Forecasting Volatility of Stock Indices with HMM-SV Models

dc.contributor.authorNkemnole, E. B
dc.contributor.authorAbass, O
dc.contributor.authorWulu, J. T
dc.date.accessioned2017-10-12T14:36:18Z
dc.date.available2017-10-12T14:36:18Z
dc.date.issued2017
dc.descriptionStaff Publicationen_US
dc.description.abstractThe use of volatility models to generate volatility forecasts has given vent to a lot of literature. However, it is known that volatility persistence, as indicated by the estimated parameter rp , in Stochastic Volatility (SV) model is typically high. Since future values in SV models are based on the estimation of the parameters, this may lead to poor volatility forecasts. Furthermore. this high persistence, as contended by some writers, is due 10 the structure changes (e.g. shift of volatility levels) in the volatility processes, which SV model cannot capture. This work deals with the problem by bringing in the SV model based on Hidden Markov Models (HMMs), called HMM-SV model. Via hidden states, HMMs allow for periods 'with different volatility levels characterized by the hidden states. Within each state, SV model is applied to model conditional volatility. Empirical analysis shows that our model, not only takes care of the structure changes (hence giving better volatility forecasts), but also helps to establish an proficient forecasting structure for volatility modelsen_US
dc.identifier.citationNkemnole, E.B, Wulu J.T & Abass, O (2017) Forecasting Volatility of Stock Indices with HMM-SV Models Theoretical and Applied Economics Volume XXIV (2017), No. 2(611), Summer, pp. 45-60en_US
dc.identifier.issn1841-8678
dc.identifier.issn1844-0029
dc.identifier.uristore.ectap.ro/articole/1268.pdf
dc.identifier.urihttp://ir.unilag.edu.ng:8080/xmlui/handle/123456789/2176
dc.language.isoenen_US
dc.publisherEctapen_US
dc.subjectForecasting, Hidden Markov model,en_US
dc.subjectStochastic Volatility, Stock Exchangeen_US
dc.titleForecasting Volatility of Stock Indices with HMM-SV Modelsen_US
dc.typeArticleen_US
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