2017, Volume 70 - Issue 2
RSS feed citation: At RePEc
Publication date: 02 May 2017
THE EFFECT OF LABOR MARKET FREEDOM AND OTHER FACTORS ON U.S. SETTLEMENT PATTERN DECISIONS OF UNDOCUMENTED IMMIGRANTS, 2012 AND 2014Read the article
DETERMINANTS OF THE RELATIVE IMPORTANCE OF EDUCATION IN LOW-INCOME AND LOWER-MIDDLE INCOME COUNTRIESRead the article
ON THE PROTECTION OF INVESTMENT CAPITAL DURING FINANCIAL CRISIS IN THE SOUTH AFRICAN EQUITY MARKET: A RISK-BASED ASSET ALLOCATION APPROACHRead the article
THE IMPACT OF FINANCIAL DEVELOPMENT ON INVESTMENT IN BOTSWANA: AN ARDL-BOUNDS TESTING APPROACHRead the article
SYSTEMIC ANALYSIS OF TRADE LIBERALISATION: POLICY ENTREPRENUERSHIP AND BEHAVIOURAL VARIABLES IN A TWO-LEGAL GAME FRAMEWORKRead the article
John Weirstrass MUTEBA MWAMBA, Department of Economics, University of Johannesburg, Johannesburg, South Africa
Lamukanyani MANTSHIMULI, Department of Economics, University of Johannesburg, Johannesburg, South Africa
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.
C44, C63, G01, G11, G12
Risk-Based Strategies, Markowitz Mean-Variance Framework, Financial Crises, Predictive Risk Measures, Asset Allocation
Allen, G.C. (2010), The Risk Parity Approach to Asset Allocation, Callan Investments Institute, Callan Associates.
Asness, C.S., A. Frazzini and L.H. Pedersen (2012), “Leverage Aversion and Risk Parity”, Financial Analysts Journal, 68(1), 47-59.
Barber, J., S. Bennett and E. Gvozdeva (2015), “How to Choose a Strategic Multifactor Equity Portfolio?”, The Journal of Index Investing, 6(2), 34-45.
Boudt, K., P. Carl and B.G. Peterson (2013), “Asset Allocation with Conditional Value-at Risk Budgets”, Journal of Risk, 15(3), 39-68.
Boudt, K., B.G. Peterson and C. Croux (2008), “Estimation and Decomposition of Downside Risk for Portfolios with Non-Normal Returns”, Journal of Risk, 11(2), 79-103.
Brown, S.J., I. Hwang and F. In (2013), “Why Optimal Diversification cannot Outperform Naive Diversification: Evidence from Tail Risk Exposure”, <https://pdfs.semanticscholar.org/b0e3/3a41801deba4a9967613a05a5371c1db4f2e.pdf>.
Choueifaty, Y. and Y. Coignard (2008), “Toward Maximum Diversi?cation”, Journal of Portfolio Management, 35(1), 40-51.
Choueifaty, Y., T. Froidure and J. Reynier (2011), “Properties of the Most Diversi?ed Portfolio”, Journal of Investment Strategies, 2(2), 49-70.
Clarke, R.G., H. De Silva and S. Thorley (2006), “Minimum-variance Portfolios in the US Equity Market”, The Journal of Portfolio Management, 33(1), 10-24.
DeMiguel, V., L. Garlappi and R. Uppal (2009), “Optimal versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?”, Review of Financial Studies, 22(5), 1915-1953.
Jorion, P. (1991), “Bayesian and CAPM Estimators of the Means: Implications for Portfolio Selection”, Journal of Banking and Finance, 15(3), 717-727.
Kritzman, M., S. Page and D. Turkington (2010), “In Defense of Optimization: The Fallacy of 1/N”, Financial Analysts Journal, 66(2), 31-39.
Ledoit, O. and M. Wolf (2003), “Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection”, Journal of Empirical Finance, 10(5), 603-621.
Lee, W. (2011), “Risk-Based Asset Allocation: A New Answer to an Old Question?” , The Journal of Portfolio Management, 37(4), 11-28.
Maillard, S., T.Roncalli and J. Teiletche (2010), “On the Properties of Equally-Weighted Risk Contributions Portfolios”, The Journal of Portfolio Management, 36-4, 60-70.
Markowitz, H. (1952), “Portfolio Selection”, Journal of Finance, 7(1), 77-91.
Muteba Mwamba, J.W. (2012), “Implementing a Robust Market Risk Model for South African Equity Markets: A Peak-Over Threshold Approach”, South African Journal of Economics, 80(4), 459-472.
Neukirch, T. (2008), Alternative Indexing with the MSCI World Index, available at <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1106109>.
Rappoport, P. and N. Nottebohm (2012), Improving on Risk Parity. Hedging Forecast Uncertainty, JP Morgan Asset Management.
Roncalli, T. (2013), Introduction to Risk Parity and Budgeting, Chapman & Hall, CRC Financial Mathematics Series, CRC Press: USA.
Scaillet, O. (2004), “Nonparametric Estimation and Sensitivity Analysis of Expected Shortfall”, Mathematical Finance, 14 (1), 115-129.
Varadi, D., M. Kapler, H. Bee and C. Rittenhouse (2012), The Minimum Correlation Algorithm. A Practical Diversification Tool, CSS Analytics, Available at: <https://cssanalytics.wordpress.com/2012/09/21/minimum-correlation-algorithm-paper-release/>.