A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models
Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less...
Ausführliche Beschreibung
Autor*in: |
Su, Kuangxi [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023transfer abstract |
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Umfang: |
16 |
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Übergeordnetes Werk: |
Enthalten in: MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma - Mirzaei, Hamed ELSEVIER, 2016transfer abstract, IREF, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:83 ; year:2023 ; pages:35-50 ; extent:16 |
Links: |
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DOI / URN: |
10.1016/j.iref.2022.08.019 |
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Katalog-ID: |
ELV059551828 |
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520 | |a Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. | ||
520 | |a Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. | ||
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650 | 7 | |a Crude oil risk hedging |2 Elsevier | |
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700 | 1 | |a Xie, Wenzhao |4 oth | |
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10.1016/j.iref.2022.08.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica (DE-627)ELV059551828 (ELSEVIER)S1059-0560(22)00203-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.52 bkl Su, Kuangxi verfasserin aut A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models 2023transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance Elsevier Yao, Yinhong oth Zheng, Chengli oth Xie, Wenzhao oth Enthalten in Elsevier Science Mirzaei, Hamed ELSEVIER MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma 2016transfer abstract IREF Amsterdam [u.a.] (DE-627)ELV01963546X volume:83 year:2023 pages:35-50 extent:16 https://doi.org/10.1016/j.iref.2022.08.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.52 Therapie Medizin VZ AR 83 2023 35-50 16 |
spelling |
10.1016/j.iref.2022.08.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica (DE-627)ELV059551828 (ELSEVIER)S1059-0560(22)00203-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.52 bkl Su, Kuangxi verfasserin aut A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models 2023transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance Elsevier Yao, Yinhong oth Zheng, Chengli oth Xie, Wenzhao oth Enthalten in Elsevier Science Mirzaei, Hamed ELSEVIER MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma 2016transfer abstract IREF Amsterdam [u.a.] (DE-627)ELV01963546X volume:83 year:2023 pages:35-50 extent:16 https://doi.org/10.1016/j.iref.2022.08.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.52 Therapie Medizin VZ AR 83 2023 35-50 16 |
allfields_unstemmed |
10.1016/j.iref.2022.08.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica (DE-627)ELV059551828 (ELSEVIER)S1059-0560(22)00203-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.52 bkl Su, Kuangxi verfasserin aut A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models 2023transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance Elsevier Yao, Yinhong oth Zheng, Chengli oth Xie, Wenzhao oth Enthalten in Elsevier Science Mirzaei, Hamed ELSEVIER MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma 2016transfer abstract IREF Amsterdam [u.a.] (DE-627)ELV01963546X volume:83 year:2023 pages:35-50 extent:16 https://doi.org/10.1016/j.iref.2022.08.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.52 Therapie Medizin VZ AR 83 2023 35-50 16 |
allfieldsGer |
10.1016/j.iref.2022.08.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica (DE-627)ELV059551828 (ELSEVIER)S1059-0560(22)00203-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.52 bkl Su, Kuangxi verfasserin aut A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models 2023transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance Elsevier Yao, Yinhong oth Zheng, Chengli oth Xie, Wenzhao oth Enthalten in Elsevier Science Mirzaei, Hamed ELSEVIER MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma 2016transfer abstract IREF Amsterdam [u.a.] (DE-627)ELV01963546X volume:83 year:2023 pages:35-50 extent:16 https://doi.org/10.1016/j.iref.2022.08.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.52 Therapie Medizin VZ AR 83 2023 35-50 16 |
allfieldsSound |
10.1016/j.iref.2022.08.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica (DE-627)ELV059551828 (ELSEVIER)S1059-0560(22)00203-9 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.52 bkl Su, Kuangxi verfasserin aut A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models 2023transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance Elsevier Yao, Yinhong oth Zheng, Chengli oth Xie, Wenzhao oth Enthalten in Elsevier Science Mirzaei, Hamed ELSEVIER MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma 2016transfer abstract IREF Amsterdam [u.a.] (DE-627)ELV01963546X volume:83 year:2023 pages:35-50 extent:16 https://doi.org/10.1016/j.iref.2022.08.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 44.52 Therapie Medizin VZ AR 83 2023 35-50 16 |
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Enthalten in MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma Amsterdam [u.a.] volume:83 year:2023 pages:35-50 extent:16 |
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Enthalten in MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma Amsterdam [u.a.] volume:83 year:2023 pages:35-50 extent:16 |
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MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma |
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Su, Kuangxi @@aut@@ Yao, Yinhong @@oth@@ Zheng, Chengli @@oth@@ Xie, Wenzhao @@oth@@ |
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However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. 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a novel hybrid strategy for crude oil future hedging based on the combination of three minimum-cvar models |
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A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models |
abstract |
Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. |
abstractGer |
Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. |
abstract_unstemmed |
Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions. |
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A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models |
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