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| Abstract: | A bivariate semi-Pareto distribution is introduced and characterized using geometric minimization. Autoregressive minification models for bivariate random vectors with bivariate semi-Pareto and bivariate Pareto distributions are also discussed. Multivariate generalizations of the distributions and the processes are briefly indicated. |
| URI: | http://dyuthi.cusat.ac.in/xmlui/purl/2104 |
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| Bivariate semi-Pareto distributions....pdf | (446.2Kb) |
| Abstract: | This paper proposes different estimators for the parameters of SemiPareto and Pareto autoregressive minification processes The asymptotic properties of the estimators are established by showing that the SemiPareto process is α-mixing. Asymptotic variances of different moment and maximum likelihood estimators are compared. |
| URI: | http://dyuthi.cusat.ac.in/xmlui/purl/2107 |
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| ESTIMATION FOR THE SEMIPARETO PROCESSES.pdf | (587.3Kb) |
| 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. |
| URI: | http://dyuthi.cusat.ac.in/xmlui/purl/2106 |
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| Gamma Stochastic Volatility Models.pdf | (262.5Kb) |
| Abstract: | In this article it is proved that the stationary Markov sequences generated by minification models are ergodic and uniformly mixing. These results are used to establish the optimal properties of estimators for the parameters in the model. The problem of estimating the parameters in the exponential minification model is discussed in detail. |
| URI: | http://dyuthi.cusat.ac.in/xmlui/purl/2105 |
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| Parameter Estimation in Minification Processes.pdf | (212.4Kb) |
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