Hedging crude oil derivatives in GARCH-type models

被引:0
|
作者
Siu, Tak Kuen [1 ,2 ]
Nawar, Roy [3 ]
Ewald, Christian-Oliver [4 ]
机构
[1] City Univ London, Cass Business Sch, 106 Bunhill Row, London EC1Y 8TZ, England
[2] Macquarie Univ, Fac Business & Econ, Dept Appl Finance & Actuarial Studies, Sydney, NSW 2109, Australia
[3] Barclays Secur, Tokyo 1066131, Japan
[4] Univ Glasgow, Adam Smith Business Sch, Glasgow G12 8QQ, Lanark, Scotland
基金
澳大利亚研究理事会;
关键词
D O I
暂无
中图分类号
F [经济];
学科分类号
02 ;
摘要
We investigate the empirical performance of hedging strategies based on Greeks, such as Delta and Delta-Gamma, for (European-style) crude oil options in a generalized autoregressive conditional heteroscedasticity (GARCH) model environment. Particular attention is paid to studying the impacts of the conditional heteroscedasticity and the conditional nonnormality of the GARCH innovations on the option prices and the performance of these hedging strategies. To examine the empirical performance of the hedging strategies, we evaluate the value-at-risk and the expected shortfall of the terminal values of the hedging portfolios using the New York Mercantile Exchange (West Texas Intermediate) data for the period 1991-2011. Our hedging results show that GARCH with shifted gamma innovations systematically outperforms the benchmark models, namely, GARCH with normal innovations and the Black-Scholes-Merton model, in capturing tail risk across maturities and strikes for the different hedging frequencies.
引用
收藏
页码:3 / 26
页数:24
相关论文
共 50 条
  • [31] Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo
    Li, Dan
    Clements, Adam
    Drovandi, Christopher
    [J]. ECONOMETRICS AND STATISTICS, 2021, 19 : 22 - 46
  • [32] Appraisal of excess Kurtosis through outlier-modified GARCH-type models
    Alphonsus Akpan, Emmanuel
    Lasisi, Kazeem Etitayo
    Moffat, Imoh Udo
    Abasiekwere, Ubon Akpan
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023, 52 (04) : 1523 - 1537
  • [33] Wavelet Estimation of a Density in a GARCH-type Model
    Chesneau, Christophe
    [J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2013, 42 (01) : 98 - 117
  • [34] MODEL SELECTION BASED ON VALUE-AT-RISK BACKTESTING APPROACH FOR GARCH-TYPE MODELS
    Tay, Hao-Zhe
    Ng, Kok-Haur
    Koh, You-Beng
    Ng, Kooi-Huat
    [J]. JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, 2020, 16 (04) : 1635 - 1654
  • [35] Bayesian estimation of realized GARCH-type models with application to financial tail risk management
    Chen, Cathy W. S.
    Watanabe, Toshiaki
    Lin, Edward M. H.
    [J]. ECONOMETRICS AND STATISTICS, 2023, 28 : 30 - 46
  • [36] A unified approach to standardized-residuals-based correlation tests for GARCH-type models
    Chen, Yi-Ting
    [J]. JOURNAL OF APPLIED ECONOMETRICS, 2008, 23 (01) : 111 - 133
  • [37] The exponentiated half logistic skew-t distribution with GARCH-type volatility models
    Adubisi, O. D.
    Abdulkadir, A.
    Farouk, U. A.
    Chiroma, H.
    [J]. SCIENTIFIC AFRICAN, 2022, 16
  • [38] Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market
    Wang, Yuling
    Xiang, Yunshuang
    Lei, Xinyu
    Zhou, Yucheng
    [J]. ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA, 2022, 35 (01): : 2530 - 2554
  • [39] Forecasting the Volatility of Ethiopian Birr/Euro Exchange Rate Using Garch-Type Models
    Fufa D.D.
    Zeleke B.L.
    [J]. Annals of Data Science, 2018, 5 (4) : 529 - 547
  • [40] The impact of parameter and model uncertainty on market risk predictions from GARCH-type models
    Ardia, David
    Kolly, Jeremy
    Trottier, Denis-Alexandre
    [J]. JOURNAL OF FORECASTING, 2017, 36 (07) : 808 - 823