On classifying the effects of policy announcements on volatility

The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Mu...
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Gespeichert in:

Autor*in:	Gallo, Giampiero M. [verfasserIn] Lacava, Demetrio [verfasserIn] Otranto, Edoardo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Markov switching model Unconventional monetary policies Stock market volatility Multiplicative error model Smoothed probabilities Model–based clustering

Übergeordnetes Werk:	Enthalten in: International journal of approximate reasoning - Amsterdam [u.a.] : Elsevier Science, 1987, 134, Seite 23-33
Übergeordnetes Werk:	volume:134 ; pages:23-33

DOI / URN:	10.1016/j.ijar.2021.04.001

Katalog-ID:	ELV006081835

Internformat


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520			\|a The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements.
650		4	\|a Markov switching model
650		4	\|a Unconventional monetary policies
650		4	\|a Stock market volatility
650		4	\|a Multiplicative error model
650		4	\|a Smoothed probabilities
650		4	\|a Model–based clustering
700	1		\|a Lacava, Demetrio \|e verfasserin \|4 aut
700	1		\|a Otranto, Edoardo \|e verfasserin \|0 (orcid)0000-0002-3428-6313 \|4 aut
773	0	8	\|i Enthalten in \|t International journal of approximate reasoning \|d Amsterdam [u.a.] : Elsevier Science, 1987 \|g 134, Seite 23-33 \|h Online-Ressource \|w (DE-627)320416763 \|w (DE-600)2002042-9 \|w (DE-576)114818037 \|x 0888-613x \|7 nnns
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allfields	10.1016/j.ijar.2021.04.001 doi (DE-627)ELV006081835 (ELSEVIER)S0888-613X(21)00044-X DE-627 ger DE-627 rda eng 510 DE-600 54.72 bkl Gallo, Giampiero M. verfasserin (orcid)0000-0003-3556-0238 aut On classifying the effects of policy announcements on volatility 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements. Markov switching model Unconventional monetary policies Stock market volatility Multiplicative error model Smoothed probabilities Model–based clustering Lacava, Demetrio verfasserin aut Otranto, Edoardo verfasserin (orcid)0000-0002-3428-6313 aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 134, Seite 23-33 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:134 pages:23-33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 134 23-33
spelling	10.1016/j.ijar.2021.04.001 doi (DE-627)ELV006081835 (ELSEVIER)S0888-613X(21)00044-X DE-627 ger DE-627 rda eng 510 DE-600 54.72 bkl Gallo, Giampiero M. verfasserin (orcid)0000-0003-3556-0238 aut On classifying the effects of policy announcements on volatility 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements. Markov switching model Unconventional monetary policies Stock market volatility Multiplicative error model Smoothed probabilities Model–based clustering Lacava, Demetrio verfasserin aut Otranto, Edoardo verfasserin (orcid)0000-0002-3428-6313 aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 134, Seite 23-33 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:134 pages:23-33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 134 23-33
allfields_unstemmed	10.1016/j.ijar.2021.04.001 doi (DE-627)ELV006081835 (ELSEVIER)S0888-613X(21)00044-X DE-627 ger DE-627 rda eng 510 DE-600 54.72 bkl Gallo, Giampiero M. verfasserin (orcid)0000-0003-3556-0238 aut On classifying the effects of policy announcements on volatility 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements. Markov switching model Unconventional monetary policies Stock market volatility Multiplicative error model Smoothed probabilities Model–based clustering Lacava, Demetrio verfasserin aut Otranto, Edoardo verfasserin (orcid)0000-0002-3428-6313 aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 134, Seite 23-33 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:134 pages:23-33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 134 23-33
allfieldsGer	10.1016/j.ijar.2021.04.001 doi (DE-627)ELV006081835 (ELSEVIER)S0888-613X(21)00044-X DE-627 ger DE-627 rda eng 510 DE-600 54.72 bkl Gallo, Giampiero M. verfasserin (orcid)0000-0003-3556-0238 aut On classifying the effects of policy announcements on volatility 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements. Markov switching model Unconventional monetary policies Stock market volatility Multiplicative error model Smoothed probabilities Model–based clustering Lacava, Demetrio verfasserin aut Otranto, Edoardo verfasserin (orcid)0000-0002-3428-6313 aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 134, Seite 23-33 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:134 pages:23-33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 134 23-33
allfieldsSound	10.1016/j.ijar.2021.04.001 doi (DE-627)ELV006081835 (ELSEVIER)S0888-613X(21)00044-X DE-627 ger DE-627 rda eng 510 DE-600 54.72 bkl Gallo, Giampiero M. verfasserin (orcid)0000-0003-3556-0238 aut On classifying the effects of policy announcements on volatility 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements. Markov switching model Unconventional monetary policies Stock market volatility Multiplicative error model Smoothed probabilities Model–based clustering Lacava, Demetrio verfasserin aut Otranto, Edoardo verfasserin (orcid)0000-0002-3428-6313 aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 134, Seite 23-33 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:134 pages:23-33 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 134 23-33
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source	Enthalten in International journal of approximate reasoning 134, Seite 23-33 volume:134 pages:23-33
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topic_facet	Markov switching model Unconventional monetary policies Stock market volatility Multiplicative error model Smoothed probabilities Model–based clustering
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author	Gallo, Giampiero M.
spellingShingle	Gallo, Giampiero M. ddc 510 bkl 54.72 misc Markov switching model misc Unconventional monetary policies misc Stock market volatility misc Multiplicative error model misc Smoothed probabilities misc Model–based clustering On classifying the effects of policy announcements on volatility
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author_browse	Gallo, Giampiero M. Lacava, Demetrio Otranto, Edoardo
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title_sort	on classifying the effects of policy announcements on volatility
title_auth	On classifying the effects of policy announcements on volatility
abstract	The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements.
abstractGer	The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements.
abstract_unstemmed	The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements.
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title_short	On classifying the effects of policy announcements on volatility
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author2	Lacava, Demetrio Otranto, Edoardo
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doi_str	10.1016/j.ijar.2021.04.001
up_date	2024-07-06T20:09:40.771Z
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