Contents of the volume

2018, Volume 71 - Issue 2

ISSN: 2499-8265
RSS feed citation: At RePEc
Publication date: 02 May 2018

SOME INTERNATIONAL FINANCIAL CONTRIBUTIONS: EMPIRICAL RESULTS AND POLICY IMPLICATIONS

Amedeo Amato

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ASSESSING PORTFOLIO MARKET RISK IN THE BRICS ECONOMIES: USE OF MULTIVARIATE GARCH MODELS

Lumengo Bonga-Bonga, Lebogang NLEYA

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BANK COMPETITION, CONCENTRATION AND RISK-TAKING IN THE UAE BANKING INDUSTRY

Aktham Maghyereh

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LINEAR AND NONLINEAR ATTRACTORS IN PURCHASING POWER PARITY

Imad Moosa, Ming MA

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DIRECT AND INDIRECT FORECASTING OF CROSS EXCHANGE RATES

Imad Moosa, John VAZ

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DOES THE EXPECTATIONS HYPOTHESIS OF THE TERM STRUCTURE HOLD IN KOREA AFTER THE ASIAN FINANCIAL CRISIS? SOME EMPIRICAL EVIDENCE (1999-2017)

Marco Tronzano

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Genoa Chamber of Commerce
Economia Internazionale / International Economics

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Corresponding author

Lumengo BONGA-BONGA, University of Johannesburg, South Africa

Co-authors

Lebogang NLEYA, University of Johannesburg, Johannesburg,South Africa

Assessing Portfolio Market Risk in the BRICS Economies: Use of Multivariate GARCH Models

Pages

87-128

Abstract

This paper compares the performance of the different models used to estimate portfolio value-at-risk (VaR) that combines assets in the currency and equity markets in the BRICS economies. Portfolio VaR is estimated with three different multivariate risk models, namely the constant conditional correlation (CCC), the dynamic conditional correlation (DCC) and asymmetric DCC (ADCC) GARCH models. Risk performance measures such as the average deviations, quadratic probability function score and the root mean square error are used to back-test the performance of the models at 99%. The results indicate that portfolios with more weight to currency and less to equities prove to be the best way of minimizing possible losses when investing in BRICS.

