Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators

This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robus...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hung, Jui-Cheng [verfasserIn] Liu, Hung-Chun [verfasserIn] Yang, J. Jimmy [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Kapitalmarkttheorie / Finanzmarkt / Internationaler Finanzmarkt / Welt
Schlagwörter:	Bitcoin Realized GARCH model Jump-robust realized measure Realized bi-power variation Realized tri-power variation

Übergeordnetes Werk:	Enthalten in: The North American journal of economics and finance - Amsterdam [u.a.] : Elsevier Science, 1992, 52
Übergeordnetes Werk:	volume:52

DOI / URN:	10.1016/j.najef.2020.101165

Katalog-ID:	ELV003955397

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allfields	10.1016/j.najef.2020.101165 doi (DE-627)ELV003955397 (ELSEVIER)S1062-9408(20)30062-0 DE-627 ger DE-627 rda eng 330 DE-600 Hung, Jui-Cheng verfasserin aut Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing 1.1\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.2\x Finanzmarkt (DE-2867)13723-2 stw 1.3\x Internationaler Finanzmarkt (DE-2867)10807-4 stw 1.4\x Welt (DE-2867)16809-5 stw Bitcoin Realized GARCH model Jump-robust realized measure Realized bi-power variation Realized tri-power variation Liu, Hung-Chun verfasserin aut Yang, J. Jimmy verfasserin aut Enthalten in The North American journal of economics and finance Amsterdam [u.a.] : Elsevier Science, 1992 52 Online-Ressource (DE-627)320629538 (DE-600)2023759-5 (DE-576)259485373 1062-9408 nnns volume:52 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 52
spelling	10.1016/j.najef.2020.101165 doi (DE-627)ELV003955397 (ELSEVIER)S1062-9408(20)30062-0 DE-627 ger DE-627 rda eng 330 DE-600 Hung, Jui-Cheng verfasserin aut Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing 1.1\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.2\x Finanzmarkt (DE-2867)13723-2 stw 1.3\x Internationaler Finanzmarkt (DE-2867)10807-4 stw 1.4\x Welt (DE-2867)16809-5 stw Bitcoin Realized GARCH model Jump-robust realized measure Realized bi-power variation Realized tri-power variation Liu, Hung-Chun verfasserin aut Yang, J. Jimmy verfasserin aut Enthalten in The North American journal of economics and finance Amsterdam [u.a.] : Elsevier Science, 1992 52 Online-Ressource (DE-627)320629538 (DE-600)2023759-5 (DE-576)259485373 1062-9408 nnns volume:52 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 52
allfields_unstemmed	10.1016/j.najef.2020.101165 doi (DE-627)ELV003955397 (ELSEVIER)S1062-9408(20)30062-0 DE-627 ger DE-627 rda eng 330 DE-600 Hung, Jui-Cheng verfasserin aut Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing 1.1\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.2\x Finanzmarkt (DE-2867)13723-2 stw 1.3\x Internationaler Finanzmarkt (DE-2867)10807-4 stw 1.4\x Welt (DE-2867)16809-5 stw Bitcoin Realized GARCH model Jump-robust realized measure Realized bi-power variation Realized tri-power variation Liu, Hung-Chun verfasserin aut Yang, J. Jimmy verfasserin aut Enthalten in The North American journal of economics and finance Amsterdam [u.a.] : Elsevier Science, 1992 52 Online-Ressource (DE-627)320629538 (DE-600)2023759-5 (DE-576)259485373 1062-9408 nnns volume:52 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 52
allfieldsGer	10.1016/j.najef.2020.101165 doi (DE-627)ELV003955397 (ELSEVIER)S1062-9408(20)30062-0 DE-627 ger DE-627 rda eng 330 DE-600 Hung, Jui-Cheng verfasserin aut Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing 1.1\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.2\x Finanzmarkt (DE-2867)13723-2 stw 1.3\x Internationaler Finanzmarkt (DE-2867)10807-4 stw 1.4\x Welt (DE-2867)16809-5 stw Bitcoin Realized GARCH model Jump-robust realized measure Realized bi-power variation Realized tri-power variation Liu, Hung-Chun verfasserin aut Yang, J. Jimmy verfasserin aut Enthalten in The North American journal of economics and finance Amsterdam [u.a.] : Elsevier Science, 1992 52 Online-Ressource (DE-627)320629538 (DE-600)2023759-5 (DE-576)259485373 1062-9408 nnns volume:52 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 52
