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  1. What drives financial sector development in Africa?
    insights from machine learning
    Autor*in: Ofori, Isaac Kwesi; Quaidoo, Christopher; Ofori, Pamela E.
    Erschienen: [2021]
    Verlag:  African Governance and Development Institute, [Yaoundé]

    This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques- the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 524
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    keine Fernleihe

     

    This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques- the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and the Elasticnet are trained based on a dataset containing 86 covariates of financial development for the period 1990 - 2019. The results show that variables such as cell phones, economic globalisation, institutional effectiveness, and literacy are crucial for financial sector development in Africa. Evidence from the Partialing-out lasso instrumental variable regression reveals that while inflation and agricultural sector employment suppress financial sector development, cell phones and institutional effectiveness are remarkable in spurring financial sector development in Africa. Policy recommendations are provided in line with the rise in globalisation, and technological progress in Africa.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/249082
    Schriftenreihe: AGDI working paper ; WP/21, 074
    Schlagworte: Africa; Elasticnet; Financial Development; Financial Inclusion; Lasso; Regularization; Variable Selection
    Umfang: 1 Online-Ressource (circa 37 Seiten), Illustrationen
  2. Regularized maximum diversification investment strategy
    Autor*in: Koné, N'Golo
    Erschienen: 1-2021
    Verlag:  Department of Economics, Queen's University, Kingston, Ontario, Canada

    The maximum diversification portfolio as defined by Choueifaty (2011) depends on the vector of asset volatilities and the inverse of the covariance matrix of the asset return. In practice, these two quantities need to be replaced by their sample... mehr

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    DS 216
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    The maximum diversification portfolio as defined by Choueifaty (2011) depends on the vector of asset volatilities and the inverse of the covariance matrix of the asset return. In practice, these two quantities need to be replaced by their sample statistics. The estimation error associated with the use of these sample statistics may be amplified due to (near) singularity of the covariance matrix, in financial markets with many assets. This in turn may lead to the selection of portfolios that are far from the optimal regarding standard portfolio performance measures of the financial market. To address this problem, we investigate three regularization techniques, including the ridge, the spectral cut-off, and the Landweber-Fridman approaches in order to stabilize the inverse of the covariance matrix. These regularization schemes involve a tuning parameter that needs to be chosen. In light of this fact, we propose a data-driven method for selecting the tuning parameter. We show that the selected portfolio by regularization is asymptotically efficient with respect to the diversification ratio. In empirical and Monte Carlo experiments, the resulting regularized rules are compared to several strategies, such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements.

     
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/230602
    Schriftenreihe: Queen's Economics Department working paper ; no. 1450
    Schlagworte: Portfolio selection; Maximum diversification; Regularization
    Umfang: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  3. Catching the drivers of inclusive growth in Sub-Saharan Africa
    an application of machine learning
    Autor*in: Ofori, Isaac Kwesi
    Erschienen: [2021]
    Verlag:  African Governance and Development Institute, [Yaoundé]

    A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum... mehr

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    DS 524
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    A conspicuous lacuna in the literature on Sub-Saharan Africa (SSA) is the lack of clarity on variables key for driving and predicting inclusive growth. To address this, I train the machine learning algorithms for the Standard lasso, the Minimum Schwarz Bayesian Information Criterion (Minimum BIC) lasso, and the Adaptive lasso to study patterns in a dataset comprising 97 covariates of inclusive growth for 43 SSA countries. First, the regularization results show that only 13 variables are key for driving inclusive growth in SSA. Further, the results show that out of the 13, the poverty headcount (US$1.90) matters most. Second, the findings reveal that 'Minimum BIC lasso' is best for predicting inclusive growth in SSA. Policy recommendations are provided in line with the region's green agenda and the coming into force of the African Continental Free Trade Area.

     
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    Sprache: Englisch
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    Weitere Identifier:
    hdl: 10419/244219
    Schriftenreihe: AGDI working paper ; WP/21, 044
    Schlagworte: Clean Fuel; Economic Growth; Machine Learning; Lasso; Sub-Saharan Africa; Regularization; Poverty
    Umfang: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  4. Sparse and stable international portfolio optimization and currency risk management
    Autor*in: Burkhardt, Raphael; Ulrych, Urban
    Erschienen: 2022
    Verlag:  Swiss Finance Institute, Geneva

    This paper introduces a sparse and stable optimization approach for a multi-currency asset allocation problem. We study the benefits of joint optimization of assets and currencies as opposed to the standard industry practice of managing currency risk... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    This paper introduces a sparse and stable optimization approach for a multi-currency asset allocation problem. We study the benefits of joint optimization of assets and currencies as opposed to the standard industry practice of managing currency risk via so-called currency overlay strategies. In our setting, a classical mean-variance problem in an international framework is augmented by several extensions that aim at reducing parameter uncertainty related to the input parameters and induce sparsity and stability of the asset and currency weights. These extensions integrate maximal net exposure to foreign currencies, shrinkage of the input parameters, and constraints on the norms of the asset- and currency-weight vectors. The empirical performance of the portfolio optimization strategies based on the proposed regularization techniques and the joint (i.e., asset and currency) optimization is tested out of sample. We demonstrate that the sparse and stable joint optimization approach consistently outperforms the standard currency overlay as well as the equally-weighted and the non-regularized global portfolio benchmarks net of transaction costs. This result shows that the common industry practice of employing currency overlay strategies is suboptimal and can be improved by a joint optimization over assets and currencies

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
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    Weitere Identifier:
    Auflage/Ausgabe: This Version: January 2022
    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 22, 07
    Schlagworte: International Asset Allocation; Currency Risk Management; Currency Overlay; Shrinkage Estimation; Regularization; Mean-Variance Optimization
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  5. Monopsony, efficiency, and the regularization of undocumented immigrants
    Autor*in: Borjas, George J.; Edo, Anthony
    Erschienen: July 2023
    Verlag:  CEPII, Paris

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: CEPII working paper ; no. 2023-18 (July 2023)
    Schlagworte: Monopsony; Regularization; Undocumented Immigrants; Labor Market
    Umfang: 1 Online-Ressource (circa 68 Seiten), Illustrationen
  6. Regularized empirical likelihood as a solution to the no moment problem
    the linear case with many instruments
    Autor*in: Chaussé, Pierre
    Erschienen: November 29, 2017
    Verlag:  Department of Economics, University of Waterloo, Waterloo, Ontario

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: [Waterloo economic series ; # 17, 008]
    Schlagworte: Many Instruments; Weak Instruments; Regularization; Cross-validation
    Umfang: 1 Online-Ressource (circa 26 Seiten), Illustrationen
  7. Additive nonparametric instrumental regressions
    a guide to implementation
    Autor*in: Centorrino, Samuele; Fève, Frédérique; Florens, Jean-Pierre
    Erschienen: October 19, 2015
    Verlag:  [Department of Economics, Stony Brook University], [Stony Brook, NY]

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Export in Literaturverwaltung   RIS-Format
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    Hinweise zum Inhalt
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Auflage/Ausgabe: This version: October 19, 2015
    Schriftenreihe: [Departmental working papers / Stony Brook University, College of Arts & Sciences, Department of Economics ; 17-06]
    Schlagworte: Nonparametric; Endogeneity; Instrumental Variables; Ill-posed inverse problem; Regularization; Petrov-Galerkin; Landweber-Fridman; Tikhonov; Simulations; EngelCurve
    Umfang: 1 Online-Ressource (circa 30 Seiten), Illustrationen