Gamma stochastic volatility models

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Gamma stochastic volatility models

Show simple item record Abraham, Bovas Balakrishna, N Sivakumar, Ranjini 2011-03-17T08:37:03Z 2011-03-17T08:37:03Z 2006
dc.identifier.issn 1099-131X
dc.description.abstract This paper presents gamma stochastic volatility models and investigates its distributional and time series properties. The parameter estimators obtained by the method of moments are shown analytically to be consistent and asymptotically normal. The simulation results indicate that the estimators behave well. The insample analysis shows that return models with gamma autoregressive stochastic volatility processes capture the leptokurtic nature of return distributions and the slowly decaying autocorrelation functions of squared stock index returns for the USA and UK. In comparison with GARCH and EGARCH models, the gamma autoregressive model picks up the persistence in volatility for the US and UK index returns but not the volatility persistence for the Canadian and Japanese index returns. The out-of-sample analysis indicates that the gamma autoregressive model has a superior volatility forecasting performance compared to GARCH and EGARCH models. en_US
dc.language.iso en en_US
dc.publisher John Wiley & Sons en_US
dc.subject Stochastic volatility en_US
dc.subject GARCH en_US
dc.subject gamma sequences en_US
dc.subject Moment estimation en_US
dc.subject Financial time series en_US
dc.title Gamma stochastic volatility models en_US
dc.type Working Paper en_US
dc.contributor.faculty Science en_US

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