Now showing items 1-11 of 11
| 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: | We propose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box–Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data. |
| URI: | http://dyuthi.cusat.ac.in/purl/2856 |
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| Dyuthi-P00399.pdf | (427.5Kb) |
| 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: | The thesis deals with some of the non-linear Gaussian and non-Gaussian time models and mainly concentrated in studying the properties and application of a first order autoregressive process with Cauchy marginal distribution. In this thesis some of the non-linear Gaussian and non-Gaussian time series models and mainly concentrated in studying the properties and application of a order autoregressive process with Cauchy marginal distribution. Time series relating to prices, consumptions, money in circulation, bank deposits and bank clearing, sales and profit in a departmental store, national income and foreign exchange reserves, prices and dividend of shares in a stock exchange etc. are examples of economic and business time series. The thesis discuses the application of a threshold autoregressive(TAR) model, try to fit this model to a time series data. Another important non-linear model is the ARCH model, and the third model is the TARCH model. The main objective here is to identify an appropriate model to a given set of data. The data considered are the daily coconut oil prices for a period of three years. Since it is a price data the consecutive prices may not be independent and hence a time series based model is more appropriate. In this study the properties like ergodicity, mixing property and time reversibility and also various estimation procedures used to estimate the unknown parameters of the process. |
| URI: | http://dyuthi.cusat.ac.in/purl/40 |
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| Dyuthi-T0177.pdf | (1.709Mb) |
| Abstract: | The average availability of a repairable system is the expected proportion of time that the system is operating in the interval [0, t]. The present article discusses the nonparametric estimation of the average availability when (i) the data on 'n' complete cycles of system operation are available, (ii) the data are subject to right censorship, and (iii) the process is observed upto a specified time 'T'. In each case, a nonparametric confidence interval for the average availability is also constructed. Simulations are conducted to assess the performance of the estimators. |
| URI: | http://dyuthi.cusat.ac.in/purl/2857 |
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| Dyuthi-P00400.pdf | (135.8Kb) |
| 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) |
| Abstract: | When variables in time series context are non-negative, such as for volatility, survival time or wave heights, a multiplicative autoregressive model of the type Xt = Xα t−1Vt , 0 ≤ α < 1, t = 1, 2, . . . may give the preferred dependent structure. In this paper, we study the properties of such models and propose methods for parameter estimation. Explicit solutions of the model are obtained in the case of gamma marginal distribution |
| Description: | Statistics and Probability Letters 82 (2012) 1530–1537 |
| URI: | http://dyuthi.cusat.ac.in/purl/4725 |
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| Product autoreg ... non-negative variables.pdf | (260.7Kb) |
| URI: | http://dyuthi.cusat.ac.in/purl/1667 |
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| Dyuthi-T0149.pdf | (3.609Mb) |
| Abstract: | The standard models for statistical signal extraction assume that the signal and noise are generated by linear Gaussian processes. The optimum filter weights for those models are derived using the method of minimum mean square error. In the present work we study the properties of signal extraction models under the assumption that signal/noise are generated by symmetric stable processes. The optimum filter is obtained by the method of minimum dispersion. The performance of the new filter is compared with their Gaussian counterparts by simulation. |
| URI: | http://dyuthi.cusat.ac.in/purl/2855 |
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| Dyuthi-P00398.pdf | (724.3Kb) |
| Abstract: | In this paper we try to fit a threshold autoregressive (TAR) model to time series data of monthly coconut oil prices at Cochin market. The procedure proposed by Tsay [7] for fitting the TAR model is briefly presented. The fitted model is compared with a simple autoregressive (AR) model. The results are in favour of TAR process. Thus the monthly coconut oil prices exhibit a type of non-linearity which can be accounted for by a threshold model. |
| URI: | http://dyuthi.cusat.ac.in/purl/2858 |
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| Dyuthi-P00401.pdf | (389.1Kb) |
Now showing items 1-11 of 11
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