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
Gespeichert in:
| Autor*in: |
Su, Kuangxi [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
16 |
|---|
| Übergeordnetes Werk: |
Enthalten in: MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma - Mirzaei, Hamed ELSEVIER, 2016transfer abstract, IREF, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:83 ; year:2023 ; pages:35-50 ; extent:16 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.iref.2022.08.019 |
|---|
| Katalog-ID: |
ELV059551828 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV059551828 | ||
| 003 | DE-627 | ||
| 005 | 20230626053208.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 221219s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.iref.2022.08.019 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica |
| 035 | |a (DE-627)ELV059551828 | ||
| 035 | |a (ELSEVIER)S1059-0560(22)00203-9 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.52 |2 bkl | ||
| 100 | 1 | |a Su, Kuangxi |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models |
| 264 | 1 | |c 2023transfer abstract | |
| 300 | |a 16 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 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. | ||
| 650 | 7 | |a Hybrid model |2 Elsevier | |
| 650 | 7 | |a Minimum-CVaR |2 Elsevier | |
| 650 | 7 | |a Crude oil risk hedging |2 Elsevier | |
| 650 | 7 | |a Hedging performance |2 Elsevier | |
| 700 | 1 | |a Yao, Yinhong |4 oth | |
| 700 | 1 | |a Zheng, Chengli |4 oth | |
| 700 | 1 | |a Xie, Wenzhao |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Mirzaei, Hamed ELSEVIER |t MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma |d 2016transfer abstract |d IREF |g Amsterdam [u.a.] |w (DE-627)ELV01963546X |
| 773 | 1 | 8 | |g volume:83 |g year:2023 |g pages:35-50 |g extent:16 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.iref.2022.08.019 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 44.52 |j Therapie |x Medizin |q VZ |
| 951 | |a AR | ||
| 952 | |d 83 |j 2023 |h 35-50 |g 16 | ||
| author_variant |
k s ks |
|---|---|
| matchkey_str |
sukuangxiyaoyinhongzhengchenglixiewenzha:2023----:nvlyrdtaeyocueiftrhdigaeoteobntoo |
| hierarchy_sort_str |
2023transfer abstract |
| bklnumber |
44.52 |
| publishDate |
2023 |
| allfields |
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 |
| language |
English |
| source |
Enthalten in MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma Amsterdam [u.a.] volume:83 year:2023 pages:35-50 extent:16 |
| sourceStr |
Enthalten in MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma Amsterdam [u.a.] volume:83 year:2023 pages:35-50 extent:16 |
| format_phy_str_mv |
Article |
| bklname |
Therapie |
| institution |
findex.gbv.de |
| topic_facet |
Hybrid model Minimum-CVaR Crude oil risk hedging Hedging performance |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma |
| authorswithroles_txt_mv |
Su, Kuangxi @@aut@@ Yao, Yinhong @@oth@@ Zheng, Chengli @@oth@@ Xie, Wenzhao @@oth@@ |
| publishDateDaySort_date |
2023-01-01T00:00:00Z |
| hierarchy_top_id |
ELV01963546X |
| dewey-sort |
3610 |
| id |
ELV059551828 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059551828</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626053208.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.iref.2022.08.019</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059551828</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1059-0560(22)00203-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.52</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Su, Kuangxi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">16</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</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. 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. 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="650" ind1=" " ind2="7"><subfield code="a">Hybrid model</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Minimum-CVaR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Crude oil risk hedging</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hedging performance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Yinhong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zheng, Chengli</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Wenzhao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Mirzaei, Hamed ELSEVIER</subfield><subfield code="t">MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma</subfield><subfield code="d">2016transfer abstract</subfield><subfield code="d">IREF</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV01963546X</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:83</subfield><subfield code="g">year:2023</subfield><subfield code="g">pages:35-50</subfield><subfield code="g">extent:16</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.iref.2022.08.019</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.52</subfield><subfield code="j">Therapie</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">83</subfield><subfield code="j">2023</subfield><subfield code="h">35-50</subfield><subfield code="g">16</subfield></datafield></record></collection>
|
| author |
Su, Kuangxi |
| spellingShingle |
Su, Kuangxi ddc 610 bkl 44.52 Elsevier Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models |
| authorStr |
Su, Kuangxi |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV01963546X |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 44.52 bkl A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance Elsevier |
| topic |
ddc 610 bkl 44.52 Elsevier Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance |
| topic_unstemmed |
ddc 610 bkl 44.52 Elsevier Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance |
| topic_browse |
ddc 610 bkl 44.52 Elsevier Hybrid model Elsevier Minimum-CVaR Elsevier Crude oil risk hedging Elsevier Hedging performance |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
y y yy c z cz w x wx |
| hierarchy_parent_title |
MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma |
| hierarchy_parent_id |
ELV01963546X |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV01963546X |
| title |
A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models |
| ctrlnum |
(DE-627)ELV059551828 (ELSEVIER)S1059-0560(22)00203-9 |
| title_full |
A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models |
| author_sort |
Su, Kuangxi |
| journal |
MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma |
| journalStr |
MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
zzz |
| container_start_page |
35 |
| author_browse |
Su, Kuangxi |
| container_volume |
83 |
| physical |
16 |
| class |
610 VZ 44.52 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Su, Kuangxi |
| doi_str_mv |
10.1016/j.iref.2022.08.019 |
| dewey-full |
610 |
| title_sort |
a novel hybrid strategy for crude oil future hedging based on the combination of three minimum-cvar models |
| title_auth |
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. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models |
| url |
https://doi.org/10.1016/j.iref.2022.08.019 |
| remote_bool |
true |
| author2 |
Yao, Yinhong Zheng, Chengli Xie, Wenzhao |
| author2Str |
Yao, Yinhong Zheng, Chengli Xie, Wenzhao |
| ppnlink |
ELV01963546X |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1016/j.iref.2022.08.019 |
| up_date |
2024-07-06T22:20:27.445Z |
| _version_ |
1803869935314665472 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059551828</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626053208.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.iref.2022.08.019</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001970.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059551828</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1059-0560(22)00203-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.52</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Su, Kuangxi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">16</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</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. 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. 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="650" ind1=" " ind2="7"><subfield code="a">Hybrid model</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Minimum-CVaR</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Crude oil risk hedging</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hedging performance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Yinhong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zheng, Chengli</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Wenzhao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Mirzaei, Hamed ELSEVIER</subfield><subfield code="t">MicroRNAs as potential diagnostic and prognostic biomarkers in melanoma</subfield><subfield code="d">2016transfer abstract</subfield><subfield code="d">IREF</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV01963546X</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:83</subfield><subfield code="g">year:2023</subfield><subfield code="g">pages:35-50</subfield><subfield code="g">extent:16</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.iref.2022.08.019</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.52</subfield><subfield code="j">Therapie</subfield><subfield code="x">Medizin</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">83</subfield><subfield code="j">2023</subfield><subfield code="h">35-50</subfield><subfield code="g">16</subfield></datafield></record></collection>
|
| score |
7.3993883 |