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On the Protection of Investment Capital During Financial Crisis in the South African Equity Market: A Risk-Based Asset Allocation Approach

Author

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  • MUTEBA MWAMBA, John Weirstrass

    (Department of Economics, University of Johannesburg, South Africa)

  • MANTSHIMULI, Lamukanyani

    (Department of Economics, University of Johannesburg, South Africa)

Abstract

This paper constructs six portfolios using six risk-based asset allocation techniques and compares the performance of these portfolios with that of the market portfolio proxied by the Johannesburg All Share Index (JSE ALSI). We make use of the daily closing prices of eleven JSE sector indices starting from August 2004 to September 2015. We divide this sample period into three overlapping sub-samples representing the pre-crisis period, the crisis period, and the post-crisis period. The performance analysis is based on the Sharpe and the Sortino ratios. The covariance matrix, the most important input in the construction of these risk-based portfolios is assumed to be constant, and time varying respectively. When it is assumed to be constant our results show that during the pre-crisis period risk-based portfolios performed poorly than the market portfolio. But during the crisis and post-crisis periods we find that risk-based portfolios performed better than the market portfolio with the minimum correlation portfolio generating the highest Sharpe and Sortino ratios. More investment capital during these two sample periods is found to be mostly allocated to the property sector. However, when the covariance matrix is assumed to be time varying the pre-crisis period is used as the in-sample space while the crisis and post-crisis periods are used as the out-sample space. The forecasts of the time varying covariances in the out-sample space are obtained with a multivariate GARCH model based on a sixty rolling window forecast. Our results with forecasted covariances show that during the crisis period all risk-based portfolios performed better than the market portfolio due to their ability to protect investor’s capital during financial crisis. We find mixed results during the post-crisis period: the equally weighted, the risk parity, and the minimum correlation portfolios performed poorly while the rest of the risk –based portfolios performed better than the market portfolio with the minimum variance portfolio generating the highest Sharpe and Sortino ratios. More investment capital is found to be allocated in the property, telecommunication, consumer services, and health sectors when the forward looking approach is employed. La protezione del capitale investito durante la crisi finanziaria nel mercato azionario sudafricano: un approccio risk-based Questo paper crea sei portafogli utilizzando tecniche di investimento risk-based e paragona l’andamento di questi portafogli all’andamento del portafoglio di mercato rappresentato dall’indice Johannesburg All Share Index (JSE ALSI). Si utilizzano i prezzi alla chiusura di undici indici JSE nel periodo agosto 2004-settembre 2015. Poi si divide questo campione in tre sottogruppi sovrapponibili che rappresentano il periodo pre-crisi, quello durante la crisi e il post-crisi. L’analisi dell’andamento si basa sui rapporti Sharpe e Sortino. La matrice di covarianza, l’elemento più importante nella costruzione di questi portafogli risk-based, si presume rispettivamente costante e time-varying. Quando si considera costante i risultati mostrano che durante il periodo pre-crisi i portafogli risk-based hanno rendimenti più bassi del portafoglio di mercato. Invece durante la crisi e nel post-crisi i portafogli risk-based hanno un andamento migliore del portafoglio di mercato e il portafoglio con correlazione minima genera i livelli più alti di rapporti Sharpe e Sortino. Si riscontra che in questi due periodi campione vi è stato un maggiore investimento di capitale prevalentemente nel settore immobiliare. Comunque, quando la matrice di covarianza si considera time varying il periodo pre-crisi viene utilizzato come lo spazio intra-campione mentre i periodi della crisi e post-crisi sono usati come spazi extra-campione. Le previsioni delle covarianze time varying nello spazio extra-campione sono state ottenute tramite un modello GARCH multivariato. I risultati delle covarianze studiate mostrano che durante la crisi tutti i portafogli risk-based hanno avuto un andamento migliore del portafoglio di mercato a causa della loro capacità di proteggere il capitale investito durante la crisi finanziaria. I risultati del periodo post-crisi sono invece eterogenei: i portafogli bilanciati, a parità di rischio e a correlazione minima, hanno avuto un andamento peggiore mentre il resto dei portafogli risk-based ha avuto una performance migliore del portafoglio di mercato e quello a varianza minima ha generato i più alti rapporti Sharpe e Sortino. Si riscontra che c’è stato maggior investimento di capitale nel settore immobiliare, delle telecomunicazioni, dei servizi ai consumatori e della sanità quando è stato adottato un approccio di lungo periodo.

Suggested Citation

  • MUTEBA MWAMBA, John Weirstrass & MANTSHIMULI, Lamukanyani, 2017. "On the Protection of Investment Capital During Financial Crisis in the South African Equity Market: A Risk-Based Asset Allocation Approach," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 70(2), pages 165-192.
  • Handle: RePEc:ris:ecoint:0799
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    References listed on IDEAS

    as
    1. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    2. Roncalli, Thierry, 2013. "Introduction to Risk Parity and Budgeting," MPRA Paper 47679, University Library of Munich, Germany.
    3. John Muteba Mwamba, 2012. "Implementing A Robust Risk Model For South African Equity Markets: A Peak-Over-Threshold Approach," South African Journal of Economics, Economic Society of South Africa, vol. 80(4), pages 459-472, December.
    4. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    5. Jorion, Philippe, 1991. "Bayesian and CAPM estimators of the means: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 15(3), pages 717-727, June.
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    More about this item

    Keywords

    Risk-Based Strategies; Markowitz Mean-Variance Framework; Financial Crises; Predictive Risk Measures; Asset Allocation;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G01 - Financial Economics - - General - - - Financial Crises
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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