JEL classification

C58, G11, G15

Keywords

Portfolio Value-at-Risk, Multivariate GARCH, Risk Performance Measures, BRICS

Index

  1. Introduction
  2. Literature review
  3. Methodology
  4. Data and estimation of results
  5. Conclusion

Bibliography

Aniūnas, P., J. Nedzveckas and R. Krušinskas (2015), “Variance-Covariance Risk Value Model for Currency Market”, Engineering Economics, 61(1), 18-27.
Bollerslev, T. (1990), “Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model”, Review of Economics and Statistics, 72(3), 498-505.
Bonga-Bonga, L. (2017). “Assessing the Readiness of the BRICS Grouping for Mutually Beneficial Financial Integration”, Review of Development Economics, 21(4), e204-e219.
Bonga-Bonga, L. and Hoveni, J. (2013), “Volatility Spillovers between the Equity Market and Foreign Exchange Market in South Africa in the 1995-2010 Period”, South African Journal of Economics, 81(2), 260-274.
Bonga-Bonga, L. and Mutema, G. (2009), “Volatility Forecasting and Value-at-Risk Estimation in Emerging Markets: the Case of the Stock Market Index Portfolio in South Africa”, South African journal of Economics and Management sciences, 12(4), 401-411.
Boubakar, H. and S.A. Raza (2017), “A Wavelet Analysis of Mean and Volatility Spillovers between Oil and BRICS Stock Markets”, Energy Economics, 64(May), 105-117.
Cabedo, J.D. and I. Moya (2003), “Estimating Oil Price ‘Value at Risk’ using the Historical Simulation”, Energy Economics, 25(3), 239-253.
Cappiello, L., R. Engle and K. Sheppard (2006), “Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns”, Journal of Financial Econometrics, 4(4), 537-572.
Christoffersen, P. F. (1998), “Evaluating Interval Forecasts”, International Economic Review, 39(4), 841-862.
Engle, R. (2002), “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models”, Journal of Business & Economic Statistics, 20(3), 339-350.
Glasserman, P., P. Heidelberger and P. Shahabuddin (2000), “Efficient Monte Carlo Methods for Value at Risk”, Working Paper, Columbia University.
Ghosh, S. and M. Saggar (2017), “Volatiltiy Spillover to the Emerging Financial Markets during Taper Talk and Actual Tapering”, Applied Economic Letters, 24(2), 122-127.
Hendricks, D.  (1996), “Evaluation of Value-at-Risk Models using Historical Data”, Economic Policy Review, 2 (1), 39-67.
Hsu Ku, Y-H. (2008), “Student-t Distribution based VAR-MGARCH: An Application of the DCC Model on International Portfolio Risk Management”, Applied Economics 40(13),1685-1697.
Hsu Ku, Y-H. and J.J. Wang, (2008), “Estimating Portfolio Value-at-Risk via Dynamic Conditional Correlation MGARCH Model – An Empirical Study on Foreign Exchange Rates”, Applied Economics Letters, 15(7), 533-538.
Jorion, P. (1996), “Measuring the Risk in Value at Risk”, Financial Analysts’ journal, 52(6), 47-56.
Jorion, P. (2007), Value at Risk, 3rd Ed.: The New Benchmark for Managing Financial Risk, McGraw-Hill: New York.
Kupiec, P. (1995), “Techniques for Verifying the Accuracy of Risk Measurement Models”, Journal of Derivatives, 3(2), 73-84.
Lee, M.C., J-S. Chiou and C-M. Lin (2006), “A Study of Value-at-Risk on Portfolio in Stock Return using DCC Multivariate GARCH”, Applied Financial Economics Letters, 2(3), 183-188.
Lopez, J.A. (1999), “Regulatory Evaluation of Value-at-Risk Models”, FRB of New York Staff Report No. 33.
Mensi W., S. Hammoudeh and S.H. Kang (2017), “Dynamic Linkages between Developed and BRICS Stock Markets: Portfolio Risk Analysis”, Financial Research Letters, 21(1), 26-33.
Morimoto, T. and Kawasaki, Y. (2008), “Empirical Comparison of Multivariate GARCH Models for Estimation of Intraday Value at Risk” < http://ssrn.com/abstract=1090807> (accessed 17 June 2015).
Nyssanov, A. (2013), “An Empirical Study in Risk Management: Estimation of Value at Risk with GARCH Family Models”, Master’s Thesis, Department of Statistics, Uppsala University: Sweden.
O’Neill, J. (2001), “Building Better Global Economic BRICs”, Global Economics Paper No. 66, Goldman Sachs, <http://www.goldmansachs.com/our-thinking/archive/archive-pdfs/build-better-brics.pdf>.
Patterson, M. and S. Chen (2011), “BRIC Decade Ends with record fund outflows as Growth Slows”,<http://www.bloomberg.com/news/articles/2011-12-27/bric-decade-ends-with-record-stock-outflows-as-goldman-says-growth-peaked> (accessed 6 October 2015).
Rombouts, J.V.K. and M. Verbeek (2009), “Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models”, Quantitative Finance, 9(6), 737-745.
Santos, A. A.R., F.J. Nogales and E. Ruiz (2013), Comparing Univariate and Multivariate Models to Forecast Portfolio Value-at-Risk”, Journal of Financial Econometrics, 11(2), 400-441.
Silvennoinen, A. and T. Teräsvirta (2005), “Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations”, SSE/EFI Working Paper Series in Economics and Finance No. 0577.
Tsay, R. S. (2010), Analysis of Financial Time Series, 3rd Edition, John Wiley & Sons: New Jersey.
Tse, Y.K. and A.K.C. Tsui (2002), “A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations”, Journal of Business & Economic Statistics, 20(3), 351-362.