Su, Yongyang and Lau, Chi Keung Marco and Tan, Na (2013): Hedging China’s Energy Oil Market Risks.
Preview |
PDF
MPRA_paper_47134.pdf Download (528kB) | Preview |
Abstract
This paper is the first study to examine the effectiveness of the Shanghai Fuel Oil Futures Contract (SHF) in risk reduction on the Chinese energy oil market. We find that the SHF contract can help investors reduce risk by approximately 45%, lower than empirical evidence in developed markets, when weekly data are applied. In contrast, when using daily data SHF contract can only help reduce risk by approximately 9%. The Tokyo Oil Futures Contract (TKF), however, performs two times better, reducing risk by around 17%. The empirical results are robust when variance complicated bivariate GARCH (BGARCH) and bivariate distributions are used. Our results imply the energy oil futures market in China is not well-established and further policy is needed to improve market efficiency.
Item Type: | MPRA Paper |
---|---|
Original Title: | Hedging China’s Energy Oil Market Risks |
Language: | English |
Keywords: | China Energy Oil Market, Hedging Risk Performance, Bivariate GARCH model. |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting |
Item ID: | 47134 |
Depositing User: | Dr Chi Keung Lau |
Date Deposited: | 21 May 2013 18:49 |
Last Modified: | 04 Oct 2019 08:30 |
References: | Baillie, R., and Myers, R.J., 1991, "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, 6, 109-124. Bauwens, L., and Laurents, S., 2005, "A New Class of Multivariate Skew Densities with Application to GARCH Models," Journal of Business and Economic Statistics, 23, 346-354. Bollerslev, T., 1990, "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," Review of Economics and Statistics, 52, 5-59. Chakraborty, A., and Barkoulas, T. J., 1999, Dynamic Futures Hedging in Currency Markets, European Journal of Finance, 5, 299-314. Chan, K., Chan K., and Karolyi G., 1991, Intraday Volatility in the Stock Index and Stock Index Futures Markets, Review of Financial Studies, 4, 652-684. Collins, R. A., 2000, The Risk Management Effectiveness of Multivariate Hedging Models in the Soy Complex, Journal of Futures Markets, 20, 189--204. Engle, R., 2002, "Dynamic Conditional Correlation: A Simple Class of Multivariate Gneralized Autoregressive Conditional Heteroskedasticity Models," Journal of Business and Economic Statistics, 20, 339-360. Lien, D., Tse, Y. K., and Tsui, A. K., 2002, Evaluating the Hedging Performance of Constant-Correlation GARCH Model, Applied Financial Economics, 12, 791--798. Lien, D., 2009, A Note on the Hedging Effectiveness of GARCH Models, International Review of Economics and Finance, 18, 110-112. Park, S. Y., and Jie, S. Y., 2010, Estimation and hedging effectiveness of time-varying hedge ratio: Flexible bivariate garch approaches,Journal of Futures Markets. 30, 1, 71–99. Sharon Brown-hruska, Gregory Kuserk, 1995, Volatility, Volume and the Notion of Balance in the S&P 500 Cash and Futures Market, Journal of futures markets, 15, 677-689. Tse, Y.K., and Tsui, A.K.C., 2002, "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-varying Correlations," Journal of Business and Economic Statistics, 20, 351-362. West, K.D. and D. Cho (1995). The Predictive Ability of Several Models of Exchange Rate Volatility, Journal of Econometrics, 69, 367-391. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/47134 |