Conditional CAPM in financial risk management: a quantile autoregression approach

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dc.creatorKube, Ananda
dc.creatorLeo, Odongo
dc.creatorMwita , Peter
dc.date05/23/2013
dc.dateThu, 23 May 2013
dc.dateThu, 23 May 2013 12:23:57
dc.dateThu, 23 May 2013 12:24:20
dc.date.accessioned2015-03-18T11:28:58Z
dc.date.available2015-03-18T11:28:58Z
dc.descriptionPaper presented at Strathmore International Math Research Conference on July 23 - 27, 2012
dc.descriptionThe study aims to provide a comprehensive description of dependence pattern of a stock by studying a range of betas derived as quantiles of conditional return distribution using quantile regression based on moving window regression.We investigate predictability of various parts of the conditional return distribution in a linear, autoregressive framework. We also aim to capture a state of dependence at dierent quantiles of the conditional return distribution. A good (bad) state is associated with upper (lower) quantiles,thus the impact of lagged returns is dierent across quantiles. Our empirical ndings are based on daily returns of major European stocks-sample data. Lower quantiles exhibit positive dependence with past returns while upper quantiles are marked by negative dependence. Central quantiles exhibit weak dependence. Keeping the sign of returns, we discover that positive previous day's return leads to strong positive returns with today's positive return and marked negative with today's negative return. The opposite pattern is visible for past negative returns.
dc.description.abstractThe study aims to provide a comprehensive description of dependence pattern of a stock by studying a range of betas derived as quantiles of conditional return distribution using quantile regression based on moving window regression.We investigate predictability of various parts of the conditional return distribution in a linear, autoregressive framework. We also aim to capture a state of dependence at di erent quantiles of the conditional return distribution. A good (bad) state is associated with upper (lower) quantiles,thus the impact of lagged returns is di erent across quantiles. Our empirical ndings are based on daily returns of major European stocks-sample data. Lower quantiles exhibit positive dependence with past returns while upper quantiles are marked by negative dependence. Central quantiles exhibit weak dependence. Keeping the sign of returns, we discover that positive previous day's return leads to strong positive returns with today's positive return and marked negative with today's negative return. The opposite pattern is visible for past negative returns.
dc.formatNumber of Pages:16 p.
dc.identifier.urihttp://hdl.handle.net/11071/3570
dc.languageeng
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dc.subjectMoving window regression
dc.subjectCAPM
dc.subjectBeta
dc.subjectQuantile autoregression
dc.subjectReurns.
dc.titleConditional CAPM in financial risk management: a quantile autoregression approach
dc.typeConference Paper
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