Option market trading activity and the estimation of the pricing kernel: A Bayesian approach
We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amou...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Barone-Adesi, Giovanni [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Umfang: |
20 |
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| Übergeordnetes Werk: |
Enthalten in: Contents - 2017, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:216 ; year:2020 ; number:2 ; pages:430-449 ; extent:20 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jeconom.2019.11.001 |
|---|
| Katalog-ID: |
ELV049960474 |
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| 520 | |a We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. | ||
| 520 | |a We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. | ||
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10.1016/j.jeconom.2019.11.001 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000970.pica (DE-627)ELV049960474 (ELSEVIER)S0304-4076(19)30226-X DE-627 ger DE-627 rakwb eng 610 VZ 630 640 VZ 48.00 bkl Barone-Adesi, Giovanni verfasserin aut Option market trading activity and the estimation of the pricing kernel: A Bayesian approach 2020transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. G10 Elsevier G13 Elsevier G14 Elsevier G17 Elsevier Fusari, Nicola oth Mira, Antonietta oth Sala, Carlo oth Enthalten in Elsevier Contents 2017 Amsterdam [u.a.] (DE-627)ELV014891271 volume:216 year:2020 number:2 pages:430-449 extent:20 https://doi.org/10.1016/j.jeconom.2019.11.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_11 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_235 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 216 2020 2 430-449 20 |
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10.1016/j.jeconom.2019.11.001 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000970.pica (DE-627)ELV049960474 (ELSEVIER)S0304-4076(19)30226-X DE-627 ger DE-627 rakwb eng 610 VZ 630 640 VZ 48.00 bkl Barone-Adesi, Giovanni verfasserin aut Option market trading activity and the estimation of the pricing kernel: A Bayesian approach 2020transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. G10 Elsevier G13 Elsevier G14 Elsevier G17 Elsevier Fusari, Nicola oth Mira, Antonietta oth Sala, Carlo oth Enthalten in Elsevier Contents 2017 Amsterdam [u.a.] (DE-627)ELV014891271 volume:216 year:2020 number:2 pages:430-449 extent:20 https://doi.org/10.1016/j.jeconom.2019.11.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_11 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_235 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 216 2020 2 430-449 20 |
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10.1016/j.jeconom.2019.11.001 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000970.pica (DE-627)ELV049960474 (ELSEVIER)S0304-4076(19)30226-X DE-627 ger DE-627 rakwb eng 610 VZ 630 640 VZ 48.00 bkl Barone-Adesi, Giovanni verfasserin aut Option market trading activity and the estimation of the pricing kernel: A Bayesian approach 2020transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. G10 Elsevier G13 Elsevier G14 Elsevier G17 Elsevier Fusari, Nicola oth Mira, Antonietta oth Sala, Carlo oth Enthalten in Elsevier Contents 2017 Amsterdam [u.a.] (DE-627)ELV014891271 volume:216 year:2020 number:2 pages:430-449 extent:20 https://doi.org/10.1016/j.jeconom.2019.11.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_11 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_235 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 216 2020 2 430-449 20 |
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10.1016/j.jeconom.2019.11.001 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000970.pica (DE-627)ELV049960474 (ELSEVIER)S0304-4076(19)30226-X DE-627 ger DE-627 rakwb eng 610 VZ 630 640 VZ 48.00 bkl Barone-Adesi, Giovanni verfasserin aut Option market trading activity and the estimation of the pricing kernel: A Bayesian approach 2020transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. G10 Elsevier G13 Elsevier G14 Elsevier G17 Elsevier Fusari, Nicola oth Mira, Antonietta oth Sala, Carlo oth Enthalten in Elsevier Contents 2017 Amsterdam [u.a.] (DE-627)ELV014891271 volume:216 year:2020 number:2 pages:430-449 extent:20 https://doi.org/10.1016/j.jeconom.2019.11.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_11 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_235 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 216 2020 2 430-449 20 |
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10.1016/j.jeconom.2019.11.001 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000970.pica (DE-627)ELV049960474 (ELSEVIER)S0304-4076(19)30226-X DE-627 ger DE-627 rakwb eng 610 VZ 630 640 VZ 48.00 bkl Barone-Adesi, Giovanni verfasserin aut Option market trading activity and the estimation of the pricing kernel: A Bayesian approach 2020transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. G10 Elsevier G13 Elsevier G14 Elsevier G17 Elsevier Fusari, Nicola oth Mira, Antonietta oth Sala, Carlo oth Enthalten in Elsevier Contents 2017 Amsterdam [u.a.] (DE-627)ELV014891271 volume:216 year:2020 number:2 pages:430-449 extent:20 https://doi.org/10.1016/j.jeconom.2019.11.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-FOR GBV_ILN_11 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_65 GBV_ILN_235 48.00 Land- und Forstwirtschaft: Allgemeines VZ AR 216 2020 2 430-449 20 |
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Option market trading activity and the estimation of the pricing kernel: A Bayesian approach |
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Option market trading activity and the estimation of the pricing kernel: A Bayesian approach |
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Barone-Adesi, Giovanni |
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Contents |
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Barone-Adesi, Giovanni |
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Elektronische Aufsätze |
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Barone-Adesi, Giovanni |
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10.1016/j.jeconom.2019.11.001 |
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610 630 640 |
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option market trading activity and the estimation of the pricing kernel: a bayesian approach |
| title_auth |
Option market trading activity and the estimation of the pricing kernel: A Bayesian approach |
| abstract |
We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. |
| abstractGer |
We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. |
| abstract_unstemmed |
We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the amount of trading activity in deep-out-of-the-money options. We use the option-adjusted physical measure to construct an option-adjusted pricing kernel. An empirical investigation on the S&P 500 Index from 2002 to 2015 shows that the option-adjusted pricing kernel is consistently monotonically decreasing, regardless of the level of volatility, thus providing an explanation to the well known U-shaped pricing kernel puzzle. |
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| title_short |
Option market trading activity and the estimation of the pricing kernel: A Bayesian approach |
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https://doi.org/10.1016/j.jeconom.2019.11.001 |
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Fusari, Nicola Mira, Antonietta Sala, Carlo |
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