta Kulikova, Maria Taylor, David Mahomed, Obeid Mathematical Finance One of the central debates in finance concerns the Efficient Market Hypothesis (EMH)—wherein markets are assumed to be efficient in the absolute sense. However, the possibility of time-varying weak-form market efficiency has received increasing attention in recent years. Under the Adaptive Market Hypothesis (AMH) it is postulated that market efficiency is dynamic, which advocates using models with non-constant coefficients. The concept of evolving efficiency has yielded a Test for Evolving Efficiency (TEE) and following that, a Generalised Test for Evolving Efficiency (GTEE) – both with an associated Kalman filtering (KF) technique. Unfortunately, these methods assume that the inherent stochastic processes are Gaussian despite widespread evidence that many real financial time series are nonGaussian. Unlike the classical KF, modern filters such as the maximum correntropy Kalman filters (MCC-KF) have been shown to be less sensitive to non-Gaussian uncertainties. These filters utilise a similarity measure known as correntropy– which incorporates higher order information than the mean square criterion that is utilised in the classical KF. As a result, they have been shown to improve filter robustness against outliers or impulsive noises. In this paper, the South African and American stock markets are tested for adaptive market efficiency using both the standard KF and the MCC-KF. A simulation study shows that the MCC-KF is a more robust estimator of adaptive efficiency but it less accurately estimates unknown system parameters. The South African stock market is found to be inefficient prior to August 2004 but achieves efficiency thereafter. Testing the S&P500 does not provide evidence of inefficiency in the American stock markets. The GTEE, implemented with the MCC-KF, is selected as the bestperforming test for the S&P500. 2020-02-25T10:30:55Z 2020-02-25T10:30:55Z 2019 2020-02-25T09:05:41Z Master Thesis Masters MPhil http://hdl.handle.net/11427/31299 eng application/pdf African Institute of Financial Markets and Risk Management Faculty of Commerce |
| spellingShingle | Mathematical Finance Wakandigara, Vykta Testing adaptive market efficiency in the presence of non-Gaussian uncertainties |
| thesis_degree_str | Master's |
| title | Testing adaptive market efficiency in the presence of non-Gaussian uncertainties |
| title_full | Testing adaptive market efficiency in the presence of non-Gaussian uncertainties |
| title_fullStr | Testing adaptive market efficiency in the presence of non-Gaussian uncertainties |
| title_full_unstemmed | Testing adaptive market efficiency in the presence of non-Gaussian uncertainties |
| title_short | Testing adaptive market efficiency in the presence of non-Gaussian uncertainties |
| title_sort | testing adaptive market efficiency in the presence of non gaussian uncertainties |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/31299 |
| work_keys_str_mv | AT wakandigaravykta testingadaptivemarketefficiencyinthepresenceofnongaussianuncertainties |