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Good volatility, bad volatility, and the cross-section of stock returns at different investment horizons
dc.contributor.advisorBaruník, Jozef
dc.creatorSako, Tony Ryan Hlali
dc.date.accessioned2018-07-27T13:26:48Z
dc.date.available2018-07-27T13:26:48Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/20.500.11956/99532
dc.description.abstractStarting with the assumption that different investors have different investment time preferences and different risk tolerances within their given investment time-frames, this paper investigates the value of employing multiresolution analysis to model volatility and risk-pricing. In terms of estimation and fore- casting performance we were able to reduce by at least half the volatility fore- casting errors, with even better results at longer horizons. In regards to risk pricing we learn that extreme aggregate volatility (i.e. tail risk) is priced but regular volatility is not. Additionally we find that whilst aggregate volatility is generally more important over the long-horizon, during periods of market turmoil it is much more significant over the short-horizon. Finally we show that stocks with high sensitivity to aggregate volatility have lower subsequent returns supporting the idea that they become attractive as a hedge against market volatility. JEL Classification C12, C13, C21, C22, C31, C32, C51, C52, C53 Keywords Realized Volatility, Wavelet, Long-Memory Models, Cross-Section, Volatility Forecast, High-Frequency Data Author's e-mail tony sako@yahoo.com Supervisor's e-mail barunik@fsv.cuni.czen_US
dc.languageEnglishcs_CZ
dc.language.isoen_US
dc.publisherUniverzita Karlova, Fakulta sociálních vědcs_CZ
dc.subjectRealized Volatilitycs_CZ
dc.subjectWaveletcs_CZ
dc.subjectLong-Memory Modelscs_CZ
dc.subjectCross-Sectioncs_CZ
dc.subjectVolatility Forecastcs_CZ
dc.subjectHigh-Frequency Datacs_CZ
dc.subjectRealized Volatilityen_US
dc.subjectWaveleten_US
dc.subjectLong-Memory Modelsen_US
dc.subjectCross-Sectionen_US
dc.subjectVolatility Forecasten_US
dc.subjectHigh-Frequency Dataen_US
dc.titleGood volatility, bad volatility, and the cross-section of stock returns at different investment horizonsen_US
dc.typediplomová prácecs_CZ
dcterms.created2018
dcterms.dateAccepted2018-06-20
dc.description.departmentInstitut ekonomických studiícs_CZ
dc.description.departmentInstitute of Economic Studiesen_US
dc.description.facultyFakulta sociálních vědcs_CZ
dc.description.facultyFaculty of Social Sciencesen_US
dc.identifier.repId191477
dc.title.translatedGood volatility, bad volatility, and the cross-section of stock returns at different investment horizonscs_CZ
dc.contributor.refereeKukačka, Jiří
dc.identifier.aleph002192429
thesis.degree.nameMgr.
thesis.degree.levelnavazující magisterskécs_CZ
thesis.degree.disciplineEconomics and Financeen_US
thesis.degree.disciplineEkonomie a financecs_CZ
thesis.degree.programEconomicsen_US
thesis.degree.programEkonomické teoriecs_CZ
uk.faculty-name.csFakulta sociálních vědcs_CZ
uk.faculty-name.enFaculty of Social Sciencesen_US
uk.faculty-abbr.csFSVcs_CZ
uk.degree-discipline.csEkonomie a financecs_CZ
uk.degree-discipline.enEconomics and Financeen_US
uk.degree-program.csEkonomické teoriecs_CZ
uk.degree-program.enEconomicsen_US
thesis.grade.csVelmi dobřecs_CZ
thesis.grade.enVery gooden_US
uk.abstract.enStarting with the assumption that different investors have different investment time preferences and different risk tolerances within their given investment time-frames, this paper investigates the value of employing multiresolution analysis to model volatility and risk-pricing. In terms of estimation and fore- casting performance we were able to reduce by at least half the volatility fore- casting errors, with even better results at longer horizons. In regards to risk pricing we learn that extreme aggregate volatility (i.e. tail risk) is priced but regular volatility is not. Additionally we find that whilst aggregate volatility is generally more important over the long-horizon, during periods of market turmoil it is much more significant over the short-horizon. Finally we show that stocks with high sensitivity to aggregate volatility have lower subsequent returns supporting the idea that they become attractive as a hedge against market volatility. JEL Classification C12, C13, C21, C22, C31, C32, C51, C52, C53 Keywords Realized Volatility, Wavelet, Long-Memory Models, Cross-Section, Volatility Forecast, High-Frequency Data Author's e-mail tony sako@yahoo.com Supervisor's e-mail barunik@fsv.cuni.czen_US
uk.file-availabilityV
uk.publication.placePrahacs_CZ
uk.grantorUniverzita Karlova, Fakulta sociálních věd, Institut ekonomických studiícs_CZ
thesis.grade.codeC


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