Chang, Chia-Lin and González Serrano, Lydia and Jiménez Martín, Juan Ángel (2012) Currency Hedging Strategies Using Dynamic Multivariate GARCH. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 07, 2012, ] (Unpublished)
Creative Commons Attribution Non-commercial.
Official URL: http://eprints.ucm.es/14831/
This paper examines the effectiveness of using futures contracts as hedging instruments
of: (1) alternative models of volatility for estimating conditional variances and
covariances; (2) alternative currencies; and (3) alternative maturities of futures contracts.
For this purpose, daily data of futures and spot exchange rates of three major
international currencies, Euro, British pound and Japanese yen, against the American
dollar, are used to analyze hedge ratios and hedging effectiveness resulting from using
two different maturity currency contracts, near-month and next-to-near-month contract.
Following Chang et al. , we estimate four multivariate volatility models (namely
CCC, VARMA-AGARCH, DCC and BEKK), and calculate optimal portfolio weights
and optimal hedge ratios to identify appropriate currency hedging strategies. The
hedging effectiveness index suggests that the best results in terms of reducing the
variance of the portfolio are for the USD/GBP exchange rate. The empirical results
show that futures hedging strategies are slightly more effective when the near-month
future contract is used for the USD/GBP and USD/JPY currencies. Moreover, the CCC
and AGARCH models provide similar hedging effectiveness, which suggests that
dynamic asymmetry may not be crucial empirically, although some differences appear
when the DCC and BEKK models are used.
|Item Type:||Working Paper or Technical Report|
JEL Classifications: G32, G11, G17, C53, C22.
|Uncontrolled Keywords:||Multivariate GARCH, Conditional correlations, Exchange rates, Optimal hedge ratio, Optimal portfolio weights, Hedging strategies.|
|Subjects:||Social sciences > Economics > Econometrics|
|Series Name:||Documentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)|
P. Araújo Santos, M.I. Fraga Alves, A new class of independence tests for interval forecasts evaluation, Computational Statistics and Data Analysis (2010) in press, doi:10.1016/j.csda2010.10.002.
P. Araújo Santos, Interval forecasts evaluation: R programs for a new independence test, Notas e Comunicações CEAUL 17/2010.
R.T. Baillie, T. Bollerslev, The message in daily exchange rates: a conditionalvariance tale, Journal of Business and Economic Statistics, 7 (1989) 295-307.
R.T. Baillie, R.J Myers, Bivariate GARCH estimation of the optimal commodity futures hedge, Journal of Applied Econometrics, 6 (1991) 109-124.
A.A. Balkema, L. de Haan, Residual life time at great age, Annals of Probability, 2 (1974) 792-804.
L. Bauwens, S. Laurent, J. Rombouts, Multivariate GARCH models: A survey, Journal of Applied Economics, 21 (2006) 79-109.
F. Black, Studies of stock market volatility changes, 1976 Proceedings of the American Statistical Association, Business and Economic Statistics Section, 177-181.
T. Bollerslev, Generalised autoregressive conditional heteroscedasticity, Journal of Econometrics, 31 (1986) 307-327.
T. Bollerslev, A conditional heteroscedastic time series model for speculative prices and rates of return, Review of Economics and Statistics, 69 (1987) 542-547.
T. Bollerslev, Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model, Review of Economics and Statistics, 72 (1990) 498–505.
H. Bystrom, Managing extreme risks in tranquil and volatile markets using conditional extreme value theory, International Review of Financial Analysis, 13 (2) (2004) 133-152.
M. Caporin, M. McAleer, Scalar BEKK and indirect DCC, Journal of Forecasting, 27 (2008) 537-549.
M. Caporin, M. McAleer, The Ten Commandments for managing investments, Journal of Economic Surveys, 24 (2010a)196-200.
M. Caporin, M. McAleer, to appear in L. Bauwens, C. Hafner and S. Laurent (eds.), Model selection and testing of conditional and stochastic volatility models, Handbook on Financial Engineering and Econometrics: Volatility Models and Their Applications, Wiley, New York, (2010b) (Available at SSRN: http://ssrn.com/abstract=1676826).
A. Chakraborty, J.T. Barkoulas, Dynamic futures hedging in currency markets, The European Journal of Finance, 5 (1999) 299-314.
W.H. Chan, Dynamic Hedging with Foreign Currency Futures in the Presence of Jumps, Studies in Nonlinear Dynamics & Econometrics, V. 12, Issue 2 (2008) 1-24.
C.L. Chang, M. Mc Aleer, R. Tansuchat, Crude oil hedging strategies using dynamic multivariate GARCH, Energy Economics, 33(5) (2011) 912-923.
P. Christoffersen P., Evaluating intervals forecasts, International Economic Review, 39 (1998) 841-862.
F.X. Diebold, Empirical Modeling of Exchange Rate Dynamics, Springer Verlag, New York, 1988.
F.X. Diebold, T. Schuermann, J.D. Stroughair, Pitfalls and opportunities in the use of extreme value theory in risk Management, Working Paper, (1998) 98-10, Wharton School, University of Pennsylvania.
L.H. Ederington, The hedging performance of the new futures markets, Journal of Finance, 34 (1979) 157-170.
P. Embrechts, C. Klüppelberg, T. Mikosch, Modeling extremal events for insurance and finance, Springer, Berlín, 1997.
R.F. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50 (1982) 987-1007.
