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Please use this identifier to cite or link to this item: http://purl.org/purl/4732

Title: Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models
Authors: Shiji, K
Dr.Balakrishna, N
Keywords: Stochastic Processes
time series models
Gumbel Extreme Value Autoregressive
non-Gaussian volatility sequences
Conditional Least Squares
Quasi Maximum Likelihood
Maximum Likelihood
Issue Date: Mar-2014
Publisher: Cochin University of Science And Technology
Abstract: The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis
Description: Department of Statistics, Cochin University of Science and Technology
URI: http://dyuthi.cusat.ac.in/purl/4732
Appears in Collections:Faculty of Sciences

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