allfieldsSound	10.1016/j.najef.2020.101165 doi (DE-627)ELV003955397 (ELSEVIER)S1062-9408(20)30062-0 DE-627 ger DE-627 rda eng 330 DE-600 Hung, Jui-Cheng verfasserin aut Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing 1.1\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.2\x Finanzmarkt (DE-2867)13723-2 stw 1.3\x Internationaler Finanzmarkt (DE-2867)10807-4 stw 1.4\x Welt (DE-2867)16809-5 stw Bitcoin Realized GARCH model Jump-robust realized measure Realized bi-power variation Realized tri-power variation Liu, Hung-Chun verfasserin aut Yang, J. Jimmy verfasserin aut Enthalten in The North American journal of economics and finance Amsterdam [u.a.] : Elsevier Science, 1992 52 Online-Ressource (DE-627)320629538 (DE-600)2023759-5 (DE-576)259485373 1062-9408 nnns volume:52 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 AR 52
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author	Hung, Jui-Cheng
spellingShingle	Hung, Jui-Cheng ddc 330 stw Kapitalmarkttheorie stw Finanzmarkt stw Internationaler Finanzmarkt stw Welt misc Bitcoin misc Realized GARCH model misc Jump-robust realized measure misc Realized bi-power variation misc Realized tri-power variation Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators
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topic_title	330 DE-600 Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators 1.1\x Kapitalmarkttheorie (DE-2867)12210-1 stw 1.2\x Finanzmarkt (DE-2867)13723-2 stw 1.3\x Internationaler Finanzmarkt (DE-2867)10807-4 stw 1.4\x Welt (DE-2867)16809-5 stw Bitcoin Realized GARCH model Jump-robust realized measure Realized bi-power variation Realized tri-power variation
topic	ddc 330 stw Kapitalmarkttheorie stw Finanzmarkt stw Internationaler Finanzmarkt stw Welt misc Bitcoin misc Realized GARCH model misc Jump-robust realized measure misc Realized bi-power variation misc Realized tri-power variation
topic_unstemmed	ddc 330 stw Kapitalmarkttheorie stw Finanzmarkt stw Internationaler Finanzmarkt stw Welt misc Bitcoin misc Realized GARCH model misc Jump-robust realized measure misc Realized bi-power variation misc Realized tri-power variation
topic_browse	ddc 330 stw Kapitalmarkttheorie stw Finanzmarkt stw Internationaler Finanzmarkt stw Welt misc Bitcoin misc Realized GARCH model misc Jump-robust realized measure misc Realized bi-power variation misc Realized tri-power variation
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title	Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators
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title_full	Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators
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author_browse	Hung, Jui-Cheng Liu, Hung-Chun Yang, J. Jimmy
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title_sort	improving the realized garch’s volatility forecast for bitcoin with jump-robust estimators
title_auth	Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators
abstract	This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing
abstractGer	This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing
abstract_unstemmed	This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. Empirical results show that considerable price jumps occurred in the Bitcoin market, suggesting that a jump-robust realized measure is crucial to estimate Bitcoin volatility. The RGARCH model, especially the one with tri-power variation, outperforms the standard GARCH model. Additionally, the RGARCH model with jump-robust realized measures can provide steady forecasting performance. This study is timely given that the CME may release a Bitcoin option product and our results are relevant to option pricing
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title_short	Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators
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author2	Liu, Hung-Chun Yang, J. Jimmy
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doi_str	10.1016/j.najef.2020.101165
up_date	2024-07-06T21:21:27.215Z
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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">ELV003955397</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524165841.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230502s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.najef.2020.101165</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV003955397</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1062-9408(20)30062-0</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hung, Jui-Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This study employs the realized GARCH (RGARCH) model to estimate the volatility of Bitcoin returns and measure the benefits of various scaled realized measures in forecasting volatility. 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Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators

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