R.F. Engle, Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business and Economic Statistics, 20 (2002) 339-350.
R.F. Engle., K.F. Kroner, Multivariate simultaneous generalized ARCH, Econometric Theory, 11 (1995) 122-150.
P.H. Franses, D. van Dijk, Nonlinear Time Series Models in Empirical Finance, Cambridge University Press, Cambridge, 1999.
R. Giacomini, I. Komunjer, Evaluation and combination of conditional quantile forecasts, Journal of Business & Economic Statistics, 23 (2005) 416-431.
L. Glosten, R. Jagannathan, D. Runkle, On the relation between the expected value and volatility of nominal excess return on stocks, Journal of Finance, 46 (1992) 1779-1801.
A. Hakim, M. McAleer, Forecasting conditional correlations in stock, bond and foreign exchange markets, Mathematics and Computers in Simulation, 79 (2009) 2830–2846.
S. Haqmmoudeh, Y. Yuan, M. McAleer, M.A. Thomson, M.A., Precious metalsexchange rate volatility transmissions and hedging strategies, International Review of Economics and Finance, 19 (2010) 633-647.
A.F. Herbs, D.D. Kare, J.F. Marshall, A time varying convergence adjusted, minimum risk futures hedge ratio, Advances in futures and option research, vol. 6 April (1993) 137-155.
L.L. Johnson, The theory of hedging and speculation in commodity futures, Review of Economic Studies, 27 (1960) 139-151.
K.F. Kroner, V. Ng, Modelling asymmetric movements of asset prices, Review of Financial Studies, 11 (1998) 844-871.
K.F. Kroner, J. Sultan, in S.G. Rhee and R.P. Change (eds.), Foreign currency futures and time varying hedge ratios, Pacific-Basin Capital Markets Research, Vol. II, Amsterdam North-Holland, pp. 397-412, 1991.
K.F. Kroner, J. Sultan, Time-varying distributions and dynamic hedging with foreign currency futures, Journal of Financial and Quantitative Analysis, 28 (1993) 535-551.
P. Kupiec, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives, 3 (1995) 73-84.
Y.H. Ku, H.C. Chen, K.H. Chen, (2007), On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios, Applied Economics Letters, 14 (2007) 503-509.
W.K. Li, S. Ling, M. McAleer, M., Recent theoretical results for time series models with GARCH errors, Journal of Economic Surveys, 16 (2002) 245-269. Reprinted in M. McAleer and L. Oxley (eds.), Contributions to Financial Econometrics: Theoretical and Practical Issues, Blackwell, Oxford, pp. 9-33.
D. Lien, Y.K. Tse, A.K. Tsui, Evaluating the hedging performance of the constantcorrelation GARCH model, Applied Financial Economics, 12 (2002) 791-798.
S. Ling, M. McAleer, Stationarity and the existence of moments of a family of GARCH processes, Journal of Econometrics, 106 1 (2002a) 9-117.
S. Ling, M. McAleer, Necessary and sufficient moment conditions for the GARCH(r,s) and asymmetric power GARCH(r, s) models, Econometric Theory, 18 (2002b) 722-729.
S. Ling, M. McAleer, Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory, 19 (2003) 278-308.
M. McAleer, Automated inference and learning in modelling financial volatility, Econometric Theory, 21 (2005) 232-261.
M. McAleer, F. Chan, F., D. Marinova, An econometric analysis of asymmetric volatility: theory and application to patents, Journal of Econometrics, 139 (2007) 259-284.
M. McAleer, S. Hoti, F. Chan, Structure and asymptotic theory for multivariate asymmetric conditional volatility, Econometric Reviews, 28 (2009) 422-440.
M. McAleer, J.A. Jiménez-Martín, T. Pérez- Amaral, T., International Evidence on GFC-robust Forecasts for Risk Management under the Basel Accord, 2011 (Available at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1741565).
A.J. McNeil, R. Frey, Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach, Journal of Empirical Finance, 7 (2000) 271-300.
D.B. Nelson, Conditional heteroscedasticity in asset returns: A new approach, Econometrica, 59 (1991) 347-370.
J. Pickands III, Statistical inference using extreme value order statistics, Annals of Statistics, 3 (1975) 119-131.
R.D. Ripple, I.A. Moosa, Hedging effectiveness and futures contract maturity: the case of NYMEX crude oil futures, Applied Financial Economics, 17 (2007) 683-689.
Riskmetrics, J.P. Morgan Technical Document, 4th Edition, J.P. Morgan, New York, 1996.
R Development Core Team, A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2008. ISBN 3-900051-07-0, URL http://www.R-project.org.
N. Shephard, in O.E. Barndorff-Nielsen, D.R. Cox and D.V. Hinkley (eds.), Statistical aspects of ARCH and stochastic volatility, Statistical Models in Econometrics, Finance and Other Fields, Chapman & Hall, London, 1996, pp. 1-67.
R. Smith, Estimating tails of probability distributions. Annals of Statististics, 15 (1987) 1174-1207.
G. Stahl, Three cheers, Risk, 10 (1997) 67-69.
J.L. Stein, The simultaneous determination of spot and futures prices, American Economic Review, 51 (1961) 1012-1025.
G. Zumbauch, A Gentle Introduction to the RM 2006 Methodology, Riskmetrics Group, New York, 2007.
|Deposited On:||16 Apr 2012 12:29|
|Last Modified:||09 Jan 2014 11:45|
Repository Staff Only: item control page