Cotter, John and Hanly, James (2007): Hedging Effectiveness under Conditions of Asymmetry.
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We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of short and long hedgers using Oil futures contracts. The metrics used include Lower Partial Moments (LPM), Value at Risk (VaR) and Conditional Value at Risk (CVAR). Comparisons are applied to a number of hedging strategies including OLS and both Symmetric and Asymmetric GARCH models. Our findings show that asymmetry reduces in-sample hedging performance and that there are significant differences in hedging performance between short and long hedgers. Thus, tail specific performance metrics should be applied in evaluating hedging effectiveness. We also find that the Ordinary Least Squares (OLS) model provides consistently good performance across different measures of hedging effectiveness and estimation methods irrespective of the characteristics of the underlying distribution. Keywords: Hedging Performance; Asymmetry; Downside Risk; Value at Risk, Conditional Value at Risk. JEL classification: G10, G12, G15. ____________________________________________________________________ John Cotter, Director of Centre for Financial Markets, Department of Banking and Finance, University College Dublin, Blackrock, Co. Dublin, Ireland, tel 353 1 716 8900, e-mail firstname.lastname@example.org. Jim Hanly, School of Accounting and Finance, Dublin Institute of Technology, tel 353 1 402 3180, e-mail email@example.com. The authors would like to thank the participants at the Global Finance Annual Conference for their constructive comments.
|Item Type:||MPRA Paper|
|Original Title:||Hedging Effectiveness under Conditions of Asymmetry|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets
G - Financial Economics > G1 - General Financial Markets > G13 - Contingent Pricing; Futures Pricing
|Depositing User:||John Cotter|
|Date Deposited:||12. Jun 2007|
|Last Modified:||18. Feb 2013 10:30|
Alexander, C, & Barbosa, A. (2005). The Spider in the Hedge. Working Paper, University of Reading. Artzner, P., Delbaen, F., Eber, J., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 203 - 228. Baillie, R.T., & Myers, R.J. (1991). Bivariate GARCH estimation of the optimal futures hedge. Journal of Applied Econometrics, 6, 109 – 124. Bawa, S. (1975). Optimal rules for ordering uncertain prospects. Journal of Financial Economics, 2, 95-121. Bekaert., G, & Wu, G. (2000). Asymmetric volatility and risk in equity markets, Review of Financial Studies, 13, 1-42. Black, F. 1976. Studies of stock price volatility changes. Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economical Statistics Section, 177-181. Bollerslev, T., Engle, R.F., & Wooldridge, J.M. (1988). A capital asset pricing model with time-varying covariances. Journal of Political Economy, 96, 116 - 131. Brooks, C., Henry, O.T., & Persand, G. (2002). The effects of asymmetries on optimal hedge ratios. Journal of Business, 75, 333 – 352. Cecchetti, S.G., Cumby, R.E., & Figlewski, S. (1988). Estimation of the optimal futures hedge. Review of Economics and Statistics, 70, 623 – 630. Conrad, J., Gultekin, M., & Kaul, G. (1991). Asymmetric predictability of conditional variances. Review of Financial Studies, 4, 597-622. Cotter, J., & Hanly, J. (2006). Re-examining Hedging Performance. Journal of Futures Markets,Volume etc to go here. De Goeij, P., & Marquering, W. (2004). Modeling the conditional covariance between stock and bond returns: a multivariate GARCH approach, Journal of Financial Econometrics, 4, 531–564. Demirer, R., & Lien, D. (2003). Downside risk for short and long hedgers. International Review of Economics and Finance, 12, 25 – 44. Demirer, R., Lien, D., & Shaffer, D. (2005). Comparisons of short and long hedge performance: The case of Taiwan. Journal of Multinational Financial Management, 15, 51-66. Ederington, L. (1979). The hedging performance of the new futures markets. Journal of Finance, 34, 157 – 170. Engle. R.F., & Ng, V.K. (1993). Measuring and Testing the Impact of News on Volatility, Journal of Finance, 48, 1749 – 1778. 23 Fishburn, P. (1977). Mean-risk analysis with risk associated with below-target returns. The American Economic Review, 67, 116–126. Giot, P., & Laurent, S. (2003).Value-at-Risk for long and short trading positions. Journal of Applied Econometrics, 18, 641-663. Glosten, L., Jagannathan, R., & Runkle, D. (1993). On the relationship between the expected value and the volatility of the normal excess return on stocks, Journal of Finance, 48, 1779-1801. Kroner, K.F., & Sultan, J. (1993). Time varying distribution and dynamic hedging with foreign currency futures. Journal of Financial and Quantitative Analysis, 28, 535-551. Lee, W., & Rao, R. (1988). Mean lower partial moment valuation and lognormally distributed returns, Management Science, 34, 446–453. Lien, D. (2005). The use and abuse of the hedging effectiveness measure. International Review of Financial Analysis, 14, 277– 282 Lien, D., & Tse, Y.K. (2002). Some recent developments in futures hedging. Journal of Economic Surveys, 16, 357 – 396. Lien, D., & Tse, Y.K. (1998). Hedging time-varying downside risk. Journal of Futures Markets, 18, 705 – 722. Lien, D. & Tse, Y. K. (2000). Hedging downside risk with futures contracts. Applied Financial Economics, 10, 163 — 170. Lien, D. & Wilson, B.K. (2001). Multiperiod hedging in the presence of stochastic volatility. International Review of Financial Analysis, 10, 395–406 Lin, J. W., Najand, M., & Yung, K. (1994). Hedging with currency futures: OLS versus GARCH. Journal of Multinational Financial Management, 4, 45 – 67. Price, K., Price, B. & Nantell, T. (1982). Variance and lower partial moment measures of systematic risk: Some analytical and empirical results. Journal of Finance, 37, 843- 855.